Determination of the optimal policy in inventory problems from a game-theoretic perspective.
Reverse engineer a regular expression pattern for the characters
    contained in an R object. Individual characters can be categorised into
    digits, letters, punctuation or spaces and encoded into run-lengths. This
    can be used to summarise the structure of a dataset or identify non-standard
    entries. Many non-character inputs such as numeric vectors and data frames
    are supported.
Functions to facilitate inverse estimation (e.g., calibration) in
    linear, generalized linear, nonlinear, and (linear) mixed-effects models. A
    generic function is also provided for plotting fitted regression models with
    or without confidence/prediction bands that may be of use to the general
    user.
Light weight implementation of the standard distribution
  functions for the inverse gamma distribution, wrapping those for the gamma
  distribution in the stats package.
invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
Provides two functions for the numerical inversion of Laplace-Transformed functions, returning the value of the standard (time) domain function at a specified value.  The first algorithm is the first optimum contour algorithm described by Evans and Chung (2000)[1].
    The second algorithm uses the Bromwich contour as per the definition of the inverse Laplace Transform.  The latter is unstable for numerical inversion and mainly included for comparison or interest.  There are also some additional functions provided for utility, including plotting and some simple Laplace Transform examples, for which there are known analytical solutions.  Polar-cartesian conversion functions are included in this package and are used by the inversion functions.
    [1] Evans & Chung, 2000: Laplace transform inversions using optimal contours in the complex plane; International Journal of Computer Mathematics v73 pp531-543.
Interaction and analysis of multiple response data,
    along with other tools for analysing these types of data including
    missing value analysis and calculation of standard errors for
    a range of covariance matrix results (proportions, multinomial,
    independent samples, and multiple response).
Simple plotting function(s) for exploratory data analysis with flexible options allowing for easy plot customisation. The goal is to make it easy for beginners to start exploring a dataset through simple R function calls, as well as provide a similar interface to summary statistics and inference information. Includes functionality to generate interactive HTML-driven graphs. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a suite of functions to use with regression models, including summaries, residual plots, and factor comparisons. Used as part of the Model Fitting module of 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding.
Provides a collection of functions for working with time series data, including functions for drawing, decomposing, and forecasting. Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) <doi:10.1016/j.ijforecast.2003.09.015>, Winters (1960) <doi:10.1287/mnsc.6.3.324>, Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".
One function to read files. One function to write files. One
    function to direct plots to screen or file. Automatic file format inference
    and directory structure creation.
Calculates fundamental IO matrices (Leontief, Wassily W. (1951) <doi:10.1038/scientificamerican1051-15>); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) <doi:10.1080/09535319200000013>, Blair and Miller (2009) <ISBN:978-0-521-73902-3>, Antras et al (2012) <doi:10.3386/w17819>, Hummels, Ishii, and Yi (2001) <doi:10.1016/S0022-1996(00)00093-3>); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) <doi:10.1080/09535319300000017>, Sonis, Hewings, and Guo (2006) <doi:10.1080/09535319600000002>); and a variety of table operators.
The data analysis module for the Iterative Optimization Heuristics
    Profiler ('IOHprofiler'). This module provides statistical analysis methods for the 
    benchmark data generated by optimization heuristics, which can be visualized through a 
    web-based interface. The benchmark data is usually generated by the 
    experimentation module, called 'IOHexperimenter'. 'IOHanalyzer' also supports
    the widely used 'COCO' (Comparing Continuous Optimisers) data format for benchmarking.
The benchmarking module for the Iterative Optimization Heuristics
    Profiler ('IOHprofiler'). This module provides benchmarking in the 'IOHprofiler'
    format, which can be visualized using the 'IOHanalyzer' module.
Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.
Two methods for calculation of the number of required flows for semiconductor sequencing: 1. Using a simulation, the number of flows can be calculated for a concrete list of amplicons. 2. An exact combinatorial model is evaluated to calculate the number of flows for a random ensemble of sequences.
Provides item exclusion procedure, which is a formal method to 
    test 'Indifference Of iNdicator' (ION). When a latent personality 
    trait-outcome association is assumed, then the association strength 
    should not depend on which subset of indicators (i.e. items) has been 
    chosen to reflect the trait. Personality traits are  often measured 
    (reflected) by  a sum-score of a certain set of indicators. 
    Item exclusion procedure randomly excludes items from a sum-score and 
    tests, whether the sum-score - outcome correlation changes. ION has been 
    achieved, when any item can be excluded from the sum-score without the 
    sum-score - outcome correlation substantially changing . For more details, 
    see Vainik, Mottus et. al, (2015) "Are Trait-Outcome Associations Caused
    by Scales or Particular Items? Example Analysis of Personality Facets and
    BMI",European Journal of Personality  DOI: <10.1002/per.2009> .
Collection of functions for IO Psychologists.
Density, spectral density, and regression estimation using infinite
    order flat-top kernels.
Pre-processing and basic analytical tasks related to working
    with Eurostat's symmetric input-output tables and provide basic
    input-output economics calculations. The package is part of rOpenGov
    <http://ropengov.github.io/> to open source open government initiatives.
Basic I/O tools for streaming and data parsing.
Enables the user to find the country, region, city, coordinates, zip code, time zone, ISP, domain name, connection type, area code, weather station code, weather station name, mobile, usage type, address type and IAB category that any IP address or hostname originates from. Supported IPv4 and IPv6.
        Please visit <https://www.ip2location.com> to learn more. You may also want to visit <https://lite.ip2location.com> for free database download.
        This package requires 'IP2Location Python' module. At the terminal, please run 'pip install IP2Location' to install the module.
A R package to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits.
    The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit <https://lite.ip2location.com> for free database download.
Converts character vectors between phonetic
    representations.  Supports IPA (International Phonetic Alphabet),
    X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and
    ARPABET (used by the CMU Pronouncing Dictionary).
Classes and functions for working with IP (Internet Protocol)
    addresses and networks, inspired by the Python 'ipaddress' module.
    Offers full support for both IPv4 and IPv6 (Internet Protocol versions
    4 and 6) address spaces. It is specifically designed to work well with
    the 'tidyverse'.
A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) <doi:10.1101/2020.03.21.001206>.
Provides tools for passing messages between R processes. 
    Shiny Examples are provided showing how to perform useful tasks such as: 
    updating reactive values from within a future, progress bars for long running 
    async tasks, and interrupting async tasks based on user input.
An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.
Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials.
Basic checks needed with an individual level participant data from randomised controlled trial. This 
    checks files for existence, read access and individual columns for formats. The checks on format is currently implemented for gender and age formats.
An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. ’s digitize package (2011)  <doi:10.32614/RJ-2011-004> . For more complex and tangled together graphs, digitizing software, such as 'DigitizeIt' (for MAC or windows) or 'ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) <doi:10.1186/1471-2288-12-9> with some modifications.
Functions are provided to interpolate geo-referenced point data via
    Inverse Path Distance Weighting. Useful for coastal marine applications where
    barriers in the landscape preclude interpolation with Euclidean distances.
Allows direct access to the macroeconomic, 
             financial and regional database maintained by 
             Brazilian Institute for Applied Economic Research ('Ipea').
             This R package uses the 'Ipeadata' API. For more information, 
             see <http://www.ipeadata.gov.br/>.
Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model.
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.
A fast (C) implementation of the iterative proportional fitting
    procedure. Based on corresponding code from the networkTomography package.
Performs iterative proportional updating given a seed table and
  an arbitrary number of marginal distributions. This is commonly used in
  population synthesis, survey raking, matrix rebalancing, and other
  applications. For example, a household survey may be weighted to match the
  known distribution of households by size from the census. An origin/
  destination trip matrix might be balanced to match traffic counts.
  The approach used by this package is based on a paper from
  Arizona State University (Ye, Xin, et. al. (2009)
  <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf>).
  Some enhancements have been made to their work including primary and 
  secondary target balance/importance, general marginal agreement, and weight 
  restriction.
Algorithms and utility functions for indoor positioning using fingerprinting techniques. 
    These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data 
    sets, estimation of positions,comparison of the performance of different models, and graphical 
    visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or
    probabilistic fingerprinting are implemented in this package to perform analysis
    and estimations over RSSI data sets.
Combining genomic prediction with Monte Carlo simulation, three different 
    strategies are implemented to select parental lines for multiple traits in plant 
    breeding. The selection strategies include (i) GEBV-O considers only genomic 
    estimated breeding values (GEBVs) of the candidate individuals; (ii) GD-O 
    considers only genomic diversity (GD) of the candidate individuals; and (iii) 
    GEBV-GD considers both GEBV and GD. The above method can be seen in Chung PY, 
    Liao CT (2020) <doi:10.1371/journal.pone.0243159>. Multi-trait genomic best 
    linear unbiased prediction (MT-GBLUP) model is used to simultaneously estimate 
    GEBVs of the target traits, and then a selection index is adopted to evaluate 
    the composite performance of an individual.
Provides functions and data sets to accompany the  book 'Integrated Population Models: Theory and Ecological Applications with R and JAGS' by Michael Schaub and Marc Kéry (ISBN: 9780128205648).
Flexibly implements Integral Projection Models using a
  mathematical(ish) syntax. This package will not help with the vital rate
  modeling process, but will help convert those regression models into an
  IPM. 'ipmr' handles density dependence and environmental stochasticity, with a
  couple of options for implementing the latter. In addition, provides functions
  to avoid unintentional eviction of individuals from models. Additionally, 
  provides model diagnostic tools, plotting functionality, 
  stochastic/deterministic simulations, and analysis tools.
  Integral projection models are described in depth by Easterling et al. (2000) 
  <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) 
  <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>,
  and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. 
  Williams et al. (2012) <doi:10.1890/11-2147.1> discuss the problem of 
  unintentional eviction.
Computes IPM for assessing variable importance for random forests. See details at I. Epifanio (2017) <DOI:10.1186/s12859-017-1650-8>.
Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.
Uncertainty quantification and propagation in the framework of 
    Dempster-Shafer Theory and imprecise probabilities. This toolbox offers 
    easy-to-use methods for using imprecise probabities for applied uncertainty
    modelling and simulation. The package comprises the basic functionality 
    needed, with usability similar to standard probabilistic analysis:
    - Fit imprecise probability distributions from data,
    - Define imprecise probabilities based on distribution functions,
    - Combine with various aggregation rules (e. g. Dempster's rule),
    - Plotting tools,
    - Propagate through arbitrary functions / simulations via Monte Carlo,
    - Perform sensitivity analyses with imprecise distributions,
    - Example models for a quick start.
Let us consider a sample of patients who can suffer from several diseases simultaneously, in a given set of diseases. The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient.
Improved predictive models by indirect classification and
  bagging for classification, regression and survival problems 
  as well as resampling based estimators of prediction error.
Provides methods to perform and analyse I-prior regression models.
    Estimation is done either via direct optimisation of the log-likelihood or 
    an EM algorithm.
Functions that wrap popular phylogenetic software for sequence
    alignment, masking of sequence alignments, and estimation of phylogenies and
    ancestral character states.
A toolkit for manipulating, validating and testing 'IP' addresses and
    ranges, along with datasets relating to 'IP' addresses. Tools are also provided
    to map 'IPv4' blocks to country codes. While it primarily has support for the 'IPv4'
    address space, more extensive 'IPv6' support is intended.
An easy way to import census, survey and geographic data provided by 'IPUMS'
    into R plus tools to help use the associated metadata to make analysis easier. 'IPUMS'
    data describing 1.4 billion individuals drawn from over 750 censuses and surveys is
    available free of charge from our website <https://ipums.org>.
Generate plots based on the Item Pool Visualization concept for
    latent constructs. Item Pool Visualizations are used to display the
    conceptual structure of a set of items (self-report or psychometric).
    Dantlgraber, Stieger, & Reips (2019) <doi:10.1177/2059799119884283>.
Functions to estimate the probability to receive the observed treatment, based on
    individual characteristics. The inverse of these probabilities can be used as weights when
	estimating causal effects from observational data via marginal structural models. Both point
	treatment situations and longitudinal studies can be analysed. The same functions can be used to
	correct for informative censoring.
Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or  modelled  using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.
An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighted (IPW) estimation of average treatment effect (ATE) with misclassified binary outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals.
Use inverse-propensity-weighted estimation approaches to estimating the treatment effect from a partially nested design where one study arm (the treatment arm) is nested and the other study arm (the control arm) is not. Two estimators are provided: IPW mean difference and IPW multilevel modeling. <https://github.com/xliu12/IPWpn>.
An implementation of the maximal
    peptide ratio extraction module of the MaxLFQ algorithm by
    Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a complete
    pipeline for processing proteomics data in data-independent acquisition mode
    (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>).
    It offers additional options for protein quantification using the N most intense 
    fragment ions, using all
    fragment ions, and a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160).
Builds statistical control charts with exact limits for
        univariate and multivariate cases.
Estimate an optimal dynamic treatment regime using Interactive Q-learning.
Iterated race is an extension of the Iterated F-race method for
             the automatic configuration of optimization algorithms, that is,
             (offline) tuning their parameters by finding the most appropriate
             settings given a set of instances of an optimization problem.
             M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle,
             and M. Birattari (2016) <doi:10.1016/j.orp.2016.09.002>.
Provides a flexible integrative algorithm that allows information from prior data, such as protein protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference.
Check the irrepresentable condition (IRC) in both L1-regularized regression <doi:10.1109/TIT.2006.883611>
            and Gaussian graphical models. The IRC requires that the important and unimportant variables 
            are not correlated, at least not all that much, and it is necessary for consistent model
            selection. Exploring the IRC as a function of the number of variables, assumed sparsity,
            and effect size can provide valuable insights into the model selection 
            properties of L1-regularization.
Provides implementation of various correlation coefficients of common use in
  Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient
  as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008)
  <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b
  developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.
An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>.
    Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.
Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.
Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.
Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.
Provides examples of code for analyzing data or accomplishing tasks that may be useful to institutional or educational researchers.
Iteratively grows feature weighted random forests and finds high-order feature interactions in a stable fashion.
Fits a double logistic function to NDVI time series and calculates 
             instantaneous rate of green (IRG) according to methods described
             in Bischoff et al. (2012) <doi:10.1086/667590>.
Researchers and analysts have access to more than 7,500 innovative companies worldwide, which are or have been part of the top 1,000 innovative companies. They can access the six parameters that compose the global IRI Scoreboard's data on R&D: Country, Year, Company's name, Industry, Indicator and Company's rank. Please cite: Warin, Th. (2020) "iiriR: An R Package for the EU Industrial R&D Investment Scoreboard", <doi:10.6084/m9.figshare.11774640.v5>.
Provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.
Classes and functions for metrics calculation as part of the
             'IRIS DMC MUSTANG' project. The functionality in this package 
             builds upon the base classes of the 'IRISSeismic' package.
             Metrics include basic statistics as well as higher level
             'health' metrics that can help identify problematic seismometers.
Provides classes and methods for seismic data analysis. The
             base classes and methods are inspired by the python code found in
             the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and 
             methods support data returned by web services provided by the 'IRIS DMC'
             <http://service.iris.edu/>.
The R kernel for the 'Jupyter' environment executes R code which the front-end
    ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
Fast and memory efficient methods for truncated singular value
    decomposition and principal components analysis of large sparse and dense
    matrices.
The Row-column designs are widely recommended for experimental situations when there are two well-identified factors that are cross-classified representing known sources of variability. These designs are expected to result a gain in accuracy of estimating treatment comparisons in an experiment as they eliminate the effects of the row and column factors. However, these designs are not readily available when the number of treatments is more than the levels of row and column blocking factors. This package named 'iRoCoDe' generates row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs. (Mcsorley, J.P., Phillips, N.C., Wallis, W.D. and Yucas, J.L. (2005).<doi:10.1007/s10623-003-6149-9>).
Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson's A, Kendall's W, Cohen's Kappa, ...
Calculates various chance-corrected agreement coefficients (CAC) among 2 or more raters are provided. 
	Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 
	coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. All of these statistical 
	procedures are described in details in Gwet, K.L. (2014,ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
	Advanced Analytics, LLC.
Analysis of longitudinal data for which the times of observation are random variables that are potentially associated with the outcome process. The package includes inverse-intensity weighting methods (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>) and multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>).
Calculates various intraclass correlation coefficients used to quantify 
  inter-rater and intra-rater reliability.  The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented
  in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
  Advanced Analytics, LLC.
Provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) <doi:10.1037/1082-989X.1.1.30> and the Coefficient of Concordance according to Kendall & Babington Smith (1939) <doi:10.1214/aoms/1177732186>.
Builds ensemble survival tree models to reveal variable interactions when the response is a time-to-events outcome. Codes contain randomization, interaction modeling, and prediction subroutines to be used in addition to the following R packages: 'survival' for Kaplan-Meier and Cox regression modeling, 'randomForestSRC' for RSF modeling, and optionally 'ggRandomForests' for Random Forest exploration/visualization. The current version contains additional R codes in folder "/inst/doc" for the analysis and generation of results shown in the corresponding article (Dazard et al. (2018) <doi:10.1515/sagmb-2017-0038>).
A collection of Item Response Theory (IRT) and Computerized 
     Adaptive Testing (CAT) functions that are used in psychometrics.
Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Wang (2018) <https:www.ncme.org/news/past-meetings/2018-recap>; (4) the BEMM algorithm for 4PL model proposed by Zhang, Guo, & Zheng (2018) <https:www.ncme.org/news/past-meetings/2018-recap>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.
Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.
Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.
A collection of functions useful in learning and practicing IRT,
    which can be combined into larger programs. Provides basic CTT analysis,
    a simple common interface to the estimation of item
    parameters in IRT models for binary responses with three different programs
    (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible 
    values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord,
    and the complete Hebaera method), and a rich array of parametric and 
    non-parametric (kernel) plots. Estimates and plots Haberman's interaction model
    when all items are dichotomously scored.
Multidimensional Person Item Response Theory probability distributions
Helper functions and example data sets accompanying De
        Boeck, P. and Partchev, I. (2012) IRTrees: Tree-Based Item
        Response Models of the GLMM Family, Journal of Statistical
        Software - Code Snippets, 48(1), 1-28.
Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
Interactive shiny application for running Item Response Theory
    analysis. Provides graphics for characteristic and information curves.
The ISA is a biclustering algorithm that finds modules 
  in an input matrix. A module or bicluster is a block of the
  reordered input matrix.
ISA-Tab (Investigation/Study/Assay (ISA) tab-delimited (TAB) format) is a
  general purpose framework for storing complex metadata in omics
  applications. It is notoriously hard to manipulate due to the fact that
  it is a graph rather than a tab-delimited data frame. The 'isaeditor' package is meant
  to facilitate reading, writing, displaying, manipulating, modifying and populating
  ISA-Tab files in R.
Reads the output of the 'PerkinElmer InForm' software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.
ISA is a metadata framework to manage an increasingly diverse set 
    of life science, environmental and biomedical experiments. In isatabr 
    methods for reading, modifying and writing of files in the ISA-Tab format
    are implemented. It also contains methods for processing assay data.
Selection of features for sparse regression estimation (like the LASSO). Selection of blocks of features when the regression parameter is sparse and constant by blocks (like the Fused-LASSO). Application to cgh arrays.
Implementation of functions to assign corresponding common job prestige scores (SIOPS, ISEI), the official job or group title and the ISCO-88 code to given ISCO-08 codes. ISCO-08 is the latest version of the International Standard Classification of Occupations which is used to organise information on labour and jobs.
Implementation of a KL-based scoring rule to assess the quality of different missing value imputations in the broad sense as introduced in Michel et al. (2021)  <arXiv:2106.03742>.
Provides datasets for Introduction to Statistical Data Analysis for the Life Sciences
Functions for predicting and mapping potential and realized distributions of invasive species within the invaded range.
Tools for parsing 'NOAA' Integrated Surface Data ('ISD') files,
    described at <https://www.ncdc.noaa.gov/isd>. Data includes for example,
    wind speed and direction, temperature, cloud data, sea level pressure,
    and more. Includes data from approximately 35,000 stations worldwide,
    though best coverage is in North America/Europe/Australia. Data is stored
    as variable length ASCII character strings, with most fields optional.
    Included are tools for parsing entire files, or individual lines of data.
An implementation of integrative dimension reduction analysis procedures for multi-source data, which provides integrative sparse partial least squares, integrative sparse principal component analysis, integrative sparse canonical correlation analysis and corresponding meta-analysis versions, etc. The work is built and improved upon the references: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). "Integrative sparse principal component analysis." Journal of Multivariate Analysis, <doi:10.1016/j.jmva.2018.02.002>. (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). "Integrative sparse partial least squares." Statistics in Medicine, <doi:10.1002/sim.8900>.
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided.
Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.
An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper: Cilluffo, G., Sottile, G., La Grutta, S. and Muggeo, V. (2019) The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression. <doi:10.1177/0962280219842890>, and discussed in a tutorial: Sottile, G., Cilluffo, G., and Muggeo, V. (2019) The R package islasso: estimation and hypothesis testing in lasso regression. <doi:10.13140/RG.2.2.16360.11521>.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.
The development of ISM was made by Warfield in 1974. 
      ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure.
      To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's.
      Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524>
      Warfield,J.N. (1974, E-ISSN:2168-2909).
Functions to support the computations carried out in
  `An Introduction to Statistical Modeling of Extreme Values' by
  Stuart Coles. The functions may be divided into the following 
  groups; maxima/minima, order statistics, peaks over thresholds
  and point processes.
The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For a tutorial, download at <https://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Check if an 'externalptr' is a null pointer.
    R does currently not have a native function for that purpose.
    This package contains a C function that returns TRUE in case of a null pointer.
Linear order and unimodal order (univariate)
	     isotonic regression; bivariate isotonic regression
	     with linear order on both variables.
A fast C++ implementation to generate contour lines (isolines) and
  contour polygons (isobands) from regularly spaced grids containing elevation data.
In classification problems a monotone relation between some
  predictors and the classes may be assumed. In this package 'isoboost' 
  we propose new boosting algorithms, based on LogitBoost, that 
  incorporate this isotonicity information, yielding more accurate 
  and easily interpretable rules.
A set of functions to run simple and composite box-models to describe the 
    dynamic or static distribution of stable isotopes in open 
    or closed systems. The package also allows the sweeping of many 
    parameters in both static and dynamic conditions. It also comes 
    with a post-run plotting interface built under shiny.
    The mathematical models used in this package are derived from Albarede, 1995, 
    Introduction to Geochemical Modelling, Cambridge University Press,
    Cambridge <doi:10.1017/CBO9780511622960>.
Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Farquhar, O'Leary, and Berry (1982) <doi:10.1071/PP9820121>.
This resource provides tools to create, compare, and post-process 
    spatial isotope assignment models of animal origin. It generates 
    probability-of-origin maps for individuals based on user-provided tissue and 
    environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] 
    <doi:10.1111/2041-210X.12147>) using the framework established in Bowen et al.
    (2010) <doi:10.1146/annurev-earth-040809-152429>). The package 'isocat' can then 
    quantitatively compare and cluster these maps to group individuals by 
    similar origin. It also includes techniques for applying four approaches 
    (cumulative sum, odds ratio, quantile only, and quantile simulation) with 
    which users can summarize geographic origins and probable distance traveled 
    by individuals. Campbell et al. [2020] establishes several of the functions
    included in this package <doi:10.1515/ami-2020-0004>.
Contains functions to check the isomorphism of multi-stage factorial designs with randomisation restrictions based on balanced spreads and balanced covering stars of PG(n-1,2) as described in Spencer, Ranjan and Mendivil (2019) <doi:10.1007/s42519-019-0064-5>.
Some functions for confidence intervals for current status data based on transformations and bootstrap.
A bunch of functions to deal with circular data under order restrictions.
ISO language, territory, currency, script and character codes.
  Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217
  currency codes, ISO 15924 script codes, and the ISO 8859 character codes
  as well as the UN M.49 area codes.
A series of functions that allow an easy and fast correction for drift and carry-over in continuous isotopic measurements. This implementation provides queries allowing users to perform the implemented corrections according to their needs. These functions further enable the processing of large datasets and can provides apt visualizations of the corrections performed.
Distributional regression under stochastic order restrictions for
    numeric and binary response variables and partially ordered covariates. See
    Henzi, Ziegel, Gneiting (2020) <arXiv:1909.03725>.
Offers framework for testing for monotonic relationship between gene expression and doses in a microarray experiment. Several testing procedures including the global likelihood-ratio test (Bartholomew, 1961), Williams (1971, 1972), Marcus (1976), M (Hu et al. 2005) and the modified M (Lin et al. 2007) are used to test for the monotonic trend in gene expression with respect to doses. BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2004) FDR controlling procedures are applied to adjust the raw p-values obtained from the permutations.
This toolbox makes working with carbonate oxygen,
    carbon, and clumped isotope data reproducible and straightforward.
    Use it to quickly calculate isotope fractionation factors,
    and apply paleothermometry equations.
Implementation of Isolation kernel (Qin et al. (2019) <doi:10.1609/aaai.v33i01.33014755>).
Isopam clustering algorithm and utilities. 
 Isopam optimizes clusters and optionally cluster numbers in 
 a brute force style and aims at an optimum separation  
 by all or some descriptors (typically species).
The function calculates the isotopic pattern (fine
        structures) for a given chemical formula.
Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington' and 'Ludwig' approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.
Provides a graphical user interface to the 'IsoplotR' package for radiometric geochronology. The GUI runs in an internet browser and can either be used offline, or hosted on a server to provide online access to the 'IsoplotR' toolbox.
Deconvolution of mixed tumour profiles into normal and cancer for each patient, using 
	the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires 
	mixed tumour profiles and a set of unmatched "basis" normal profiles.
Interface to the raw data file formats commonly encountered in scientific disciplines that make use of stable isotopes.
Building isoscapes using mixed models and inferring the geographic
  origin of samples based on their isotopic ratios. This package is essentially a
  simplified interface to several other packages which implements a new
  statistical framework based on mixed models. It uses 'spaMM' for fitting and
  predicting isoscapes, and assigning an organism's origin depending on its
  isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for
  plotting the maps produced with 'raster' using 'lattice'.
IsoSpec is a fine structure calculator used for obtaining the most
    probable masses of a chemical compound given the frequencies of the composing
    isotopes and their masses. It finds the smallest set of isotopologues with
    a given probability. The probability is assumed to be that of the product of
    multinomial distributions, each corresponding to one particular element and
    parametrized by the frequencies of finding these elements in nature. These
    numbers are supplied by IUPAC - the International Union of Pure and Applied
    Chemistry. See: Lacki, Valkenborg, Startek (2020) <DOI:10.1021/acs.analchem.0c00959>
    and Lacki, Startek, Valkenborg, Gambin (2017) <DOI:10.1021/acs.analchem.6b01459>
    for the description of the algorithms used.
Nonparametric estimation on survival analysis under order restrictions. It estimates monotone increasing or decreasing covariate effects in the proportional hazards model. Yunro Chung et al. (2018) <doi:10.1093/biomet/asx064>.
Contains two main functions: one for
        solving general isotone regression problems using the
        pool-adjacent-violators algorithm (PAVA); another one provides
        a framework for active set methods for isotone optimization
        problems with arbitrary order restrictions. Various types of
        loss functions are prespecified.
Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Estimates diet contributions from isotopic sources using JAGS.
    Includes estimation of concentration dependence and measurement error.
Implements Bayesian models to analyze data from tracer addition
    experiments. The implemented method was originally described in the article
    "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from
    Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020)
    <doi:10.1086/708546>.
Fast and multi-threaded implementation of
	isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>),
	extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>),
	SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>),
	and fair-cut forest (Cortes (2019) <arXiv:1911.06646>),
	for isolation-based outlier detection, clustered outlier detection, distance or similarity
	approximation (Cortes (2019) <arXiv:1910.12362>),
	and imputation of missing values (Cortes (2019) <arXiv:1911.06646>),
	based on random or guided decision tree splitting. Provides simple heuristics for fitting the model to
	categorical columns and handling missing data, and offers options for varying between random and guided
	splits, and for using different splitting criteria.
The wiDB...() functions provide an interface to the public API 
    of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: 
    build, check and submit queries, and receive and 
    unpack responses. Data analysis functions support Bayesian 
    inference of the source and source isotope composition of water 
    samples that may have experienced evaporation. Algorithms 
    adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).
This is an substitute for the %V and %u formats which are
        not implemented on Windows. In addition, the package offers
        functions to convert from standard calender format yyyy-mm-dd
        to and from ISO 8601 week format yyyy-Www-d.
A collection of several functions related to construction and analysis of incomplete split-plot designs. The package contains functions to obtain  and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are  incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments.
Accomplish high performance simulations in quantitative genetics. 
    The molecular genetic components are represented by R6/C++ classes and methods. 
    The core computational algorithm is implemented using bitsets according to 
    <doi:10.1534/g3.119.400373>. A mix between low and high level interfaces 
    provides great flexibility and allows user defined extensions and a wide range of 
    applications.
Algorithms to handle with PCA-based missing data with high correlation. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
This is the central location for data and tools for the development,
    maintenance, analysis, and deployment of the International Soil Radiocarbon Database
    (ISRaD). ISRaD was developed as a collaboration between the U.S. Geological Survey
    Powell Center and the Max Planck Institute for Biogeochemistry. This R package provides
    tools for accessing and manipulating ISRaD data, compiling local data using the ISRaD
    data structure, and simple query and reporting functions for ISRaD. For more detailed
    information visit the ISRaD website at: <https://soilradiocarbon.org/>.
You can access to open data published in Instituto Canario De Estadistica (ISTAC) APIs at <https://datos.canarias.es/api/estadisticas/>.
Launches a 'shiny' based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.
Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations.
Independent Surrogate Variable Analysis is an algorithm
        for feature selection in the presence of potential confounding
        factors (see Teschendorff AE et al 2011, <doi: 10.1093/bioinformatics/btr171>).
Data sets and scripts for text examples and exercises in 
  P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Provides two record linkage data sets on the Italian Survey on Household and Wealth, 2008 and 2010, a sample survey conducted by the Bank of Italy every two years. The 2010 survey covered 13,702 individuals, while the 2008 survey covered 13,734 individuals. The following categorical variables are included in this data set: year of birth, working status, employment status, branch of activity, town size, geographical area of birth, sex, whether or not Italian national, and highest educational level obtained. Unique identifiers are available to assess the accuracy of one’s method. Please see Steorts (2015) <DOI:10.1214/15-BA965SI> to find more details about the data set.
Functions for analyzing multiple choice items. These analyses include
  the convertion of student response into binaty data (correct/incorrect),
  the computation of the number of corrected responses and grade for each subject,
  the calculation of item difficulty and discrimination, the computation of the
  frecuency and point-biserial correlation for each distractor and the graphical
  analysis of each item.
Three methods for Individual Tree Crowns (ITCs) delineation on remote sensing data: one is based on LiDAR data in x,y,z format and one on imagery data in raster format.
The routine, itdr(), which allows to estimate the sufficient dimension reduction subspaces, i.e., central mean subspace or central subspace in regression, using Fourier transformation proposed by Zhu and Zeng (2006) <https:doi.org/10.1198/016214506000000140>, convolution transformation proposed by  Zeng and Zhu (2010) <https:doi.org/10.1016/j.jmva.2009.08.004>
  and iterative Hessian transformation methods proposed by Cook and Li (2002) <https:doi.org/10.1214/aos/1021379861>. The predictor variables can be consider to have a multivariate normal distribution or an elliptical contoured distribution. If the distribution of the predictor variables is unknown, then the predictors' distribution can be estimated by the kernel density estimation method. Moreover, each of these routines is supported with a bootstrap procedure to estimate their tuning parameters. That is, wx() estimates the tuning parameter for the predictor variables, wy() estimates the tuning parameter for the response variable, and wh() estimates the bandwidth parameter for the kernel density estimation method. The function invFM() estimates the central subspace using Fourier transform approach for inverse dimension reduction method proposed by Weng and Yin (2018) <https:doi.org/10.1080/10485252.2018.1515432>. The function d.test() estimates the dimension of the central mean subspace using hypothesis under invFM(). Moreover, the dsp() function provides the two distance measures between two subspaces spanned by the columns of two matrices; Vector correlation proposed by Hooper (1959) <https:doi.org/10.2307/1909445>, and Trace correlation proposed by Hotelling (1936) <https:doi.org/10.2307/2333955>.
Iterates through a phylogenetic tree to identify regions
        of rate variation using the parametric rate comparison test.
Support for iterators, which allow a programmer to traverse
    through all the elements of a vector, list, or other collection of data.
The iterLap (iterated Laplace approximation) algorithm approximates a
             general (possibly non-normalized) probability density on R^p, by repeated
             Laplace approximations to the difference between current approximation 
             and true density (on log scale). The final approximation is a mixture of
             multivariate normal distributions and might be used for example as a
             proposal distribution for importance sampling (eg in Bayesian applications). 
             The algorithm can be seen as a computational generalization of the Laplace 
             approximation suitable for skew or multimodal densities.
Iterator for generating permutations and combinations. They
    can be either drawn with or without replacement, or with distinct/ non-distinct 
    items (multiset). The generated sequences are in lexicographical order 
    (dictionary order). The algorithms to generate permutations and combinations are 
    memory efficient. These iterative algorithms enable users to process all sequences 
    without putting all results in the memory at the same time. 
    The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer
    being maintained. Users are recommended to switch to 'arrangements'.
Various tools for creating iterators, many patterned after
        functions in the Python itertools module, and others patterned
        after functions in the 'snow' package.
A port of Python's excellent itertools module to R for efficient
    looping.
Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.
Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).
Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter 
    scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing 
    XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. 
    A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. 
    Principle component analysis (PCA), cluster analysis and associated multivariate methods are 
    implemented with appropriate data transformation.
Maximin-projection learning (MPL, Shi, et al., 2018) is implemented for 
	recommending a meaningful and reliable individualized treatment regime for future 
	groups of patients based on the observed data from different populations with
	heterogeneity in individualized decision making. Q-learning and A-learning are
	implemented for estimating the groupwise contrast function that shares the same
	marginal treatment effects. The packages contains classical Q-learning and A-learning
	algorithms for a single stage study as a byproduct. More functions will be added
	at later versions.
Sequential advantage selection (SAS, Fan, Lu and Song, 2016) <arXiv:1405.5239> 
	and penalized A-learning (PAL, Shi, et al., 2018) methods are implement for selecting 
	important variables involved in optimal individualized (dynamic) treatment regime in 
	both single-stage or multi-stage studies.
Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999)
    as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear
    regression analysis which is particularly useful for time course data such as
    EEG, pupil dilation, gaze data (eye tracking), and articulography recordings,
    but also for behavioral data such as reaction times and response data. As time
    course measures are sensitive to autocorrelation problems, GAMMs implements
    methods to reduce the autocorrelation problems. This package includes functions
    for the evaluation of GAMM models (e.g., model comparisons, determining regions
    of significance, inspection of autocorrelational structure in residuals)
    and interpreting of GAMMs (e.g., visualization of complex interactions, and
    contrasts).
Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <http://www.springer.com/us/book/9781475777505>.
This package includes functions to incorporate
        uncertainties in estimated propensity scores and provide
        adjusted standard errors for making valid causal inference.
Mendelian randomization (MR) analysis is a special case of instrumental variable analysis with genetic instruments. It is used to estimate the unconfounded causal effect of an exposure. This package implements estimating and testing methods in Zhang et al. (2019) for MR analysis in case-control studies. It (1) estimates the causal effect of a quantitative exposure by the quasi empirical likelihood approach; (2) uses Lagrange multiplier test for testing the presence of causal; (3) provides a test for the presence of confounder.
Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <arXiv:2103.06328>.
An implementation of randomization-based hypothesis 
    testing for three different estimands in a cluster-randomized 
    encouragement experiment. The three estimands include (1) testing
    a cluster-level constant proportional treatment effect (Fisher's
    sharp null hypothesis), (2) pooled effect ratio, and (3) average 
    cluster effect ratio. To test the third estimand, user needs to install
    'Gurobi' (>= 9.0.1) optimizer via its R API. Please refer to 
    <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.
Uses data and researcher's beliefs on measurement error and
    instrumental variable (IV) endogeneity to generate the space of consistent 
    beliefs across measurement error, instrument endogeneity, and instrumental 
    relevance for IV regressions. 
    Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.
Fit an Instrumental least square dummy variable model
A method that estimates
    an IV-optimal individualized treatment rule. An individualized
    treatment rule is said to be IV-optimal if it minimizes the 
    maximum risk with respect to the putative IV and the set of
    IV identification assumptions. Please refer to 
    <arXiv:2002.02579> for more details on the methodology and 
    some theory underpinning the method. Function IV-PILE() uses
    functions in the package 'locClass'. Package 'locClass' can be
    accessed and installed from the 'R-Forge' repository via the following link: 
    <https://r-forge.r-project.org/projects/locclass/>.
    Alternatively, one can install the package by entering the following in R:
    'install.packages("locClass", repos="<http://R-Forge.R-project.org>")'.
Carries out instrumental variable
    estimation of causal effects, including power analysis, sensitivity analysis,
    and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.
The marginal treatment effect was introduced by Heckman and
    Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a
    choice-theoretic interpretation to instrumental variables models that
    maintain the monotonicity condition of Imbens and Angrist (1994)
    <doi:10.2307/2951620>. This interpretation can be used to extrapolate from
    the compliers to estimate treatment effects for other subpopulations. This
    package provides a flexible set of methods for conducting this
    extrapolation. It allows for parametric or nonparametric sieve estimation,
    and allows the user to maintain shape restrictions such as monotonicity. The
    package operates in the general framework developed by Mogstad, Santos and
    Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point
    identification or partial identification (bounds). In the partially
    identified case, bounds are computed using either linear programming
    or quadratically constrained quadratic programming. Support for
    four solvers is provided. Gurobi and the Gurobi R API
    can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained
    from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex'
    and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be
    obtained from <https://www.mosek.com/>. The lp_solve library is freely
    available from <http://lpsolve.sourceforge.net/5.5/>, and is included when
    installing its API 'lpSolveAPI', which is available from CRAN.
This package contains functions for carrying out instrumental variable estimation of causal effects and power analyses for instrumental variable studies.
Fit the instrumental panel data models: the fixed effects, random
    effects and between models.
Compute the instrumental variables probit model using  the Amemiya's Generalized Least Squares estimators (Amemiya, Takeshi, (1978) <doi: 10.2307/1911443>).
Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.
Allows you to create an evidence factor (EX analysis) in an 
	instrumental variables regression model. Additionally, performs Sensitivity 
	analysis for OLS analysis, 2SLS analysis and EX analysis with interpretable 
	plotting and printing features.
An instrumental variable estimator under structural cumulative additive intensity model is fitted, that leverages initial randomization as the IV. The estimator can be used to fit an additive hazards model under time to event data which handles treatment switching (treatment crossover) correctly. We also provide a consistent variance estimate.
Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.
Drawing statistical inference on the coefficients
    of a short- or long-horizon predictive regression with persistent
    regressors by using the IVX method of Magdalinos and Phillips (2009)
    <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and
    Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.
For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. 
    Leaves are stacked vertically. At most twenty leaves are shown; For high frequency, each leaflet may represent more than 
    one observation with multiplicity declared in the subtitle.
Improved methods based on inverse probability weighting
        and outcome regression for causal inference and missing data
        problems
A powerful system for estimating an improved wavelet-based index
    of magnetic storm activity, storm activity preindex (from individual station) and SQ variations.
    It also serves as a flexible visualization tool.
Code to specify, run, and then visualize and analyze the results of 
    Ixodidae (hard-bodied ticks) population and infection dynamics models. Such 
    models exist in the literature, but the source code to run them is not 
    always available. 'IxPopDyMod' provides an easy way for these models to be 
    written and shared.
Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and 
    likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) 
    <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate 
    random deviates from zero inflated or hurdle models and obtain  maximum likelihood estimate
    of unknown parameters in these models.
Calculate statistical significance of Jaccard/Tanimoto similarity
    coefficients for binary data.
Symbolic calculation and evaluation of the Jack polynomials, zonal polynomials, and Schur polynomials. Mainly based on Demmel & Koev's paper (2006) <doi:10.1090/S0025-5718-05-01780-1>. Zonal polynomials and Schur polynomials are particular cases of Jack polynomials. Zonal polynomials appear in random matrix theory. Schur polynomials appear in the field of combinatorics.
Simply and efficiently
    simulates (i) variants from reference genomes and (ii) reads from both Illumina 
    <https://www.illumina.com/>
    and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. 
    It can either read reference genomes from FASTA files or simulate new ones.
    Genomic variants can be simulated using summary statistics, phylogenies, 
    Variant Call Format (VCF) files, and coalescent simulations—the latter of which
    can include selection, recombination, and demographic fluctuations.
    'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, 
    as well as PacBio reads.
    These simulations include sequencing errors, mapping qualities, multiplexing,
    and optical/polymerase chain reaction (PCR) duplicates.
    Simulating Illumina sequencing is based on ART
    by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>.
    PacBio sequencing simulation is based on 
    SimLoRD  by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>.
    All outputs can be written to standard file formats.
Computing the original and modified jackknife estimates of Kaplan-Meier estimators.
Provides method used to check whether data have outlier in efficiency measurement 
  of big samples with data envelopment analysis (DEA). In this jackstrap method, the package 
  provides two criteria to define outliers: heaviside and k-s test. The technique was developed 
  by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting 
  Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.
Test for association between the observed data
	and their systematic patterns of variations.
	The jackstraw enables statistical testing for association between observed variables and latent variables, as captured by principal component analysis (PCA), factor analysis (FA), or other estimates. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds subpopulations among the observed variables. The jackstraw estimates statistical significance of cluster membership, including unsupervised evaluation of cell identities in single cell RNA-seq. P-values and posterior probabilities allows one to rigorously evaluate the strength of cluster membership assignments. See the GitHub repository for the latest developments and further helps.
Implements the classical Jacobi algorithm for the
    eigenvalues and eigenvectors of a real symmetric matrix, both in 
    pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example 
    for teaching purposes.
Uses the Jaccard similarity index to account for population
    structure in sequencing studies. This method was specifically
    designed to detect population stratification based on rare variants, hence it
    will be especially useful in rare variant analysis.
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
All the data and functions used to produce the book. We do not expect
    most people to use the package for any other reason than to get simple access to the
    'JAGS' model files, the data, and perhaps run some of the simple examples.
    The authors of the book are David Lucy (now sadly deceased) and James Curran. It is 
    anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with
    bibliographic details to follow at that point. Until such time, further information
    can be obtained by emailing James Curran.
Bayesian data analysis usually incurs long runtimes
  and cumbersome custom code.
  A pipeline toolkit tailored to Bayesian statisticians,
  the 'jagstargets' R package is leverages
  'targets' and 'R2jags' to ease this burden.
  'jagstargets' makes it super easy to set up scalable
  JAGS pipelines that automatically parallelize the computation
  and skip expensive steps when the results are already up to date.
  Minimal custom code is required, and there is no need to manually
  configure branching, so usage is much easier than 'targets' alone.
  For the underlying methodology, please refer
  to the documentation of 'targets' <doi:10.21105/joss.02959> and 'JAGS'
  (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.
A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags').  A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.
Jalali, also known as Persian, Solar Hijri and Hijri Shamsi calendar is 
    the official calendar of Iran and Afghanistan. It begins on Nowruz, the March equinox, 
    as determined by astronomical calculation and has years of 365 or 366 days. 
    Adapting the algorithms in <https://jdf.scr.ir/>, this package provides tools 
    for converting the Jalali and Gregorian dates.
Provides an interface to 'Jamendo' API <https://developer.jamendo.com/v3.0>.
              Pull audio, features and other information for a given 
              'Jamendo' user (including yourself!) or enter an artist's -, album's -, 
              or track's name and retrieve the available information in seconds.
Analyze and visualize results of studies performed with the
             analysis tools in 'JAMES', a modern object-oriented Java
             framework for discrete optimization using local search
             metaheuristics (see http://www.jamesframework.org).
Full texts for Jane Austen's 6 completed novels, ready for text
    analysis. These novels are "Sense and Sensibility", "Pride and Prejudice",
    "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion".
The main janitor functions can: perfectly format data.frame column
    names; provide quick counts of variable combinations (i.e., frequency
    tables and crosstabs); and isolate duplicate records. Other janitor functions
    nicely format the tabulation results. These tabulate-and-report functions
    approximate popular features of SPSS and Microsoft Excel. This package
    follows the principles of the "tidyverse" and works well with the pipe function
    %>%. janitor was built with beginning-to-intermediate R users in mind and is
    optimized for user-friendliness. Advanced R users can already do everything
    covered here, but with janitor they can do it faster and save their thinking for
    the fun stuff.
Client for the Directory of Open Access Journals ('DOAJ')
    (<https://doaj.org/>). API documentation at
    <https://doaj.org/api/v1/docs>. Methods included for working with
    all 'DOAJ' API routes: fetch article information by identifier,
    search for articles, fetch journal information by identifier,
    and search for journals.
Functions for the Japanese regional mesh codes defined in 'JIS
    X 0410'
    (<https://www.jisc.go.jp/app/jis/general/GnrJISNumberNameSearchList?show&jisStdNo=X0410>).
    Conversion between regional mesh codes and longitude/latitude, and
    between mesh codes of different scales.
Provides tools for using the API of 'e-Stat' (<https://www.e-stat.go.jp/>), a portal site for Japanese government statistics.
    Includes functions for automatic query generation, data collection and formatting.
Provides a new class of Bayesian meta-analysis models that we called "The Hierarchical Meta-Regression" (HMR). The aim of HMR is to incorporate into the meta-analysis, the data collection process, which results in a model for the internal and external validity bias. In this way, it is possible to combine studies of different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS). The statistical methods and their applications are described in Verde (2019) <doi:10.1002/bimj.201700266> and in Verde (2017) <doi:10.5772/intechopen.70231>.
R modules for JASPAR data processing and visualization
Graphics device routing all graphics commands to a Java
	     program. The actual functionality of the JavaGD depends
	     on the Java-side implementation. Simple AWT and Swing
	     implementations are included.
Maximization or Minimization of a fitness function using Jaya Algorithm (JA).
   A population based method which repeatedly modifies a population of individual solutions.
   Capable of solving both constrained and unconstrained optimization problems.
   It does not contain any hyperparameters. 
   For further details: R.V. Rao (2016) <doi:10.5267/j.ijiec.2015.8.004> .
Provides an R interface to the JBrowse 2 genome browser.
    Enables embedding a JB2 genome browser in a Shiny app or R Markdown
    document. The browser can also be launched from an interactive R console.
    The browser can be loaded with a variety of common genomics data types,
    and can be used with a custom theme.
Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather 
             types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.
Contains a selection of color palettes and 'ggplot2' themes designed by the package author.
Procedures for joint detection of changes in both expectation and variance in univariate sequences. Performs a statistical test of the null hypothesis of the absence of change points. In case of rejection performs an algorithm for change point detection. Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547.
The Impact Factor of a journal reported by Journal Citation Reports ('JCR') of  'Clarivate' 'Analytics' is provided. The impact factor is available for those journals only that were included Journal Citation Reports 'JCR'.
Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) <doi:10.1086/260062>] or jump diffusion processes [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other.
Customized R Markdown templates for authoring articles
    for Journal of Data Science.
Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeek) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) <arXiv:1806.00548>.
Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search.
Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.
On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.
Implements the audit sampling workflow as discussed in Derks et al. (2019) <doi:10.31234/osf.io/9f6ub>. The package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with the International Standards on Auditing. Next to classical audit sampling methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) <doi:10.1111/ijau.12240> and Derks et al. (2021) <doi:10.31234/osf.io/kzqp5>.
Support the analysis of financial and econometric time series, including recursive forecasts for machine learning.
Color palettes used by the Pacific Northwest National Laboratory - Joint Global Change Research Institute for maps and charts.
Fits two different joint generalized estimating equation models to multivariate longitudinal data.
The Joint Graphical Lasso is a generalized method for
        estimating Gaussian graphical models/ sparse inverse covariance
        matrices/ biological networks on multiple classes of data.  We
        solve JGL under two penalty functions: The Fused Graphical
        Lasso (FGL), which employs a fused penalty to encourage inverse
        covariance matrices to be similar across classes, and the Group
        Graphical Lasso (GGL), which encourages similar network
        structure between classes.  FGL is recommended over GGL for
        most applications. Reference: Danaher P, Wang P, Witten DM. (2013) 
        <doi:10.1111/rssb.12033>.
Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS.
Chinese text segmentation, keyword extraction and speech tagging
    For R.
jiebaR is a package for Chinese text segmentation, keyword extraction
   and speech tagging. This package provides the data files required by jiebaR.
Template engine powered by the 'inja' C++ library. Users
    write a template document, using syntax inspired by the 'Jinja' Python
    package, and then render the final document by passing data from R.
    The template syntax supports features such as variables, loops,
    conditions and inheritance.
Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.
Allows to interact with the 'JIRA SERVER REST API' to analyze the retrieved data in R. For further information about the API visit <https://docs.atlassian.com/software/jira/docs/api/REST/8.9.1/>.
Set of common functions used for manipulating colors,
    detecting and interacting with 'RStudio', modeling, formatting, determining
    users' operating system, feature scaling, and more!
Implements the tree-based approach to joint modeling of time-to-event and longitudinal data. This approach looks for a tree-based partitioning such that within each estimated latent class defined by a terminal node, the time-to-event and longitudinal responses display a lack of association. See Zhang and Simonoff (2018) <arXiv:1812.01774>.
Joint latent space models for social networks and multivariate attributes using a fast inference approach (Wang et al. (2019) <arXiv:1910.12128>).
Shared parameter models for the joint modeling of longitudinal and time-to-event data.
Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.
Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).
Fit joint mean-covariance models for longitudinal data. The models
    and their components are represented using S4 classes and methods. The core
    computational algorithms are implemented using the 'Armadillo' C++ library
    for numerical linear algebra and 'RcppArmadillo' glue.
Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.
Performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown.
The main purpose of this package is to make it easy for userR's to interact with 'jMetrik' an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by 'jMetrik'. It also allows useR's to read *.jmetrik files (e.g. output from an analysis) for follow-up analysis in R. The *.jmetrik format is a flat file that includes a multiline header and the data as comma separated values. The header includes metadata about the file and one row per variable with the following information in each row: variable name, data type, item scoring, special data codes, and variable label.
Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.
Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA
    algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly),
    and characteristic pattern discovery along with interpretable time series classification.
Performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test.  Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions.  Performs power calculations for the binomial test.
A suite of common statistical methods such as descriptives,
    t-tests, ANOVAs, regression, correlation matrices, proportion tests,
    contingency tables, and factor analysis. This package is also useable from
    the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more
    information).
Methods to access data sets from the 'jamovi' statistical
  spreadsheet (see <https://www.jamovi.org> for more information) from R.
A framework for creating rich interactive analyses for the jamovi
    platform (see <https://www.jamovi.org> for more information).
The free and open a statistical spreadsheet 'jamovi' (www.jamovi.org)
    aims to make statistical analyses easy and intuitive. 'jamovi' produces
    syntax that can directly be used in R (in connection with the R-package 'jmv').
    Having import / export routines for the data files 'jamovi' produces ('.omv')
    permits an easy transfer of analyses between 'jamovi' and R.
Call job::job({<code here>}) to run R code as an RStudio job and keep your console free in the meantime. This allows for a productive workflow while testing (multiple) long-running chunks of code. It can also be used to organize results using the RStudio Jobs GUI or to test code in a clean environment. Two RStudio Addins can be used to run selected code as a job.
Computing and plotting joint confidence regions and intervals. Regions include classical ellipsoids, minimum-volume or minimum-length regions, and an empirical Bayes region. Intervals include the TOST procedure with ordinary or expanded intervals and a fixed-sequence procedure. Such regions and intervals are useful e.g., for the assessment of multi-parameter (bio-)equivalence. Joint confidence regions for the mean and variance of a normal distribution are available as well.
In a typical experiment for the intuitive judgment of frequencies (JoF) different stimuli with different frequencies are presented. The participants consider these stimuli with a constant duration and give a judgment of frequency. These judgments can be simulated by formal models: PASS 1 and PASS 2 based on Sedlmeier (2002, ISBN:978-0198508632), MINERVA 2 baesd on Hintzman (1984) <doi:10.3758/BF03202365> and TODAM 2 based on Murdock, Smith & Bai (2001) <doi:10.1006/jmps.2000.1339>. The package provides an assessment of the frequency by determining the core aspects of these four models (attention, decay, and presented frequency) that can be compared to empirical results.
RE.Johnson performs the Johnson Transformation to increase the normality.
Analysis of repeated measurements and time-to-event data via random
    effects joint models. Fits the joint models proposed by Henderson and colleagues
    <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and
    colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a
    single continuous repeated measure. The time-to-event data is modelled using a 
    (cause-specific) Cox proportional hazards regression model with time-varying 
    covariates. The longitudinal outcome is modelled using a linear mixed effects
    model. The association is captured by a latent Gaussian process. The model is 
    estimated using am Expectation Maximization algorithm. Some plotting functions 
    and the variogram are also included. This project is funded by the Medical 
    Research Council (Grant numbers G0400615 and MR/M013227/1).
Fits joint models of the type proposed by Henderson and colleagues 
    (2000) <doi:10.1093/biostatistics/1.4.465>, but extends to the multi-study, 
    meta-analytic case. Functions for meta-analysis of a single longitudinal and 
    a single  time-to-event outcome from multiple studies using joint models.  
    Options to produce plots for multi study joint data, to pool joint model 
    fits from 'JM' and 'joineR' packages in a two stage meta-analysis, and to 
    model multi-study joint data in a one stage meta-analysis.
Fits the joint model proposed by Henderson and colleagues (2000) 
    <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple 
    continuous longitudinal measures. The time-to-event data is modelled using a 
    Cox proportional hazards regression model with time-varying covariates. The 
    multiple longitudinal outcomes are modelled using a multivariate version of the 
    Laird and Ware linear mixed model. The association is captured by a multivariate
    latent Gaussian process. The model is estimated using a Monte Carlo Expectation 
    Maximization algorithm. This project was funded by the Medical Research Council 
    (Grant number MR/M013227/1).
Implements high-dimensional multivariate regression by stacked generalisation (Rauschenberger 2021 <doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).
Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death.
 Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model.
 The methods are applicable for meta-analytic data containing individual-patient information from several studies.
 Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).
 Methodologies were published in
 Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>,
 Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>,
 Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>.
 See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>.
 Survival data from ovarian cancer patients are also available.
Joint analysis and imputation of incomplete data in the Bayesian
    framework, using (generalized) linear (mixed) models and extensions there of,
    survival models, or joint models for longitudinal and survival data, as
    described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>.
    Incomplete covariates, if present, are automatically imputed.
    The package performs some preprocessing of the data and creates a 'JAGS'
    model, which will then automatically be passed to 'JAGS' 
    <https://mcmc-jags.sourceforge.io/> with the help of 
    the package 'rjags'.
Different algorithms to perform approximate joint diagonalization
	of a finite set of square matrices. Depending on the algorithm,
	orthogonal or non-orthogonal diagonalizer is found. These algorithms
	are particularly useful in the context of blind source separation. 
	Original publications of the algorithms can be found in 
	Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, 
	Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, 
	Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of 
	application in the context of Brain-Computer Interfaces EEG denoising
	can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.
Jointly estimates two-group means and covariances
    for matrix-variate data and calculates test statistics.  
    This package implements the algorithms defined in 
    Hornstein, Fan, Shedden, and Zhou (2018) 
    <doi:10.1080/01621459.2018.1429275>.
Joint fit of a semiparametric regression model for longitudinal responses and a semiparametric transformation model for time-to-event data.
An end-to-end package for learning multiple sparse Gaussian graphical models and nonparanormal models from Heterogeneous Data with Additional Knowledge. It is able to simulate multiple related graphs as well as produce samples drawn from them. Multiple state-of-the-art sparse Gaussian graphical model estimators are included to both multiple and difference estimation. Graph visualization is available in 2D as well as 3D, designed specifically for brain. Moreover, a set of evaluation metrics are integrated for easy exploration with model validity. Finally, classification using graphical model is achieved with Quadratic Discriminant Analysis. The package comes with multiple demos with datasets from various fields. Methods references: SIMULE (Wang B et al. (2017) <doi:10.1007/s10994-017-5635-7>), WSIMULE (Singh C et al. (2017) <arXiv:1709.04090v2>), DIFFEE (Wang B et al. (2018) <arXiv:1710.11223>), JEEK (Wang B et al. (2018) <arXiv:1806.00548>), JGL(Danaher P et al. (2012) <arXiv:1111.0324>) and kdiffnet (Sekhon A et al, preprint for publication).
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.
A bivariate integration method to estimate risk caused by two extreme and dependent forcing variables.
Methods for fast segmentation of multivariate
    signals into piecewise constant profiles and for generating realistic
    copy-number profiles. A typical application is the joint segmentation of total
    DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism
    (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, 
    Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.
Performs Joins and Minus Queries on 'Excel' Files
    fulljoinXL() Merges all rows of 2 'Excel' files based upon a common column in the files.
    innerjoinXL() Merges all rows from base file and join file when the join condition is met.
    leftjoinXL() Merges all rows from the base file, and all rows from the join file
    if the join condition is met.
    rightjoinXL() Merges all rows from the join file, and all rows from the base file if the join
    condition is met.
    minusXL() Performs 2 operations source-minus-target and target-minus-source
    If the files are identical all output files will be empty.
    Choose two 'Excel' files via a dialog box, and then follow prompts at the console to
    choose a base or source file and columns to merge or minus on.
Similarly to Schafer's package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi: 10.1002/9781119942283>.
 Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
Functions and data to reproduce all plots in the book "Practical Smoothing. 
 The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).
A Jordan algebra is an algebraic object originally
   designed to study observables in quantum mechanics.  Jordan
   algebras are commutative but non-associative; they satisfy the
   Jordan identity.  The package follows the ideas and notation of
   K. McCrimmon (2004, ISBN:0-387-95447-3) "A Taste of Jordan
   Algebras".
Implementation of some unit and area level EBLUP estimators as well as the estimators of their MSE also under heteroscedasticity. The package further documents the publications Breidenbach and Astrup (2012) <DOI:10.1007/s10342-012-0596-7>, Breidenbach et al. (2016) <DOI:10.1016/j.rse.2015.07.026> and Breidenbach et al. (2018 in press). The vignette further explains the use of the implemented functions.
Josa in Korean is often determined by judging the previous word. 
            When writing reports using Rmd, a function that prints the 
            appropriate investigation for each case is helpful. 
            The 'josaplay' package then evaluates the previous word 
            to determine which josa is appropriate.
Read and write JSON Web Keys (JWK, rfc7517), generate and verify JSON
    Web Signatures (JWS, rfc7515) and encode/decode JSON Web Tokens (JWT, rfc7519).
    These standards provide modern signing and encryption formats that are natively
    supported by browsers via the JavaScript WebCryptoAPI, and used by services 
    like OAuth 2.0, LetsEncrypt, and Github Apps.
Since the reference management software (such as 'Zotero', 'Mendeley') exports Bib file journal abbreviation is not detailed enough, the 'journalabbr' package only abbreviates the journal field of Bib file, and then outputs a new Bib file for generating reference format with journal abbreviation on other software (such as 'texstudio'). The abbreviation table is from 'JabRef'. At the same time, 'Shiny' application is provided to generate 'thebibliography', a reference format that can be directly used for latex paper writing based on 'Rmd' files.
Implements under/oversampling for probability estimation.  To be
    used with machine learning methods such as AdaBoost, random forests, etc.
Tool for diagnosing table joins. It combines the speed
    `data.table`, the flexibility of `dplyr`, and the diagnosis and features of
    the `merge` command in `Stata`.
This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.
A Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.
Helpful functions for using mesh code (80km to 100m) data in Japan. Visualize mesh code using 'ggplot2' and 'leaflet', etc.
We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap.
Client for 'jq', a 'JSON' processor (<https://stedolan.github.io/jq/>), 
    written in C. 'jq' allows the following with 'JSON' data: index into, parse, 
    do calculations, cut up and filter, change key names and values, perform 
    conditionals and comparisons, and more.
Obtain any major version of 'jQuery' (<https://code.jquery.com/>) and use it in any webpage generated by 'htmltools' (e.g. 'shiny', 'htmlwidgets', and 'rmarkdown').
    Most R users don't need to use this package directly, but other R packages (e.g. 'shiny', 'rmarkdown', etc.) depend on this package to avoid bundling redundant copies of 'jQuery'.
An 'httpuv' based bridge between R and 'JavaScript'. Provides an easy way to exchange commands and data between a web page and a currently running R session.
Simultaneous estimation of multiple related networks.
These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). 
   The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, 
   as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area 
   in a conservation analysis.
   The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.
Psychometric analysis and scoring of judgment data using polytomous Item-Response Theory (IRT) models, as described in Myszkowski and Storme (2019) <doi:10.1037/aca0000225>. A convenience function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions.
Implements the basic financial analysis
    functions similar to (but not identical to) what
    is available in most spreadsheet software. This
    includes finding the IRR and NPV of regularly
    spaced cash flows and annuities. Bond pricing and
    YTM calculations are included. In addition, Black
    Scholes option pricing and Greeks are also
    provided.
A set of utilities for working with JavaScript syntax in R.
    Includes tools to parse, tokenize, compile, validate, reformat, optimize 
    and analyze JavaScript code.
Provides a 'RStudio' addin to send some 'JavaScript' code to the 'V8' console. The user can send an entire 'JavaScript' file or only some selected lines. This is useful to test the code.
The jscore() function in the package calculates the J-Score metric between two clustering
    assignments. The score is designed to address some problems with existing common metrics such 
    as problem of matching. The details of J-score is described in Ahmadinejad and Liu. (2021) <arXiv:2109.01306>.
Fits joint species distribution models ('jSDM')
    in a hierarchical Bayesian framework (Warton et al. 2015
    <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written
    in C++. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation
    efficiency.
The function 'jskm()' creates publication quality Kaplan-Meier plot with at risk tables below. 'svyjskm()' provides plot for weighted Kaplan-Meier estimator.
Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data. Refer to the Journal of Statistical Software article: <doi:10.18637/jss.v093.i02>.
A set of helper functions to conduct joint-significance tests
    for mediation analysis, as recommended by Yzerbyt, Muller, Batailler,
    & Judd. (2018) <doi:10.1037/pspa0000132>.
'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
Encode/Decode 'base64', with support for JSON format, using two functions: j_encode() and j_decode().    'Base64' is a group of similar binary-to-text encoding schemes that represent binary data in an ASCII string format by translating it into a radix-64 representation, used when there is a need to encode binary data that needs to be stored and transferred over media that are designed to deal with textual data, ensuring that the data will remain intact and without modification during transport. <https://developer.mozilla.org/en-US/docs/Web/API/WindowBase64/Base64_encoding_and_decoding>    On the other side, JSON (JavaScript Object Notation) is a lightweight data-interchange format. Easy to read, write, parse and generate. It is based on a subset of the JavaScript Programming Language. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. JSON structure is built around name:value pairs and ordered list of values. <https://www.json.org>    The first function, j_encode(), let you transform a data.frame or list to a 'base64' encoded JSON (or JSON string).    The j_decode() function takes a 'base64' string (could be an encoded JSON) and transform it to a data.frame (or list, depending of the JSON structure).
This package enables users to access MongoDB by running queries
    and returning their results in R data frames. Usually, data in MongoDB is
    only available in the form of a JSON document. jSonarR uses data
    processing and conversion capabilities in the jSonar Analytics Platform
    and the JSON Studio Gateway (http://www.jsonstudio.com), to convert it to
    a tabular format which is easy to use with existing R packages.
Conversions between 'R' objects and Javascript Object Notation (JSON) using 
    the 'rapidjsonr' library <https://CRAN.R-project.org/package=rapidjsonr>.
JSON-LD is a light-weight syntax for expressing linked data. It is primarily
    intended for web-based programming environments, interoperable web services and for 
    storing linked data in JSON-based databases. This package provides bindings to the 
    JavaScript library for converting, expanding and compacting JSON-LD documents.
A reasonably fast JSON parser and generator, optimized for statistical 
    data and the web. Offers simple, flexible tools for working with JSON in R, and
    is particularly powerful for building pipelines and interacting with a web API. 
    The implementation is based on the mapping described in the vignette (Ooms, 2014).
    In addition to converting JSON data from/to R objects, 'jsonlite' contains 
    functions to stream, validate, and prettify JSON data. The unit tests included 
    with the package verify that all edge cases are encoded and decoded consistently 
    for use with dynamic data in systems and applications.
Interface to 'JSON-stat' <https://json-stat.org/>,
  a simple lightweight 'JSON' format for data dissemination.
Fast manipulation of JSON strings. Allows to extract or delete an element in a JSON string, merge two JSON strings, and more.
A toolbox for working with JSON vectors similar to
    the functions 'Postgres' provides to work with JSON columns. It supports
    in parsing and formatting JSON, extracting data from JSON, and
    modifying JSON data.
Uses the node library 'is-my-json-valid' or 'ajv' to
    validate 'JSON' against a 'JSON' schema.  Drafts 04, 06 and 07 of
    'JSON' schema are supported.
Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Create and customize interactive trees using the
       'jQuery' 'jsTree' <https://www.jstree.com/> plugin
       library and the 'htmlwidgets' package. These trees can
       be used directly from the R console, from 'RStudio', in
       Shiny apps and R Markdown documents.
Creates interactive trees that can be included in 'Shiny' apps and R markdown documents. A tree allows to represent hierarchical data (e.g. the contents of a directory). Similar to the 'shinyTree' package but offers more features and options, such as the grid extension, restricting the drag-and-drop behavior, and settings for the search functionality. It is possible to attach some data to the nodes of a tree and then to get these data in 'Shiny' when a node is selected. Also provides a 'Shiny' gadget allowing to manipulate one or more folders.
Minimal and memory efficient implementation of the junction tree
	     algorithm using the Lauritzen-Spiegelhalter scheme;
	     S. L. Lauritzen and D. J. Spiegelhalter (1988) 
	     <https://www.jstor.org/stable/2345762?seq=1>.
This is a collection of tools that the author (Jacob) has written
  for the purpose of more efficiently understanding and sharing the results of
  (primarily) regression analyses. There are also a number of miscellaneous
  functions for statistical and programming purposes. Just about everything 
  supports models from the survey package.
Transforming univariate non-normal data to normality using Johnson 
  families of distributions. Johnson family is a comprehensive distribution 
  family that accommodates many kinds of non-normal distributions. A bunch of 
  distributions with various parameters will be fit and the corresponding 
  p-values under a user-specified normality test will be given. The final 
  transformation will be the one with the largest p-value under the given 
  normality test.
A long-term forecast model called "Jubilee-Tectonic model" is implemented to forecast future returns of the U.S. stock market, Treasury yield, and gold price. The five-factor model forecasts the 10-year and 20-year future equity returns with high R-squared above 80 percent. It is based on linear growth and mean reversion characteristics in the U.S. stock market. This model also enhances the CAPE model by introducing the hypothesis that there are fault lines in the historical CAPE, which can be calibrated and corrected through statistical learning. In addition, it contains a module for business cycles, optimal interest rate, and recession forecasts.
Provides a GUI interface for automating data extraction from 
  multiple images containing scatter and bar plots, semi-automated tools to tinker 
  with extraction attempts, and a fully-loaded point-and-click manual extractor 
  with image zoom, calibrator, and classifier. Also provides detailed and 
  R-independent extraction reports as fully-embedded .html records.
Generates image data for fractals (Julia and Mandelbrot
        sets) on the complex plane in the given region and resolution.
Provides an R interface to 'Julia',
    which is a high-level, high-performance dynamic programming language
    for numerical computing, see <https://julialang.org/> for more information.
    It provides a high-level interface as well as a low-level interface.
    Using the high level interface, you could call any 'Julia' function just like
    any R function with automatic type conversion. Using the low level interface,
    you could deal with C-level SEXP directly while enjoying the convenience of
    using a high-level programming language like 'Julia'.
Allows to import functions and whole packages from 'Julia' in R.
    Imported 'Julia' functions can directly be called as R functions.
    Data structures can be translated between 'Julia' and R.
Primarily used to convert human readable track and field results into dataframes for further analysis.  Results can come from central repositories like <https://www.flashresults.com/> or <http://www.deltatiming.com/>, or from individual team sites, like those for colleges.   Also contains functions useful for working with track and field data.
A fast simulation on stochastic volatility model, with jump tests, p-values pooling, and FDR adjustments.
Individual based simulations of hybridizing populations,
    where the accumulation of junctions is tracked. Furthermore,
    mathematical equations are provided to verify simulation outcomes.
    Both simulations and mathematical equations are based on Janzen
    (2018, <doi:10.1101/058107>) and Janzen (2020,
    <doi:10.1101/2020.09.10.292441>).
Leverages the 'yum' package to
             implement a 'YAML' ('YAML Ain't Markup Language', a human
             friendly standard for data serialization; see <https:yaml.org>)
             standard for documenting justifications, such as for decisions
             taken during the planning, execution and analysis of a study
             or during the development of a behavior change intervention
             as illustrated by Marques & Peters (2019)
             <doi:10.17605/osf.io/ndxha>. These justifications are both
             human- and machine-readable, facilitating efficient extraction
             and organisation.
Functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>).
Provides functions to standardize and whiten data, and to perform
  Principal Component Analysis (PCA).  The main advantage of this
  package over alternatives like prcomp() is, that jvcoords makes it
  easy to convert (additional) data between the original and the
  transformed coordinates.  The package also provides a class coords,
  which can represent affine coordinate transformations.  This class
  forms the basis of the transformations provided by the package, but
  can also be used independently.  The implementation has been
  optimized to be of comparable speed (and sometimes even faster) than
  existing alternatives.
Many method to compute, predict and back-test VaR. For more detail, see the report: Value at Risk <researchgate.net>.
Miscellaneous tools and functions,
    including: generate descriptive statistics tables,
    format output, visualize relations among variables or check
    distributions, and generic functions for residual and
    model diagnostics.
Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' 
    and the piping syntax from 'magrittr'. Function 'kable()' is a light weight 
    table generator coming from 'knitr'. This package simplifies the way to 
    manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows 
    users to construct complex tables and customize styles using a readable 
    syntax.
Implementation of various kernel adaptive methods in nonparametric curve
    estimation like density estimation as introduced in Stute and Srihera (2011)
    <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013)
    <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression
    as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.
A toolkit for absolute dating and analysis of chronological
    patterns. This package includes functions for chronological modeling
    and dating of archaeological assemblages from count data. It allows to
    compute time point estimates and density estimates of the occupation and 
    duration of an archaeological site.
Implements methods for clustering mixed-type data,
  specifically combinations of continuous and nominal data. Special attention
  is paid to the often-overlooked problem of equitably balancing the
  contribution of the continuous and categorical variables. This package
  implements KAMILA clustering, a novel method for clustering
  mixed-type data in the spirit of k-means clustering. It does not require
  dummy coding of variables, and is efficient enough to scale to rather large
  data sets. Also implemented is Modha-Spangler clustering, which uses a
  brute-force strategy to maximize the cluster separation simultaneously in the
  continuous and categorical variables. For more information, see Foss, Markatou,
  Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou
  (2018) <doi:10.18637/jss.v083.i13>.
Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel (Friedrichs et al., 2017, <doi:10.1155/2017/6742763>).
Identify and sort genes overexpressed and associated to transmembrane protein in Affymetrix expression set or any other results of microarray experiment.
Computes the Kantorovich distance between two probability measures on a finite set. The Kantorovich distance is also known as the Monge-Kantorovich distance or the first Wasserstein distance.
Sequences encoding by using the chaos game representation.
    Löchel et al. (2019) <doi:10.1093/bioinformatics/btz493>.
Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019) <doi:10.1080/03610918.2018.1538457> Jun-mo Nam (2003) <doi:10.1111/j.0006-341X.2003.00118.x> Jun-mo Nam (2005) <doi:10.1002/sim.2321>Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) <doi:10.2307/3315487> Allan Donner, Michael Eliasziw, Neil Klar (1996) <doi:10.2307/2533154>.
Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss' Kappa. The calculation of kappa statistics is done using the R package 'irr', so that 'KappaGUI' is essentially a Shiny front-end for 'irr'.
S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
Contains basic tools for sample size estimation in studies of interobserver/interrater agreement (reliability).  Includes functions for both the power-based and confidence interval-based methods, with binary or multinomial outcomes and two through six raters.
this package allows to quantify the congruence between two patchy
    mosaics or landscapes. This "vectorial Kappa" approach extends the
    principle of Cohen's Kappa index by calculating areas of intersected
    patches between two mosaics rather than agreement between pixels. It
    provides an exact alternative for patchy mosaics when a Kappa index is
    needed.
This package provides some routines to conduct the K-adaptive parititioning (kaps) algorithm for survival data. A function kaps is an implementation version of our algorithm.
Attempts to remove vocals from a stereo '.wav' recording of a song.
This is the R implementation of Karel the robot, a programming 
  language created by Dr. R. E. Pattis at Stanford University in 1981. Karel is 
  an useful tool to teach introductory concepts about general programming, such 
  as algorithmic decomposition, conditional statements, loops, etc., in an 
  interactive and fun way, by writing programs to make Karel the robot achieve 
  certaing tasks in the world she lives in. Originally based on Pascal, Karel 
  was implemented in many languages through these decades, including 'Java', 'C++', 
  'Ruby' and 'Python'. This is the first package implementing Karel in R.
An R code with a GUI for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) <doi:10.1016/j.physrep.2006.11.001>) and filling methods (Moffat et al. (2007) <doi:10.1016/j.agrformet.2007.08.011>), as well as tools to manipulate easily time series and gap sets.
Convert latex math expressions to HTML and 'MathML' for use in 
    markdown documents or package manual pages. The rendering is done in 
    R using the V8 engine (i.e. server-side), which eliminates the need 
    for embedding the 'MathJax' library into your web pages. In addition
    a 'math-to-rd' wrapper is provided to automatically render beautiful
    math in R documentation files.
Provides data for Kaya identity variables (population, gross 
             domestic product, primary energy consumption, and energy-related 
             CO2 emissions) for the world and for individual nations, and 
             utility functions for looking up data,  plotting trends of 
             Kaya variables, and plotting the fuel mix for a given country
             or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, 
             "Environment, Energy, and Economy: Strategies for Sustainability" 
             (United Nations University Press, 1998) and 
             <https://en.wikipedia.org/wiki/Kaya_identity>) expresses a nation's 
             or region's greenhouse gas emissions in terms of its population, 
             per-capita Gross Domestic Product, the energy intensity of its 
             economy, and the carbon-intensity of its energy supply.
Many data science problems reduce to operations on very tall,
    skinny matrices.  However, sometimes these matrices can be so tall that they
    are difficult to work with, or do not even fit into main memory.  One
    strategy to deal with such objects is to distribute their rows across
    several processors.  To this end, we offer an 'S4' class for tall, skinny,
    distributed matrices, called the 'shaq'.  We also provide many useful
    numerical methods and statistics operations for operating on these
    distributed objects.  The naming is a bit "tongue-in-cheek", with the class
    a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he
    starred in the film 'Kazaam'.
Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearson’s coefficient of skewness, Bowley’s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) <doi: 10.1007/s41060-018-0106-1>.
Create, Simulate, Fit, Solve k-Cube Thurstonian IRT Models
Provides the density and distribution function of the bivariate 
    K-copula by Wollschläger and Schäfer (2016) <doi:10.21314/JOR.2016.342>.
The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.
It predicts any attribute (categorical) given a set of input numeric predictor values. Note that only numeric input predictors should be given.
             The k value can be chosen according to accuracies provided. The attribute to be predicted can be selected from the dropdown provided (select categorical attribute).
             This is because categorical attributes cannot be given as inputs here. A 'handsontable' is also provided to enter the input predictor values.
Predicts any variable in any categorical dataset for given values of predictor variables.
    If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. 
    The user can upload their own datasets and select what variable they want to predict.
    A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown.
Provides an efficient implementation of univariate local polynomial
    kernel density estimators that can handle bounded and discrete data. See 
    Geenens (2014) <arXiv:1303.4121>, 
    Geenens and Wang (2018) <arXiv:1602.04862>, 
    Nagler (2018a) <arXiv:1704.07457>, 
    Nagler (2018b) <arXiv:1705.05431>.
Provides fast implementations of kernel smoothing techniques for
    bivariate copula densities, in particular density estimation and resampling,
    see Nagler (2018) <doi:10.18637/jss.v084.i07>.
Handles univariate non-parametric density estimation with 
    parametric starts and asymmetric kernels in a simple and flexible way. 
    Kernel density estimation with parametric starts involves fitting a
    parametric density to the data before making a correction with kernel 
    density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
    Asymmetric kernels make kernel density estimation more efficient on bounded
    intervals such as (0, 1) and the positive half-line. Supported asymmetric 
    kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
    the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
    copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
    User-supplied kernels, parametric starts, and bandwidths are supported.
A non-parametric method for identifying potential
    outlying observations in a collection of phylogenetic trees based
    on the methods of Owen and Provan (2011). Such discordant trees
    may indicate problems with sequence annotation or tree
    reconstruction, or they may represent interesting biological
    phenomena, such as horizontal gene transfers.
Implements the vine copula based kernel density estimator of
    Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does
    not suffer from the curse of dimensionality and is therefore well suited for
    high-dimensional applications.
Density, distribution function, quantile function and random generation
             for the K-distribution. A plotting function that plots data on Weibull
             paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.
Estimate entropy of multidimensional data set.
Provides various tools for working with multidimensional
  data in R and C++, including extremely fast nearest-neighbor- and range-
  queries without the overhead of linked tree nodes.
Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data.
Provides arrays with flexible control over dimension dropping when subscripting.
Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.
Uses Bessel functions to calculate the
    fundamental and complementary analytic solutions to the
    Kelvin differential equation.
Computes the Kendall rank correlation and Mann-Kendall
        trend test. See documentation for use of block bootstrap when
        there is autocorrelation.
Kendall random walks are a continuous-space Markov chains generated
             by the Kendall generalized convolution. This package provides tools
             for simulating these random walks and studying distributions
             related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) <arXiv:1412.0220>.
Calculate the kernel-smoothed nonparametric estimator for the exposure distribution in presence of detection limits.
Demo and dataset accompaying the books :
	De l'analyse des réseaux expérimentaux à la méta-analyse: Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales (Published 2018-06-28, Quae, for french version) by David Makowski, Francois Piraux and Francois Brun - <https://www.quae.com/produit/1514/9782759228164/de-l-analyse-des-reseaux-experimentaux-a-la-meta-analyse>
	Knowledge Synthesis in Agriculture : from Experimental Network to Meta-Analysis (in preparation for 2018-06, Springer , for English version) by David Makowski, Francois Piraux and Francois Brun
	A full description of all the material is in both books.
	ACKNOWLEDGMENTS : The French network "RMT modeling and data analysis for agriculture" (<http://www.modelia.org>) have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France.
Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi: 10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi: 10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Interface to 'Keras' <https://keras.io>, a high-level neural
  networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Provides a consistent interface to the 'Keras' Deep Learning Library
  directly from within R. 'Keras' provides specifications for describing dense
  neural networks, convolution neural networks (CNN) and recurrent neural networks
  (RNN) running on top of either 'TensorFlow' or 'Theano'. Type conversions between
  Python and R are automatically handled correctly, even when the default
  choices would otherwise lead to errors. Includes complete R documentation
  and many working examples.
'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans. 
             It aims at making the life of AI practitioners, hypertuner 
             algorithm creators and model designers as simple as possible by 
             providing them with a clean and easy to use API for hypertuning. 
             'Keras Tuner' makes moving from a base model to a hypertuned one quick and 
             easy by only requiring you to change a few lines of code.
Nonparametric kernel distribution function estimation is
        performed. Three automatic bandwidth selection methods for
        nonparametric kernel distribution function estimation are
        implemented: the plug-in of Altman and Leger, the plug-in of
        Polansky and Baker, and the modified cross-validation of
        Bowman, Hall and Prvan. The exceedance function, the mean
        return period and the return level are also computed.
An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.
Gaussian process regression with an emphasis on kernels.
    Quantitative and qualitative inputs are accepted. Some pre-defined
    kernels are available, such as radial or tensor-sum for
    quantitative inputs, and compound symmetry, low rank, group kernel
    for qualitative inputs. The user can define new kernels and
    composite kernels through a formula mechanism. Useful methods
    include parameter estimation by maximum likelihood, simulation,
    prediction and leave-one-out validation.
A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.
Smoothed bootstrap and functions for random generation from
             univariate and multivariate kernel densities. It does not
             estimate kernel densities.
Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.
In self-reported or anonymised data the user often encounters
    heaped data, i.e. data which are rounded (to a possibly different degree
    of coarseness). While this is mostly a minor problem in parametric density
    estimation the bias can be very large for non-parametric methods such as kernel
    density estimation. This package implements a partly Bayesian algorithm treating
    the true unknown values as additional parameters and estimates the rounding
    parameters to give a corrected kernel density estimate. It supports various
    standard bandwidth selection methods. Varying rounding probabilities (depending
    on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
    Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
    as well as data aggregated on areas is supported.
The kernel independent component analysis (kernel ICA) method introduced by Bach and Jordan (2003) <doi:10.1162/153244303768966085>. The incomplete Cholesky decomposition used in kernel ICA is provided as separate function.
Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
Contains kernel smoothing tools designed for use by historical
    dialectologists and philologists for exploring spatial and temporal patterns
    in noisy historical language data, such as that obtained from historical texts.
    The main way in which these might differ from other implementations of kernel
    smoothing is that they assume that the function (linguistic variable) being
    explored has the form of the relative frequency of a series of discrete
    possibilities (linguistic variants). This package also offers a way of exploring
    distributions in 2-dimensional space and in time with separate kernels, and
    tools for identifying appropriate bandwidths for these.
Different post-selection inference strategies for kernel 
  selection, as described in "kernelPSI: a Post-Selection Inference Framework
  for Nonlinear Variable Selection", Slim et al., Proceedings of Machine 
  Learning Research, 2019, <http://proceedings.mlr.press/v97/slim19a/slim19a.pdf>. The strategies rest upon quadratic kernel
  association scores to measure the association between a given kernel and an
  outcome of interest. The inference step tests for the joint effect of the
  selected kernels on the outcome. A fast constrained sampling algorithm is
  proposed to derive empirical p-values for the test statistics.
Producing kernel estimates of the unconditional and conditional hazard
        function for right-censored data including methods of bandwidth selection.
Kernel-based machine learning methods for classification,
        regression, clustering, novelty detection, quantile regression
        and dimensionality reduction.  Among other methods 'kernlab'
        includes Support Vector Machines, Spectral Clustering, Kernel
        PCA, Gaussian Processes and a QP solver.
Provides wind energy practitioners with an effective machine learning-based
    tool that estimates a multivariate power curve and predicts the wind power output
    for a specific environmental condition.
Kernel Machine Score Test for Pathway Analysis in the Presence of 
    Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) 
    <doi: 10.1177/0962280216653427>.
Functions for kernel smoothing (and density estimation)
  corresponding to the book: 
  Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
Fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools. The kernel smoothing is based on methods described in Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.
New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data.
Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2020) <arXiv:2004.05964>.
We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) <doi:10.1158/1078-0432.CCR-17-0220>, Li DH, Whitmore JB, Guo W, Ji Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>,  Liu S, Johnson VE (2016) <doi:10.1093/biostatistics/kxv040>, Zhou Y, Lee JJ, Yuan Y (2019) <doi:10.1002/sim.8475>, Pan H, Lin R, Yuan Y (2020) <doi:10.1016/j.cct.2020.105972). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials.
Control your keyboard and mouse with R code by simulating key presses and mouse clicks. The input simulation is implemented with the Windows API.
Tools for keeping track of information, named
    "keys", about rows of data frame like objects. This is done by
    creating special attribute "keys" which is updated after every change
    in rows (subsetting, ordering, etc.).  This package is designed to
    work tightly with 'dplyr' package.
Computes group centrality scores and identifies the most central group of players in a network.
Wait for a single key press at the 'R' prompt.
    This works in terminals, but does not currently work
    in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'),
    in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in
    'R Studio'. In these cases 'keypress' stops with an
    error message.
Platform independent 'API' to access the operating system's
    credential store. Currently supports: 'Keychain' on 'macOS', Credential
    Store on 'Windows', the Secret Service 'API' on 'Linux', and a simple,
    platform independent store implemented with environment variables.
    Additional storage back-ends can be added easily.
Decrypts passwords stored in the Gnome Keyring, macOS Keychain and
    strings encrypted with the Windows Data Protection API.
Assign and listen to keyboard shortcuts in 'shiny' using the 
  'Mousetrap' Javascript library.
Convert keys and other values to memorable phrases. 
  Includes some methods to build lists of words.
Provides functions to explore possible factor structures for a set of variables and
	helps identify plausible and replicable structures via *k*-fold cross validation. The process 
	combines the exploratory and confirmatory factor analytic approach to scale development 
	(Flora & Flake, 2017) <doi:10.1037/cbs0000069> with a cross validation technique that 
	maximizes the available data. Also available are functions to determine *k* by drawing on 
	power analytic techniques for covariance structures (MacCallum, Browne, & Sugawara, 1996) 
	<doi:10.1037/1082-989X.1.2.130>, generate model syntax, and summarize results in a report.
State space modelling is an efficient and flexible framework for 
    statistical inference of a broad class of time series and other data. KFAS 
    includes computationally efficient functions for Kalman filtering, smoothing, 
    forecasting, and simulation of multivariate exponential family state space models, 
    with observations from Gaussian, Poisson, binomial, negative binomial, and gamma 
    distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA).
    There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions.
    In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly.
    This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.
A streamlined cross-referencing system for R Markdown documents
    generated with 'knitr'. R Markdown is  an authoring format for generating
    dynamic content from R. 'kfigr' provides a hook for anchoring code
    chunks and a function to cross-reference document elements generated from
    said chunks, e.g. figures and tables.
Naive implementation of the Kalman filter, smoother and disturbance 
  smoother for state space models.
Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA(). Moreover, least square estimates of functional principal component scores are also provided. Refer to <arXiv:2102.00911>.
Aids in identifying the Koeppen-Geiger (KG) climatic zone for 
    a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system
    to classify regions of the earth by their relative heat and humidity through the year, for 
    the benefit of human health, plant and agriculture and other human activity [1]. This climate
    zone classification system, applicable to all of the earths surface, has continued to be 
    developed by scientists up to the present day.  Recently one of use (FZ) has published updated,
    higher accuracy KG climate zone definitions [2]. In this package we use these updated 
    high-resolution maps as the data source [3]. We provide functions that return the KG climate zone 
    for a given longitude and lattitude, or for a given United States zip code. In addition
    the CZUncertainty() function will check climate zones nearby to check if the given location
    is near a climate zone boundary. In addition an interactive shiny app is provided to define 
    the KG climate zone for a given longitude and lattitude, or United States zip code. 
    Digital data, as well as animated maps, showing the shift of the climate zones are provided 
    on the following website <http://koeppen-geiger.vu-wien.ac.at>.
    This work was supported by the DOE-EERE SunShot award DE-EE-0007140.
     [1] W. Koeppen, (2011) <doi:10.1127/0941-2948/2011/105>.
     [2] F. Rubel and M. Kottek, (2010) <doi:10.1127/0941-2948/2010/0430>.
     [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) <doi:10.1127/metz/2016/0816>.
The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <http://jmlr.org/proceedings/papers/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.
Tools for training and evaluating k-gram language models in R, 
        supporting several probability smoothing techniques, 
        perplexity computations, random text generation and more.
Restore underlining numeric data from rating history graph of 
    KGS (an online platform of the game of go, <http://www.gokgs.com/>). 
    A shiny application is also provided.
The King's Health Questionnaire (KHQ) is a disease-specific, 
  self-administered questionnaire designed specific to assess the impact of 
  Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed 
  by Kelleher and collaborators (1997) <doi:10.1111/j.1471-0528.1997.tb11006.x>. 
  It is a simple, acceptable and reliable measure to use in the clinical setting 
  and a research tool that is useful in evaluating UI treatment outcomes. 
  The KHQ five dimensions (KHQ5D) is a condition-specific preference-based 
  measure developed by Brazier and collaborators (2008) <doi:10.1177/0272989X07301820>. 
  Although not as popular as the SF6D <doi:10.1016/S0895-4356(98)00103-6> and 
  EQ-5D <https://euroqol.org/>, the KHQ5D measures health-related quality of 
  life (HRQoL) specifically for UI, not general conditions like the others 
  two instruments mentioned. The KHQ5D ca be used in the clinical and economic 
  evaluation of health care. The subject self-rates their health in terms of 
  five dimensions: Role Limitation (RL), Physical Limitations (PL), Social 
  Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these 
  five dimensions are converted to a single utility index using country specific 
  value sets, which can be used in the clinical and economic evaluation of 
  health care as well as in population health surveys. This package provides 
  methods to calculate scores for each dimension of the KHQ; converts KHQ item 
  scores to KHQ5D scores; and also calculates the utility index of the KHQ5D.
Colour schemes ready for each type of data (qualitative,
    diverging or sequential), with colours that are distinct for all
    people, including colour-blind readers. This package provides an
    implementation of Paul Tol (2018) and Fabio Crameri (2018) <doi:
    10.5194/gmd-11-2541-2018> colour schemes for use with 'graphics' or
    'ggplot2'. It provides tools to simulate colour-blindness and to test
    how well the colours of any palette are identifiable. Several
    scientific thematic schemes (geologic timescale, land cover, FAO
    soils, etc.) are also implemented.
An interface to store, retrieve, search, join and share datasets, based on Elasticsearch (ES) API. As a decentralized, FAIR and collaborative search engine and database effort, it proposes a simple push/pull/search mechanism only based on ES, a tool which can be deployed on nearly any hardware. It is a high-level R-ES binding to ease data usage using 'elastic' package (S. Chamberlain (2020)) <https://docs.ropensci.org/elastic/>, extends joins from 'dplyr' package (H. Wickham et al. (2020)) <https://dplyr.tidyverse.org/> and integrates specific biological format importation with Bioconductor packages such as 'rtracklayer' (M. Lawrence and al. (2009) <doi:10.1093/bioinformatics/btp328>) <http://bioconductor.org/packages/rtracklayer>, 'Biostrings' (H. Pagès and al. (2020) <doi:10.18129/B9.bioc.Biostrings>) <http://bioconductor.org/packages/Biostrings>, and 'Rsamtools' (M. Morgan and al. (2020) <doi:10.18129/B9.bioc.Rsamtools>) <http://bioconductor.org/packages/Rsamtools>, but also a long list of more common ones with 'rio' (C-h. Chan and al. (2018)) <https://cran.r-project.org/package=rio>.
Contains kidney care oriented functions.
			 Current version contains functions for calculation of:
			 - Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors by Rao et al. (2009) <doi:10.1097/TP.0b013e3181ac620b>.
			 - Estimated glomerular filtration rate by CKD-EPI, MDRD and other equations.
			 Citation: Bikbov B. R open source programming code for calculation of the Kidney Donor Profile Index and Kidney Donor Risk Index. Kidney Diseases (2018) 4:269–272 <doi:10.1159/000492427> (the only citation for the whole package).
Miscellaneous functions to simplify and expedite analyses of experimental data. Examples include a function that plots sample means of groups in a factorial experimental design, a function that conducts robust regressions with bootstrapped samples, and a function that conducts robust two-way analysis of variance. Many of the functions will require installing package(s) listed in the Selected References. Selected References: Canty & Ripley (2021) <https://CRAN.R-project.org/package=boot>. Chen & Popovich (2002) <doi:10.4135/9781412983808>. Cohen (1988) <doi:10.4324/9780203771587>. DeCarlo (1997) <doi:10.1037/1082-989X.2.3.292>. Dinno (2018) <https://CRAN.R-project.org/package=paran>. Doane & Seward (2011) <doi:10.1080/10691898.2011.11889611>. Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Edwards et al. (2020) <https://CRAN.R-project.org/package=lemon>. Field (2001). <doi:10.1037/1082-989X.6.2.161> Fox et al. (2020) <https://CRAN.R-project.org/package=car>. Hedges & Olkin (1985, ISBN: 0123363802) Hester et al. (2020) <https://CRAN.R-project.org/package=remotes>. Ioannidis (2005) <doi:10.1371/journal.pmed.0020124> Kim (2021) <doi:10.5281/zenodo.4445388>. Kim (2020) <https://CRAN.R-project.org/package=ezr>. Long (2020) <https://CRAN.R-project.org/package=interactions>. Mair & Wilcox (2021) <https://CRAN.R-project.org/package=WRS2>. Pasek et al. (2020) <https://CRAN.R-project.org/package=weights>. Simmons et al. (2011) <doi:10.1177/0956797611417632>. Steiger (1980) <doi:10.1037/0033-2909.87.2.245> Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>. Torchiano (2020) <https://CRAN.R-project.org/package=effsize>. Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>. Wilke (2021) <https://CRAN.R-project.org/package=ggridges>.
A collection of useful functions not found anywhere else,
        mainly for programming: Pretty intervals, generalized lagged
        differences, checking containment in an interval, and an
        alternative interface to assign().
Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific 
 cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are
 retrospectively built from relatives of probands for whom the genotype is known. Currently 
 the method of moments and marginal maximum likelihood are implemented. Confidence intervals 
 are calculated from bootstrap samples.
 Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee.
Functions for simulating and estimating kinship-related dispersal. Based
    on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. 
    Hoffmann (2019) <doi:10.1111/1755-0998.13043> "A genomic approach to inferring kinship 
    reveals limited intergenerational dispersal in the yellow fever mosquito".
    Assumes an additive variance model of dispersal in two dimensions, compatible with 
    Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, 
    as well as the functions needed to estimate parent-offspring dispersal for simulated or 
    empirical data, and to undertake sampling design for future field studies of dispersal. 
    For ease of use an integrated Shiny app is also included.
Allows analyzing time series representing two-dimensional movements.
    It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns,
    and returns several dynamical properties such as speed, acceleration or curvature.
Data cleaning, processing, visualisation and analysis for manual (Goldmann) and automated (Octopus 900) kinetic visual field data.
Data on houses in and around Seattle WA are included. Basic 
    characteristics are given along with sale prices.
Routines to handle family data with a pedigree object (2014, <doi: 10.1159/000363105>). The initial purpose was to create correlation structures that describe family relationships such as kinship and identity-by-descent, which can be used to model family data in mixed effects models, such as in the coxme function. Also includes a tool for pedigree drawing which is focused on producing compact layouts without intervention. Recent additions include utilities to trim the pedigree object with various criteria, and kinship for the X chromosome.
Multi-modal magnetic resonance imaging ('MRI')
    data from the 'Kirby21' 'reproducibility' study
    <https://www.nitrc.org/projects/multimodal/>, including functional
    and structural imaging.
Functional magnetic resonance imaging ('fMRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
Structural T1 magnetic resonance imaging ('MRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, stochastic migration model. Providing accessibility for considering species migration in niche-based species distribution models.
Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.
Provides tools to calculate the theoretical hydrodynamic response
    of an aquifer undergoing harmonic straining or pressurization, or analyze
    measured responses. There are
    two classes of models here, designed for use with confined
    aquifers: (1) for sealed wells, based on the model of 
    Kitagawa et al (2011, <doi:10.1029/2010JB007794>), 
    and (2) for open wells, based on the models of
    Cooper et al (1965, <doi:10.1029/JZ070i016p03915>), 
    Hsieh et al (1987, <doi:10.1029/WR023i010p01824>), 
    Rojstaczer (1988, <doi:10.1029/JB093iB11p13619>), 
    Liu et al (1989, <doi:10.1029/JB094iB07p09453>), and
    Wang et al (2018, <doi:10.1029/2018WR022793>). Wang's 
    solution is a special exception which
    allows for leakage out of the aquifer 
    (semi-confined); it is equivalent to Hsieh's model
    when there is no leakage (the confined case).
    These models treat strain (or aquifer head) as an input to the
    physical system, and fluid-pressure (or water height) as the output. The
    applicable frequency band of these models is characteristic of seismic
    waves, atmospheric pressure fluctuations, and solid earth tides.
A wrapper for querying 'WISKI' databases via the 'KiWIS' 'REST' API. 'WISKI' is an 'SQL' relational database 
  used for the collection and storage of water data developed by KISTERS and 'KiWIS' is a 'REST' service that provides
  access to 'WISKI' databases via HTTP requests (<https://water.kisters.de/en/technology-trends/kisters-and-open-data/>). 
  Contains a list of default databases (called 'hubs') and also allows users to provide their own 'KiWIS' URL. 
  Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles.
Weighted k-Nearest Neighbors for Classification, Regression and Clustering.
Miscellaneous functions for classification and visualization,
	 e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, 
	 an interface to 'svmlight' and stepclass() wrapper variable selection 
   	 for supervised classification, partimat() visualization of classification rules 
         and shardsplot() of cluster results as well as kmodes() clustering for categorical data, 
	 corclust() variable clustering, variable extraction from different variable clustering models 
         and weight of evidence preprocessing.
Functions to search, retrieve and apply classifications 
  and codelists using Statistics Norway's API <https://www.ssb.no/klass> 
  from the system 'KLASS'. Retrieves classifications by date with options 
  to choose language, hierarchical level and formatting.
A set of functions designed to quickly generate results of a
    multiple choice test. Generates detailed global results, lists for
    anonymous feedback and personalised result feedback (in LaTeX and/or PDF
    format), as well as item statistics like Cronbach's alpha or disciminatory
    power. klausuR also includes a plugin for the R GUI and IDE RKWard,
    providing dialogs for its basic features. To use them, install RKWard from
    http://rkward.sf.net (plugins are detected automatically). Due to some
    restrictions on CRAN, the full package sources are only available from the
    project homepage.
Most importantly, calculates Kullback-Leibler Divergence (KLD), Turing's perspective estimator
 and their confidence intervals.
Six relational 'tibbles' from the Kootenay Lake Large Trout Exploitation study.
  The study which ran from 2008 to 2014 caught, tagged and released large Rainbow Trout and Bull Trout
  in Kootenay Lake by boat angling. 
  The fish were tagged with internal acoustic tags and/or high reward external tags
  and subsequently detected by an acoustic receiver array as well as reported by anglers.
  The data are analysed by Thorley and Andrusak (1994) <doi:10.7717/peerj.2874>
  to estimate the natural and fishing mortality of both species.
Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) <doi:10.1088/1742-6596/1955/1/012047>.
Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.
An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) <DOI:10.1007/978-3-319-11257-2_20>, which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette.
Used to create dynamic, interactive 'D3.js' 
  based parallel coordinates and principal component plots in 'R'.
  The plots make visualizing k-means or other clusters simple and informative.
Computes various confidence intervals for the Kaplan-Meier
        estimator, namely: Petos CI, Rothman CI, CI's based on
        Greenwoods variance, Thomas and Grunkemeier CI and the
        simultaneous confidence bands by Nair and Hall and Wellner.
Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015)<doi: 10.1007/s00180-015-0567-9> and Mai Zhou and Yifan Yang (2017) <10.1002/wics.1400>. More applications could be found in Mai Zhou (2015) <doi: 10.1201/b18598>.
Compute p-values of metabolite differential expression analysis using the kernel-based approach.
Algorithms of distance-based k-medoids clustering: simple and fast 
  k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. 
  Calculate distances for mixed variable data such as Gower, Podani, Wishart, 
  Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and 
  relative criteria. The internal criteria includes silhouette index and shadow 
  values. The relative criterium applies bootstrap procedure producing a heatmap 
  with a flexible reordering matrix algorithm such as complete, ward, or average 
  linkages. The cluster result can be plotted in a marked barplot or pca biplot.
Contains tools for rapidly computing distance matrices 
    and clustering large sequence datasets using fast alignment-free 
    k-mer counting and recursive k-means partitioning. 
    See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> 
    for a review of k-mer counting methods and applications for 
    biological sequence analysis.
Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 <doi:10.1093/biostatistics/kxq018>).
An implementation of k-means specifically design
        to cluster longitudinal data. It provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical
        interface for choosing the 'best' number of clusters.
An implementation of k-means specifically design
        to cluster joint trajectories (longitudinal data on
        several variable-trajectories).
        Like 'kml', it provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
        interface for choosing the 'best' number of clusters. In addition, the 3D graph
        representing the mean joint-trajectories of each cluster can be exported through
        LaTeX in a 3D dynamic rotating PDF graph.
K-means for longitudinal data using shape-respecting distance and shape-respecting means.
An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, "Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.
Data sets and functions for Klein and Moeschberger (1997),
        "Survival Analysis, Techniques for Censored and Truncated
        Data", Springer.
Produce Kaplan–Meier plots in the style recommended
    following the KMunicate study by Morris et al. (2019)
    <doi:10.1136/bmjopen-2019-030215>. The KMunicate style consists of
    Kaplan-Meier curves with confidence intervals to quantify uncertainty
    and an extended risk table (per treatment arm) depicting the number of
    study subjects at risk, events, and censored observations over time.
    The resulting plots are built using 'ggplot2' and can be further
    customised to a certain extent, including themes, fonts, and colour
    scales.
The sampl.mcmc() function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints.
Given a set of points around a knee curve,
    analyzes first and second derivatives to find knee points.
Provides the ability to create dynamic citations
    in which the bibliographic information is pulled from the web rather
    than having to be entered into a local database such as 'bibtex' ahead of
    time. The package is primarily aimed at authoring in the R 'markdown'
    format, and can provide outputs for web-based authoring such as linked
    text for inline citations.  Cite using a 'DOI', URL, or
    'bibtex' file key.  See the package URL for details.
Provides several helper functions for working with 'knitr' and 'LaTeX'.
  It includes 'xTab' for creating traditional 'LaTeX' tables, 'lTab' for generating
  'longtable' environments, and 'sTab' for generating a 'supertabular' environment.
  Additionally, this package contains a knitr_setup() function which fixes a
  well-known bug in 'knitr', which distorts the 'results="asis"' command when used
  in conjunction with user-defined commands; and a com command (<<com=TRUE>>=)
  which renders the output from 'knitr' as a 'LaTeX' command.
Provides a general-purpose tool for dynamic report generation in R
    using Literate Programming techniques.
A framework to create Bootstrap <http://getbootstrap.com/> HTML reports from 'knitr'
    'rmarkdown'.
Implements a data language engine for incorporating data directly in 
    'rmarkdown' documents so that they can be made completely standalone.
Provides a progress bar similar to 'dplyr' that can write progress out to a 
    variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown',
    and you still want to see progress of calculations in the terminal.
Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix.
    The implementation is based on cover trees introduced by
    Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.
This package provides the implementation of an exact formula of the
    ith nearest neighbour distance distribution and implementations of tests of
    independence based on that formula. Furthermore the package provides a
    general framework to benchmark tests of independence.
Two main functionalities are provided. One of them is predicting values with 
    k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm.
    These are carried out in parallel using multiple threads.
An interactive document on  the topic of K-nearest neighbour (KNN) using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/KNNShiny/>.
k Nearest Neighbors with variable selection, combine grid search and forward selection to achieve variable selection in order to improve k Nearest Neighbors predictive performance.
Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <https://github.com/mtrupiano1/knnwtsim/blob/main/README.md>.
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
  when performing variable selection. 
  For more information, see the website below and the accompanying paper: Candes et al., 
  "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", 
  J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.
Functions for identification of putative causal loci in whole-genome sequencing data. The functions allow genome-wide association scan. It also includes an efficient knockoff generator for genetic data.
Using this package, users can access to the largest collection of public data and statistics on the Internet featuring about 2.5 billion time series from thousands of sources collected in 'Knoema' repository and use rich R calculations in order to analyze the data. Because data in 'Knoema' is time series data, 'Knoema' function offers data in a number of formats usable in R such as 'ts', 'xts' or 'zoo'. For more information about 'Knoema' API go to <https://knoema.com/dev/docs>.
Makes nice pictures of knots using Bezier curves and
  numerical optimization.
It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.
Random Forest (RF) and Regularized Random Forest can be used for feature selection. Moreover, by Guided Regularized Random Forest, statistical-based weights are used to guide the regularization of random forest and further used for feature selection. This package can integrate prior information from multiple domains (statistical based and knowledge domain) to guide the regularization of random forest and feature selection. For more details, see reference: Guan X., Liu L. (2018) <doi:10.1007/978-3-319-78759-6_1>.
Retrieve data from 'kintone' (<https://www.kintone.com/>) via its API.
    'kintone' is an enterprise application platform.
A novel strategy for conducting variable selection without prior model topology 
             knowledge using the knockoff method (Barber and Candes (2015) <doi:10.1214/15-AOS1337>)
             with extreme boosted tree models (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>). 
             This method is inspired by the original knockoff method, where the differences between 
             original and knockoff variables are used for variable selection with false discovery rate 
             control. In addition to the original knockoff generating methods, two new sampling methods 
             are available to be implemented, namely the sparse covariance and principal component 
             knockoff methods. As results, the indices of selected variables are returned.
An unsupervised and semi-supervised learning algorithm that performs feature extraction 
  from noisy and high-dimensional data. It facilitates identification of patterns representing underlying 
  groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. 
  (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) 
  Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.
Read Swiss time series data from the 'KOF Datenservice' API, <https://datenservice.kof.ethz.ch>. The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.
Provides a function that uses a genetic algorithm to search for a subset
 of size k from the integers 1:n, such that a user-supplied objective function 
 is minimized at that subset.  The selection step is done by tournament selection 
 based on ranks, and elitism may be used to retain a portion of the best solutions 
 from one generation to the next. Population objective function values may 
 optionally be evaluated in parallel.
Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government.
    See <https://nlftp.mlit.go.jp/index.html> for more information.
Provides an alternative, more efficient evaluation of extreme
  probabilities of Kolmogorov's goodness-of-fit measure, Dn, when compared to
  the original implementation of Wang, Marsaglia, and Tsang. These
  probabilities are used in Kolmogorov-Smirnov tests when comparing two
  samples.
Write beautiful yet customizable letters in R Markdown and
  directly obtain the finished PDF. Smooth generation of PDFs is realized by
  'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class.
  'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes
  with emphasis on typography and versatility. 'KOMA-Script' is particularly
  useful for international writers as it handles various paper formats well, 
  provides layouts for many common window envelope types (e.g. German, US, 
  French, Japanese) and lets you define your own layouts. The package comes 
  with a default letter layout based on 'DIN 5008B'.
Statistical methods that quantify the conditions necessary to alter
    inferences, also known as sensitivity analysis, are becoming increasingly
    important to a variety of quantitative sciences. A series of recent works,
    including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al.
    (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses
    by considering the characteristics of omitted variables or unobserved cases
    that would change an inference if such variables or cases were observed. These
    analyses generate statements such as "an omitted variable would have to be
    correlated at xx with the predictor of interest (e.g., treatment) and outcome
    to invalidate an inference of a treatment effect". Or "one would have to replace
    pp percent of the observed data with null hypothesis cases to invalidate the
    inference". We implement these recent developments of sensitivity analysis and
    provide modules to calculate these two robustness indices and generate such
    statements in R. In particular, the functions konfound(), pkonfound() and 
    mkonfound() allow users to calculate the robustness of inferences for a user's 
    own model, a single published study and multiple studies respectively.
The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2019, <arXiv:1901.05739v1>). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions.
A set of tools to analyze texts. Includes, amongst others, functions for
          automatic language detection, hyphenation, several indices of lexical diversity
          (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch,
          SMOG, LIX, Dale-Chall). Basic import functions for language corpora are also
          provided, to enable frequency analyses (supports Celex and Leipzig Corpora
          Collection file formats) and measures like tf-idf. Note: For full functionality
          a local installation of TreeTagger is recommended. It is also recommended to
          not load this package directly, but by loading one of the available language
          support packages from the 'l10n' repository
          <https://undocumeantit.github.io/repos/l10n/>. 'koRpus' also includes a plugin
          for the R GUI and IDE RKWard, providing graphical dialogs for its basic
          features. The respective R package 'rkward' cannot be installed directly from a
          repository, as it is a part of RKWard. To make full use of this feature, please
          install RKWard from <https://rkward.kde.org> (plugins are detected
          automatically). Due to some restrictions on CRAN, the full package sources are
          only available from the project homepage. To ask for help, report bugs, request
          features, or discuss the development of the package, please subscribe to the
          koRpus-dev mailing list (<https://korpusml.reaktanz.de>).
Adds support for the English language to the 'koRpus' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the
                    package, please consider subscribing to the koRpus-dev mailing list (<https://korpusml.reaktanz.de>).
Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <arXiv:1907.03153>.
Cubic spline fitting along with knot selection, includes support for additional variables.
Implementations of two empirical versions the kernel partial correlation (KPC) coefficient
    and the associated variable selection algorithms. KPC is a measure of the strength of conditional
    association between Y and Z given X, with X, Y, Z being random variables taking values in
    general topological spaces. As the name suggests, KPC is defined in terms of kernels on
    reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number
    between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if
    and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on
    geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is
    consistent under very weak conditions. The other empirical estimator, defined using conditional
    mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable
    conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph
    based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm
    based on the RKHS based estimator. For more details on KPC, its empirical estimators and its
    application on variable selection, see Huang, Z., N. Deb, and B. Sen (2020). “Kernel partial
    correlation coefficient – a measure of conditional dependence” <arXiv:2012.14804>. When X is
    empty, KPC measures the unconditional dependence between Y and Z, which has been described
    in Deb, N., P. Ghosal, and B. Sen (2020), “Measuring association on topological spaces using
    kernels and geometric graphs” <arXiv:2010.01768>, and it is implemented in the functions
    KMAc() and Klin() in this package. The latter can be computed in near linear time.
Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise.
The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary,  the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.
Functions that implement the known population median test.
Software for k-means clustering of partially 
    observed data from Chi, Chi, and Baraniuk (2016) <doi:10.1080/00031305.2015.1086685>.
Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, <https://hbr.org/1983/09/purchasing-must-become-supply-management>)
    for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and
    uses this information to place products and services within the Kraljic Matrix.
Provides basic functions for Continuation-Passing Style development.
Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.
Simple and highly optimized ordinary kriging algorithm to plot geographical data
Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Provides tools for applying Krippendorff's Alpha methodology <DOI:10.1080/19312450709336664>. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Bootstrap inference is permitted, and the computation can be done in parallel.
Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) <DOI:10.1093/biostatistics/kxu056>.
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
Kernel smoothers for univariate and multivariate data, including densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) <doi:10.1201/9780429485572>.
Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests 
	with different rank score criteria, Steel's multiple comparison test, and the 
        Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact 
        P-values, all valid under randomization, with or without ties, or conditionally 
        under random sampling from populations, given the observed tie pattern.  Except for 
        Steel's test and the JT test it also combines these tests across several blocks of 
	samples.  Also analyzed are 2 x t contingency tables and their blocked combinations 
	using the Kruskal-Wallis criterion.  Steel's test is inverted to provide simultaneous 
	confidence bounds for shift parameters.  A plotting function compares tail probabilities
	obtained under asymptotic approximation with those obtained via simulation or exact 
	calculations.
Implements the Lilliefors-corrected Kolmogorov-Smirnov test for use
    in goodness-of-fit tests, suitable when population parameters are unknown and
    must be estimated by sample statistics. P-values are estimated by simulation.
    Can be used with a variety of continuous distributions, including normal,
    lognormal, univariate mixtures of normals, uniform, loguniform, exponential,
    gamma, and Weibull distributions. Functions to generate random numbers and
    calculate density, distribution, and quantile functions are provided for use
    with the log uniform and mixture distributions.
An adaptation of Kernelized Stein Discrepancy, this package provides a goodness-of-fit test of whether a given i.i.d. sample is drawn from a given distribution. It works for any distribution once its score function (the derivative of log-density) can be provided. This method is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <arXiv:1602.03253>.
Infers relative kinase activity from phosphoproteomics data
    using the method described by Casado et al. (2013) <doi:10.1126/scisignal.2003573>.
Selection of k in k-means clustering based on Pham et al. paper
    ``Selection of k in k-means clustering''.
Computes a p-value of the one-sample two-sided (or one-sided, as a special case) Kolmogorov-Smirnov (KS) statistic, for any fixed critical level, and an arbitrary, possibly large sample size for a pre-specified purely discrete, mixed or continuous cumulative distribution function (cdf) under the null hypothesis. If a data sample is supplied, 'KSgeneral' computes the p-value corresponding to the value of the KS test statistic computed based on the user provided data sample. The package 'KSgeneral' implements a novel, accurate and efficient method named Exact-KS-FFT, expressing the p-value as a double-boundary non-crossing probability for a homogeneous Poisson process, which is then efficiently computed using Fast Fourier Transform (FFT). The package can also be used to compute and plot the complementary cdf of the KS statistic which is known to depend on the hypothesized distribution when the latter is discontinuous (i.e. purely discrete or mixed). To cite this package in publication use: Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42. <doi:10.18637/jss.v095.i10>.
Clustering typically assigns data points into discrete groups, but the clusters can sometimes be indistinct. Cluster sharpening adjusts an existing clustering to create contrast between groups. This package provides a general interface for cluster sharpening along with several implementations based on different excision criteria.
Prediction with k* nearest neighbor algorithm 
    based on a publication by Anava and Levy (2016) <arXiv:1701.07266>.
To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain. The 'kst' package provides 
   basic functionalities to generate, handle, and manipulate knowledge 
   structures and knowledge spaces.
Tools for estimate (joint) cumulants and (joint) products of cumulants of a random sample using (multivariate) k-statistics and (multivariate) polykays, unbiased estimators with minimum variance. Tools for generating univariate and multivariate Faa di Bruno's formula and related polynomials, such as Bell polynomials, generalized complete Bell polynomials, partition polynomials and generalized partition polynomials. For more details see Di Nardo E., Guarino G., Senato D. (2009) <arXiv:0807.5008>, <arXiv:1012.6008>.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain.  The 'kstIO' package 
   provides basic functionalities to read and write KST data from/to 
   files to be used together with the 'kst', 'kstMatrix', 'pks' or 
   'DAKS' packages.
Knowledge space theory by Doignon and Falmagne (1999)
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize
   knowledge structures in a particular domain. The 'kstMatrix' package 
   provides basic functionalities to generate, handle, and manipulate 
   knowledge structures and knowledge spaces. Opposed to the 'kst'
   package, 'kstMatrix' uses matrix representations for knowledge
   structures. Furthermore, 'kstMatrix' contains several knowledge spaces
   developed by the research group around Cornelia Dowling through
   querying experts.
A clustering algorithm similar to K-Means is implemented, it has two main advantages, 
    namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when
    there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, 
    if there are no outliers in the sample, results will be similar than those obtained by a classic algorithm (K-Means).
    Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale.
    (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).
Provides a function called COTUCKER3() (Co-Inertia Analysis
    + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of
    matrices, as well as other functions called PCA() (Principal Component Analysis)
    and BGA() (Between-Groups Analysis), which perform analysis of one matrix,
    COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA()
    (Partial Triadic Analysis), STATIS(), STATISDUAL() and TUCKER3(), which perform analysis of a sequence
    of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO()
    (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS
    method), which also perform analysis of two sequences of matrices.
This is designed for use with an arbitrary set of equations with
 an arbitrary set of unknowns.
 The user selects "fixed" values for enough unknowns to leave as many variables as
 there are equations, which in most cases means the system is properly
 defined and a unique solution exists. The function, the fixed values
 and initial values for the remaining unknowns are fed to a nonlinear backsolver.  
 The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.
This function performs the two-sample Kuiper test to assess the anomaly of continuous, one-dimensional probability distributions. References used for this method are (1). Kuiper, N. H. (1960). <DOI:10.1016/S1385-7258(60)50006-0> and (2). Paltani, S. (2004). <DOI:10.1051/0004-6361:20034220>.
Provides various functions and data sets from experiments at the Faculty of Life Sciences, University of Copenhagen. This package will be discontinued and archived, and the functions and datasets will be maintained and updated in the MESS package
Computes measures of multivariate kurtosis, matrices of fourth-order moments and cumulants, kurtosis-based projection pursuit.
             Franceschini, C. and Loperfido, N. (2018, ISBN:978-3-319-73905-2). "An Algorithm for Finding Projections with Extreme Kurtosis".
             Loperfido, N. (2017,ISSN:0024-3795). "A New Kurtosis Matrix, with Statistical Applications".
Tools for data importation, recoding, and inspection.
    There are functions to create new project
    folders, R code templates, create uniquely named output
    directories, and to quickly obtain a visual summary for each
    variable in a data frame.  The main feature here is the systematic
    implementation of the "variable key" framework for data
    importation and recoding.  We are eager to have community feedback
    about the variable key and the vignette about it. In version 1.7,
    the function 'semTable' is removed. It was deprecated since 1.67.
    That is provided in a separate package, 'semTable'.
The format KVH is a lightweight format that can be read/written both by humans and machines.
   It can be useful in situations where XML or alike formats seem to be an overkill.
   We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.
Calculation groundwater mounding beneath an
    infiltration basin based on the Hantush (1967) equation
    (<doi:10.1029/WR003i001p00227>). The correct implementation is shown
    with a verification example based on a USGS report (page 25,
    <https://pubs.usgs.gov/sir/2010/5102/support/sir2010-5102.pdf#page=35>).
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
Separation of wave motions in different scales and directions based on
     Kolmogorov-Zurbenko Periodograms and Kolmogorov-Zurbenko Fourier Transform.
A colletion of functions to implement Kolmogorov-Zurbenko
        Fourier transform based periodograms and smoothing methods
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection).
    The algorithms are based on coordinate descent and local combinatorial search.
    For more details, check the paper by Hazimeh and Mazumder (2020);
    the link is provided in the URL field below.
Provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.
LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.
Fitting the regularization path of the L1-regularized multi-state models since they can exploit sparsity structure of input. Different tuning regularization parameter methods are provided. The cumulative hazard rate estimation and the transition probability predictions can be made from the fitted models.
L1 estimation for linear regression, density, distribution function,
    quantile function and random number generation for univariate and multivariate
    Laplace distribution.
Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a robust spectral clustering using Lasso regularization" (2021).
Efficient implementation of Friedman's boosting algorithm with
    l2-loss function and coordinate direction (design matrix columns) basis
    functions.
Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.
Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>.
Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design.
A variety of ordination and community analyses
   useful in analysis of data sets in community ecology.  
   Includes many of the common ordination methods, with 
   graphical routines to facilitate their interpretation, 
   as well as several novel analyses.
The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
Functions which provide a range of axis labeling algorithms.
Work with labelled data imported from 'SPSS'
    or 'Stata' with 'haven' or 'foreign'. This package
    provides useful functions to deal with "haven_labelled" and
    "haven_labelled_spss" classes introduced by 'haven' package.
Assign meaningful labels to data frame columns.
    'labelmachine' manages your label assignment rules in 'yaml' files
    and makes it easy to use the same labels in multiple projects.
An implementation of distance-based ranking algorithms to predict rankings of labels.  Two common algorithms are included: the naive Bayes and the nearest neighbor algorithms.
Labels are a common construct in statistical software providing a 
  human readable description of a variable. While variable names are succinct,
  quick to type, and follow a language's naming conventions, labels may 
  be more illustrative and may use plain text and spaces. R does not provide
  native support for labels. Some packages, however, have made this feature
  available.  Most notably, the 'Hmisc' package provides labelling methods
  for a number of different object. Due to design decisions, these methods
  are not all exported, and so are unavailable for use in package development.
  The 'labelVector' package supports labels for atomic vectors in a light-weight
  design that is suitable for use in other packages.
Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.
Allows the user to map multilingual free-text of occupations to a broad range
    of standardized classifications. The package facilitates automatic occupation coding
    (see, e.g., Gweon et al. (2017) <doi:10.1515/jos-2017-0006> and Turrell et al. (2019)
    <doi:10.3386/w25837>), where the ISCO to ESCO mapping is exploited to extend the
    occupations hierarchy, Le Vrang et al. (2014) <doi:10.1109/mc.2014.283>. Document
    vectorization is performed using the multilingual ESCO corpus. A method based on the 
    nearest neighbor search is used to suggest the closest ISCO occupation.
Dati, scripts e funzioni per il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale" (Datasets, scripts and functions to support the book "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale").
Simplex optimization algorithms as firstly proposed by Spendley
    et al. (1962) <doi:10.1080/00401706.1962.10490033> and later modified by 
    Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> for laboratory and 
    manufacturing processes. The package also provides tools for graphical 
    representation of the simplexes and some example response surfaces that 
    are useful in illustrating the optimization process.
Insieme di funzioni di supporto al volume "Laboratorio di
        Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006.
        This package contains sets of functions defined in "Laboratorio
        di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia,
        2006. Function names and docs are in italian as well.
Contains data sets to accompany the book: Lazic SE
    (2016). "Experimental Design for Laboratory Biologists: Maximising Information
    and Improving Reproducibility". Cambridge University Press.
Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details.
Connect to the 'Less Annoying CRM' API with ease to get your crm data in a clean and tidy format. 'Less Annoying CRM' is a simple CRM built for small businesses, more information is available on their website <https://www.lessannoyingcrm.com/>.
Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Easily analyze
    the methods Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), Dmax, Lactate Turning Point (LTP),
    and Lactate / Intensity ratio (LTratio) in cycling, running, or swimming.
    Beaver WL, Wasserman K, Whipp BJ (1985) <doi:10.1152/jappl.1985.59.6.1936>.
    Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W (1985) <doi:10.1055/s-2008-1025824>.
    Kindermann W, Simon G, Keul J (1979) <doi:10.1007/BF00421101>.
    Skinner JS, Mclellan TH (1980) <doi:10.1080/02701367.1980.10609285>.
    Berg A, Jakob E, Lehmann M, Dickhuth HH, Huber G, Keul J (1990) <PMID:2408033>.
    Zoladz JA, Rademaker AC, Sargeant AJ (1995) <doi:10.1113/jphysiol.1995.sp020959>.
    Cheng B, Kuipers H, Snyder A, Keizer H, Jeukendrup A, Hesselink M (1992) <doi:10.1055/s-2007-1021309>.
    Bishop D, Jenkins DG, Mackinnon LT (1998) <doi:10.1097/00005768-199808000-00014>.
    Hughson RL, Weisiger KH, Swanson GD (1987) <doi:10.1152/jappl.1987.62.5.1975>.
    Jamnick NA, Botella J, Pyne DB, Bishop DJ (2018) <doi:10.1371/journal.pone.0199794>.
    Hofmann P, Tschakert G (2017) <doi:10.3389/fphys.2017.00337>.
    Hofmann P, Pokan R, von Duvillard SP, Seibert FJ, Zweiker R, Schmid P (1997) <doi:10.1097/00005768-199706000-00005>.
    Pokan R, Hofmann P, Von Duvillard SP, et al. (1997) <doi:10.1097/00005768-199708000-00009>.
    Dickhuth H-H, Yin L, Niess A, et al. (1999) <doi:10.1055/s-2007-971105>.
Functions for estimating the gliding box lacunarity (GBL),
    covariance, and pair-correlation of a random closed set (RACS) in 2D
    from a binary coverage map (e.g. presence-absence land cover maps).
    Contains a number of newly-developed covariance-based estimators of
    GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and
    balanced estimators, proposed by Picka (2000)
    <http://www.jstor.org/stable/1428408>, for covariance, centred
    covariance, and pair-correlation.  Also contains methods for
    estimating contagion-like properties of RACS and simulating 2D Boolean
    models.  Binary coverage maps are usually represented as raster images
    with pixel values of TRUE, FALSE or NA, with NA representing
    unobserved pixels.  A demo for extracting such a binary map from a
    geospatial data format is provided.  Binary maps may also be
    represented using polygonal sets as the foreground, however for most
    computations such maps are converted into raster images.  The package
    is based on research conducted during the author's PhD studies.
LAD (Least Absolute Deviations) estimation for linear regression, confidence intervals, tests of hypotheses, methods for outliers detection, measures of leverage, methods of diagnostics for LAD regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005)  <doi:10.1080/0094965042000223680>, Elian et al. (2000) <doi:10.1080/03610920008832518> and Dodge (1997) <doi:10.1006/jmva.1997.1666>. This package also has two datasets "houses" and "pollution", respectively, from Narula and Wellington (1977) <doi:10.2307/1268628> and Santos et al. (2016) <doi:10.1371/journal.pone.0163225>.
Estimation of indicators on social exclusion and poverty, as well
    as Pareto tail modeling for empirical income distributions.
The package contains functions to compare and group means.
Methods for fast access to large ASCII files.  Currently the
    following file formats are supported: comma separated format (CSV) and fixed
    width format. It is assumed that the files are too large to fit into memory,
    although the package can also be used to efficiently access files that do
    fit into memory. Methods are provided to access and process files blockwise.
    Furthermore, an opened file can be accessed as one would an ordinary
    data.frame. The LaF vignette gives an overview of the functionality
    provided.
Provides classes and methods for lagged objects.
Client for programmatic access to the Lake
    Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions
    for accessing lake water quality and ecological context data for the US.
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided.
Model-based linear model trees adjusting for spatial correlation using a
             simultaneous autoregressive spatial lag, Wagner and Zeileis (2019)
	     <doi:10.1111/geer.12146>.
Lag-sequential analysis is a method of assessing of patterns (what tends to 
        follow what?) in sequences of codes. The codes are typically for discrete 
        behaviors or states. The functions in this package read a stream of codes,
        or a frequency transition matrix, and produce a variety of lag sequential  
        statistics, including transitional frequencies, expected transitional frequencies, 
        transitional probabilities, z values, adjusted residuals, Yule's Q values, 
        likelihood ratio tests of stationarity across time and homogeneity across groups 
        or segments, transformed kappas for unidirectional dependence, bidirectional 
        dependence, parallel and nonparallel dominance, and significance levels based on 
        both parametric and randomization tests. The methods are described in
  Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>,
  O'Connor (1999) <doi:10.3758/BF03200753>,
  Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and
  Wampold (1995, ISBN:0-89391-919-5).
Provides the tables from the 'Sean Lahman Baseball Database' as
    a set of R data.frames. It uses the data on pitching, hitting and fielding
    performance and other tables from 1871 through 2019, as recorded in the 2020
    version of the database. Documentation examples show how many baseball
    questions can be investigated.
A collection of tools for the calculation of freewater metabolism.
Lake morphometry metrics are used by limnologists to understand,
    among other things, the ecological processes in a lake. Traditionally, these
    metrics are calculated by hand, with planimeters, and increasingly with
    commercial GIS products. All of these methods work; however, they are either
    outdated, difficult to reproduce, or require expensive licenses to use. The
    'lakemorpho' package provides the tools to calculate a typical suite
    of these metrics from an input elevation model and lake polygon. The metrics
    currently supported are: fetch, major axis, minor axis, major/minor axis 
    ratio, maximum length, maximum width, mean width, maximum depth, mean depth, 
    shoreline development, shoreline length, surface area, and volume.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Includes some procedures for latent variable modeling with a 
    particular focus on multilevel data.
    The 'LAM' package contains mean and covariance structure modelling
    for multivariate normally distributed data (mlnormal(); Longford, 1987;
    <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm 
    (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and 
    penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 
    2014; <doi:10.1093/aje/kwt245>).
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Currently the package includes 14 methods for calculating internal
    consistency reliability but is still growing.  The package allows users
    access to whichever reliability estimator is deemed most appropriate for
    their situation.
Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'.
Runtime for serving containers that can execute R code on the 
    'AWS Lambda' serverless compute service <https://aws.amazon.com/lambda/>.
    Provides the necessary functionality for handling the various endpoints
    required for accepting new input and sending responses.
Lambert W x F distributions are a generalized framework to analyze
    skewed, heavy-tailed data. It is based on an input/output system, where the
    output random variable (RV) Y is a non-linearly transformed version of an input
    RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
    The transformed RV Y has a Lambert W x F distribution. This package contains
    functions to model and analyze skewed, heavy-tailed data the Lambert Way:
    simulate random samples, estimate parameters, compute quantiles, and plot/
    print results nicely. The most useful function is 'Gaussianize',
    which works similarly to 'scale', but actually makes the data Gaussian.
    A do-it-yourself toolkit allows users to define their own Lambert W x
    'MyFavoriteDistribution' and use it in their analysis right away.
Log-analytic methods intended for testing multiplicative effects.
Implements both real-valued branches of the Lambert-W function
    (Corless et al, 1996) <doi:10.1007/BF02124750> without the need for
    installing the entire GSL.
Using Liquid Association for Network Dynamics Detection.
Provides functions to estimate survival and a treatment effect using a landmark estimation approach.
Functions and tools for spatial interpolation and/or prediction of environmental variables (points to grids) 
   based on using Ensemble Machine Learning with geographical distances. Package also provides access to Global 
   Environmental Layers (<https://www.OpenLandMap.org>) produced by the OpenGeoHub.org foundation and collaborators. Some functions have been migrated and adopted from the
   Global Soil Information Facilities package.
Provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.
Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options.
Functions for converted Landsat 8 multispectral satellite imagery
    rescaled to the top of atmosphere (TOA) reflectance, radiance
    and/or at satellite brightness temperature using radiometric
    rescaling coefficients provided in the metadata file (MTL file).
Calculates landscape metrics for categorical landscape patterns in 
    a tidy workflow. 'landscapemetrics' reimplements the most common metrics from
    'FRAGSTATS' (<https://www.umass.edu/landeco/research/fragstats/fragstats.html>) 
    and new ones from the current literature on landscape metrics.
    This package supports 'raster' spatial objects and takes 
    RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the
    'raster' package as input arguments. It further provides utility functions
    to visualize patches, select metrics and building blocks to develop new 
    metrics.
Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.
Provides utility functions for some of the less-glamorous tasks involved
    in landscape analysis. It includes functions to coerce raster data to the
    common tibble format and vice versa, it helps with flexible reclassification
    tasks of raster data and it provides a function to merge multiple raster.
    Furthermore, 'landscapetools' helps landscape scientists to visualize their
    data by providing optional themes and utility functions to plot single
    landscapes, rasterstacks, -bricks and lists of raster.
A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution 
    of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. 
    It is based on a spatial geometry for describing the landscape and allocation of different cultivars, 
    a dispersal kernel for the dissemination of the pathogen, and a SEIR 
    ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step.
    It provides a useful tool to assess the performance of a wide range of deployment options with 
    respect to their epidemiological, evolutionary and economic outcomes.
    Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, 
    Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
Estimate drift and diffusion functions from time series and
    generate synthetic time series from given drift and diffusion coefficients.
Improve your text analysis with languagelayer
        <https://languagelayer.com>, a powerful language detection
        API.
Data sets exemplifying statistical methods, and some
        facilitatory utility functions used in ``Analyzing Linguistic
        Data: A practical introduction to statistics using R'',
        Cambridge University Press, 2008.
An implementation of the Language Server Protocol
    for R. The Language Server protocol is used by an editor client to
    integrate features like auto completion. See
    <https://microsoft.github.io/language-server-protocol/> for details.
Allows to install the R 'languageserver' with all dependencies
    into a separate library and use that independent installation
    automatically when R is instantiated as a language server process.
    Useful for making language server seamless to use without
    running into package version conflicts.
R interface for working with nanometer scale secondary ion mass 
    spectrometry (NanoSIMS) data exported from Look at NanoSIMS.
Solves the problem of Laplace deconvolution with noisy discrete
    non-equally spaced observations on a finite time interval based on expansions
    of the convolution kernel, the unknown function and the observed signal over
    Laguerre functions basis. It implements the methodology proposed in the paper
    "Laplace deconvolution on the basis of time domain data and its application to
    Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y.
    Rozenholc in ArXiv (http://arxiv.org/abs/1405.7107).
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
This package is intended for researchers studying historical labour relations (see http://www.historyoflabourrelations.org). The package allows for easy access of excel files in the standard defined by the Global Collaboratory on the History of Labour Relations. The package also allows for visualisation of labour relations according to the Collaboratory's format.
Auxiliary package for better/faster analytics, visualization, data mining, and machine learning
    tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling,
    Exploratory, and Scrapper, it helps the analyst or data scientist to get quick and robust
    results, without the need of repetitive coding or extensive programming skills.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
Measurements of leaf area are important in the studies of plant biological characteristics. High-throughput plant phenotyping using image analysis is the key area in the domain of plant phenotyping. For determining the leaf area, the RGB image is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions, respectively. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected leaf area.
Functions to write or append a R list to a file, as well as read, remove, modify elements from it without restoring the whole list.
Compute lifetime attributable risk of 
    radiation-induced cancer reveals that it can be helpful with 
    enhancement of the flexibility in research with fast calculation 
    and various options. Important reference papers include
    Berrington de Gonzalez et al. (2012) <doi:10.1088/0952-4746/32/3/205>,
    National Research Council (2006, ISBN:978-0-309-09156-5).
Efficient procedures for fitting an entire lasso sequence
        with the cost of a single least squares fit. Least angle
        regression and infinitesimal forward stagewise regression are
        related to the lasso, as described in the paper below.
Routines and documentation for solving regression problems
  while imposing an L1 constraint on the estimates, based on
  the algorithm of Osborne et al. (1998).
Implementation of the algorithm introduced in Shah, R. D.
    (2016) <http://www.jmlr.org/papers/volume17/13-515/13-515.pdf>.
    Data with thousands of predictors can be handled. The algorithm
    performs sequential Lasso fits on design matrices containing
    increasing sets of candidate interactions. Previous fits are used to greatly
    speed up subsequent fits so the algorithm is very efficient.
Fits generalized estimating equations with L1 regularization to longitudinal data with high dimensional covariates. Use a efficient iterative composite gradient descent algorithm.
Contains functions to estimate a penalized regression model using 3CoSE algorithm, see Weber, Striaukas, Schumacher Binder (2018) <doi:10.2139/ssrn.3211163>.
Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values.
Use the lasso regression method to perform approximate inference
    in high dimensions, by penalizing the effects of nuisance parameters.
L1 regularized regression (Lasso) solver using the Cyclic
        Coordinate Descent algorithm aka Lasso Shooting is fast. This
        implementation can choose which coefficients to penalize. It
        support coefficient-specific penalities and it can take X'X and
        X'y instead of X and y.
Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2017) <arxiv:1611.06655>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.
A graph
  proposed by Rosenbaum is useful
  for checking some properties of various
  sorts of latent scale, this program generates commands
  to obtain the graph using 'dot' from 'graphviz'.
The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated),
             comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation.
             The estimation is based on latent copula Gaussian models.
             For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017) <doi:10.1111/rssb.12168>.
             For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>.
             For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>.
             For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>.
Three methods are provided to estimate graphical models with latent variables: (1) Jin, Y., Ning, Y., and Tan, K. M. (2020) (preprint available); (2) Chandrasekaran, V., Parrilo, P. A. & Willsky, A. S. (2012) <doi:10.1214/11-AOS949>; (3) Tan, K. M., Ning, Y., Witten, D. M. & Liu, H. (2016) <doi:10.1093/biomet/asw050>.
Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <10.1016/j.socnet.2009.04.001>.
Executes arbitrary R or C functions some time after the current
    time, after the R execution stack has emptied. The functions are scheduled
    in an event loop.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Parses and converts LaTeX math formulas to R's plotmath
    expressions, used to enter mathematical formulas and symbols to be rendered as
    text, axis labels, etc. throughout R's plotting system.
Produces a PDF diff of two 'rmarkdown', Sweave or TeX files,  using
    the external 'latexdiff' utility.
Converts table-like objects to stand-alone PDF or PNG.
 Can be used to embed tables and arbitrary content in PDF or Word
 documents. Provides a low-level R interface for creating 'LaTeX'
 code, e.g. command() and a high-level interface for creating PDF
 documents, e.g. as.pdf.data.frame(). Extensive customization is
 available via mid-level functions, e.g. as.tabular(). See also 
 'package?latexpdf'. Support for PNG is experimental; see
 'as.png.data.frame'. Adapted from 'metrumrg' 
 <https://r-forge.r-project.org/R/?group_id=1215>.
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
A framework for clustering longitudinal datasets in a standardized way. Provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease.
Back-end connections to 'LattE' (<https://www.math.ucdavis.edu/~latte>) 
	for counting lattice points and integration inside convex polytopes and 
	'4ti2' (<http://www.4ti2.de/>) for algebraic, geometric, and combinatorial 
	problems on linear spaces and front-end tools facilitating their use in the 
	'R' ecosystem.
A powerful and elegant high-level data visualization
  system inspired by Trellis graphics, with an emphasis on
  multivariate data. Lattice is sufficient for typical graphics needs,
  and is also flexible enough to handle most nonstandard requirements.
  See ?Lattice for an introduction.
Functions that compute the
  lattice-based density estimator of Barry and McIntyre, which
  accounts for point processes in two-dimensional regions with 
  irregular boundaries and holes.  The package also implements
  two-dimensional non-parametric regression for similar regions.
Lattice-based space-filling designs with fill or separation distance properties including
  interleaved lattice-based minimax distance designs proposed in Xu He (2017)
  <doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed
  in Xu He (2018) <doi:10.1093/biomet/asy069>, (sliced) rotated sphere packing designs 
  proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289> and Xu He (2019) 
  <doi:10.1080/00401706.2018.1458655>, and densest packing-based maximum projections 
  designs proposed in Xu He (2020) <doi:10.1093/biomet/asaa057> and Xu He (2018) 
  <arXiv:1709.02062v2>.
Building on the infrastructure provided by the lattice
	     package, this package provides several new high-level
	     functions and methods, as well as additional utilities
	     such as panel and axis annotation functions.
A general implementation of Structural Equation Models
	with latent variables (MLE, 2SLS, and composite likelihood
	estimators) with both continuous, censored, and ordinal
	outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
	Mixture latent variable models and non-linear latent variable models
	(Holst and Budtz-Joergensen (2019) <doi:10.1093/biostatistics/kxy082>).
	The package also provides methods for graph exploration (d-separation,
	back-door criterion), simulation of general non-linear latent variable
	models, and estimation of influence functions for a broad range of
	statistical models.
Fit a variety of latent variable models, including confirmatory
   factor analysis, structural equation modeling and latent growth curve models.
Interactive shiny application for working with different kinds of
    latent variable analysis, with the 'lavaan' package. Graphical output for models
    are provided and different estimators are supported.
Fit structural equation models (SEM) including factor analysis,
    multivariate regression models with latent variables and many other latent
    variable models while correcting estimates, standard errors, and
    chi-square-derived fit measures for a complex sampling design. 
    Incorporate clustering, stratification, sampling weights, and 
    finite population corrections into a SEM analysis.
    Wrapper around packages lavaan and survey.
Plots path diagrams from models in 'lavaan' using the plotting
    functionality from the 'DiagrammeR' package. 'DiagrammeR' provides nice path diagrams 
    via 'Graphviz', and these functions make it easy to generate these diagrams from a
    'lavaan' path model without having to write the DOT language graph specification.
Estimation of a multi-group count regression models (i.e., Poisson, 
    negative binomial) with latent covariates. This packages provides two extensions
    compared to ordinary count regression models based on a generalized linear model:
    First, measurement models for the predictors can be specified allowing to account 
    for measurement error. Second, the count regression can be simultaneously estimated 
    in multiple groups with stochastic group weights. The marginal maximum likelihood 
    estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.
The lava estimation is used to recover signals that is the sum of a sparse signal and a dense signal. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.
Tools for model specification in the latent variable framework
    (add-on to the 'lava' package). The package contains three main functionalities:
    Wald tests/F-tests with improved control of the type 1 error in small samples,
    adjustment for multiple comparisons when searching for local dependencies,
    and adjustment for multiple comparisons when doing inference for multiple latent variable models.
The lava estimation is a new technique to recover signals that is the sum of a sparse and dense signals. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.
A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.
Performs adjusted inferences based on model objects fitted, using 
    maximum likelihood estimation, by the extreme value analysis packages
    'eva' <https://cran.r-project.org/package=eva>, 
    'evd' <https://cran.r-project.org/package=evd>, 
    'evir' <https://cran.r-project.org/package=evir>, 
    'extRemes' <https://cran.r-project.org/package=extRemes>, 
    'fExtremes' <https://cran.r-project.org/package=fExtremes>, 
    'ismev' <https://cran.r-project.org/package=ismev>, 
    'mev' <https://cran.r-project.org/package=mev>, 
    'POT' <https://cran.r-project.org/package=POT> and
    'texmex' <https://cran.r-project.org/package=texmex>. 
    Adjusted standard errors and an adjusted loglikelihood are provided, using    
    the 'chandwich' package <https://cran.r-project.org/package=chandwich>
    and the object-oriented features of the 'sandwich' package 
    <https://cran.r-project.org/package=sandwich>. The adjustment is based on a 
    robust sandwich estimator of the parameter covariance matrix, based on the 
    methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This 
    can be used for cluster correlated data when interest lies in the 
    parameters of the marginal distributions, or for performing inferences that 
    are robust to certain types of model misspecification.  Univariate extreme 
    value models, including regression models, are supported.
By combining constant, linear, and quadratic local models,
        lazy estimates the value of an unknown multivariate function on
        the basis of a set of possibly noisy samples of the function
        itself.  This implementation of lazy learning automatically
        adjusts the bandwidth on a query-by-query basis through a
        leave-one-out cross-validation.
Multi-threaded serialization of compressed array that 
    fully utilizes modern solid state drives. It allows 
    to store and load extremely large data on demand within seconds
    without occupying too much memories. With data stored on hard drive, 
    a lazy-array data can be loaded, shared across multiple R sessions.
    For arrays with partition mode on, multiple R sessions can write to 
    a same array simultaneously along the last dimension (partition). 
    The internal storage format is provided by 'fstcore' package geared by 
    'LZ4' and 'ZSTD' compressors.
A simple progress bar showing estimated remaining time. 
    Multiple forecast methods and user defined forecast method for 
    the remaining time are supported.
Supplies a LazyData facility for packages which have data
		sets but do not provide LazyData: true.  A single function is
		is included, requireData, which is a drop-in replacement for
		base::require, but carrying the additional
		functionality. By default, it suppresses package
		startup messages as well.  See argument 'reallyQuitely'.
An alternative approach to non-standard evaluation using
    formulas. Provides a full implementation of LISP style 'quasiquotation',
    making it easier to generate code with other code.
Provides an R wrapper for an efficient C++ implementation of the Lazy-Greedy spanning algorithm of Bouts, ten Brink, and Buchin (2014) <DOI:10.1145/2582112.2582154>.
Read raster data at a specified resolution on-demand via 'GDAL' 
 (the Geospatial Data Abstraction Library <https://gdal.org/>). Augments the 
 'raster' package by never reading data from a raster source until necessary for 
 generating an in-memory 'raster' object. A 'lazyraster' object may be cropped 
 and converted to 'raster' object, and by default will only read a small amount 
 of data sufficient for an overall summary. The amount of data read can be 
 controlled by specifying the output dimensions.
Lazy read for drawings. A 'dplyr' back end for data sources supported by 
    'GDAL' vector drivers, that allows working with local or remote sources as if they 
    are in-memory data frames. Basic features works with any drawing format ('GDAL vector 
    data source') supported by the 'sf' package.
Helper functions to build SQL statements
    for dbGetQuery or dbSendQuery under program control.
    They are intended to increase speed of coding and
    to reduce coding errors. Arguments are carefully checked,
    in particular SQL identifiers such as names of tables or columns.
    More patterns will be added as required.
Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading.
    Main goal is to process information within "Decision Support System" to come up with analysis or predictions.
    There are several utilities such as dynamic and adaptive risk management using reinforcement learning
    and even functions to generate predictions of price changes using pattern recognition deep regression learning.
    Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, 
    Market Type research of Van Tharp Institute: <https://www.vantharp.com/>,
    Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.
Provides the functionality to write LaTeX code from within R
    without having to learn LaTeX.  Functionality also exists to create HTML
    and Markdown code.  While the functionality still exists to write
    complete documents with lazyWeave, it is generally easier to do so with
    with markdown and knitr.  lazyWeave's main strength now is the ability
    to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.
The logistic Box-Cox model is a formal method, which can accommodate the non-linear 
              relationship between the log-odds and exposure via a shape parameter. Thus, it is 
              superior to the common two-step approach, which adds a data transformation step 
              before fitting a logistic regression model to accommodate any non-linearity in the 
              disease-exposure relationship. This package includes key functions in our model and 
              dataset for simulation. Simulation experiments can be reproduced following the Vignette.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0 
        (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>)
        limited memory BFGS minimizer with bounds on parameters. 
        This is a fork of 'lbfgsb3'. 
	This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same 
	function types and optimization as the optim() function (see writing 'R' extensions
        and source for details).  This package also adds more stopping criteria as well 
        as allowing the adjustment of more tolerances.
The package offers various length-bias corrections to
        survival curve estimation.
Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of 
    LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on 
    the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize 
    the model without losing any information, and fits the model on the standard fitting algorithm 
    with no convergence issues.
Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.
Simulate expected equilibrium length composition, yield-per-recruit, and
    the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR
    model to length data to estimate  selectivity, relative apical fishing mortality, and
    the spawning potential ratio for data-limited fisheries.
    See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the
    LBSPR assessment method.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
Latent Class Analysis of
        phenotypic measurements in pedigrees and model selection
        based on one of two methods: likelihood-based cross-validation
        and Bayesian Information Criterion. Computation of individual
        and triplet child-parents weights in a pedigree is performed using an
        upward-downward algorithm. The model takes into account the familial
        dependence defined by the pedigree structure by considering
        that a class of a child depends on his parents classes via
        triplet-transition probabilities of the classes. The package
        handles the case where measurements are available on all
        subjects and the case where measurements are available only on
        symptomatic (i.e. affected) subjects. Distributions for
        discrete (or ordinal) and continuous data are currently
        implemented. The package can deal with missing data.
Provides Shiny widgets and theme that support a 'Library Computer Access/Retrieval System' (LCARS) aesthetic for Shiny apps. 
    The package also includes functions for adding a minimal LCARS theme to static 'ggplot2' graphs. 
    More details about LCARS can be found at <https://en.wikipedia.org/wiki/LCARS>.
Variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package implements a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative. The variable selection method is based on the approach of Fop et al. (2017) <doi:10.1214/17-AOAS1061> and Dean and Raftery (2010) <doi:10.1007/s10463-009-0258-9>. Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search. Concomitant covariates used to predict the class membership probabilities can also be included in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.
Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) <doi:10.1007/s13253-018-0321-1>.  In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation.
Estimation of latent class models with individual covariates for capture-recapture data. See Forcina and Bartolucci (2021)<arxiv:2106.03811>.
Local Discrimination via Latent Class Models
Baseline correction, normalization and linear combination fitting (LCF) 
    of X-ray absorption near edge structure (XANES) spectra.
    The package includes data loading of .xmu files exported from 'ATHENA' (Ravel and Newville, 2005) <doi:10.1107/S0909049505012719>. 
    Loaded spectra can be background corrected and all standards can be fitted at once.
    Two linear combination fitting functions can be used:
    (1) fit_athena(): Simply fitting combinations of standards as in ATHENA, 
    (2) fit_float(): Fitting all standards with changing baseline correction and edge-step normalization parameters.
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.
A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.
The core of this 'Rcpp' based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the 'Cuhre' algorithm from the 'Cuba' library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the 'Cuhre' function are included in this package. The maximization process is carried out using Brent's algorithm, with the 'C++' code file from John Burkardt and John Denker (Brent, R.,Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).
Bayesian population size estimation using non parametric latent-class models.
Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
Provides functions to upload vectorial data and derive landscape
    connectivity metrics in habitat or matrix systems. Additionally, includes an 
    approach to assess individual patch contribution to the overall landscape 
    connectivity, enabling the prioritization of habitat patches. The computation
    of landscape connectivity and patch importance are very useful in Landscape 
    Ecology research. The metrics available are: number of components, number of 
    links, size of the largest component, mean size of components, class coincidence
    probability, landscape coincidence probability, characteristic path length, 
    expected cluster size, area-weighted flux and integral index of connectivity.
    Pascual-Hortal, L., and Saura, S. (2006) <doi:10.1007/s10980-006-0013-z>
    Urban, D., and Keitt, T. (2001) <doi:10.2307/2679983>
    Laita, A., Kotiaho, J., Monkkonen, M. (2011) <doi:10.1007/s10980-011-9620-4>.
Collections of functions allowing random number generations and
    estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) <doi:10.1016/j.jmva.2017.05.008>.
Longitudinal genomics data and survival outcome are common in biomedical studies. It is of great interest to select genes related to the survival outcome. LCox is a computationally efficient tool for selecting genes related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome.
An implementation of the Log Cumulative Probability Model (LCPM) 
  and Proportional Probability Model (PPM) for which the Maximum Likelihood Estimates are determined using constrained optimization. 
  This implementation accounts for the implicit constraints on the parameter space. Other 
  features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization.
For fitting Bayesian joint latent class and regression models using
    Gibbs sampling. See the documentation for the model.
    The technical details of the model implemented here are described in Elliott,
    Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham,
    Belinda L., "Methods to account for uncertainty in latent class assignments when
    using latent classes as predictors in regression models, with application to
    acculturation strategy measures" (2020) In press at Epidemiology
    <doi:10.1097/EDE.0000000000001139>.
Helper functions to implement univariate and bivariate latent change score models in R using the 'lavaan' package.
  For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>.
  The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints.
  The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually.
  Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications.
  Estimated model parameters and fit statistics can be extracted as data frames.
  Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects.
  This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together.
Provides researchers and educators with easy-to-learn user friendly tools for calculating 
    key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. 
    These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, 
    Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation 
    (Global and Local Moran's I), several Geographically Weighted Regression techniques and other 
    Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for 
    measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Analysis of stock data ups and downs trend, the stock technical analysis indicators function have trend line, reversal pattern and market trend.
Implements latent Dirichlet allocation (LDA)
	     and related models.  This includes (but is not limited
	     to) sLDA, corrLDA, and the mixed-membership stochastic
	     blockmodel.  Inference for all of these models is
	     implemented via a fast collapsed Gibbs sampler written
	     in C.  Utility functions for reading/writing data
	     typically used in topic models, as well as tools for
	     examining posterior distributions are also included.
Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.
Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.
Contains the development of a tool that provides a
    web-based graphical user interface (GUI) to perform a review of the
    scientific literature under the Bayesian approach of Latent Dirichlet
    Allocation (LDA)and machine learning algorithms. The application
    methodology is framed by the well known procedures in topic modelling
    on how to clean and process data. Contains methods described by 
    Blei, David M., Andrew Y. Ng, and Michael I. Jordan (2003) 
    <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf> 
    Allocation"; Thomas L. Griffiths and Mark Steyvers (2004) 
    <doi:10.1073/pnas.0307752101> ; Xiong Hui, et al (2019) 
    <doi:10.1016/j.cie.2019.06.010>.
Tools for working with vectors and data sets that are too large
    to keep in memory. Extends the basic functionality provided in the 'lvec'
    package. Provides basis statistical functionality of 'lvec' objects, such
    as arithmetic operations and calculating means and sums. Also implements
    'data.frame'-like objects storing its data in 'lvec' objects.
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial
    time series methods in a two-stage analysis to quantify dynamics in
	high-dimensional temporal data. LDA decomposes multivariate data into 
    lower-dimension latent groupings, whose relative proportions are modeled
    using generalized Bayesian time series models that include abrupt 
    changepoints and smooth dynamics. The methods are described in Blei
    et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp
   (2004) <doi:10.1093/pan/mph023>, Venables and Ripley 
   (2002, ISBN-13:978-0387954578), and Christensen et al. 
   (2018) <doi:10.1002/ecy.2373>.
For this first version only metrics to estimate the best fitting
    number of topics are implemented.
Tools to create an interactive web-based visualization of a
    topic model that has been fit to a corpus of text data using
    Latent Dirichlet Allocation (LDA). Given the estimated parameters of
    the topic model, it computes various summary statistics as input to
    an interactive visualization built with D3.js that is accessed via
    a browser. The goal is to help users interpret the topics in their
    LDA topic model.
Flexible procedures to compute local density-based outlier scores for ranking outliers.
  Both exact and approximate nearest neighbor search can be implemented, while also accommodating
  multiple neighborhood sizes and four different local density-based methods. It allows for
  referencing a random subsample of the input data or a user specified reference data set
  to compute outlier scores against, so both unsupervised and semi-supervised outlier
  detection can be implemented.
Computations related to group sequential boundaries.
             Includes calculation of bounds using the Lan-DeMets
             alpha spending function approach.
Four measures of linkage disequilibrium are provided: the usual r^2
        measure, the r^2_S measure (r^2 corrected by the structure
        sample), the r^2_V (r^2 corrected by the relatedness of
        genotyped individuals), the r^2_VS measure (r^2 corrected by
        both the relatedness of genotyped individuals and the structure
        of the sample).
Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021).
Produces a graphical display, as a heat map, of measures
        of pairwise linkage disequilibria between single nucleotide polymorphisms (SNPs). Users may
        optionally include the physical locations or genetic map
        distances of each SNP on the plot. The methods are described in 
        Shin et al. (2006) <doi:10.18637/jss.v016.c03>. Users should note that the
        imported package 'snpStats' and the suggested packages 
        'rtracklayer', 'GenomicRanges', 'GenomInfoDb' and 'IRanges' are all
        BioConductor packages <https://bioconductor.org>.
Hidden Markov Model (HMM) based on symmetric lambda distribution
    framework is implemented for the study of return time-series in the financial
    market. Major features in the S&P500 index, such as regime identification,
    volatility clustering, and anti-correlation between return and volatility,
    can be extracted from HMM cleanly. Univariate symmetric lambda distribution
    is essentially a location-scale family of exponential power distribution.
    Such distribution is suitable for describing highly leptokurtic time series
    obtained from the financial market. It provides a theoretically solid foundation
    to explore such data where the normal distribution is not adequate. The HMM
    implementation follows closely the book: "Hidden Markov Models for Time Series",
    by Zucchini, MacDonald, Langrock (2016).
A wide variety of ways to calculate (through equations) or predict (using 9 Machine learning methods as well as a stack algorithm combination of them all) the Low Density Lipoprotein values of patients based on the values of three other metrics as Total Cholesterol , Triglyceride and High Density Lipoprotein.
Provides access to the 'LDlink' API (<https://ldlink.nci.nih.gov/?tab=apiaccess>)
    using the R console.  This programmatic access facilitates researchers who are 
    interested in performing batch queries in 1000 Genomes Project (2015) <doi:10.1038/nature15393> 
    data using 'LDlink'. 'LDlink' is an interactive and powerful suite of web-based tools for querying 
    germline variants in human population groups of interest. For more details, please see 
    Machiela et al. (2015) <doi:10.1093/bioinformatics/btv402>.
This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.
this package provides functions for Finding Locally
        D-optimal designs for Logistic, Negative Binomial, Poisson,
        Michaelis-Menten, Exponential, Log-Linear, Emax, Richards,
        Weibull and Inverse Quadratic regression models and also
        functions for auto-constructing Fisher information matrix and
        Frechet derivative based on some input variables and without
        user-interfere.
Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D. Tasche), algorithm proposed by M. van der Burgt, K. Pluto and D. Tasche's most prudent estimation methodology.
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces <doi:10.1007/978-81-322-3643-6_7>.
Estimate haplotypic or composite pairwise linkage disequilibrium
    (LD) in polyploids, using either genotypes or genotype likelihoods. 
    Support is provided to estimate the popular measures of LD: the LD 
    coefficient D, the standardized LD coefficient D', and the Pearson 
    correlation coefficient r. All estimates are returned with corresponding 
    standard errors. These estimates and standard errors can then be used
    for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(),
    sldest(), plot.lddf(), format_lddf(), and ldshrink(). Details of the methods
    are available in Gerard (2021a) <doi:10.1111/1755-0998.13349>
    and Gerard (2021b) <doi:10.1038/s41437-021-00462-5>.
Streamflow (and climate) reconstruction using Linear Dynamical Systems. 
    The advantage of this method is the additional state trajectory which can reveal more information 
    about the catchment or climate system. For details of the method please refer to Nguyen and Galelli 
    (2018) <doi:10.1002/2017WR022114>.
The algorithm Leabra (local error driven and associative
    biologically realistic algorithm) allows for the construction of artificial
    neural networks that are biologically realistic and balance supervised and
    unsupervised learning within a single framework. This package is based on
    the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on
    the description of the algorithm by Randall O'Reilly (1996)
    <ftp://grey.colorado.edu/pub/oreilly/thesis/oreilly_thesis.all.pdf>. For
    more general (not 'R' specific) information on the algorithm Leabra see
    <https://grey.colorado.edu/emergent/index.php/Leabra>.
The leader clustering algorithm provides
 a means for clustering a set of data points. Unlike many other clustering
 algorithms it does not require the user to specify the number of clusters,
 but instead requires the approximate radius of a cluster as its primary
 tuning parameter. The package provides a fast implementation of this
 algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
 and infinity) as well as for spatial data using the Haversine
 formula, which takes latitude/longitude pairs as inputs and clusters
 based on great circle distances.
An interface for the image processing program 'ImageJ', which
    allows a rapid digital image analysis for particle sizes. This package includes
    function to write an 'ImageJ' macro which is optimized for a leaf area analysis by
    default.
Provides drill down functionality for 'leaflet' choropleths in 'shiny' apps.
Provides extensions for packages 'leaflet' & 'mapdeck', 
    many of which are used by package 'mapview'. 
    Focus is on functionality readily available in 
    Geographic Information Systems such as 'Quantum GIS'. Includes functions
    to display coordinates of mouse pointer position, query image values via 
    mouse pointer and zoom-to-layer buttons. Additionally, provides a feature 
    type agnostic function to add points, lines, polygons to a map.
Provides bindings to the 'Leaflet.glify' JavaScript library which extends the 'leaflet' JavaScript library to render large data in the browser using 'WebGl'.
Provides extensions to the 'leaflet' package to 
    customize legends with images, text styling, orientation, sizing,
    and symbology.
Create and customize interactive maps using the 'Leaflet'
    JavaScript library and the 'htmlwidgets' package. These maps can be used
    directly from the R console, from 'RStudio', in Shiny applications and R Markdown
    documents.
An add-on package to the 'leaflet' package, which provides bindings for 'ESRI' services. This package allows a user to add 'ESRI' provided services such as 'MapService', 'ImageMapService', 'TiledMapService' etc. to a 'leaflet' map.
The 'leaflet' JavaScript library provides many plugins some of which
    are available in the core 'leaflet' package, but there are many more. It is not
    possible to support them all in the core 'leaflet' package. This package serves
    as an add-on to the 'leaflet' package by providing extra functionality via 'leaflet'
    plugins.
Several 'leaflet' plugins are integrated, which are available as extension to the 'leaflet' package.
Add and modify small charts on an interactive map created with 
    package 'leaflet'. These charts can be used to represent at same time multiple 
    variables on a single map.
Extends Leaflet for R by adding widget to control opacity of multiple layers.
Extends the 'leaflet' R package with the 'Leaflet.OpacityControls' JavaScript 
    plugin. Adds controls to the leaflet map for adjusting the 
    opacity of a layer.
Contains third-party map tile provider information from
    'Leaflet.js', <https://github.com/leaflet-extras/leaflet-providers>, to be
    used with the 'leaflet' R package. Additionally, 'leaflet.providers'
    enables users to retrieve up-to-date provider information between package
    updates.
An R gallery for China and other geojson choropleth map in leaflet. Contains the geojson data for provinces, cities in China.
Display your spatial data on interactive web-maps using the open-source JavaScript library Leaflet. 'leafletR' provides basic web-mapping functionality to combine vector data and online map tiles from different sources. See <http://leafletjs.com> for more information on Leaflet.
A collection of tools for interactive manipulation of (spatial) data 
    layers on leaflet web maps. Tools include editing of existing layers, creation 
    of new layers through drawing of shapes (points, lines, polygons), deletion 
    of shapes as well as cutting holes into existing shapes. Provides control over 
    options to e.g. prevent self-intersection of polygons and lines or to enable/disable 
    snapping to align shapes.
Creates 'HTML' strings to embed tables, images or graphs in pop-ups
  of interactive maps created with packages like 'leaflet' or 'mapview'. Handles
  local images located on the file system or via remote URL. Handles graphs created 
  with 'lattice' or 'ggplot2' as well as interactive plots created with 'htmlwidgets'.
A set of functions for analyzing the structure 
  of forests based on the leaf area density (LAD) and leaf area index (LAI) measures 
  calculated from Airborne Laser Scanning (ALS), i.e., scanning lidar (Light Detection 
  and Ranging) data. The methodology is discussed and described in 
  Almeida et al. (2019) <doi:10.3390/rs11010092> and 
  Stark et al. (2012) <doi:10.1111/j.1461-0248.2012.01864.x>.
Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.
Create small multiples of several leaflet web maps with (optional) 
    synchronised panning and zooming control. When syncing is enabled all maps 
    respond to mouse actions on one map. This allows side-by-side comparisons
    of different attributes of the same geometries. Syncing can be adjusted
    so that any combination of maps can be synchronised.
Use the 'leaflet-timeline' plugin with a leaflet widget to add an
    interactive slider with play, pause, and step buttons to explore temporal
    geographic spatial data changes.
Provides access to the 'Leanpub' API
    <https://leanpub.com/help/api> for gathering information about 
    publications and submissions to the 'Leanpub' platform.
Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) <DOI:10.1093/bioinformatics/btw676>]
 Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method
 computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values.
 The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.
Advances in sequencing technology now allow researchers to capture the expression profiles of individual cells. Several algorithms have been developed to attempt to account for these effects by determining a cell's so-called `pseudotime', or relative biological state of transition. By applying these algorithms to single-cell sequencing data, we can sort cells into their pseudotemporal ordering based on gene expression. LEAP (Lag-based Expression Association for Pseudotime-series) then applies a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.
Contains LEAPFrOG Gradient Optimisation and Expectation Maximisation functions for inferring parental admixture proportions from an offspring with SNP genotypes.
These functions take a gene expression value matrix, a
        primary covariate vector, an additional known covariates
        matrix.  A two stage analysis is applied to counter the effects
        of latent variables on the rankings of hypotheses.  The
        estimation and adjustment of latent effects are proposed by
        Sun, Zhang and Owen (2011).  "leapp" is developed in the
        context of microarray experiments, but may be used as a general
        tool for high throughput data sets where dependence may be
        involved.
Regression subset selection, including exhaustive search.
Provides methods for estimating borders of uniform distribution on
  the interval (one-dimensional) and on the elliptical domain (two-dimensional)
  under measurement errors. For one-dimensional case, it also estimates the
  length of underlying uniform domain and tests the hypothesized length against
  two-sided or one-sided alternatives. For two-dimensional case, it estimates
  the area of underlying uniform domain. It works with numerical inputs as well
  as with pictures in JPG format.
A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step.
  It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. 
  The package explains non expert users how hierarchical clustering algorithms work.
Contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.
Crabs in the English channel, deer skulls, English
    monarchs, half-caste Manga characters, Jamaican cities,
    Shakespeare's The Tempest, drugged up cyclists and sexually
    transmitted diseases.
Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use.
Provides tools to teach students elemental statistics. The main topics covered are descriptive statistics, probability models (discrete and continuous variables) and statistical inference (confidence intervals and hypothesis tests). One of the main advantages of this package is that allows the user to read quite a variety of types of data files with one unique command. Moreover it includes shortcuts to simple but up-to-now not in R descriptive features such a complete frequency table or an histogram with the optimal number of intervals. Related to model distributions (both discrete and continuous), the package allows the student to easy plot the mass/density function, distribution function and quantile function just detailing as input arguments the known population parameters. The inference related tools are basically confidence interval and hypothesis testing. Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared).
The Programme for International Student Assessment (PISA) is a global study conducted by the Organization for Economic Cooperation and Development (OECD) in member and non-member countries to assess educational systems by assessing 15-year-old school students academic performance in mathematics, science, and reading. This datasets contains information on their scores and other socioeconomic characteristics, information about their school and its infrastructure, as well as the countries that are taking part in the program.
Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
Conducts various numerical analyses and simulations in population genetics and evolutionary theory, primarily for the purpose of teaching (and learning about) key concepts in population & quantitative genetics, and evolutionary theory.
Create interactive tutorials using R Markdown. Use a combination
  of narrative, figures, videos, exercises, and quizzes to create self-paced
  tutorials for learning about R and R packages.
Data, scripts and code from chunks used as examples in the book 
   "Learn R: As a Language" by Pedro J. Aphalo.
   ISBN 9780367182533 (pbk); ISBN 9780367182557 (hbk); ISBN 9780429060342 (ebk).
Allows students to use R as an interactive educational environment
    for statistical concepts, ranging from p-values to confidence intervals
	to stability in time series.
Provides functionality to calculate cost surfaces based on slope (e.g. Herzog, 2010; Llobera and Sluckin, 2007 <doi:10.1016/j.jtbi.2007.07.020>; París Roche, 2002; Tobler, 1993), traversing slope (Bell and Lock, 2000), and landscape features (Llobera, 2000) to be used when modelling pathways and movement potential within a landscape (e.g. Llobera, 2015; Verhagen, 2013; White and Barber, 2012 <doi:10.1016/j.jas.2012.04.017>).
Utilities for querying plain text accounting files from 'Ledger', 'HLedger', and 'Beancount'.
Functions to download data from the SIE, which is the Statistical Information System (Sistema de Información Estadística) in the Statistical Portal of the Government of Castilla y León (Spain) <https://estadistica.jcyl.es>.
Creates matrix population models for use in population ecological
             analyses. Specializes on the construction of historical matrices,
             which are 2d matrices comprising 3 consecutive times of demographic
             information. Estimates both raw and function-based forms of
             historical and standard ahistorical matrices. It also estimates
             function-based age-by-stage matrices. Methodology based on Ehrlen
             (2000) <doi:10.1890/0012-9658(2000)081[1675:TDOPPD]2.0.CO;2> and
             deVries and Caswell (2018) <doi:10.1007/s12080-017-0353-0>.
Fetching data from Hong Kong Legislative Council's open data API in R. 
    Functions correspond to the data endpoints of the API. 
    Documentations of supported API databases:
    <https://www.legco.gov.hk/odata/english/billsdb.html>, 
    <https://www.legco.gov.hk/odata/english/hansard-db.html>,
    <https://www.legco.gov.hk/odata/english/attendance-db.html>,
    <https://www.legco.gov.hk/odata/english/schedule-db.html> and
    <https://www.legco.gov.hk/odata/english/vrdb.html>.
Functions implementing multivariate state space models for purposes of time series analysis and forecasting.
             The focus of the package is on multivariate models, such as Vector Exponential Smoothing,
             Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential
             Smoothing (VES, de Silva et al., 2010, <doi:10.1177/1471082X0901000401>), Vector ETS and
             simulation function for VES.
Facilitates access to the Comparative Legislators Database (CLD). The CLD includes political, sociodemographic, career, online presence, public attention, and visual information for over 45,000 contemporary and historical politicians from ten countries.
Constructs genotype x environment interaction (GxE) models where
    G is a weighted sum of genetic variants (genetic score) and E is a weighted
    sum of environments (environmental score) using the alternating optimization algorithm 
    by Jolicoeur-Martineau et al. (2017) <arXiv:1703.08111>. This approach has greatly 
    enhanced predictive power over traditional GxE models which include only a single 
    genetic variant and a single environmental exposure. Although this approach was 
    originally made for GxE modelling, it is flexible and does not require the use of 
    genetic and environmental variables. It can also handle more than 2 latent variables 
    (rather than just G and E) and 3-way interactions or more. The LEGIT model produces 
    highly interpretable results and is very parameter-efficient thus it can even be 
    used with small sample sizes (n < 250). Tools to determine the type of interaction
    (vantage sensitivity, diathesis-stress or differential susceptibility), with any 
    number of genetic variants or environments, are available <arXiv:1712.04058>.
Provides a dataset containing several color naming conventions established by multiple sources, along with associated color metadata.
    The package also provides related helper functions for mapping among the different Lego color naming conventions and between Lego colors, hex colors, and 'R' color names, 
    making it easy to convert any color palette to one based on existing Lego colors while keeping as close to the original color palette as possible.
    The functions use nearest color matching based on Euclidean distance in RGB space. 
    Naming conventions for color mapping include those from 'BrickLink' (<https://www.bricklink.com>), 'The Lego Group' (<https://www.lego.com>), 'LDraw' (<https://www.ldraw.org/>), and 'Peeron' (<http://www.peeron.com/>).
Implements the 'Python leidenalg' module to be called in R.
    Enables clustering using the leiden algorithm for partition a graph into communities.
    See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg>
    Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: <https://github.com/vtraag/leidenalg>. The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) <doi:10.1038/s41598-019-41695-z>.
Estimate the slope and intercept of a bivariate
	linear relationship by calculating a posterior density
	that is invariant to interchange and scaling of the
	coordinates.
Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.
Functions for working with legends and axis lines of 'ggplot2',
    facets that repeat axis lines on all panels, and some 'knitr' extensions.
Provides tools for creating and using lenses to simplify data manipulation. Lenses are composable getter/setter pairs for working with data in a purely functional way. Inspired by the 'Haskell' library 'lens' (Kmett, 2012) <https://hackage.haskell.org/package/lens>. For a fairly comprehensive (and highly technical) history of lenses please see the 'lens' wiki <https://github.com/ekmett/lens/wiki/History-of-Lenses>.
An implementation of the Input-Output model developed by
    Wassily Leontief that represents the interdependencies between different 
    sectors of a national economy or different regional economies.
Finds and downloads Landscape Evaporative Response
    Index (LERI) data, then reads the data into 'R' using the 'raster'
    package. The LERI product measures anomalies in actual
    evapotranspiration, to support drought monitoring and early warning
    systems. More info on LERI is available at
    <https://www.esrl.noaa.gov/psd/leri/>.
Generates quotes from 'Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.
Each function accomplishes the work of several or more standard R functions. For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate. Other functions provide for descriptive statistics, a comprehensive regression analysis, analysis of variance and t-test, plotting including the introduced here Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, color themes, Trellis graphics and a built-in help system. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, and generation and rendering of R markdown instructions for interpretative output.
Functions for vectorised conditional recoding of
    variables. case_when() enables you to vectorise multiple if and else
    statements (like 'CASE WHEN' in 'SQL'). if_else() is a stricter and
    more predictable version of ifelse() in 'base' that preserves
    attributes. These functions are forked from 'dplyr' with all package
    dependencies removed and behave identically to the originals.
Some simple objects and functions to do 
             statistics using linear models and a Bayesian framework.
Handling, processing, and analyzing geographic
    data on species' distributions and environmental variables. 
    Read Vilela & Villalobos (2015) <doi: 10.1111/2041-210X.12401> for details.
Provides string similarity calculations inspired by the
    Python 'fuzzywuzzy' package. Compare strings by edit distance,
    similarity ratio, best matching substring, ordered token matching and
    set-based token matching. A range of edit distance measures are
    available thanks to the 'stringdist' package.
Implements code to identify lexical competitors in a given list
  of words. We include many of the standard competitor types used in spoken word
  recognition research, such as functions to find cohorts, neighbors, and
  rhymes, amongst many others. The package includes documentation for using a
  variety of lexicon files, including those with form codes made up of multiple
  letters (i.e., phoneme codes) and also basic orthographies. Importantly, the
  code makes use of multiple CPU cores and vectorization when possible, making
  it extremely fast and able to handle large lexicons. Additionally, the package
  contains documentation for users to easily write new functions, allowing
  researchers to examine other relationships within a lexicon. 
  Preprint: <https://psyarxiv.com/8dyru/>. Open access: <https://link.springer.com/epdf/10.3758/s13428-021-01667-6?sharing_token=9WlO9soCc9y0uSuwWSUYfJAH0g46feNdnc402WrhzyrdKcK8uzZx_hDEtgbYzn3gvxdG5Cuj0j0cC4lVMFBqYCGTQmE2blN2Gwo74LJ8ro1pEOAYDRFy6Lhf1nc719vD-zU7GDvKOQxDAwPbrisvPBeXSIu0NkqXF7Jx3IuUwIs%3D>. 
  Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). 
  <doi:10.3758/s13428-021-01667-6>.
A collection of lexical hash tables, dictionaries, and word lists.
Provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts.
    As of now, two Portuguese lexicons are available: 'SentiLex-PT02' and 'OpLexicon' (v2.1 and v3.0).
Plots empty Lexis grids, adds lifelines and highlights certain areas of the grid, like cohorts and age groups.
An R implementation of the LexRank algorithm described by G. Erkan and D. R. Radev (2004) <DOI:10.1613/jair.1523>.
Provides an extension to factors called 'lfactor' that are similar
    to factors but allows users to refer to 'lfactor' levels by either the level or
    the label.
Shiny apps for the quantitative analysis of images from lateral flow assays (LFAs). The images are segmented and background corrected and color intensities are extracted. The apps can be used to import and export intensity data and to calibrate LFAs by means of linear, loess, or gam models. The calibration models can further be saved and applied to intensity data from new images for determining concentrations.
Functions for performing and visualizing Local Fisher Discriminant
    Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised
    Local Fisher Discriminant Analysis(SELF).
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Estimation of the local false discovery rate using the method of moments.
New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a  "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.
Transforms away factors with many levels prior to doing an OLS.
  Useful for estimating linear models with multiple group fixed effects, and for
  estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) <doi:10.1016/j.csda.2013.03.024>
  Includes support for instrumental variables, conditional F statistics for weak instruments,
  robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 <doi:10.1002/sta4.68>). 
  WARNING: This package is NOT under active development anymore, no further improvements are to be expected, and the package is at risk of being removed from CRAN.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
    rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), 
    and forecasting time-series using fuzzy rule-based ensemble (FRBE).
Fast and accurate inference of 
             gene-environment associations (GEA) in genome-wide studies 
             (Caye et al., 2019, <doi:10.1093/molbev/msz008>). 
             We developed a least-squares estimation approach for confounder and effect sizes 
             estimation that provides a unique framework for several categories of genomic data, 
             not restricted to genotypes. 
             The speed of the new algorithm is several times faster than the existing GEA approaches, 
             then our previous version of the 'LFMM' program present in the 'LEA' package 
             (Frichot and Francois, 2015, <doi:10.1111/2041-210X.12382>).
The "Manual on Low-flow Estimation and Prediction", published by
    the World Meteorological Organisation (WMO), gives a comprehensive summary on
    how to analyse stream flow data focusing on low-flows. This packages provides
    functions to compute the described statistics and produces plots similar to the
    ones in the manual.
An implementation of locally Gaussian distributions. It provides methods for
    implementing locally Gaussian multivariate density estimation, conditional density 
    estimation, various independence tests for iid and time series data, a test for conditional 
    independence and a test for financial contagion.
Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.
Spatial and spatio-temporal modelling of point patterns using the
    log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
    multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.
Functions for genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".
Interpretable nonparametric modeling of longitudinal data
    using additive Gaussian process regression. Contains functionality
    for inferring covariate effects and assessing covariate relevances.
    Models are specified using a convenient formula syntax, and can include
    shared, group-specific, non-stationary, heterogeneous and temporally
    uncertain effects. Bayesian inference for model parameters is performed
    using 'Stan'. The modeling approach and methods are described in detail in
    Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.
A flexible, feature-rich yet light-weight logging
    framework based on 'R6' classes. It supports hierarchical loggers,
    custom log levels, arbitrary data fields in log events, logging to
    plaintext, 'JSON', (rotating) files, memory buffers. For extra
    appenders that support logging to databases, email and push
    notifications see the the package lgr.app.
A largish collection of example datasets, including several classics. Many of
    these datasets are well suited for regression, classification, and visualization.
Additional appenders for the logging package 'lgr' that support 
  logging to databases, email and push notifications.
Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.
A very simple implementation of a class for 
	     longitudinal data.
Contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.
Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.
Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.
Given an RNA-seq derived cell-by-gene matrix and an DNA-seq derived copy number segmentation, LIAYSON predicts the number of clones present in a tumor, their size, the copy number profile of each clone and the clone membership of each single cell (Andor, N. & Lau, B., et al. (2018) <doi:10.1101/445932>).
Provides functions for validating and normalizing bibliographic
   codes such as ISBN, ISSN, and LCCN. Also includes functions to communicate
   with the WorldCat API, translate Call numbers (Library of Congress and
   Dewey Decimal) to their subject classifications or subclassifications,
   and provides various loadable data files such call number / subject
   crosswalks and code tables.
Basic infrastructure for linear test statistics and permutation
  inference in the framework of Strasser and Weber (1999) <https://epub.wu.ac.at/102/>.
  This package must not be used by end-users. CRAN package 'coin' implements all
  user interfaces and is ready to be used by anyone.
Provides the Open Source Geometry Engine ('GEOS') as a
  C API that can be used to write high-performance C and C++
  geometry operations using R as an interface. Headers are provided
  to make linking to and using these functions from C++ code as
  easy and as safe as possible. This package contains an internal
  copy of the 'GEOS' library to guarantee the best possible
  consistency on multiple platforms.
A wrapper around the LIBLINEAR C/C++ library for machine
        learning (available at
        <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a
        simple library for solving large-scale regularized linear
        classification and regression. It currently supports
        L2-regularized classification (such as logistic regression,
        L2-loss linear SVM and L1-loss linear SVM) as well as
        L1-regularized classification (such as L2-loss linear SVM and
        logistic regression) and L2-regularized support vector
        regression (with L1- or L2-loss). The main features of
        LiblineaR include multi-class classification (one-vs-the rest,
        and Crammer & Singer method), cross validation for model
        selection, probability estimates (logistic regression only) or
        weights for unbalanced data. The estimation of the models is
        particularly fast as compared to other libraries.
Solving the linear SVM problem with coordinate descent
    is very efficient and is implemented in one of the most often used packages,
    'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
    It has been shown that the uniform selection of coordinates can be
    accelerated by using an online adaptation of coordinate frequencies (ACF).
    This package implements ACF and is based on 'LIBLINEAR' as well as
    the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>).
    It currently supports L2-regularized L1-loss as well as L2-loss linear SVM.
    Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and
    Crammer & Singer method) and cross validation for model selection is
    supported. The training of the models based on ACF is much faster than
    standard 'LIBLINEAR' on many problems.
The 'LibOPF' is a framework to develop pattern recognition techniques based on optimum-path forests (OPF), João P. Papa and Alexandre X. Falcão (2008) <doi:10.1007/978-3-540-89639-5_89>, with methods for supervised learning and data clustering.
Contains a set of functions to create data libraries,
    generate data dictionaries, and simulate a data step.
    The libname() function will load a directory of data into 
    a library in one line of code.  The dictionary() function
    will generate data dictionaries for individual
    data frames or an entire library.  And the datestep() function
    will perform row-by-row data processing.
Automatically install, update, and load 'CRAN', 'GitHub', and 'Bioconductor' 
    packages in a single function call. By accepting bare unquoted names for packages, 
    it's easy to add or remove packages from the list.
Generate a local library copy with relevant packages. 
    All packages currently found within the search path - except base packages - 
    will be copied to the directory provided and can be used later on with the 
    .libPaths() function.
Handle 'PharmML' (Pharmacometrics Markup Language) standard output (SO) XML files.
    SO files can be created, read, manipulated and written through a
    data binding from the XML structure to a tree structure of R objects.
Tools for fast and accurate evaluation of skew stable distributions (CDF, PDF 
  and quantile functions), random number generation and parameter estimation.
The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2020) <doi: 10.1007/s00180-020-00974-4>.
Estimates predictive states from spatio-temporal data and
    consequently can provide provably optimal forecasts.
    Currently this implementation
    supports an N-dimensional spatial grid observed over equally spaced time
    intervals. E.g. a video is a 2D spatial systems observed over time. This
    package implements mixed LICORS, has plotting tools (for (1+1)D and (2+1)D
    systems), and methods for optimal forecasting.  Due to memory limitations
    it is recommend to only analyze (1+1)D systems.
Provides functions for forest analysis using airborne laser scanning (LiDAR remote sensing) data: tree detection (method 1 in Eysn et al. (2015) <doi:10.3390/f6051721>) and segmentation; forest parameters estimation and mapping with the area-based approach. It includes complementary steps for forest mapping: co-registration of field plots with LiDAR data (Monnet and Mermin (2014) <doi:10.3390/f5092307>); extraction of both physical (gaps, edges, trees) and statistical features from LiDAR data useful for e.g. habitat suitability modeling (Glad et al. (2020) <doi:10.1002/rse2.117>); model calibration with ground reference, and maps export.
Airborne LiDAR (Light Detection and Ranging) interface for data
    manipulation and visualization. Read/write 'las' and 'laz' files, computation
    of metrics in area based approach, point filtering, artificial point reduction,
    classification from geographic data, normalization, individual tree segmentation
    and other manipulations.
Classes and methods that allow the user to manage life table,
    actuarial tables (also multiple decrements tables). Moreover, functions to easily
    perform demographic, financial and actuarial mathematics on life contingencies
    insurances calculations are contained therein. See Spedicato (2013)	<doi:10.18637/jss.v055.i10>.
Provides in built datasets and three functions.
  These functions are mobility_index, nonStanTest and linkedLives.  The mobility_index
  function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the 
  linkedLives functions allow the user to determine the probability that the observed sequence data 
  was due to chance.  The linkedLives function acknowledges the fact that some individuals may
  have identical sequences.
  The datasets available provide sequence data on marital status(maritalData) 
  and mobility (mydata) for a selected group of individuals from the British Household Panel Study
  (BHPS).  In addition, personal and house ID's for 100 individuals are provided in a 
  third dataset (myHouseID) from the BHPS.
Manage the life cycle of your exported functions
    with shared conventions, documentation badges, and user-friendly
    deprecation warnings.
Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.
R6 classes to model traditional life insurance
    contracts like annuities, whole life insurances or endowments. Such life
    insurance contracts provide a guaranteed interest and are not directly linked
    to the performance of a particular investment vehicle. However, they typically
    provide (discretionary) profit participation. This package provides a framework
    to model such contracts in a very generic (cash-flow-based) way and includes
    modelling profit participation schemes, dynamic increases or more general
    contract layers, as well as contract changes (like sum increases or premium
    waivers). All relevant quantities like premium decomposition, reserves and 
    benefits over the whole contract period are calculated and potentially 
    exported to excel. Mortalities are given using the 'MortalityTables' package.
Provides a framework for combining self-data (exercise, sleep, etc.) from multiple sources (fitbit, Apple Health), creating visualizations, and experimenting on onself.
Functions supplied in this package will implement
        discriminant analysis to select an appropriate life table
        family, select an appropriate alpha level based on a desired
        life expectancy at birth, produce a model mortality pattern
        based on family and level as well as plot the results.
Compute the top decile lift and plot the lift curve. Cumulative lift curves are also supported.
Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy of Knight et al (2017) <doi:10.1007/s11222-016-9698-2>.
Persistent reproducible reporting by containerization of R Markdown documents.
Allows you to read and change the state of 'LIFX' smart light bulbs via the 'LIFX' developer api <https://api.developer.lifx.com/>.
    Covers most 'LIFX' api endpoints, including changing light color and brightness, selecting lights by id, group or location as well as activating effects.
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. The original algorithm is detailed in Subramanian et al. with 'Java' implementations available through the Broad Institute (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>). The 'liger' package provides a lightweight R implementation of this enrichment test on a list of values (Fan et al., 2017 <doi:10.5281/zenodo.887386>). Given a list of values, such as p-values or log-fold changes derived from differential expression analysis or other analyses comparing biological states, this package enables you to test a priori defined set of genes for enrichment to enable interpretability of highly significant or high fold-change genes.
Tree based algorithms can be improved by introducing boosting frameworks. 
    'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.
    This package offers an R interface to work with it.
    It is designed to be distributed and efficient with the following advantages:
        1. Faster training speed and higher efficiency.
        2. Lower memory usage.
        3. Better accuracy.
        4. Parallel learning supported.
        5. Capable of handling large-scale data.
    In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions.
    Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.
The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
   server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
   by itself cannot and will not create any visualizations.
Parse various reflectance/transmittance/absorbance spectra file
    formats to extract spectral data and metadata, as described in Gruson, White
    & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import
    files from 'Avantes' <https://www.avantes.com/>, 'CRAIC' 
    <https://www.microspectra.com/>, and 'OceanInsight' (formerly 'OceanOptics') 
    <https://www.oceaninsight.com/> brands.
Lights Out is a puzzle game consisting of a grid of lights
    that are either on or off. Pressing any light will toggle it and its
    adjacent lights. The goal of the game is to switch all the lights off. This
    package provides an interface to play the game on different board sizes,
    both through the command line or with a visual application. Puzzles can
    also be solved using the automatic solver included. View a demo
    online at http://daattali.com/shiny/lightsout/.
Performs locally induced approximate GP regression for large computer experiments and spatial datasets following Cole D.A., Christianson, R., Gramacy, R.B. (2021) Statistics and Computing, 31(3), 1-21, <arXiv:2008.12857>. The approximation is based on small local designs combined with a set of inducing points (latent design points) for predictions at particular inputs. Parallelization is supported for generating predictions over an immense out-of-sample testing set. Local optimization of the inducing points design is provided based on variance-based criteria. Inducing point template schemes, including scaling of space-filling designs, are also provided.
The goal of 'LIHKGr' is to scrape text data from LIHKG (<https://lihkg.com/>), the Hong Kong version of Reddit. LIHKG is currently protected by Google's 'reCAPTCHA', this package currently builds on 'RSelenium' and adopts a semi-manual approach to bypass it.
A Poisson Subordinated Distribution to capture major
        leptokurtic features in log-return time series of financial
        data.
Tools for maximum likelihood estimation of parameters 
  of scientific models.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Provides likelihood functions as defined by Fisher (1922)
    <doi:10.1098/rsta.1922.0009> and a function that creates likelihood 
    functions from density functions. The functions are meant to aid in 
    education of likelihood based methods.
A collection of functions that calculate the log likelihood 
    (support) for a range of statistical tests. Where possible the likelihood 
    function and likelihood interval for the observed data are displayed. The 
    evidential approach used here is based on the book "Likelihood" by A.W.F. 
    Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. 
    Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall 
    (2011) <doi:10.2105/AJPH.78.12.1568>, "Understanding 
    Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), 
    S. Glover & P. Dixon <https://link.springer.com/article/10.3758/BF03196706> 
    and others. This package accompanies "Evidence-Based Statistics" by 
    P. Cahusac (2020, ISBN-13 : 978-1119549802).
An approach to analyzing Likert response items, with an emphasis on visualizations. 
    The stacked bar plot is the preferred method for presenting Likert results. Tabular results
    are also implemented along with density plots to assist researchers in determining whether 
    Likert responses can be used quantitatively instead of qualitatively. See the likert(), 
    summary.likert(), and plot.likert() functions to get started.
A comprehensive analysis tool for metabolomics data.  It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.
Estimation of life expectancy and
    Life Years Lost (LYL, or lillies for short) for a given population, for 
    example those with a given disease or condition. In addition, the package 
    can be used to compare estimates from different populations, or to estimate 
    confidence intervals. Technical details of the method are available in
    Plana-Ripoll et al. (2020) <doi:10.1371/journal.pone.0228073> and Andersen
    (2017) <doi:10.1002/sim.7357>.
This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction <http://wbbpredictions.com/wp-content/uploads/2018/12/Redditbot_Paper.pdf>. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.
Functions that read and solve linear inverse problems (food web problems, linear programming problems).
  These problems find solutions to linear or quadratic functions:
  min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
  subject to equality constraints Ex=f and inequality constraints Gx>=h. Uses package limSolve.
When building complex models, it is often difficult to explain why
    the model should be trusted. While global measures such as accuracy are
    useful, they cannot be used for explaining why a model made a specific
    prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for
    explaining the outcome of black box models by fitting a local model around
    the point in question an perturbations of this point. The approach is
    described in more detail in the article by Ribeiro et al. (2016) 
    <arXiv:1602.04938>.
Compose interactive visualisations designed for exploratory 
        high-dimensional data analysis. With 'liminal' you can create linked
        interactive graphics to diagnose the quality of a dimension reduction
        technique and explore the global structure of a dataset with a tour. A
        complete description of the method is discussed in 
        ['Lee' & 'Laa' & 'Cook' (2020) <arXiv:2012.06077>].
Values below a specified limit of detection are stacked in
        rows in order to reduce overplotting and create a clear
        graphical representation of your data.
Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).
Functions that (1) find the minimum/maximum of a linear or quadratic function:
  min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i)
  subject to equality constraints Ex=f and/or inequality constraints Gx>=h,
  (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b,      
  (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems.
Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.
Short for 'linear binning', the linbin package provides functions
    for manipulating, binning, and plotting linearly referenced data. Although
    developed for data collected on river networks, it can be used with any interval
    or point data referenced to a 1-dimensional coordinate system. Flexible bin
    generation and batch processing makes it easy to compute and visualize variables
    at multiple scales, useful for identifying patterns within and between variables
    and investigating the influence of scale of observation on data interpretation.
Estimate and confidence/credible intervals for an unknown
    regressor x0 given an observed y0.
Sample truncated multivariate Normal distribution following Gessner, A., Kanjilal, O., & Hennig, P. (2019). Integrals over Gaussians under Linear Domain Constraints. 108. <arxiv:1910.09328>.
L-systems or Lindenmayer systems are parallel rewriting systems which can
    be used to simulate biological forms and certain kinds of fractals.
    Briefly, in an L-system a series of symbols in a string are replaced
    iteratively according to rules to give a more complex string. Eventually,
    the symbols are translated into turtle graphics for plotting. Wikipedia has
    a very good introduction: en.wikipedia.org/wiki/L-system This package
    provides basic functions for exploring L-systems.
Provides a set of streamlined functions that allow
    easy generation of linear regression diagnostic plots necessarily 
    for checking linear model assumptions.
    This package is meant for easy scheming of linear 
    regression diagnostics, while preserving merits of 
    "The Grammar of Graphics" as implemented in 'ggplot2'.
    See the 'ggplot2' website for more information regarding the
    specific capability of graphics.
Computes the probability density function, the cumulative distribution function, the hazard rate function, the quantile function and random generation for Lindley Power Series distributions, see Nadarajah and Si (2018) <doi:10.1007/s13171-018-0150-x>.
Computes  the  probability density,  the  cumulative  distribution, the quantile and
   the hazard rate functions and generates random deviates from the discrete and continuous
   Lindley  distribution  as  well  as  for 19 of  its  modifications. It  also  generates censored
   random deviates from any probability  distribution available in R.
Provides tools to manipulate formulas, such as getting x, y or contrasts from the model/formula, and functions to evaluate and check the marginal effects of a linear model.
A unified framework for simultaneous structural break detection and parameter estimation in high-dimensional linear models. The proposed method can handle a wide range of models, including change-in-mean model, multiple linear regression model, Vector auto-regressive model and Gaussian graphical model.
It is a linear algorithm to simulate quantiles of multiscale statistics 
            under hull hypothesis for multiscale change-point segmentation. 
            The reference is in preparation.
Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Tests whether the linear hypothesis of a model
    is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation 
    Specification Error Test) test is implemented and Wald tests can be carried out. 
    Although RESET test is widely used to 
    test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel 
    approach that generalizes well known specification tests such as Ramsey's. This test 
    relies on wild-bootstrap; this package implements this approach to be 
    usable with any function that fits linear models and is compatible with 
    the update() function such as 'stats'::lm(), 'lfe'::felm() and 'forecast'::Arima(), 
    for ARMA (autoregressive–moving-average) models. 
    Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov 
    Smirnov, described by the authors, and custom distributions such as Mammen (discrete 
    and continuous) and Rademacher.
    Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.
Create maps made of lines. The package contains two functions:
    linemap() and getgrid(). linemap() displays a map made of lines using a
    data frame of gridded data. getgrid() transforms a set of polygons
    (sf objects) into a suitable data frame for linemap().
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two tissues.
    Broman et al. (2015) <doi:10.1534/g3.115.019778>.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two
    tissues. This is a revised version of the 'lineup' package, to be
    more general and not tied to the 'qtl' package.
Measure similarity between texts. Offers a variety of processing
  tools and similarity metrics to facilitate flexible representation of texts and matching.
  Implements forms of Language Style Matching (Ireland & Pennebaker, 2010) <doi:10.1037/a0020386>
  and Latent Semantic Analysis (Landauer & Dumais, 1997) <doi:10.1037/0033-295X.104.2.211>.
Provides R with the Glottolog database <https://glottolog.org/> and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <https://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages <https://docs.ropensci.org/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.
Provides 'Shiny gadgets' to search, type, and insert IPA symbols
    into documents or scripts, requiring only knowledge about phonetics or 
    'X-SAMPA'. Also provides functions to facilitate the rendering of IPA
    symbols in 'LaTeX' and PDF format, making IPA symbols properly rendered
    in all output formats. A minimal R Markdown template for authoring 
    Linguistics related documents is also bundled with the package. Some
    helper functions to facilitate authoring with R Markdown is also provided.
Functions to simplify the linking of open source GIS and remote sensing related command line interfaces.
Produces high resolution, publication ready linkage maps
    and quantitative trait loci maps. Input can be output from 'R/qtl',
    simple text or comma delimited files. Output is currently
    a portable document file.
Link communities reveal the nested and overlapping
    structure in networks, and uncover the key nodes that form connections
    to multiple communities. linkcomm provides a set of tools for
    generating, visualizing, and analysing link communities in networks of
    arbitrary size and type. The linkcomm package also includes tools for
    generating, visualizing, and analysing Overlapping Cluster Generator
    (OCG) communities. Kalinka and Tomancak (2011) <doi:10.1093/bioinformatics/btr311>.
Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.
A class that links matrix-like objects (nodes) by rows or by
    columns while behaving similarly to a base R matrix. Very large matrices
    are supported if the nodes are file-backed matrices.
A linkage information based method for imputing missing diploid genotypes
Implementations of most of the existing proximity-based methods of 
  link prediction in graphs. Among the 20 implemented methods are e.g.:
  Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>,
  Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>,
  Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and
  Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.
Compute and visualize using the 'visNetwork' package all the bivariate correlations of a dataframe.
  Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau,
  distance correlation, maximal information coefficient and 
  equal-freq discretization-based maximal normalized mutual information) are used according to 
  the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).
A 'LaTeX' Letter class for 'rmarkdown', using the
 'pandoc-letter' template adapted for use with 'markdown'.
This package implements the methodology of
        Likelihood-based Imprecise Regression (LIR) for the case of
        linear regression with interval data.
Conveniently generate 'CSS' using R code.
Generate concentration-time profiles from linear pharmacokinetic
  (PK) systems, possibly with first-order absorption or zero-order infusion,
  possibly with one or more peripheral compartments, and possibly under
  steady-state conditions. Single or multiple doses may be specified. Secondary
  (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are
  computed.
This package can be used to solve Linear Programming /
        Linear Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions 
    and marginal effects for different kinds of regression models. In this version 
    linear regression models, generalized linear models, generalized additive
    models and linear mixed-effects models are supported.  
    Major features are the interactive approach and the handling of the effects of categorical covariates: 
    if two or more factors are used as covariates every combination of the levels of each 
    factor is treated separately. The automatic calculation of 
    marginal effects and a number of possibilities to customize the graphical output 
    are useful features as well.
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators,
  following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>.
  In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso,
  elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), 
    Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution,  
    projecting a vector on the convex polytope described by a system of (in)equations, 
    simplify systems by removing spurious columns and rows and collapse implied equalities, 
    test if a matrix is totally unimodular, compute variable ranges implied by linear
    (in)equalities.
Checks adherence to a given style, syntax errors and possible
    semantic issues.  Supports on the fly checking of R code edited with 'RStudio IDE', 'Emacs',
    'Vim', 'Sublime Text' and 'Atom'.
Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.
Create lipidome-wide heatmaps of statistics with the 'lipidomeR'.
    The 'lipidomeR' provides a streamlined pipeline for the systematic 
    interpretation of the lipidome through publication-ready visualizations 
    of regression models fitted on lipidomics data. 
    With 'lipidomeR', associations between covariates and the lipidome 
    can be interpreted systematically and intuitively through heatmaps, where 
    lipids are categorized by the lipid class and are presented on 
    two-dimensional maps organized by the lipid size and level of saturation. 
    This way, the 'lipidomeR' helps you gain an immediate understanding of 
    the multivariate patterns in the lipidome already at first glance.
    You can create lipidome-wide heatmaps of statistical associations, changes, 
    differences, variation, or other lipid-specific values.  
    The heatmaps are provided with publication-ready quality and the results 
    behind the visualizations are based on rigorous statistical models.
Provides three classes: Queue, PriorityQueue and Stack. Queue is just a
    "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.
Performs Bayesian linear regression and forecasting in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables) and scatter correlation, time evolution of slopes, normalization, scatters, Malmquist and Eddington bias, upper limits and break of linearity. The posterior distribution of the regression parameters is sampled with a Gibbs method exploiting the JAGS library.
Contains 128 palettes from Color Lisa. All palettes are based on 
    masterpieces from the worlds greatest artists. For more information, see 
    <http://colorlisa.com/>.
Though SRFI-1 scopes both list-processing and higher-order
        programming, we'll save some list-orthogonal functions for the
        `functional' package; this is freely a mixture of
        implementation and API.
This is an unofficial package aimed at automating the
        import of LISREL output in R.  This package or its maintainer
        is not in any way affiliated with the creators of LISREL and
        SSI, Inc.
Allows researchers to conduct multivariate
    statistical analyses of survey data with list experiments. This
    survey methodology is also known as the item count technique or
    the unmatched count technique and is an alternative to the commonly
    used randomized response method. The package implements the methods
    developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, 
    Blair and Imai (2012) <doi:10.1093/pan/mpr048>, 
    Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, 
    Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>,
    Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, 
    Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and 
    Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. 
    This includes a Bayesian MCMC implementation of regression for the 
    standard and multiple sensitive item list experiment designs and a 
    random effects setup, a Bayesian MCMC hierarchical regression model 
    with up to three hierarchical groups, the combined list experiment 
    and endorsement experiment regression model, a joint model of the 
    list experiment that enables the analysis of the list experiment as 
    a predictor in outcome regression models, a method for combining 
    list experiments with direct questions, and methods for diagnosing and
    adjusting for response error. In addition, the package implements the 
    statistical test that is designed to detect certain failures of list 
    experiments, and a placebo test for the list experiment using data from 
    direct questions.
The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].
A toolbox for R arrays. Flexibly split, bind, reshape, modify, 
    subset and name arrays.
An implementation of list comprehensions as purely syntactic
  sugar with a minor runtime overhead. It constructs nested for-loops and
  executes the byte-compiled loops to collect the results.
Syntactic shortcuts for creating synthetic lists, vectors, 
    data frames, and matrices using list comprehension.
Programmatically create R Markdown documents from lists.
Construction of list-based rules, i.e. a list of if-then clauses, to estimate the optimal dynamic treatment regime. Details can be found at Zhang et al (2018) <doi:10.1080/01621459.2017.1345743>.
List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.
Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.
R lists, especially nested lists, can be very difficult to
    visualize or represent. Sometimes 'str()' is not enough, so this suite of
    htmlwidgets is designed to help see, understand, and maybe even modify your R
    lists.  The function 'reactjson()' requires a package
    'reactR' that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.
Provides a function that, as an alternative to base::list, allows
    default values to be inherited from another list.
Temporary and permanent message queues for R. Built on top of
    'SQLite' databases. 'SQLite' provides locking, and makes it possible
    to detect crashed consumers. Crashed jobs can be automatically marked
    as "failed", or put in the queue again, potentially a limited number of times.
Dataset and functions to explore quality of literary novels. The package is a part of the Riddle of Literary Quality project, and it contains the data of a reader survey about fiction in Dutch, a description of the novels the readers rated, and the results of stylistic measurements of the novels. The package also contains functions to combine, analyze, and visualize these data.
Data sets on various litter types like beach litter, riverain
    litter, floating litter, and seafloor litter are rapidly growing. This 
    package offers a simple user interface to analyse these litter data in
    a consistent and reproducible way. It also provides functions to 
    facilitate several kinds of litter analysis, e.g., trend analysis, 
    power analysis, and baseline analysis. Under the hood, these functions 
    are also used by the user interface. See Schulz et al. (2019)
    <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are
    advised to run 'litteR' in 'RStudio'. See our vignette: Installation manual 
    for 'RStudio' and 'litteR'.
A scripting and command-line front-end
 is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
 the GNU R language and environment for statistical computing and graphics.
 While R can be used in batch mode, the r binary adds full support for
 both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
 expression as the first line in scripts) as well as command-line use in
 standard Unix pipelines. In other words, r provides the R language without
 the environment.
Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585>
  ii.  Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011>
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Liu (1993) <doi:10.1080/03610929308831027>
  v.   Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.
Interpretability of complex machine learning models is a growing concern.
    This package helps to understand key factors that drive the 
    decision made by complicated predictive model (so called black box model). 
    This is achieved through local approximations that are either based on 
    additive regression like model or CART like model that allows for 
    higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
    More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.
Provides a wrapper around LiveChat's API. The R functions allow for
    one to extract chat sessions, raw text of chats between agents and customers and
    events.
Provides a collection of helper functions that make various techniques from data science more user-friendly for non-experts. In this way, our aim is to allow non-experts to become familiar with the techniques with only a minimal level of coding knowledge. Indeed, following an ancient Persian idiom, we refer to this as "eating the liver of data science" which could be interpreted as "getting intimately close with data science". Examples of procedures we include are: data partitioning for out-of-sample testing, computing Mean Squared Error (MSE) for quantifying prediction accuracy, and data transformation (z-score and min-max). Besides such helper functions, the package also includes several interesting datasets that are useful for multivariate analysis.
Fits and tests logistic joinpoint models.
Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.
Provides functionality to train and evaluate algorithm selection models for portfolios.
R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, <https://www.imstat.org/wp-content/uploads/2018/05/BJPS397.pdf>.
LLE is a non-linear algorithm for mapping high-dimensional
        data into a lower dimensional (intrinsic) space. This package
        provides the main functions to performs the LLE alogrithm
        including some enhancements like subset selection, calculation
        of the intrinsic dimension etc.
Applies local log-linear capture-recapture models (LLLMs) for
    closed populations, as described in the doctoral thesis of Zachary Kurtz.
    The method is relevant when there are 3-5 capture occasions, with auxiliary
    covariates available for all capture occasions.  As part of estimating the
    number of missing population units, the method estimates the "rate of
    missingness" as it varies over the covariate space.  In addition,
    user-friendly functions are provided to recreate (approximately) the method
    of Zwane and van der Heijden (2004), which applied the VGAM package
    in a way that is closely related to LLLMs.
Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).
Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and phi. The parameter m is the median of the distribution.
Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research.
Adds standardized regression coefficients to objects created by lm. Also extends the S3 methods print, summary and coef with additional boolean argument standardized.
Exact significance tests for a changepoint in linear or multiple linear regression.  
  Confidence regions with exact coverage probabilities for the changepoint.  Based on 
  Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.
Various efficient and robust bootstrap methods are implemented for 
    linear models with least squares estimation.  Functions within this package 
    allow users to create bootstrap sampling distributions for model parameters, 
    test hypotheses about parameters, and visualize the bootstrap sampling or null 
    distributions.  Methods implemented for linear models include the wild bootstrap by 
    Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by
    Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by 
    Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by 
    Rubin (1981) <doi:10.1214/aos/1176345338>.
Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in  Onofri et al (2020) <DOI:10.1007/s00122-020-03716-8>.
A fast dimensionality reduction method scaleable to large numbers of samples.
  Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS,
  but rather than calculating a complete distance matrix between all pairs of samples,
  only the distances between a set of landmarks and the samples are calculated.
Fit linear and generalized linear mixed-effects models.
    The models and their components are represented using S4 classes and
    methods.  The core computational algorithms are implemented using the
    'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
This package includes a function to fit a linear
        mixed-effects model in the formulation described in Laird and
        Ware (1982) but allowing for censored normal responses. In this
        version, the with-in group errors are assumed independent and
        identically distributed.
Provides analytic derivatives and information matrices for
    fitted linear mixed effects (lme) models and generalized least squares (gls) models
    estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively.
    The package includes functions for estimating the sampling variance-covariance of variance
    component parameters using the inverse Fisher information. The variance components include
    the parameters of the random effects structure (for lme models), the variance structure,
    and the correlation structure. The expected and average forms of the Fisher information matrix
    are used in the calculations, and models estimated by full maximum likelihood or
    restricted maximum likelihood are supported. The package also includes a function for estimating
    standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>)
    based on fitted lme or gls models.
Performs QTL mapping analysis for balanced and for
  multi-environment and multi-trait analysis using mixed models.
  Balanced population, single trait, single environment QTL mapping is
  performed through marker-regression (Haley and Knott (1992) <DOI:10.1038/hdy.1992.131>,
  Martinez and Curnow (1992) <DOI:10.1007/BF00222330>,
  while multi-environment and multi-trait QTL
  mapping is performed through linear mixed models.
  These functions could use any of the following populations: double haploid,
  F2, recombinant inbred lines, back-cross, and 4-way crosses.
  Performs a Single Marker Analysis, a Single Interval Mapping,
  or a Composite Interval Mapping analysis, and then constructs a final model
  with all of the relevant QTL.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Bootstrap routines for nested linear mixed effects models fit using
    either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the
    parametric, residual, cases, random effect block (REB), and wild bootstrap 
    procedures. An overview of these procedures can be found 
    in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, 
    Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>,
    and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.
Provides p-values in type I, II or III anova and summary tables
    for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A
    Kenward-Roger method is also available via the pbkrtest package. Model selection
    methods include step, drop1 and anova-like tables for random effects (ranova).
    Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed
    effects are also available.
Add smoothing spline modelling capability to nlme. Fit
        smoothing spline terms in Gaussian linear and nonlinear
        mixed-effects models
Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.
This R package provide methods for estimation and statistical
               inference on directional and fluctuating selection in age-structured
               populations.
We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.
Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.
Introduces in-sample, out-of-sample, pseudo out-of-sample, and
    benchmark linear model forecast tests and a new class for working with 
    forecast data: Forecast.
It implements Expectation/Conditional Maximization Either (ECME)
             and rapidly converging algorithms as well as
             Bayesian inference for linear mixed models, 
             which is described in Schafer, J.L. (1998)
             "Some improved procedures for linear mixed models".
             Dept. of Statistics, The Pennsylvania State University.
In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) <doi:10.1027/1015-5759/a000624>. Hedeker, Mermelstein, and Demirtas (2008) <doi:10.1111/j.1541-0420.2007.00924.x>.
Fit right censored Multiple Ordinal Tobit (MOT) model.
Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.
An efficient and flexible system to solve sparse mixed model
    equations, for models that are often used in statistical genetics. 
    Important applications are the use of splines to model spatial or temporal
    trends. Another application area is mixed model QTL analysis for 
    multiparental populations, allowing for heterogeneous residual variance and
    random design matrices with Identity-By-Descent (IBD) probabilities.
Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
	     taught by the section of Biostatistics of the University of Copenhagen.
	     It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, unstructured).
	     Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite degrees of freedom.
	     Normalized residuals are provided to assess model misspecification.
	     Statistical inference can be performed for arbitrary linear combination(s) of model coefficients.
	     Predictions can be computed conditional to covariates only or also to outcome values.
Computes model II simple linear regression using ordinary
 least squares (OLS), major axis (MA), standard major axis (SMA), and
 ranged major axis (RMA).
A complete framework for frequency analysis is provided by 'LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x>, the fitting of various distributions as in Zaghloul et al. (2020) <doi:10.1016/j.advwatres.2020.103720> and Hosking, J.R.M. (2019) <https://CRAN.R-project.org/package=lmom>, besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) <doi:10.1016/j.advwatres.2016.05.005> for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner.
Functions related to L-moments: computation of L-moments
  and trimmed L-moments of distributions and data samples; parameter
  estimation; L-moment ratio diagram; plot vs. quantiles of an
  extreme-value distribution.
Extensive functions for L-moments (LMs) and probability-weighted moments
  (PWMs), parameter estimation for distributions, LM computation for distributions, and
  L-moment ratio diagrams. Maximum likelihood and maximum product of spacings estimation
  are also available. LMs for right-tail and left-tail censoring by known or unknown
  threshold and by indicator variable are available. Asymmetric (asy) trimmed LMs
  (TL-moments, TLMs) are supported. LMs of residual (resid) and reversed (rev) resid life
  are implemented along with 13 quantile function operators for reliability and survival
  analyses. Exact analytical bootstrap estimates of order statistics, LMs, and variances-
  covariances of LMs are provided. The Harri-Coble Tau34-squared Normality Test is available.
  Distribution support with "L" (LMs), "TL" (TLMs) and added (+) support for right-tail
  censoring (RC) encompasses: Asy Exponential (Exp) Power [L], Asy Triangular [L],
  Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L],
  Gen Extreme Value [L], Gen Lambda [L,TL], Gen Logistic [L), Gen Normal [L],
  Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L],
  Kumaraswamy [L], Laplace [L], Linear Mean Resid. Quantile Function [L], Normal [L],
  3-p log-Normal [L], Pearson Type III [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice/Rician [L],
  Slash [TL], 3-p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L].
  Multivariate sample L-comoments (LCMs) are implemented to measure asymmetric associations.
Contains functions to estimate
        L-moments and trimmed L-moments from the data. Also
        contains functions to estimate the parameters of the normal
        polynomial quantile mixture and the Cauchy polynomial quantile
        mixture from L-moments and trimmed L-moments.
It is an extension of 'lmom' R package: 'pel','cdf',qua' function
    families are lumped and called from one function per each family respectively in order to
    create robust automatic tools to fit data  with different probability
    distributions and then to estimate probability values and return periods.  The implemented functions are able to manage time series with constant and/or missing values without stopping
    the execution with error messages. The package also contains tools to  calculate several  indices based on variability (e.g. 'SPI' , Standardized
    Precipitation Index, see <https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi> and <http://spei.csic.es/>) for multiple time series or spatio-temporal gridded values.
Functions for regional frequency analysis using the methods
  of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis:
  an approach based on L-moments".
Linear model functions using permutation tests.
Implementation based on Zhang, Jie & Huang, Kun (2014) <doi:10.4137/CIN.S14021> Normalized ImQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers. Cancer informatics, 13, CIN-S14021.
Data files and a few functions used in the book 
             'Linear Models and Regression with R: An Integrated Approach' 
             by Debasis Sengupta and Sreenivas Rao Jammalamadaka (2019).
Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>,
  ii.  Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>,
  iii. Imdadullah, Aslam, and Saima (2017),
  iv.  Marquardt (1970) <doi:10.2307/1267205>.
Exact and approximation algorithms for variable-subset
  selection in ordinary linear regression models.  Either compute all
  submodels with the lowest residual sum of squares, or determine the
  single-best submodel according to a pre-determined statistical
  criterion.  Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.
A collection of tests, data sets, and examples
 for diagnostic checking in linear regression models. Furthermore,
 some generic tools for inference in parametric models are provided.
Non-parametric estimators for casual effects based on longitudinal modified treatment 
  policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, 
  and traditional longitudinal effects. Continuous, binary, and categorical treatments are allowed as well are 
  censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure 
  irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects 
  can be calculated and relative risks and odds ratios may be calculated for binary outcomes.
Runs a linear-like regression with in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.
Contains a suite of shiny applications
    meant to explore linear model inference feature through simulation
    and games.
We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>.
Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han SY., Liang YC., Li Y., et al. (2018) <doi:10.1093/bib/bby065>.
Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.
Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, <doi:10.18637/jss.v020.i07>.
Various plots and functions that make use of the lattice/trellis plotting framework. 
   The plots, which include loaPlot(), RgoogleMapsPlot() and trianglePlot(), use panelPal(), a function that 
   extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel 
   and panel-to-key synchronization/management.
A collection of functions created to study water discharge
    (Q) and suspended sediment concentration (SSC) relationship.
Computing principal component (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51> and Yamamoto, H. et al. (2021) <doi:10.3390/metabo11030149>)
             and partial least squares loading (Yamamoto, H. (2017) <doi:10.1002/cem.2883>) and its statistical hypothesis testing.
Provides intuitive functions for loading objects into
    environments, encouraging less cluttered workspaces and sharing variables
    with large or reusable data across users and sessions. The user
    provides named variables which are loaded into the variable environment for
    later retrieval.
A set of tools for inspecting and understanding R data
    structures inspired by str(). Includes ast() for visualizing abstract 
    syntax trees, ref() for showing shared references, cst() for showing 
    call stack trees, and obj_size() for computing object sizes.
Implements novel nonparametric approaches to address
    biases and confounding when comparing treatments or exposures in
    observational studies of outcomes. While designed and appropriate for use
    in studies involving medicine and the life sciences, the package can be
    used in other situations involving outcomes with multiple confounders.
    The package implements a family of methods for non-parametric bias correction
    when comparing treatments in observational studies, including survival
    analysis settings, where competing risks and/or censoring may be present.
    The approach extends to bias-corrected personalized predictions of treatment
    outcome differences, and analysis of heterogeneity of treatment effect-sizes
    across patient subgroups. For further details, please see: 
    Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl:
    An R Package for Comparative Safety and Effectiveness Research.
    Journal of Statistical Software. 2020. p. 1–32. Available from <doi:10.18637/jss.v096.i04>.
Especially when cross-sectional data are observational, effects of treatment
  selection bias and confounding are revealed by using the Nonparametric and Unsupervised 
  "preprocessing" methods central to Local Control (LC) Strategy. The LC objective is to
  estimate the "effect-size distribution" that best quantifies a potentially causal
  relationship between a numeric y-Outcome variable and a t-Treatment variable. This
  t-variable may be either binary {1 = "new" vs 0 = "control"} or a numeric measure of
  Exposure level. LC Strategy starts by CLUSTERING experimental units (patients) on their
  pre-exposure X-Covariates, forming mutually exclusive and exhaustive BLOCKS of relatively
  well-matched units. The implicit statistical model for LC is thus simple one-way ANOVA.
  The Within-Block measures of effect-size are Local Rank Correlations (LRCs) when Exposure
  is numeric with more than two levels. Otherwise, Treatment choice is Nested within
  BLOCKS, and effect-sizes are LOCAL Treatment Differences (LTDs) between within-cluster
  y-Outcome Means ["new" minus "control"]. An Instrumental Variable (IV) method is also
  provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute
  information for effect-size inferences ...assuming that X-Covariates influence only
  Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome.
  Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to
  aid Doctor-Patient communications about Personalized Medicine.
Implementation of a theoretically supported alternative to k-nearest neighbors for functional data 
  to solve problems of estimating unobserved segments of a partially observed functional data sample, 
  functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. 
  Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. 
  The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.
Computational routines for estimating local Gaussian parameters. Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48 <DOI:10.1016/j.jeconom.2012.08.001>.
Local Individual Conditional Expectation ('localICE') is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI). localICE is a model-agnostic XAI approach which provides three-dimensional local explanations for particular data instances. The approach is proposed in the master thesis of Martin Walter as an extension to ICE (see Reference). The three dimensions are the two features at the horizontal and vertical axes as well as the target represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. For classification models, the number of classes can be more than two and each class is added as a different color to the plot. The given instance is added to the plot as two dotted lines according to the feature values. The localICE-package can explain features of type factor and numeric of any machine learning model. Automatically supported machine learning packages are 'mlr', 'randomForest', 'caret' or all other with an S3 predict function. For further model types from other libraries, a predict function has to be provided as an argument in order to get access to the model. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) <arXiv:1309.6392>.
In the generalized Roy model, the marginal treatment effect (MTE) can be used as
  a building block for constructing conventional causal parameters such as the average treatment
  effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection
  equation and an outcome equation, the function mte() estimates the MTE via the semiparametric
  local instrumental variables method or the normal selection model. The function mte_at() evaluates
  MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at()
  evaluates MTE projected onto the estimated propensity score. The function ace() estimates
  population-level average causal effects such as ATE, ATT, or the marginal policy relevant
  treatment effect.
Local explanations of machine learning models describe, how features contributed to a single prediction. 
    This package implements an explanation method based on LIME 
    (Local Interpretable Model-agnostic Explanations, 
    see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable
    inputs are created based on local rather than global behaviour of each original feature.
Functionalities for calculating the local score and calculating statistical relevance (p-value) to find a local Score in a sequence of given distribution (S. Mercier and J.-J. Daudin (2001) <https://hal.archives-ouvertes.fr/hal-00714174> ; S. Karlin and S. Altschul (1990) <https://www.pnas.org/content/87/6/2264> ; S. Mercier, D. Cellier and F. Charlot (2003) <https://hal.archives-ouvertes.fr/hal-00937529v1> ; A. Lagnoux, S. Mercier and P. Valois (2017) <doi:10.1093/bioinformatics/btw699> ).
The package converts R data onto input and data for LocalSolver,
    executes optimization and exposes optimization results as R data.
    LocalSolver (http://www.localsolver.com/) is an optimization engine
    developed by Innovation24 (http://www.innovation24.fr/). It is designed to
    solve large-scale mixed-variable non-convex optimization problems.  The
    localsolver package is developed and maintained by WLOG Solutions
    (http://www.wlogsolutions.com/en/) in collaboration with Decision Support
    and Analysis Division at Warsaw School of Economics
    (http://www.sgh.waw.pl/en/).
A set of functions to locate some programs 
 available on the user machine. The package provides functions to locate 
 'Node.js', 'npm', 'LibreOffice', 'Microsoft Word', 'Microsoft PowerPoint', 
 'Microsoft Excel', 'Python', 'pip', 'Mozilla Firefox' and 'Google Chrome'.
 User can test the availability of a program with eventually a version 
 and call it with function system2() or system(). This allows the use of 
 a single function to retrieve the path to a program regardless of the 
 operating system and its configuration.
Identification of equilibrium locations in location games (Hotelling (1929) 
    <doi:10.2307/2224214>). In these games, two competing actors place
    customer-serving units in two locations simultaneously. Customers make the 
    decision to visit the location that is closest to them. The functions in 
    this package include Prim algorithm (Prim (1957) 
    <doi:10.1002/j.1538-7305.1957.tb01515.x>) to find the minimum spanning tree 
    connecting all network vertices, an implementation of Dijkstra algorithm 
    (Dijkstra (1959) <doi:10.1007/BF01386390>) to find the shortest distance and 
    path between any two vertices, a self-developed algorithm using elimination 
    of purely dominated strategies to find the equilibrium, and several plotting 
    functions.
Computation of local false discovery rates.
The main idea of the Local FDR algorithm is to estimate both proportion of null observations and the ratio of null and alternative densities. In the case that there are many null observations, this can be done reliably, through maximum likelihood or generalized linear models. This package implements this in the case that the null and alternative densities are Poisson.
Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.
Provides test of second-order stationarity for time
	series (for dyadic and arbitrary-n length data). Provides
	localized autocovariance, with confidence intervals,
	for locally stationary (nonstationary) time series.
Datasets for the third edition of "Statistics: Unlocking the Power of Data" by Lock^5
    Includes version of datasets from earlier editions.
Data sets and other utilities for 
    'Statistics: Unlocking the Power of Data'
    by Lock, Lock, Lock, Lock and Lock 
    (ISBN : 978-0-470-60187-7, http://lock5stat.com/).
Computes local polynomial estimators for 
  the regression and also density. It comprises several 
  different utilities to handle kernel estimators.
Provides the local polynomial expectile regression method and different bandwidth selection procedures. The codes include local polynomial univariate expectile regression with several data-driven methods for bandwidth selection; local linear bivariate and trivariate expectile regression; and partially linear expectile regression, allowing for different errors structures (homoscedastic error and various heteroscedastic error structures). For more details, see Adam and Gijbels (2021a) <doi:10.1007/s10463-021-00799-y> and Adam and Gijbels (2021b) <doi:10.1007/978-3-030-73249-3_8>.
Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) <doi:10.1002/gepi.22356>). Full support for sparse matrices from the R-package 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix without actually computing the similarity matrices.
Read and write access to PNG image files using the LodePNG
    library. The package has no external dependencies.
Genome-wide association (GWAS) analyses
  of a biomarker that account for the limit of detection.
Impute observed values below the limit of detection (LOD) via
    censored likelihood multiple imputation (CLMI) in single-pollutant
    models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>.
    CLMI handles exposure detection limits that may change throughout the course
    of exposure assessment. 'lodi' provides functions for imputing and
    pooling for this method.
Tools to fit linear regression model to data while taking into account covariates with lower limit of detection (LOD).
Local Ordinal embedding (LOE) is one of graph embedding methods for unweighted graphs.
Estimate model parameters to determine whether two compounds have synergy, antagonism, or Loewe's Additivity.
Logger to keep track of informational events and errors useful for debugging.
The log4r package is meant to provide a fast, lightweight,
  object-oriented approach to logging in R based on the widely-emulated
  'log4j' system and etymology.
Enables users to handle the dataset cleaning for
    conducting specific analyses with the log files from two international
    educational assessments: the Programme for International Student Assessment
    (PISA, <http://www.oecd.org/pisa/>) and the Programme for the International Assessment of Adult Competencies
    (PIAAC, <http://www.oecd.org/skills/piaac/>). An illustration of the analyses can be found on the LOGAN Shiny app
    (<https://loganpackage.shinyapps.io/shiny/>) on your browser.
Methods for fitting log-link GLMs and GAMs to binomial data,
    including EM-type algorithms with more stable convergence properties than standard methods.
Software for computing a log-concave (maximum likelihood) estimator for i.i.d. data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach and Schuhmacher (2014) <doi:10.1214/14-EJS930>.
Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Computes maximum likelihood estimate of a log-concave density  with fixed and known location of the mode. Performs inference about the mode via a likelihood ratio test. Extension of the logcondens package.
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Inspired by the the 'futile.logger' R package and 'logging' Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.
Pure R implementation of the ubiquitous log4j package. It offers hierarchic 
  loggers, multiple handlers per logger, level based filtering, space handling in messages 
  and custom formatting.
An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary
    log-writing interfaces. It provides a set of wrappings for base R's
    message(), warning(), and stop() functions that maintain identical
    functionality, but also log the handler message to an 'ndjson' log file.
    'loggit' also exports its internal 'loggit()' function for powerful and
    configurable custom logging. No change in existing code is necessary to use
    this package, and should only require additions to fully leverage the power
    of the logging system. 'loggit' also provides a log reader for reading an
    'ndjson' log file into a data frame, log rotation, and live echo of the
    'ndjson' log messages to terminal 'stdout' for log capture by external
    systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines,
    modeling work flows, and more. Please see the vignettes for detailed example
    use cases.
Implementation of the Swiss Confederation's standard analysis model for salary analyses <https://www.ebg.admin.ch/dam/ebg/en/dokumente/lohngleichheit/infos-zu-analysen/standard-analysemodellzurueberpruefungderlohngleichheitzwischenf.pdf.download.pdf/methodological_approachformonitoringcompliancewithwageequalitybe.pdf> in R. The analysis is run at company-level and the model is intended for companies with 50 or more employees (apprentices, trainees/interns and expats are not included in the analysis). Employees with at least 100 employees are required by the Gender Equality Act to conduct an equal pay analysis.
    This package allows users to run the equal salary analysis in R, providing additional transparency with respect to the methodology and simple automation possibilities.
Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.
A global statistical learning method
  which tries to find the best set of predictors and interactions
  between predictors for modeling binary or quantitative response data.
  Several search algorithms and ensembling techniques are implemented
  allowing for finetuning the method to the specific problem.
  Interactions with single quantitative covariables can be properly
  taken into account by also splitting after those or by fitting
  local four parameter logistic models.
This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
Routines for fitting Logic Regression models. Logic Regression is described
	in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte
        Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005)
        <DOI:10.1002/gepi.20042>.
Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.
Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys 
	prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal
	solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary
	maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available
	in the package as well, see Puhr, Heinze, Nold, Lusa and Geroldinger (2017) <doi:10.1002/sim.7273>.
Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
Diagnostic tests and plots for GLMs (generalized linear models)
    with binomial/ binary outcomes, particularly logistic regression.
Dimensionality reduction techniques for binary data including
    logistic PCA.
Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.
LogitNet is developed for inferring network of binary
        variables under the high-dimension-low-sample-size setting
Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. MXL models assume uncorrelated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) "Discrete Choice Methods with Simulation, 2nd Edition" <doi:10.1017/CBO9780511805271>.
Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.
r,d,p,q functions for the double log normal distribution
Functions to fit log-multiplicative models using 'gnm', with
  support for convenient printing, plots, and jackknife/bootstrap
  standard errors. For complex survey data, models can be fitted from
  design objects from the 'survey' package. Currently supported models
  include UNIDIFF (Erikson & Goldthorpe), a.k.a. log-multiplicative
  layer effect model (Xie), and several association models: Goodman's
  row-column association models of the RC(M) and RC(M)-L families
  with one or several dimensions; two skew-symmetric association
  models proposed by Yamaguchi and by van der Heijden & Mooijaart.
  Functions allow computing the intrinsic association coefficient
  (and therefore the Altham index), including via the Bayes shrinkage
  estimator proposed by Zhou; and the RAS/IPF/Deming-Stephan algorithm.
The lognormal distribution  
  (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>)
  can characterize uncertainty that is bounded by zero.
  This package provides estimation of distribution parameters, computation of
  moments and other basic statistics, and an approximation of the distribution
  of the sum of several correlated lognormally distributed variables 
  (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the 
  difference of two correlated lognormally distributed variables
  (Lo 2012 <doi:10.1155/2012/838397>).
Functions to fits simple linear regression models with log normal errors 
		and identity link, i.e. taking the responses on the original scale. See
		Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.
Uses approximations to compute the natural logarithm of the Gamma
    function for large values.
Contains functions to help create log files.  The 
    package aims to overcome the difficulty of the base R sink() command.  The
    log_print() function will print to both the console and the file log, 
    without interfering in other write operations.
LogrankA provides a logrank test across unlimited groups with the
 possibility to input aggregated survival data.
Tools for assessing equivalence of similar Logistic Regression models.
Contains routines for logspline density estimation.
	The function oldlogspline() uses the same algorithm as the logspline package
	version 1.0.x; i.e. the Kooperberg and Stone (1992) 
	algorithm (with an improved interface).  The recommended routine logspline()
	uses an algorithm from Stone et al (1997)  <DOI:10.1214/aos/1031594728>.
Kernel regression smoothing with adaptive local or global plug-in
	     bandwidth selection.
Estimation of Latent Order Logistic (LOLOG) Models for Networks.
    LOLOGs are a flexible and fully general class of statistical graph models. 
    This package provides functions for performing MOM, GMM and variational 
    inference. Visual diagnostics and goodness of fit metrics are provided. 
    See Fellows (2018) <arXiv:1804.04583> for a detailed description of the methods.
Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.
Computes the Lomb-Scargle Periodogram for unevenly sampled time series. Includes a randomization procedure to obtain exact p-values.
Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.
Methods for plotting categorical longitudinal and time-series data by mapping individuals to the vertical space (each horizontal line represents a participant), time (or repeated measures) to the horizontal space, categorical (or discrete) states as facets using color or shade, and events to points using plotting characters. Sorting individuals in the vertical space and (or) stratifying them by groups can reveal patterns in the changes over time.
Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Details in McNicholas and Murphy (2010) <doi:10.1002/cjs.10047> and McNicholas and Subedi (2012) <doi:10.1016/j.jspi.2011.11.026>.
High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). 
             Currently support mixed effect regression with or without missing observations by considering 
             covariance structures. It provides estimates by missing at random and missing not at random assumptions.
             In this R package, we present Bayesian approaches that statisticians and clinical 
             researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by 
             Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.
Contains general data structures and
  functions for longitudinal data with multiple variables, 
  repeated measurements, and irregularly spaced time points.
  Also implements a shrinkage estimator of dynamical correlation
  and dynamical covariance.
Creates a series of set of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests.
Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).
Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data.  In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.
Datasets and Functionality from
  'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall.
  Estimation of Hurst (and more) parameters for fractional Gaussian noise,
  'fARIMA' and 'FEXP' models.
Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.
Compute power and sample size for linear models of longitudinal
    data. Supported models include mixed-effects models and models fit by
    generalized least squares and generalized estimating equations. Relevant
    formulas are derived by Liu and Liang (1997) <DOI:10.2307/2533554>, 
    Diggle et al (2002) <ISBN:9780199676750>, and Lu, Luo, and Chen (2008)
    <DOI:10.2202/1557-4679.1098>.
Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.
Performs recursive partitioning of linear and nonlinear mixed effects models,
    specifically for longitudinal data. The package is an
    extension of the original 'longRPart' package by 
    Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.
Tools are provided to expand vectors of short URLs into long 'URLs'. 
    No 'API' services are used, which may mean that this operates more slowly than 
    'API' services do (since they usually cache results of expansions that every 
    user of the service requests). You can setup your own caching layer with the 
    'memoise' package if you wish to have a speedup during single sessions or add 
    larger dependencies, such as 'Redis', to gain a longer-term performance boost 
    at the expense of added complexity.
Efficient approximate leave-one-out cross-validation (LOO)
    for Bayesian models fit using Markov chain Monte Carlo, as 
    described in Vehtari, Gelman, and Gabry (2017) 
    <doi:10.1007/s11222-016-9696-4>. 
    The approximation uses Pareto smoothed importance sampling (PSIS), 
    a new procedure for regularizing importance weights. 
    As a byproduct of the calculations, we also obtain approximate 
    standard errors for estimated predictive errors and for the comparison 
    of predictive errors between models. The package also provides methods 
    for using stacking and other model weighting techniques to average 
    Bayesian predictive distributions.
Outlier detection using leave-one-out kernel density estimates and
    extreme value theory. The bandwidth for kernel density estimates is computed 
    using persistent homology, a technique in topological data analysis. Using 
    peak-over-threshold method, a generalized Pareto distribution is fitted to
    the log of leave-one-out kde values to identify outliers.
Simple functions to lookup items in key-value pairs. See  Mehta (2021) <doi:10.1007/978-1-4842-6613-7_6>.
Fits look-up tables by filling entries with the mean or median values of observations
            fall in partitions of the feature space. Partitions can be determined by user of the
            package using input argument feature.boundaries, and dimensions of the feature space
            can be any combination of continuous and categorical features provided by the data set.
            A Predict function directly fetches corresponding entry value, and a default value is
            defined as the mean or median of all available observations.
            The table and other components are represented using the S4 class lookupTable.
An extendable toolkit for interactive data visualization and exploration.
Data used as examples in the 'loon' package.
Provides a bridge between the 'loon' and  'ggplot2' packages.  Extends the grammar of ggplot to add clauses to create interactive 'loon' plots. Existing ggplot(s) can be turned into interactive 'loon' plots and 'loon' plots into static ggplot(s); the function 'loon.ggplot()' is the bridge from one plot structure to the other.
Package 'shiny' provides interactive web applications in R. Package 'loon' is an interactive toolkit engaged in open-ended, creative and unscripted data exploration. The 'loon.shiny' package can take 'loon' widgets and display a selfsame 'shiny' app.
Implement tour algorithms in interactive graphical system 'loon'.
The program can perform loop analysis and plot network
        structure (especially for food webs),including minimum spanning
        tree, loop decomposition of weighted directed graphs, and other
        network properties which may be related to food chain
        properties in ecology.
Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means sign only (i.e. increases, decreases, non change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as 'graphviz' (<http://graphviz.org>). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.
Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using 'numDeriv'. Graph-based algorithms from 'igraph' are employed for path detection.
Common coordinate-based workflows involving processed chromatin loop and genomic element data are considered and packaged into appropriate customizable functions. Includes methods for linking element sets via chromatin loops and creating consensus loop datasets.
Collection of functions to improve work-flow in survival analysis and data science.
             The package features include: the generation of balanced datasets, live retrieval of 
             protein coding genes from two public databases, generation of random 
             matrix based on covariance matrix, cache function to store function results.
             This work was supported by two grants from the Portuguese Foundation for Science
             and technology, and the EU Commission under SOUND project.
Change-point detection algorithm with
 label constraints and a penalty for each change outside of labels.
 Read TD Hocking, A Srivastava (2020) <arXiv:2006.13967> for details.
Analysis of Differential Item Functioning (DIF) for
        dichotomous and polytomous items using an iterative hybrid of
        ordinal logistic regression and item response theory (IRT).
Estimate covariance matrices that contain low rank and sparse components
The Lorentz transform in special relativity; also the gyrogroup structure of three-velocities.  Includes active and passive transforms and the ability to use units in which the speed of light is not one.  For general relativity, see the
  'schwarzschild' package.
Provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census.
  These methods use different interpolation techniques to infer the distribution of incomes within income bins.  One method is an implementation of 
  Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB).  The other method is based on a new technique, Lorenz interpolation, 
  which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data.  These methods can be used to 
  estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index.  
  Jargowsky and Wheeler (2018) <doi:10.1177/0081175018782579>.
Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <arXiv:1703.02296v4>.
Functions for simulating missing morphometric
	data randomly, with taxonomic bias and with anatomical bias. LOST also 
	includes functions for estimating linear and geometric morphometric data.
Running Lotka's Law following Pao (1985)(DOI: 10.1016/0306-4573(85)90055-X). The Law is based around the proof that the number of authors making n contributions is about 1/n^{a} of those making one contribution.
Provides a simple mechanism to specify a symmetric block diagonal matrices (often 
  used for covariance matrices).  This is based on the domain specific language implemented
  in 'nlmixr' but expanded to create matrices in R generally instead of specifying parts of matrices
  to estimate.
Performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.
Creates lowpass filters which are commonly used in ion channel recordings. It supports generation of random numbers that are filtered, i.e. follow a model for ion channel recordings, see <doi:10.1109/TNB.2018.2845126>. Furthermore, time continuous convolutions of piecewise constant signals with the kernel of lowpass filters can be computed.
Solves quadratic programming problems where the Hessian is represented as the product of two matrices.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Niederreiter-Xing sequence <DOI:10.1007/978-3-642-56046-0_30>.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Sobol sequence.
Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.
Optimization of bipartite modularity using LP-BRIM (Label propagation
  followed by Bipartite Recursively Induced Modularity).
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering.
Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.
It uses 'phenological' and productivity-related variables derived from time series of vegetation 
    indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which 
    eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator 
    is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing 
    productivity. See <https://eartharxiv.org/repository/view/2294/> for a description of the methods used in 
    the package to calculate the indicator.
A nonparametric method to approximate Laplacian graph spectra of a network with 
    ordered vertices. This provides a computationally efficient algorithm for obtaining an 
	accurate and smooth estimate of the graph Laplacian basis. The approximation results can 
	then be used for tasks like change point detection, k-sample testing, and so on. The 
	primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).
Provides a bunch of algorithms based on linear programming for estimating 
    RxC ecological contingency tables (or vote transition matrices) using exclusively aggregate
    results from voting units under the homogeneity hypothesis. References: 
    Romero, Pavia, Martin and Romero (2020) <doi:10.1080/02664763.2020.1804842>.
    Pavia and Romero (2021a) <doi:10.31124/advance.14716638.v1>.
    Pavia and Romero (2021b) Symmetry estimating R×C vote transfer matrices from aggregate data.
Estimates the intensity function, or its derivative of a given order, of a multiplicative counting process using the local polynomial method
Provides functions to estimate and plot linear as well as nonlinear impulse 
             responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>.
LP nonparametric high-dimensional K-sample comparison method that includes 
    (i) confirmatory test, (ii) exploratory analysis, and (iii) options to output a 
	data-driven LP-transformed matrix for classification. The primary reference is 
	Mukhopadhyay, S. and Wang, K. (2020, Biometrika); <arXiv:1810.01724>.
Local partial likelihood estimation by Fan, Lin and Zhou(2006)<doi:10.1214/009053605000000796> and simultaneous confidence band is a set of tools to test the covariates-biomarker interaction for survival data. Test for the covariates-biomarker interaction using the bootstrap method and the asymptotic method with simultaneous confidence band (Liu, Jiang and Chen (2015)<doi:10.1002/sim.6563>).
Apply Univariate Long Memory Models,
    Apply Multivariate Short Memory Models To Hydrological Dataset,
    Estimate Intensity Duration Frequency curve to rainfall series.
Provide nonparametric methods for mean regression model, modal regression and conditional density estimation in the presence/absence of measurement error. Bandwidth selection is also provided for each method. See Huang and Zhou (2017) <doi:10.1080/10485252.2017.1303060>, Zhou and Huang (2016) <doi:10.1214/16-EJS1210>, Huang and Zhou (2020) <doi:10.1214/20-EJS1688>, and Zhou and Huang (2019) <doi:10.1080/03610918.2017.1402044>.
Creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.
lpmodeler is a set of user-friendly functions to simplify the modelling of linear programs (LP) and mixed integer programs (MIP). It provides a unified interface compatible with optimization packages: Rsymphony.
Computes power, or sample size or the detectable difference for a repeated measures model with attrition. It requires the variance covariance matrix of the observations but can compute this matrix for several common random effects models. See Diggle, P, Liang, KY and Zeger, SL (1994, ISBN:9780198522843).
Provide methods to perform customized inference at individual level by taking 
	contextual covariates into account. Three main functions are provided 
	in this package: (i) LASER(): it generates specially-designed artificial relevant 
	samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) 
	rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be 
	found in Mukhopadhyay, S., and Wang, K (2021, <arXiv:2004.09588>).
Local Polynomial Regression with Ridging.
An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.
Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.
Lp_solve is freely available (under LGPL 2) software for
        solving linear, integer and mixed integer programs. In this
        implementation we supply a "wrapper" function in C and some R
        functions that solve general linear/integer problems,
        assignment problems, and transportation problems. This version
        calls lp_solve version 5.5.
The lpSolveAPI package provides an R interface to 'lp_solve',
    a Mixed Integer Linear Programming (MILP) solver with support for pure
    linear, (mixed) integer/binary, semi-continuous and special ordered sets
    (SOS) models.
Learned Pattern Similarity (LPS) for time series. 
			Implements a novel approach to model the dependency structure 
			in time series that generalizes the concept of autoregression to local 
			auto-patterns. Generates a pattern-based representation of time series
			along with a similarity measure called Learned Pattern Similarity (LPS).
			Introduces a generalized autoregressive kernel.This package is based on the 
			'randomForest' package by Andy Liaw.
Specially designed rank transform based Legendre Polynomial-like (LP) orthonormal transformations are implemented for non-linear signal processing.
Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.
Functions to fit quantile regression models for hierarchical
    data (2-level nested designs) as described in Geraci and
    Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>.
    A vignette is given in Geraci (2014, Journal of Statistical Software)
    <doi:10.18637/jss.v057.i13> and included in the package documents.
    The packages also provides functions to fit quantile models for
    independent	data and for count responses.
It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.
Provides functions for calculating test statistics, simulating quantiles 
	and simulating p-values of likelihood ratio contrast tests in regression models 
	with a lack of identifiability.
The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.
In the fashion of 'node.js' <https://nodejs.org/>, requires a file,
  sourcing into the current environment only the variables explicitly specified
  in the module.exports or exports list variable. If the file was already sourced,
  the result of the earlier sourcing is returned to the caller.
Implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.
When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. To deal with multicollinearity several estimators are proposed in the literature. Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Generalized Ordinary Least Square Estimator (OGOLSE), Ordinary Ridge Regression Estimator (ORRE), Ordinary Generalized Ridge Regression Estimator (OGRRE), Restricted Least Square Estimator (RLSE), Ordinary Generalized Restricted Least Square Estimator (OGRLSE), Ordinary Mixed Regression Estimator (OMRE), Ordinary Generalized Mixed Regression Estimator (OGMRE), Liu Estimator (LE), Ordinary Generalized Liu Estimator (OGLE), Restricted Liu Estimator (RLE), Ordinary Generalized Restricted Liu Estimator (OGRLE), Stochastic Restricted Liu Estimator (SRLE), Ordinary Generalized Stochastic Restricted Liu Estimator (OGSRLE), Type (1),(2),(3) Liu Estimator (Type-1,2,3 LTE), Ordinary Generalized Type (1),(2),(3) Liu Estimator (Type-1,2,3 OGLTE), Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 ALTE), Ordinary Generalized Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 OGALTE), Almost Unbiased Ridge Estimator (AURE), Ordinary Generalized Almost Unbiased Ridge Estimator (OGAURE), Almost Unbiased Liu Estimator (AULE), Ordinary Generalized Almost Unbiased Liu Estimator (OGAULE), Stochastic Restricted Ridge Estimator (SRRE), Ordinary Generalized Stochastic Restricted Ridge Estimator (OGSRRE), Restricted Ridge Regression Estimator (RRRE) and Ordinary Generalized Restricted Ridge Regression Estimator (OGRRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically.
Fit a quantile regression mixed model involved Relationship Matrix using a sparse implementation of the
  Frisch-Newton interior-point algorithm as described in 
  Portnoy and Koenker (1977, Statistical Science)  <https://www.jstor.org/stable/2246216>.
R code of a likelihood ratio test for genome-wide association under genetic
    heterogeneity.
Estimates two-dimensional local wavelet spectra.
Wavelet-based methods for testing stationarity and quadtree segmenting of images, see Taylor et al (2014) <doi:10.1080/00401706.2013.823890>.
The basic idea of latent semantic analysis (LSA) is, 
  that text do have a higher order (=latent semantic) structure which, 
  however, is obscured by word usage (e.g. through the use of synonyms 
  or polysemy). By using conceptual indices that are derived statistically 
  via a truncated singular value decomposition (a two-mode factor analysis) 
  over a given document-term matrix, this variability problem can be overcome.
Provides functions that allow for convenient working
    with latent semantic analysis (LSA) and other vector space models 
	of semantics. For actually building a vector semantic space, use 
	the package 'lsa' or other specialized software. Downloadable 
	semantic spaces can be found here: <https://sites.google.com/site/fritzgntr/software-resources>
	A description of the LSA algorithm can be found in Landauer and Dumais (1997) 
	<doi:10.1037/0033-295X.104.2.211> .
Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen
             und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, 
             C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen 
             der österreichischen Bildungsstandardüberprüfung." Wien: facultas. 
             (ISBN: 978-3-7089-1343-8, <https://www.bifie.at/node/3770>) zur 
             Verfügung.
Provides functions to simulate data from large-scale educational
  assessments, including background questionnaire data and cognitive item
  responses that adhere to a multiple-matrix sampled design. The theoretical
  foundation can be found on
  Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018)
  <doi:10.1186/s40536-018-0068-8>.
Functions for performing least-squares bilinear clustering of
    three-way data. The method uses the bilinear decomposition (or bi-additive
    model) to model two-way matrix slices while clustering over the third way.
    Up to four different types of clusters are included, one for each term of the
    bilinear decomposition. In this way, matrices are clustered simultaneously on
    (a subset of) their overall means, row margins, column margins and row-column
    interactions. The orthogonality of the bilinear model results in separability of
    the joint clustering problem into four separate ones. Three of these sub-problems
    are specific k-means problems, while a special algorithm is implemented for the
    interactions. Plotting methods are provided, including biplots for the low-rank
    approximations of the interactions.
Bandwidth selection for kernel density estimators of 2-d
    level sets and highest density regions. It applies a plug-in
    strategy to estimate the asymptotic risk function and minimize to
    get the optimal bandwidth matrix. See Doss and Weng (2018)
    <arXiv:1806.00731> for more detail.
Estimators and visualization for local statistical complexity of
    (N+1)D fields. In particular for 0, 1 and 2 dimensional space this package
    provides useful visualization.
Create lots of colorful plots in a plethora of variations. Try the LSD demotour().
Implementation of Locally Scaled Density Based Clustering (LSDBC) algorithm proposed by Bicici and Yuret (2007) <doi:10.1007/978-3-540-71618-1_82>. This package also contains some supporting functions such as betaCV() function and get_spectral() function.
Interfaces R with LSD simulation models. Reads object-oriented data in results files (.res[.gz]) produced by LSD and creates appropriate multi-dimensional arrays in R. Supports multiple core parallel threads of multi-file data reading for increased performance. Also provides functions to extract basic information and statistics from data files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente (documentation and downloads available at <https://www.labsimdev.org/>).
Tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente (documentation and downloads available at <https://www.labsimdev.org/>).
Fit a least square dummy variable regression
It contains functions that solve least squares linear
	     regression problems under linear equality/inequality
	     constraints. Functions for solving quadratic programming
	     problems are also available, which transform such problems
	     into least squares ones first. It is developed based on the
	     'Fortran' program of Lawson and Hanson (1974, 1995), which is
	     public domain and available at
	     <http://www.netlib.org/lawson-hanson/>.
Calculates the (localised) length of the shorth and
        supplies corresponding diagnostic plots.
Fits structural equation modeling via penalized likelihood.
Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) <doi:10.18637/jss.v093.i07>.
When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.
Obtain least-squares means for linear, generalized linear, 
    and mixed models. Compute contrasts or linear functions of 
    least-squares means, and comparisons of slopes. 
    Plots and compact letter displays. Least-squares means were proposed in
    Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers",
    Tech Report ARS-20-8, USDA National Agricultural Library, and discussed
    further in Searle, Speed, and Milliken (1980) "Population marginal means 
    in the linear model: An alternative to least squares means", 
    The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
    NOTE: lsmeans now relies primarily on code in the 'emmeans' package.
    'lsmeans' will be archived in the near future.
The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.
The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. 'LSMRealOptions' provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. 'LSMRealOptions' values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. 'LSMRealOptions' provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) <doi:10.1093/rfs/14.1.113>.
Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2019a, <arXiv:1804.04916>) and Cattaneo, Farrell and Feng (2019b, <arXiv:1906.00202>): lsprobust() for nonparametric point estimation of regression functions and their derivatives and for robust bias-corrected (pointwise and uniform) inference; lspkselect() for data-driven selection of the IMSE-optimal number of knots; lsprobust.plot() for regression plots with robust confidence intervals and confidence bands; lsplincom() for estimation and inference for linear combinations of regression functions from different groups.
Creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.
Linear splines with convenient parametrisations such that 
  (1) coefficients are slopes of consecutive segments or (2) coefficients are 
  slope changes at consecutive knots. Knots can be set manually or at break points
  of equal-frequency or equal-width intervals covering the range of 'x'.
  The implementation follows Greene (2003), chapter 7.2.5.
Implements the LS-PLS (least squares - partial least squares)
  method described in for instance Jørgensen, K., Segtnan, V. H., Thyholt, K.,
  Næs, T. (2004)  "A Comparison of Methods for Analysing Regression Models with
  Both Spectral and Designed Variables"
  Journal of Chemometrics, 18(10), 451--464, <doi:10.1002/cem.890>.
Fit logistic regression models using LS-PLS approaches 
	to analyse both clinical and genomic data. (C. Bazzoli and 
	S. Lambert-Lacroix. (2017) Classification using LS-PLS with 
	logistic regression based on both clinical and gene expression
	variables <https://hal.archives-ouvertes.fr/hal-01405101>).
A collection of tools intended to make introductory 
    statistics easier to teach, including wrappers for common 
    hypothesis tests and basic data manipulation. It accompanies 
    Navarro, D. J. (2015). Learning Statistics with R: A Tutorial 
    for Psychology Students and Other Beginners, Version 0.6.
Rapid satellite data streams in operational applications
    have clear benefits for monitoring land cover, 
    especially when information can be delivered as fast as changing
    surface conditions. Over the past decade,
    remote sensing has become a key tool for monitoring and predicting
    environmental variables by using satellite data. 
    This package presents the main applications in remote sensing for 
    land surface monitoring and land cover mapping (soil, vegetation, water...).
     Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.
Due to lack of proper inference procedure and software,
        the ordinary linear regression model is seldom used in practice
        for the analysis of right censored data. This paper presents an
        S-Plus/R program that implements a recently developed inference
        procedure (Jin, Lin and Ying, 2006) <doi:10.1093/biomet/93.1.147> 
        for the accelerated failure time model based on the least-squares
        principle.
Calculates Land Surface Temperature from Landsat band 10 and 11.
  Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). <doi: 10.1109/TGRS.2008.2007125>.
  Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). <doi:10.1016/j.rse.2004.02.003>.
  Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). <doi: 10.1016/j.asr.2009.01.023>.
  Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). <doi:10.1016/0034-4257(96)00039-9>.
  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). <doi:10.1080/01431169308904400>.
  Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). <doi:10.3390/rs6109829>.
  Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014).
A set of functions that allow stationary analysis and locally stationary time series analysis.
Implementations of estimation algorithm of low rank plus sparse structured VAR model by using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). It relates to the algorithm in Sumanta, Li, and Michailidis (2019) <doi:10.1109/TSP.2018.2887401>.
A word embeddings-based semisupervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>.
    LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).
    It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.
Constructs tables of counts and proportions out of data sets. It has simplified syntax appealing for novice and even for advanced user under time pressure. It is particularly suitable for exploratory data analysis or presentation to single out most appropriate pieces of tabulated information. The other important feature is possibility to insert tables to Excel and Word documents. This version also features capacity of log-linear and power analyses (original by Oleksandr Ocheredko <doi:10.35566/isdsa2019c5>) for tabulated data with GSL.
Functions for simulating realizations from the posterior distribution of
	a latent trait of an item response model. Distributions are conditional on one or
	a subset of response patterns (e.g., sum scores). Functions for computing likelihoods,
	Fisher and observed information, posterior modes, and profile likelihood confidence
	intervals are also included. These functions are designed to be easily amenable to 
	user-specified models.
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Mixture modelling of one-dimensional data using combinations of left-truncated Gamma, Weibull, and Lognormal Distributions. Blostein, Martin & Miljkovic, Tatjana. (2019) <doi:10.1016/j.insmatheco.2018.12.001>.
Targeted Maximum Likelihood Estimation (TMLE) of
    treatment/censoring specific mean outcome or marginal structural model for
    point-treatment and longitudinal data.
Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.
Implements the conditional inference forest and relative risk forest 
             algorithm to modeling left-truncated right-censored data with time-invariant        
             covariates, and (left-truncated) right-censored survival data with time-varying 
             covariates. It also provides functions to tune the parameters and evaluate the 
             model fit. See Yao et al. (2020) <arXiv:2006.00567>.
Recursive partition algorithms designed for fitting survival tree with left-truncated and right censored (LTRC) data, as well as interval-censored data.
    The LTRC trees can also be used to fit survival tree with time-varying covariates.
Methods of developing linear time series modelling.
 Methods are given for loglikelihood computation, forecasting
  and simulation.
This is a new tool to estimate Ridge and Liu estimators based on LTS method in multiple linear regression analysis.
Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.
Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <DOI:10.1080/00401706.2019.1671234>.
Sparklines are small plots (about one line of text high),
  made popular by Edward Tufte.  This package is the interface from R
  to the LaTeX package sparklines by Andreas Loeffer and Dan Luecking
  (<http://www.ctan.org/pkg/sparklines>).  It can work with Sweave or
  knitr or other engines that produce TeX.  The package can be used to
  plot vectors, matrices, data frames, time series (in ts or zoo format).
Functions to work with date-times and time-spans:
    fast and user friendly parsing of date-time data, extraction and
    updating of components of a date-time (years, months, days, hours,
    minutes, and seconds), algebraic manipulation on date-time and
    time-span objects. The 'lubridate' package has a consistent and
    memorable syntax that makes working with dates easy and fun.  Parts of
    the 'CCTZ' source code, released under the Apache 2.0 License, are
    included in this package. See <https://github.com/google/cctz> for
    more details.
Likelihood inference under covariate assumptions (LUCA) in case-control studies of a rare disease assuming independence or simple dependence of genetic and
        non-genetic covariates.
Reproduces the harmonized DB of the ESTAT survey of the same name. The survey data is served as separate spreadsheets with noticeable differences in the collected attributes. The tool here presented carries out a series of instructions that harmonize the attributes in terms of name, meaning, and occurrence, while also introducing a series of new variables, instrumental to adding value to the product. Outputs include one harmonized table with all the years, and three separate geometries, corresponding to the theoretical point, the gps location where the measurement was made and the 250m east-facing transect.
Print vectors (and data frames) of floating point numbers
    using a non-scientific format optimized for human readers.  Vectors
    of numbers are rounded using significant digits, aligned at the
    decimal point, and all zeros trailing the decimal point are dropped.
    See: Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point
    Numbers. In JSM Proceedings, Statistical Computing Section. Alexandria,
    VA: American Statistical Association. 2270-2279.
An implementation for the 'LUCID' model (Peng (2019) <doi:10.1093/bioinformatics/btz667>) to jointly estimate latent unknown clusters/subgroups with integrated data. 
  An EM algorithm is used to obtain the latent cluster assignment and model parameter estimates. 
  Feature selection is achieved by applying the L1 regularization method.
Probabilistic record linkage without direct identifiers using only 
    diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, 
    Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ;
    Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane 
    & Cai (2021) <doi: 10.1101/2021.05.02.21256490>.
Contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.
Classes and methods for spatially explicit land use change
    modelling in R.
A framework that allows for easy logging of changes in data.
    Main features: start tracking changes by adding a single line of code to 
    an existing script. Track changes in multiple datasets, using multiple 
    loggers. Add custom-built loggers or use loggers offered by other 
    packages. <doi:10.18637/jss.v098.i01>.
A collection of various R functions for the purpose of Luminescence
    dating data analysis. This includes, amongst others, data import, export,
    application of age models, curve deconvolution, sequence analysis and
    plotting of equivalent dose distributions.
Provides functions to calculate lunar and other environmental
    covariates.
Input latitude and longitude values or an 'sf/sfc' POINT 
    object and get back the time zone in which they exist. Two methods are implemented. 
    One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library
    (<https://github.com/darkskyapp/tz-lookup/>). This method also works outside of countries' 
    borders and in international waters, however speed comes at the cost of accuracy - near time 
    zone borders away from populated centres there is a chance that it will return the incorrect
    time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect 
    points with a detailed map of time zones from here: 
    <https://github.com/evansiroky/timezone-boundary-builder/>. The package also 
    contains several utility functions for helping to understand and visualize 
    time zones, such as listing of world time zones, including information about 
    daylight savings times and their offsets from UTC. You can also plot a 
    time zone to visualize the UTC offset over a year and when daylight savings 
    times are in effect.
A high level interface for 'torch' providing utilities to reduce the
    the amount of code needed for common tasks, abstract away torch details and 
    make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to
    support expressing a large range of models. It's heavily inspired by 'fastai' by 
    Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 
    'Pytorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
Provides flexible but lightweight logging facilities for R scripts.
    Supports priority levels for logs and messages, flagging messages, capturing
    script output, switching logs, and logging to files or connections.
Core functionality for working with vectors (numeric, integer,
    logical and character) that are too large to keep in memory. The vectors are
    kept (partially) on disk using memory mapping. This package contains the
    basic functionality for working with these memory mapped vectors (e.g.
    creating, indexing, ordering and sorting) and provides C++ headers which can
    be used by other packages to extend the functionality provided in this
    package.
Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D).
    The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
    The output variable of the datasets is a scalar (quantitative).
    The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM".
    The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors"
    by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>. The package is developed in IDEAL of Northwestern University.
Latent variable models for network data using fast inferential
    procedures. For more information please visit: <http://igollini.github.io/lvm4net/>.
Provides stochastic EM algorithms for latent variable models
    with a high-dimensional latent space. So far, we provide functions for confirmatory item
    factor analysis based on the multidimensional two parameter logistic (M2PL) model and the 
    generalized multidimensional partial credit model. These functions scale well for problems
    with many latent traits (e.g., thirty or even more) and are virtually tuning-free.
    The computation is facilitated by multiprocessing 'OpenMP' API.
    For more information, please refer to:
    Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale
    Full-information Item Factor Analysis. British Journal of Mathematical and Statistical
    Psychology. <doi:10.1111/bmsp.12153>.
Contains a collection of useful
    functions for basic data computation and manipulation,
    wrapper functions for generating 'ggplot2' graphics,
    including statistical model diagnostic plots, methods
    for computing statistical models quality measures (such
    as AIC, BIC, r squared, root mean squared error) and
    general utilities.
Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.
Implements the letter value 'boxplot' which extends the standard
    'boxplot' to deal with both larger and smaller number of data points
    by dynamically selecting the appropriate number of letter values to display.
The manual approach of Litchfield and Wilcoxon (1949)
    <http://jpet.aspetjournals.org/content/96/2/99.abstract>
    for evaluating dose-effect experiments
    is automated so that the computer can do the work.
Provides a flexible and easy-to use interface for the soil vegetation 
    atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran.
    The model simulates daily transpiration, interception, soil and snow evaporation, 
    streamflow and soil water fluxes through a soil profile covered with vegetation, 
    as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) 
    <doi:10.1175/1525-7541(2003)004%3C1276:SOAETS%3E2.0.CO;2>. A set of high-level functions
    for model set up, execution and parallelization provides easy access to plot-level SVAT 
    simulations, as well as multi-run and large-scale applications.
Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/master/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.
Wrapper functions for the implementation of lagged weighted quantile sum regression, as per 'Gennings et al' (2020) <doi:10.1016/j.envres.2020.109529>.
Functions to quickly read LXB parameter data.
We provide a clonality score estimator that takes full advantage of the multi-biological-replicate structure of modern sequencing experiments; it specifically takes into account the reality that, typically, the clonal coverage is well below 0.1%.
An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.
Prediction of behaviour from movement 
	characteristics using observation and random forest for the analyses of movement
	data in ecology.
	From movement information (speed, bearing...) the model predicts the
	observed behaviour (movement, foraging...) using random forest. The
	model can then extrapolate behavioural information to movement data
	without direct observation of behaviours.
	The specificity of this method relies on the derivation of multiple predictor variables from the
	movement data over a range of temporal windows. This procedure allows to capture
	as much information as possible on the changes and variations of movement and
	ensures the use of the random forest algorithm to its best capacity. The method
	is very generic, applicable to any set of data providing movement data together with
	observation of behaviour.
Persistent interface to 'Macaulay2' <http://www.math.uiuc.edu/Macaulay2/>
    and front-end tools facilitating its use in the 'R' ecosystem. For details see 
    Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.
A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.
A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.
This package contains functions to read in and manipulate
        air quality model output from Models3-formatted files.  This
        format is used by the Community Multiscale Air Quaility (CMAQ)
        model.
Data manipulation in one package and in base R.
  Minimal. No dependencies.
  'dplyr' and 'tidyr'-like in one place.
  Nothing else than base R to build the package.
A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.
Provides an extension of the shadow-test approach to computerized adaptive 
    testing (CAT) implemented in the 'TestDesign' package for the assessment framework 
    involving multiple tests administered periodically throughout the year. This framework 
    is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports 
    multiple item pools vertically scaled and multiple phases (stages) of CAT within each test. 
    Between phases and tests, transitioning from one item pool (and associated constraints) 
    to another is allowed as deemed necessary to enhance the quality of measurement.
Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.
Performs binary and multiclass boosting in maximum-margin, sparse, smooth and normal settings
 as described in "A Boosting Framework on Grounds of Online Learning" by T. Naghibi and B. Pfister, (2014). 
 For further information regarding the algorithms, please refer to http://arxiv.org/abs/1409.7202
Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency.
This is an integrated meta-analysis package for conducting
        a correlational research synthesis. One of the unique features
        of this package is in its integration of user-friendly
        functions to facilitate statistical analyses at each stage in a
        meta-analysis with correlations. It uses recommended procedures
        as described in The Handbook of Research Synthesis and
        Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Performs causal mediation analysis under confounding or correlated errors. This package includes a single level mediation model, a two-level mediation model, and a three-level mediation model for data with hierarchical structures. Under the two/three-level mediation model, the correlation parameter is identifiable and is estimated based on a hierarchical-likelihood, a marginal-likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2014), Estimating Mediation Effects under Correlated Errors with an Application to fMRI, <arXiv:1410.7217> for details.
To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. 'MACER' accepts a list of genera as a user input and uses NCBI-GenBank and BOLD as resources to download and assemble molecular sequence datasets. These datasets are then assembled by marker, aligned, trimmed, and cleaned. The use of this package allows the publication of specific parameters to ensure reproducibility. The 'MACER' package has four core functions and an example run through using all of these functions can be found in the associated repository <https://github.com/rgyoung6/MACER_example>.
Meta-package for statistical and machine learning with a unified
    interface for model fitting, prediction, performance assessment, and
    presentation of results.  Approaches for model fitting and prediction of
    numerical, categorical, or censored time-to-event outcomes include
    traditional regression models, regularization methods, tree-based methods,
    support vector machines, neural networks, ensembles, data preprocessing,
    filtering, and model tuning and selection.  Performance metrics are provided
    for model assessment and can be estimated with independent test sets, split
    sampling, cross-validation, or bootstrap resampling.  Resample estimation
    can be executed in parallel for faster processing and nested in cases of
    model tuning and selection.  Modeling results can be summarized with
    descriptive statistics; calibration curves; variable importance; partial
    dependence plots; confusion matrices; and ROC, lift, and other performance
    curves.
Download data from the Ada and Archibald MacLeish Field 
    Station in Whately, MA. The Ada 
    and Archibald MacLeish Field Station is a 260-acre patchwork of 
    forest and farmland located in West Whately, MA that provides opportunities 
    for faculty and students to pursue environmental research, outdoor 
    education, and low-impact recreation 
    (see <https://www.smith.edu/about-smith/sustainable-smith/macleish> for more information). 
    This package contains 
    weather data over several years, and spatial data on various man-made and 
    natural structures.
'Maclinical' implements class prediction using both
        microarray data and clinical parameters. It addresses the
        question of the additional predictive value of microarray data.
        Class prediction is performed using a two-step method combining
        (pre-validated) PLS dimension reduction and random forests.
Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.
Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models.  M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model.  M-activation can detect high-order interactions -- a traditionally non-trivial challenge.  Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."
Performs causal mediation analysis for count and zero-inflated
    count data without or with a post-treatment confounder; calculates power
    to detect prespecified causal mediation effects, direct effects, and 
    total effects; performs sensitivity analysis when there is a treatment-
    induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., 
    Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) 
    <doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV) 
    method to estimate the controlled (natural) direct and mediation effects, 
    and compute the bootstrap Confidence Intervals as described by Guo, Z., 
    Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This 
    software was made possible by Grant R03DE028410 from the The National 
    Institute of Dental and Craniofacial Research, a component of the National 
    Institutes of Health.
A collection of functions for conducting a meta-analysis with mean differences data.  It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
Contains the Maddison Project database, which provides estimates of
    GDP per capita for all countries in the world between AD 1 and 2010. See
    http://www.ggdc.net/maddison for more information.
A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic 
  Optimization algorithm. MADGRAD is a 'best-of-both-worlds' optimizer with the 
  generalization performance of stochastic gradient descent and at least as fast 
  convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation
  is provided based on Defazio et al (2020) <arxiv:2101.11075>.
Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without
              significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data
              manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 
              'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional
              aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' 
              conversion to 'data.table'.
An object that supports automatic differentiation
    of matrix- and multidimensional-valued functions with 
    respect to multidimensional independent variables. 
    Automatic differentiation is via 'forward accumulation'.
Tools for the analysis of population differences
    using the Major Histocompatibility Complex (MHC) genotypes of samples
    having a variable number of alleles (1-4) recorded for each
    individual.  A hierarchical Dirichlet-Multinomial model on the
    genotype counts is used to pool small samples from multiple
    populations for pairwise tests of equality.  Bayesian inference is
    implemented via the 'rstan' package.  Bootstrapped and posterior
    p-values are provided for chi-squared and likelihood ratio tests of
    equal genotype probabilities.
Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.
Provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation.
    * The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.
Performs distance sampling analyses on a number of species 
    at once and can account for unidentified sightings, model uncertainty 
    and covariate uncertainty. Unidentified sightings refer to sightings 
    which cannot be allocated to a single species but may instead be 
    allocated to a group of species. The abundance of each unidentified 
    group is estimated and then prorated to the species estimates. Model 
    uncertainty should be incorporated when multiple models give equally
    good fit to the data but lead to large differences in estimated
    density / abundance. Covariate uncertainty should be incorporated
    when covariates cannot be measured accurately, for example this is 
    often the case for group size in marine mammal surveys. Variance 
    estimation for these methods is via a non parametric bootstrap. 
    The methods implemented are described in Gerodette T. and Forcada 
    J. (2005) <doi:10.3354/meps291001> Non-recovery of two spotted and 
    spinner dolphin populations in the eastern tropical Pacific Ocean.
This function allows to generate two biological conditions synthetic 
             microarray dataset which has similar behavior to those currently 
             observed with common platforms. User provides a subset of parameters.
             Available default parameters settings can be modified.
A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.
Data class for increased interoperability working with spatial-
    temporal data together with corresponding functions and methods (conversions,
    basic calculations and basic data manipulation). The class distinguishes
    between spatial, temporal and other dimensions to facilitate the development
    and interoperability of tools build for it. Additional features are name-based
    addressing of data and internal consistency checks (e.g. checking for the right
    data order in calculations).
Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) <DOI:10.1002/gepi.22351>.
A collection of efficient, vectorized algorithms for the
 creation and investigation of magic squares and hypercubes, including
 a variety of functions for the manipulation and analysis of
 arbitrarily dimensioned arrays.  The package includes methods for
 creating normal magic squares of any order greater than 2.  The
 ultimate intention is for the package to be a computerized embodiment
 all magic square knowledge, including direct numerical verification
 of properties of magic squares (such as recent results on the
 determinant of odd-ordered semimagic squares).  Some antimagic
 functionality is included.  The package also
 serves as a rebuttal to the often-heard comment "I thought R
 was just for statistics".
Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
Magic functions to obtain results from for loops.
Bindings to 'ImageMagick': the most comprehensive open-source image
    processing library available. Supports many common formats (png, jpeg, tiff,
    pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc).
    All operations are vectorized via the Magick++ STL meaning they operate either
    on a single frame or a series of frames for working with layers, collages,
    or animation. In RStudio images are automatically previewed when printed to
    the console, resulting in an interactive editing environment. The latest 
    version of the package includes a native graphics device for creating 
    in-memory graphics or drawing onto images using pixel coordinates.
Enables us to use the functions of the package 'magick' interactively.
Set of utility functions to interact with 'WeMo Switch', a smart 
    plug that can be remotely controlled via wifi. The provided functions make 
    it possible to turn one or more 'WeMo Switch' plugs on and off in a 
    scriptable fashion. More information about 'WeMo Switch' can be found at 
    <http://www.belkin.com/us/p/P-F7C027/>.
A client for interacting with 'magma', the data warehouse of the 'UCSF
    Mount Etna Data Library'. 'magmaR' includes functions for querying and
    downloading data from 'magma', in order to enable working with such data in R,
    as well as for uploading local data to 'magma'.
Correlates variation within the meta-genome to target species
    phenotype variations in meta-genome with association studies. Follows
    the pipeline described in Chaston, J.M. et al. (2014) <doi:10.1128/mBio.01631-14>.
Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) <DOI:10.2307/2531148>. Implements Fortran 77 code for the methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>. Includes estimates of average agreement for each observer and average agreement for each subject.
Provides a mechanism for chaining commands with a
    new forward-pipe operator, %>%. This operator will forward a value, or
    the result of an expression, into the next function call/expression.
    There is flexible support for the type of right-hand side expressions.
    For more information, see package vignette.  To quote Rene Magritte,
    "Ceci n'est pas un pipe."
A generalised workflow for generation of subject weights to be 
    used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et 
    al (2012) <doi:10.1016/j.jval.2012.05.004>, Signorovitch et al (2010) 
    <doi:10.2165/11538370-000000000-00000>. In MAIC, unbiased 
    comparison between outcomes of two trials is facilitated by weighting the
    subject-level outcomes of one trial with weights derived such that the 
    weighted aggregate measures of the prognostic or effect modifying variables 
    are equal to those of the sample in the comparator trial. The functions and
    classes included in this package wrap and abstract the process demonstrated
    in the UK National Institute for Health and Care Excellence Decision 
    Support Unit (NICE DSU)'s example (Phillippo et al, (2016) [see URL]),
    providing a repeatable and easily specifiable workflow for producing 
    multiple comparison variable sets against a variety of target studies, with
    preprocessing for a number of aggregate target forms (e.g. mean, median, 
    domain limits).
Easy to use package for sending email notifications with status information from R
Perform a mail merge (mass email) using the message defined in 
    markdown, the recipients in a 'csv' file, and gmail as the mailing engine. 
    With this package you can parse markdown documents as the body of email, and 
    the 'yaml' header to specify the subject line of the email.  Any '{}' braces 
    in the email will be encoded with 'glue::glue()'. You can preview the email 
    in the RStudio viewer pane, and send (draft) email using 'gmailr'.
Interface to Apache Commons Email to send emails
    from R.
Allows the user to generate a friendly user interface for emails sending. 
    The user can choose from the most popular free email services ('Gmail', 'Outlook', 'Yahoo') and his default email application. 
    The package is a wrapper for the 'Mailtoui' 'JavaScript' library.  See <https://mailtoui.com/#menu> for more information.
Implements methodologies for modelling interval data by Normal
    and Skew-Normal distributions, considering appropriate parameterizations of
    the variance-covariance matrix that takes into account the intrinsic nature of
    interval data, and lead to four different possible configuration structures.
    The Skew-Normal parameters can be estimated by maximum likelihood, while Normal
    parameters may be estimated by maximum likelihood or robust trimmed maximum
    likelihood methods.
Implements methods for querying backlink data from 'Majestic' using its API (<https://developer-support.majestic.com/api/>).
      'Majestic' API uses a basic authentication with an API key. This package is used in the "Do You Need Backlinks for SEO" tutorial <https://remibacha.com/en/backlinks-r/>.
A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.
Create dummy variables from categorical data.
    This package can convert categorical data (factor and ordered) into
    dummy variables and handle multiple columns simultaneously.
    This package enables to select whether a dummy variable for base group
    is included (for principal component analysis/factor analysis) or
    excluded (for regression analysis) by an option.
    'makedummies' function accepts 'data.frame', 'matrix', and
    'tbl' (tibble) class (by 'tibble' package).
    'matrix' class data is automatically converted to 'data.frame' class.
A user-friendly interface for the construction of
        'Makefiles'.
A user-friendly tool for visualizing categorical or group movement.
Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model.
A suite of tools for transforming an existing workflow into a
    self-documenting pipeline with very minimal upfront costs. Segments of
    the pipeline are specified in much the same way a 'Make' rule is, by
    declaring an executable recipe (which might be an R script), along
    with the corresponding targets and dependencies. When the entire
    pipeline is run through, only those recipes that need to be executed
    will be. Meanwhile, execution metadata is captured behind the scenes
    for later inspection.
This package creates an empty framework of files and
        directories for the "Load, Clean, Func, Do" structure described
        by Josh Reich.
A collection of helper functions that interface with the appropriate
    system utilities to learn about the build environment. Lets you explore 'make' 
    rules to test the local configuration, or query 'pkg-config' to find compiler
    flags and libs needed for building packages with external dependencies. Also
    contains tools to analyze which libraries that a installed R package linked to
    by inspecting output from 'ldd' in combination with information from your
    distribution package manager, e.g. 'rpm' or 'dpkg'. Finally the package provides
    Windows-specific utilities to automatically find or install the suitable version
    of the 'Rtools' build environment, and diagnose some common problems.
MAle Lineage ANalysis by simulating 
    genealogies backwards and imposing short tandem repeats (STR) mutations forwards. 
    Intended for forensic Y chromosomal STR (Y-STR) haplotype analyses. 
    Numerous analyses are possible, e.g. number of matches and meiotic distance to matches. 
    Refer to papers mentioned in citation("malan") (DOI's: 
    <doi:10.1371/journal.pgen.1007028>, 
    <doi:10.21105/joss.00684> and 
    <doi:10.1016/j.fsigen.2018.10.004>).
Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks.
A suite of tools to allow you to download all 
  publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from 
  the 'Malaria Atlas Project' <https://malariaatlas.org/> servers as well as utility functions for plotting
  the downloaded data.
It is designed to work with text written in Bahasa Malaysia. We provide 
    functions and data sets that will make working with Bahasa Malaysia text much easier. 
    For word stemming in particular, we will look up the Malay words in a dictionary and
    then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, 
    Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, 
    Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi
    (2017) <https://ijrest.net/vol-4-issue-12.html> . This package includes
    a dictionary of Malay words that may be used to perform word stemming.
A complete analysis pipeline for matrix-assisted laser
        desorption/ionization-time-of-flight (MALDI-TOF) and other
        two-dimensional mass spectrometry data. In addition to commonly
        used plotting and processing methods it includes distinctive
        features, namely baseline subtraction methods such as
        morphological filters (TopHat) or the statistics-sensitive
        non-linear iterative peak-clipping algorithm (SNIP), peak
        alignment using warping functions, handling of replicated
        measurements as well as allowing spectra with different
        resolutions.
Functions for reading (tab, csv, Bruker fid, Ciphergen
        XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and
        writing (tab, csv, mMass MSD, mzML, imzML) different file
        formats of mass spectrometry data into/from 'MALDIquant'
        objects.
Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) <doi:10.1093/bioinformatics/btx628>).
This package allows you to train topic models in mallet and load results directly into R.
Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.
Providing the kubernetes-like class 'ManagedCloudProvider' as a child class of 
  the 'CloudProvider' class in the 'DockerParallel' package. The class is able to
  manage the cloud instance made by the non-kubernetes cloud service.
  For creating a provider for the non-kubernetes cloud service, the developer
  needs to define a reference class inherited from 'ManagedCloudProvider' and
  define the method for the generics runDockerWorkerContainers(),
  getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see
  the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.
This will allow easier management of a CRAN-style repository on
    local networks (i.e. not on CRAN). This might be necessary where hosted
    packages contain intellectual property owned by a corporation.
High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.
Several functions can be used to analyze neuroimaging data using multivariate methods based on the 'msma' package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained. Please also see Kawaguchi et al. (2017) <doi:10.1093/biostatistics/kxx011> and Kawaguchi (2019) <DOI:10.5772/intechopen.80531>.
Estimates membership for the Mandelbrot set.
Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted.
Datasets to be used primarily in conjunction with Mango Solutions
    training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9).
    Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with
    training materials, whilst retaining compatibility with the book.
Methods for performing genetic risk
        prediction from genotype data.  You can use it to perform risk
        prediction for individuals, or for families with missing data.
Create interactive manhattan, Q-Q and volcano plots that are usable from the R console, 
    in 'Dash' apps, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps.
    Hover the mouse pointer over a point to show details or drag a rectangle to
    zoom. A manhattan plot is a popular graphical method for visualizing results
    from high-dimensional data analysis such as a (epi)genome wide association study
    (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter
    plot against their genomic position. Manhattan plots are used for visualizing
    potential regions of interest in the genome that are associated with a phenotype.
    Interactive manhattan plots allow the inspection of specific value (e.g. rs number or
    gene name) by hovering the mouse over a cell, as well as zooming into a region of the
    genome (e.g. a chromosome) by dragging a rectangle around the relevant area.
    This work is based on the 'qqman' package and the 'plotly.js'
    engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq'
    functions in the 'qqman' package, with the advantage of including extra annotation 
    information and interactive web-based visualizations directly from R. 
    Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) 
    can be viewed and modified in a web browser.
This plot integrates annotation into a manhattan plot. The plot is implemented as a heatmap, which is binned using -log10(p-value) and chromosome position. Annotation currently supported is minor allele frequency and gene function high impact variants.
Provides access to coded election programmes from the Manifesto
    Corpus and to the Manifesto Project's Main Dataset and routines to analyse this
    data. The Manifesto Project <https://manifesto-project.wzb.eu> collects and
    analyses election programmes across time and space to measure the political
    preferences of parties. The Manifesto Corpus contains the collected and
    annotated election programmes in the Corpus format of the package 'tm' to enable
    easy use of text processing and text mining functionality. Specific functions
    for scaling of coded political texts are included.
An R interface to version 0.3 of the 'ROPTLIB' optimization library
    (see <http://www.math.fsu.edu/~whuang2> for more information). Optimize real-
    valued functions over manifolds such as Stiefel, Grassmann, and Symmetric
    Positive Definite matrices. For details see Martin et. al. (2020) <doi:10.18637/jss.v093.i01>.
Interactive plotting functions for use within RStudio.
  The manipulate function accepts a plotting expression and a set of
  controls (e.g. slider, picker, checkbox, or button) which are used
  to dynamically change values within the expression. When a value is
  changed using its corresponding control the expression is
  automatically re-executed and the plot is redrawn.
Like package 'manipulate' does for static graphics, this package
    helps to easily add controls like sliders, pickers, checkboxes, etc. that 
    can be used to modify the input data or the parameters of an interactive 
    chart created with package 'htmlwidgets'.
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.
Chromatin immunoprecipitation followed by high-throughput
    sequencing (ChIP-seq) is the premier technology for profiling genome-wide
    localization of chromatin-binding proteins, including transcription
    factors and histones with various modifications.
    This package provides a robust method for normalizing ChIP-seq
    signals across individual samples or groups of samples. It also designs
    a self-contained system of statistical models for calling differential
    ChIP-seq signals between two or more biological conditions as well as
    for calling hypervariable ChIP-seq signals across samples. Refer to
    Tu et al. (2021) <doi:10.1101/gr.262675.120> and
    Chen et al. (2021) <doi:10.1101/2021.07.27.453915>
    for associated statistical details.
Implemented are various tests for semi-parametric repeated measures
    and general MANOVA designs that do neither assume multivariate normality nor
    covariance homogeneity, i.e., the procedures are applicable for a wide range
    of general multivariate factorial designs. In addition to asymptotic inference
    methods, novel bootstrap and permutation approaches are implemented as well. These provide more
    accurate results in case of small to moderate sample sizes. Furthermore, post-hoc 
    comparisons are provided for the multivariate analyses.
    Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.
Frequently one needs a convenient way to build and tune
             several models in one go.The goal is to provide a number of machine learning convenience 
             functions. It provides the ability to build, tune and obtain predictions of 
             several models in one function. The models are built using functions from
             'caret' with easier to read syntax.
             Kuhn(2014) <arXiv:1405.6974>.
Performs the multiple testing procedures of Cox (2011) <doi:10.5170/CERN-2011-006> and Wong and Cox (2007) <doi:10.1080/02664760701240014>.
A set of evolutionary algorithms to solve many-objective optimization. 
    Hybridization between the algorithms are also facilitated. Available algorithms are:
    'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008>
    'NSGA-III' <doi:10.1109/TEVC.2013.2281535>
    'MO-CMA-ES' <doi:10.1145/1830483.1830573>
    The following many-objective benchmark problems are also provided: 
    'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and
    'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.
Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).
Electronic health records (EHR) linked with biorepositories are 
    a powerful platform for translational studies. A major bottleneck exists 
    in the ability to phenotype patients accurately and efficiently. 
    Towards that end, we developed an automated high-throughput 
    phenotyping method integrating International 
    Classification of Diseases (ICD) codes and narrative data extracted 
    using natural language processing (NLP). Specifically, our proposed method, 
    called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent 
    mixture models on aggregated ICD and NLP counts along with healthcare 
    utilization. The MAP algorithm yields a predicted probability of phenotype 
    for each patient and a threshold for classifying subjects with phenotype 
    yes/no (See Katherine P. Liao, et al. (2019) <doi:10.1101/587436>.).
Allows the user to generate a list of features (gene, pseudo, RNA,
    CDS, and/or UTR) directly from NCBI database for any species with a current 
    build available. Option to save downloaded and formatted files is available, 
    and the user can prioritize the feature list based on type and assembly builds 
    present in the current build used. The user can then use the list of features 
    generated or provide a list to map a set of markers (designed for SNP markers 
    with a single base pair position available) to the closest feature based on 
    the map build. This function does require map positions of the markers to be 
    provided and the positions should be based on the build being queried through 
    NCBI.
Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.
Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman (2014) <doi:10.1080/01431161.2014.930207>.
Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in `mrgsolve`, typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format.
Includes support for 'Mapbox' Navigation APIs, including directions, 
  isochrones, and route optimization; the Search API for forward and reverse geocoding; 
  the Maps API for interacting with 'Mapbox' vector tilesets and visualizing 
  'Mapbox' maps in R; and the 'tippecanoe' tile-generation utility. 
  See <https://docs.mapbox.com/api/> for more information about the 'Mapbox' APIs.
Makes 'Mapbox GL JS' <https://docs.mapbox.com/mapbox-gl-js/api/>,
  an open source JavaScript library that uses WebGL to render interactive maps,
  available within R via the 'htmlwidgets' package. Visualizations can be used from the R console,
  in R Markdown documents and in Shiny apps.
A variety of functions that make it easy to plot standard choropleth maps as well as choropleth alternatives in 'ggplot2'.
Geospatial shapefile data of China administrative divisions to the county/district-level.
Supplement to maps package, providing the larger and/or
	higher-resolution databases.
Provides a mechanism to plot an interactive map using 'Mapbox GL' 
		(<https://docs.mapbox.com/mapbox-gl-js/api/>), a javascript library for interactive maps,
		and 'Deck.gl' (<https://deck.gl/>), a javascript library which uses 'WebGL' for 
		visualising large data sets.
It covers approaches to multivariate analysis of variance, PERMANOVA and a Bayesian analysis, presenting an assumption test section together with a decision diagram that will allow selecting the appropriate technique for the analysis. The presentation of results is through an interactive panel, in which you can view automatic conclusions based on the tests, dynamic graphics through 'Highchart', also the package uses 'Stan' for Bayesian analysis.
Suite of interactive functions and helpers for selecting and editing
    geospatial data.
Estimation methods for phase-type
  distribution (PH) and Markovian arrival process (MAP) from
  empirical data (point and grouped data) and density function.
Contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (submitted).  Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
Mapping Averaged Pairwise Information (MAPI) is an
    exploratory method providing graphical representations summarizing the
    spatial variation of pairwise metrics (eg. distance, similarity
    coefficient, ...) computed between georeferenced samples.
Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors.
A genetic analysis tool and variance component 
    model for identifying marginal epistasis between pathways 
    and the rest of the genome. 'MAPITR' uses as input a matrix 
    of genotypes, a vector of phenotypes, and a list of 
    pathways. 'MAPITR' then iteratively tests each pathway for 
    epistasis between any variants within the pathway versus 
    any variants remaining in the rest of the genome. 'MAPITR'
    returns results in the form of p-values for every pathway 
    indicating whether the null model of there being no 
    epistatic interactions between a pathway and the rest of 
    the genome can be rejected.
MAPLES is a general method for the estimation of age
        profiles that uses standard micro-level demographic survey
        data. The aim is to estimate smoothed age profiles and relative
        risks for time-fixed and time-varying covariates.
A minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections.
Maps are an important tool to visualise variables distribution across different spatial object. The mapping process require to link the data with coordinates and then generate the correspondent map. This package provide coordinates, linking and mapping functions for an automatic, flexible and easy approach of mapping workflow of different geographical statistical unit.Geographical coordinates are provided in the package and automatically linked with the input data to generate maps with internal provided functions or external functions.provide an easy, flexible and automatic approach to potentially download updated coordinates, to link statistical units with coordinates and to aggregate variables based on the spatial hierarchy of units. The object returned from the package can be used for thematic maps with the build-in functions provided in mapping or with other packages already available.
Easily create functions to map between different sets of values,
  such as for re-labelling categorical variables.
Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.
Construction of genetic maps in autopolyploid full-sib populations. 
             Uses pairwise recombination fraction estimation as the first 
             source of information to sequentially position allelic variants 
             in specific homologues. For situations where pairwise analysis has 
             limited power, the algorithm relies on the multilocus likelihood 
             obtained through a hidden Markov model (HMM). For more detail,
             please see  Mollinari and Garcia (2019) <doi:10.1534/g3.119.400378> 
             and Mollinari et al. (2020) <doi:10.1534/g3.119.400620>.
Converts latitude/longitude into projected coordinates.
Utilities for visualizing species occurrence data. Includes
    functions to visualize occurrence data from 'spocc', 'rgbif',
    and other packages. Mapping options included for base R plots, 'ggplot2',
    'leaflet' and 'GitHub' 'gists'.
Display of maps.  Projection code and larger maps are in
             separate packages ('mapproj' and 'mapdata').
Interface to the 'Google Maps' APIs: (1) routing directions based on the 'Directions' API, returned as 'sf' objects, either as single feature per alternative route, or a single feature per segment per alternative route; (2) travel distance or time matrices based on the 'Distance Matrix' API; (3) geocoded locations based on the 'Geocode' API, returned as 'sf' objects, either points or bounds; (4) map images using the 'Maps Static' API, returned as 'stars' objects.
Enables preparation of maps to be printed and drawn on.
    Modified maps can then be scanned back in, and hand-drawn marks
    converted to spatial objects.
Create and integrate thematic maps in your workflow. This package
    helps to design various cartographic representations such as proportional
    symbols, choropleth or typology maps. It also offers several functions to
    display layout elements that improve the graphic presentation of maps
    (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on
    'base' graphics.
Administrative Boundaries of Spain at several levels
    (Autonomous Communities, Provinces, Municipalities) based on the
    'GISCO' 'Eurostat' database <https://ec.europa.eu/eurostat/web/gisco>
    and 'CartoBase SIANE' from 'Instituto Geografico Nacional'
    <https://www.ign.es/>.  It also provides a 'leaflet' plugin and the
    ability of downloading and processing static tiles.
Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.
Local adaptation and evaluation of digital soil maps in raster 
    format by use of point location soil property data.
Automated calculation and visualization of survey data statistics on a color-coded map.
To create maps from tiles, 'maptiles' downloads, composes and
    displays tiles from a large number of providers (e.g. 'OpenStreetMap',
    'Stamen', 'Esri', 'CARTO', or 'Thunderforest').
Please note that 'maptools' will be retired by the end of 2023, plan transition at your earliest convenience; some functionality will be moved to 'sp'. Set of tools for manipulating geographic data. It includes binary access to 'GSHHG' shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat.geom', 'maps', and others.
Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis,
	as described in Taddy (2012) 'On estimation and selection for topic models'.  Previous versions of this code were included as part of the 'textir' package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Functions with example data for graphing, pruning, and
        mapping models from hierarchical clustering, and classification
        and regression trees.
Quickly and conveniently create interactive
    visualisations of spatial data with or without background maps.
    Attributes of displayed features are fully queryable via pop-up
    windows. Additional functionality includes methods to visualise true-
    and false-color raster images and bounding boxes.
R functions for multivariate autoregressive analysis
Multiplicative AR(1) with Seasonal is a stochastic
  process model built on top of AR(1). The package provides the
  following procedures for MAR(1)S processes: fit, compose, decompose,
  advanced simulate and predict.
Computation of various Markovian models for categorical data
    including homogeneous Markov chains of any order, MTD models, Hidden Markov
    models, and Double Chain Markov Models.
A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>.  Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment.  Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.
Datasets, constants, conversion factors, and utilities for 'MArine', 'Riverine',
             'Estuarine', 'LAcustrine' and 'Coastal' science. 
             The package contains among others: (1) chemical and
             physical constants and datasets, e.g. atomic weights, gas
             constants, the earths bathymetry; (2) conversion factors
             (e.g. gram to mol to liter, barometric units,
             temperature, salinity); (3) physical functions, e.g. to
             estimate concentrations of conservative substances, gas
             transfer and diffusion coefficients, the Coriolis force
             and gravity; (4) thermophysical properties of the
             seawater, as from the UNESCO polynomial or from the more
             recent derivation based on a Gibbs function.
Local recombination rates are graphically estimated across a genome using Marey maps.
Likelihood inference based on higher order approximations 
  for linear nonnormal regression models.
Fits joint marginal conditional models for multivariate longitudinal data, as in Proudfoot, Faig, Natarajan, and Xu (2018) <doi:10.1002/sim.7552>. Development of this package was supported by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Compute, summarize, and plot marginal effects, adjusted predictions, contrasts, and marginal means for a wide variety of models.
Estimates risk as a function of a marker by integrating over other covariates in a conditional risk model.
Provides the ability to perform "Marginal Mediation"--mediation
   wherein the indirect and direct effects are in terms of the average marginal effects 
   (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). 
   The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package.
   This framework allows the use of categorical mediators and outcomes with little change in interpretation
   from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details 
   on the method.
An R port of Stata's 'margins' command, which can be used to
    calculate marginal (or partial) effects from model objects.
Multivariate ARIMA and ARIMA-X estimation using Spliid's 
    algorithm (marima()) and simulation (marima.sim()).
Miscellaneous functions and wrappers for development in other 
    packages created, maintained by Jordan Mark Barbone.
Provides R bindings to the 'Sundown' Markdown rendering library
    (<https://github.com/vmg/sundown>). Markdown is a plain-text formatting
    syntax that can be converted to 'XHTML' or other formats. See
    <http://en.wikipedia.org/wiki/Markdown> for more information about Markdown.
An R-Shiny module containing a "markdownInput". This input allows
    the user to write some markdown code and to preview the result. This input
    has been inspired by the "comment" window of <https://github.com/>.
Functions for fitting various models to capture-recapture data
    including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and
    the multi-variate state model structure for survival
    estimation and POPAN structured Jolly-Seber models for abundance estimation.
    There are also Hidden Markov model (HMM) implementations of CJS and multistate
    models with and without state uncertainty and a simulation capability for HMM
    models.
Implementation of the 'MarkerPen' algorithm, short for marker gene detection
    via penalized principal component analysis, described in the paper by Qiu, Wang, Lei,
    and Roeder (2020, <doi:10.1101/2020.11.07.373043>). 'MarkerPen' is a semi-supervised
    algorithm for detecting marker genes by combining prior marker information with bulk
    transcriptome data.
For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).
Facilitates tidy calculation of popular quantitative marketing 
    metrics. It also includes functions for doing analysis that will help
    marketers and data analysts better understand the drivers and/or trends
    of these metrics. These metrics include Customer Experience Index 
    <https://go.forrester.com/analytics/cx-index/> and Net Promoter Score 
    <https://www.netpromoter.com/know/>.
Automatic marking of R assignments for students and teachers based
    on 'testthat' test suites.
Allows for fitting of maximum likelihood models using Markov chains
    on phylogenetic trees for analysis of discrete character data. Examples of such
    discrete character data include restriction sites, gene family presence/absence,
    intron presence/absence, and gene family size data. Hypothesis-driven user-
    specified substitution rate matrices can be estimated. Allows for biologically
    realistic models combining constrained substitution rate matrices, site rate
    variation, site partitioning, branch-specific rates, allowing for non-stationary
    prior root probabilities, correcting for sampling bias, etc. See Dang and Golding 
    (2016) <doi:10.1093/bioinformatics/btv541> for more details.
Functions and S4 methods to create and manage discrete time Markov
    chains more easily. In addition functions to perform statistical (fitting
    and drawing random variates) and probabilistic (analysis of their structural
    proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>.
Functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.
The inference in multi-state models is traditionally performed under
             a Markov assumption that claims that past and future of the process
             are independent given the present state. In this package, we 
             consider tests of the Markov assumption that are applicable to 
             general multi-state models. Three approaches using existing 
             methodology are considered: a simple method based on including
             covariates depending on the history in Cox models for the transition
             intensities; methods based on measuring the discrepancy of the 
             non-Markov estimators of the transition probabilities to the 
             Markov Aalen-Johansen estimators; and, finally, methods that were
             developed by considering summaries from families of log-rank 
             statistics where patients are grouped by the state occupied of the
             process at a particular time point (see Soutinho G, Meira-Machado L
             (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H
             (2020) <doi:10.1093/biostatistics/kxaa030>).
A collection of tools for analyzing significance of 
    Markowitz portfolios.
Functions provided allow data simulation; construction of weighted relative likelihood functions; clustering and principal component analysis based on weighted relative likelihood functions.
Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <https://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <https://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
This algorithm provides a numerical solution to the
        problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than
        the Gauss-Newton-like algorithm when starting from points very
        far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2020 <arXiv:2009.03840>.
Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data.  Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided.  MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.  Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks.  The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.  Type RShowDoc("UserGuide", package="MARSS") at the R command line to open the MARSS user guide.  Online workshops (lectures and computer labs) at <https://nwfsc-timeseries.github.io/>  See the NEWS file for update information.
Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.
Simulate, estimate (by t-MLE), select and forecast mixed causal-noncausal autoregressive models with possibly exogenous regressors, using methods proposed in Lanne and Saikkonen (2011) <doi:10.2202/1941-1928.1080> and Hecq et al. (2016) <doi:10.15609/annaeconstat2009.123-124.0307>.
An S4 implementation of the unbiased extension of
    the model- assisted synthetic-regression estimator proposed by
    Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013)
    <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014)
    <DOI:10.1139/cjfr-2013-0449>.  It yields smaller variances than the
    standard bias correction, the generalised regression estimator.
A set of model-assisted survey estimators and corresponding
    variance estimators for single stage, unequal probability, without replacement
    sampling designs.  All of the estimators can be written as a generalized 
    regression estimator with the Horvitz-Thompson, ratio, post-stratified, and 
    regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6).
    Two of the estimators employ a statistical learning model as the assisting model:
    the elastic net regression estimator, which is an extension of the lasso regression
    estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the 
    regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. 
    The variance estimators which approximate the joint inclusion probabilities can
    be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the
    bootstrap variance estimator is presented in Mashreghi et al. (2016) 
    <doi:10.1214/16-SS113>.
Implements the multivariate adaptive shrinkage (mash)
    method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for
    estimating and testing large numbers of effects in many conditions
    (or many outcomes). Mash takes an empirical Bayes approach to
    testing and effect estimation; it estimates patterns of similarity
    among conditions, then exploits these patterns to improve accuracy
    of the effect estimates. The core linear algebra is implemented in
    C++ for fast model fitting and posterior computation.
Three key functionalities are present. It is able to mask confidential data using multiplicative noise. It is able to unmask this data while still preserving confidentiality. It is able to calculate the numerical joint density function of the original data from the unmasked data, as well as obtaining a sample from the marginal density functions of the unmasked data. The final results are a reasonable approximation to the original data for the purposes of analysis (Lin et al. (2018) <http://www.tdp.cat/issues16/abs.a271a17.php>).
Mask ranges based on expert 
    knowledge or remote sensing layers. These tools can 
    be combined to quantitatively and reproducibly 
    generate a new map or to update an existing map.
    Methods include expert opinion and data-driven 
    tools to generate thresholds for binary masks.
Use a consistent syntax to create data structures
    of common statistical techniques that can be continued in a pipe
    chain.  Design the analysis, add settings and variables, construct the
    results, and polish the final structure. Rinse and repeat for any
    number of statistical techniques.
Functions and datasets to support Venables and Ripley,
  "Modern Applied Statistics with S" (4th edition, 2002).
Mass measurement corrections and uncertainties using calibration
    data, as recommended by EURAMET's guideline No. 18 (2015) 
    ISBN:978-3-942992-40-4 . The package provides classes, functions, and 
    methods for storing information contained in calibration certificates
    and converting balance readings to both conventional mass and real mass.
    For the latter, the Magnitude of the Air Buoyancy Correction factor employs
    models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and
    Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to estimate the local air
    density using measured environmental conditions.
Some enhancements, extensions and additions
    to the facilities of the recommended 'MASS' package 
    that are useful mainly for teaching purposes, with
    more convenient default settings and user interfaces.
    Key functions from 'MASS' are imported and re-exported
    to avoid masking conflicts.  In addition we provide
    some additional functions mainly used to illustrate
    coding paradigms and techniques, such as Gramm-Schmidt
    orthogonalisation and generalised eigenvalue problems.
Constructing matrices for quick prototyping can be a nuisance, 
    requiring the user to think about how to fill the matrix with values using 
    the matrix() function. The %<-% operator solves that issue by allowing the 
    user to construct matrices using code that shows the actual matrices.
Estimation equations are from a variety of sources and associated error estimation.
The primary aim of 'MasterBayes' is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data.  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled 'C++' for efficiency.
Analyzes production and dispersal of seeds dispersed from trees and recovered in seed traps.  Motivated by long-term inventory plots where seed collections are used to infer seed production by each individual plant.
Calculates Model-Averaged Tail Area Wald
    (MATA-Wald) confidence intervals, and MATA-Wald confidence densities
    and distributions, which are constructed using single-model
    frequentist estimators and model weights. See Turek and Fletcher
    (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019)
    <doi:10.1007/s10651-019-00432-5> for details.
Conveniently log everything you type into the R console. Logs are
    are stored as tidy data frames which can then be analyzed using 'tidyverse'
    style tools.
Multivariate matching in observational studies typically has two goals: 1. to construct 
    treated and control groups that have similar distribution of observed covariates and 2. to produce 
    matched pairs or sets that are homogeneous in a few priority variables. This packages implements a
    network-flow-based method built around a tripartite graph that can simultaneously achieve both goals.
    A detailed explanation of the workflow and numerous examples are given in the vignette.
Provides a wrapper for the some basic functionality around
    the 'Matchbook' <http://www.matchbook.com> REST API. It features calls to get events, markets and runners
    in data frame format. It features functions for bet placement and position
    management and also allows reporting of settled bet transactions. Note: this
    package uses the back-lay format. The default for odds type, currency and
    language are set according to those of the registered user account.
Provides functions for multivariate and propensity score matching 
             and for finding optimal balance based on a genetic search algorithm. 
             A variety of univariate and multivariate metrics to
             determine if balance has been obtained are also provided. For
             details, see the paper by Jasjeet Sekhon 
             (2007, <doi:10.18637/jss.v042.i07>).
Implements structural estimators to correct for
    the sample selection bias from observed outcomes in matching
    markets. This includes one-sided matching of agents into
    groups as well as two-sided matching of students to schools.
    The package also contains algorithms to find stable matchings
    in the three most common matching problems: the stable roommates
    problem, the college admissions problem, and the house
    allocation problem.
Computes matching algorithms quickly using Rcpp.
    Implements the Gale-Shapley Algorithm to compute the stable
    matching for two-sided markets, such as the stable marriage
    problem and the college-admissions problem. Implements Irving's
    Algorithm for the stable roommate problem. Implements the top
    trading cycle algorithm for the indivisible goods trading problem.
Selects matched samples of the original treated and
    control groups with similar covariate distributions -- can be
    used to match exactly on covariates, to match on propensity
    scores, or perform a variety of other matching procedures.  The
    package also implements a series of recommendations offered in
    Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 
    'gurobi' package, which is not on CRAN, is optional and comes with 
    an installation of the Gurobi Optimizer, available at 
    <https://www.gurobi.com>.)
Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
Provides flexible dictionary-based
    cleaning that allows users to specify implicit and explicit missing data,
    regular expressions for both data and columns, and global matches, while
    respecting ordering of factors. This package is part of the 'RECON'
    (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Performs multilevel matches for data with cluster-level treatments and individual-level outcomes using a network optimization algorithm.  Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis.  Details in Pimentel et al. (2017+), forthcoming in the Annals of Applied Statistics.
Inspired by pattern matching and enum types in Rust
    and many functional programming languages, this package offers
    an updated version of the 'switch' function called 'Match' that
    accepts atomic values, functions, expressions, and enum variants.
    Conditions and return expressions are separated by '->' and 
    multiple conditions can be associated with the same return expression
    using '|'. 'Match' also includes support for 'fallthrough'. The 
    package also replicates the Result and Option enums from Rust.
Provides the necessary tools for the pre-processing techniques of matching and weighting multiply imputed datasets to control for effects of confounders and to reduce the degree of dependence on certain modeling assumptions in studying the causal associations between an exposure and an outcome. This package includes functions to perform matching within and across the multiply imputed datasets using several matching methods, to estimate weights of units in the imputed datasets using several weighting methods, to calculate the causal effect estimate in each matched or weighted dataset using parametric or non-parametric statistical models, and to pool the obtained estimates from these models according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for details).
Transferring over a code base from Matlab to R is often a repetitive
    and inefficient use of time. This package provides a translator for Matlab /
    Octave code into R code. It does some syntax changes, but most of the heavy
    lifting is in the function changes since the languages are so similar.
    Options for different data structures and the functions that can be changed
    are given. The Matlab code should be mostly in adherence to the standard
    style guide but some effort has been made to accommodate different number of
    spaces and other small syntax issues. This will not make the code more R
    friendly and may not even run afterwards. However, the rudimentary syntax,
    base function and data structure conversion is done quickly so that the
    maintainer can focus on changes to the design structure.
You can apply image processing effects that modifies the perceived material properties of objects
    in photos, such as gloss, smoothness, and blemishes. This is an implementation of the algorithm proposed by
    Boyadzhiev et al. (2015) "Band-Sifting Decomposition for Image Based Material Editing".
    Documentation and practical tips of the package is available at <https://github.com/tsuda16k/materialmodifier>.
Provides 'MathJax' and macros to enable its use within Rd files for rendering equations in the HTML help files.
Given an image of a formula (typeset or handwritten) this package
    provides calls to the 'Mathpix' service to produce the 'LaTeX' code which should
    generate that image, and pastes it into a (e.g. an 'rmarkdown') document. 
    See <https://docs.mathpix.com/> for full details. 'Mathpix' is an external service 
    and use of the API is subject to their terms and conditions.
Uses a ratio of weighted distributions to estimate association between variables in a data set.
Provides users to call MATLAB from using the "system" command.
    Allows users to submit lines of code or MATLAB m files.
    This is in comparison to 'R.matlab', which creates a MATLAB server.
A collection of matrix functions for teaching and learning matrix
    linear algebra as used in multivariate statistical methods. These functions are
    mainly for tutorial purposes in learning matrix algebra ideas using R. In some
    cases, functions are provided for concepts available elsewhere in R, but where
    the function call or name is not obvious. In other cases, functions are provided
    to show or demonstrate an algorithm. In addition, a collection of functions are
    provided for drawing vector diagrams in 2D and 3D.
A set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.
Matrix clustering with finite mixture models.
A general framework for computing powers of matrices.  A
   key feature is the capability for users to write callback functions,
   called after each iteration, thus enabling customization for specific
   applications.  Diverse types of matrix classes/matrix multiplication
   are accommodated.  If the multiplication type computes in parallel,
   then the package computation is also parallel.
An analysis platform for metagenomics combining 
 specialized tools and workflows, easy handling of the BIOM 
 format, and transparent access to MG-RAST resources.  Integrates 
 easily with other R packages and non-R software.
Provides functions, which make matrix creation conciser 
             (such as the core package's function m() for rowwise matrix definition or 
             runifm() for random value matrices).
             Allows to set multiple matrix values at once, by using list of formulae. 
             Provides additional matrix operators and dedicated plotting function.
A rich hierarchy of matrix classes, including triangular,
   symmetric, and diagonal matrices, both dense and sparse and with
   pattern, logical and numeric entries.   Numerous methods for and
   operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.
Implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and matrix-like objects.
A collection of functions to support matrix calculations
        for probability, econometric and numerical analysis. There are
        additional functions that are comparable to APL functions which
        are useful for actuarial models such as pension mathematics.
        This package is used for teaching and research purposes at the
        Department of Finance and Risk Engineering, New York
        University, Polytechnic Institute, Brooklyn, NY 11201.
        Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326.
        Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507.
        Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563.
Computation and visualization of matrix correlation coefficients.
    The main method is the Similarity of Matrices Index, while various related
    measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's
    linear correlation and Coxhead's coefficient are included
    for comparison and flexibility.
Tools for homogeneous and in-homogeneous phase-type distributions. 
    Methods for functional evaluation, simulation and estimation using the 
    expectation-maximization (EM) algorithm are provided.
    The methods of this package are based on the following references.
    Asmussen, S., Nerman, O., & Olsson, M. (1996) <https://www.jstor.org/stable/4616418>,
    Olsson, M. (1996) <https://www.jstor.org/stable/4616419>.
    Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>
    Albrecher, H., Bladt, M., & Yslas, J. (2020) <doi:10.1111/sjos.12505>
    Bladt, M., & Yslas, J. (2020) <arXiv:2011.03219>.
Matrix eQTL is designed for fast eQTL analysis on large datasets.
        Matrix eQTL can test for association between genotype 
        and gene expression using linear regression 
        with either additive or ANOVA genotype effects.
        The models can include covariates to account for factors 
        as population stratification, gender, and clinical variables. 
        It also supports models with heteroscedastic and/or correlated errors,
        false discovery rate estimation and 
        separate treatment of local (cis) and distant (trans) eQTLs.
        For more details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.
Extends sparse matrix and vector classes from the 'Matrix' package by providing: 
  (a) Methods and operators that work natively on CSR formats (compressed sparse row, 
  a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), 
  mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); 
  (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, 
  including the 'float32' type from 'float'; 
  (c) Coercion methods between pairs of classes which are not present in 'Matrix', 
  such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; 
  (d) Utility functions for sparse matrices such as sorting the indices or removing 
  zero-valued entries; 
  (e) Fast transposes that work by outputting in the opposite storage format;
  (f) Faster replacements for many 'Matrix' methods for all sparse types, such as
  slicing and elementwise multiplication.
  (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.
Constructs the normalized Laplacian matrix of a square matrix, returns the eigenvectors (singular vectors) and visualization of normalized Laplacian map.
Fits the penalized matrix-normal model to be used for linear discriminant analysis with matrix-valued predictors. For a description of the method, see Molstad and Rothman (2018) <doi:10.1080/10618600.2018.1476249>.
Implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) <arXiv:2005.03861>. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.
Modelling with sparse and dense 'Matrix' matrices, using
  modular prediction and response module classes.
Computes densities, probabilities, and random deviates of the Matrix Normal (Iranmanesh et al. (2010) <doi:10.7508/ijmsi.2010.02.004>). Also includes simple but useful matrix functions. See the vignette for more information.
Partial Least Squares Path Modeling
    algorithm and related algorithms. The algorithm implementations aim for
    computational efficiency using matrix algebra and covariance data. The
    package is designed toward Monte Carlo simulations and includes functions
    to perform simple Monte Carlo simulations.
A simple and the early stage package for matrix profile based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh (2016) <DOI:10.1109/ICDM.2016.0179>. This package calculates all-pairs-similarity for a given window size for time series data.
This is the core functions needed by the 'tsmp' package.  The
    low level and carefully checked mathematical functions are here.
    These are implementations of the Matrix Profile concept that was
    created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Provides samplers for various matrix variate distributions: Wishart, inverse-Wishart, normal, t, inverted-t, Beta type I, Beta type II, Gamma, confluent hypergeometric. Allows to simulate the noncentral Wishart distribution without the integer restriction on the degrees of freedom.
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.  There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
Tests for block-diagonal structure in symmetric matrices (e.g. correlation matrices) under the null hypothesis of exchangeable off-diagonal elements. As described in Segal et al. (2019), these tests can be useful for construct validation either by themselves or as a complement to confirmatory factor analysis. Monte Carlo methods are used to approximate the permutation p-value with Hubert's Gamma (Hubert, 1976) and a t-statistic. This package also implements the chi-squared statistic described by Steiger (1980). Please see Segal, et al. (2019) <doi:10.1007/s11336-018-9647-4> for more information.
Functions to perform fast statistical hypothesis tests on rows/columns of matrices.
  The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use,
  3) compatibility with tests implemented in R (like those available in the 'stats' package).
An implementation of matrix mathematics wherein operations are performed "by name."
Provides functions to collapse a tidy data frame into matrices in a data frame
    and expand a data frame of matrices into a tidy data frame.
Performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017)  <doi: 10.1002/sta4.143>.
This package in includes functions for receiver operating
        characteristic (ROC) analyses as well as Monte Carlo
        simulation.  It includes specific graphical functions for
        interpreting the output of these techniques.
Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations.
Identifying maturation stages across young athletes is paramount for talent identification. Furthermore, the concept of biobanding, or grouping of athletes based on their biological development, instead of their chronological age, has been widely researched. The goal of this package is to help professionals working in the field of strength & conditioning and talent ID obtain common maturation metrics and as well as to quickly visualize this information via several plotting options. For the methods behind the computed maturation metrics implemented in this package refer to Khamis, H. J., & Roche, A. F. (1994) <https://pubmed.ncbi.nlm.nih.gov/7936860/>, Mirwald, R.L et al., (2002) <https://pubmed.ncbi.nlm.nih.gov/11932580/> and Cumming, Sean P. et al., (2017) <doi:10.1519/SSC.0000000000000281>.
Provides functions for the creation, evaluation and test of decision models based in
    Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities
    for a set of indexes, compute utilities and weights for the whole decision tree defining the
    decision model and simulate weights employing Dirichlet distributions under addition constraints 
    in weights.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is commonly accompanied by 'BEAUti 2' (<https://www.beast2.org>),
    which, among others, allows one to install 'BEAST2' package. 
    This package allows to work with 'BEAST2' packages from 'R'.
Functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). Methods for selecting the best dimension are also included. Xia (2002) <doi:10.1111/1467-9868.03411>; Xia (2007) <doi:10.1214/009053607000000352>; Wang (2008) <doi:10.1198/016214508000000418>.
This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance).
             If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated.
             If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too.
             Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented.
             The program computes the p-values and FDR (false discovery rate) q-values for all gene sets.
             The p-values for individual genes in a significant gene set are also listed.
             MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set.
             A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.
Perform exact MAX3 or MAX2 test for one-locus genetic
        association analysis and trend test for dominant, recessive and
        additive models. It can also calculate approximated p-value
        with the normal approximation method.
Fits a linear combination of predictors by maximizing a smooth approximation to the estimated covariate-adjusted area under the receiver operating characteristic curve (AUC) for a discrete covariate. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper421/>.)
Functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) <doi:10.1177/0962280220931560>.  The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo.
Complex niche models show low performance in identifying
    the most important range-limiting environmental variables and in
    transferring habitat suitability to novel environmental conditions
    (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al.,
    2014 <DOI:10.1111/ddi.12160>). This package helps to identify the
    most important set of uncorrelated variables and to fine-tune Maxent's
    regularization multiplier. In combination, this allows to constrain
    complexity and increase performance of Maxent niche models (assessed
    by information criteria, such as AICc (Akaike, 1974
    <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver
    operating characteristic (AUC) (Fielding and Bell, 1997
    <DOI:10.1017/S0376892997000088>). Users of this package should be
    familiar with Maxent niche modelling.
Constructs a space-filling design under the criterion of maximum-minimum distance. Both discrete and continuous searches are provided.
Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in <arXiv:2011.07568>. It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.
Functions for Maximum Likelihood (ML) estimation, non-linear
   optimization, and related tools.  It includes a unified way to call
   different optimizers, and classes and methods to handle the results from
   the Maximum Likelihood viewpoint.  It also includes a number of convenience
   tools for testing and developing your own models.
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
Computes the maximum matching for unweighted graph and maximum
    matching for (un)weighted bipartite graph efficiently.
An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.
Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options.  See the 'Maxent' website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.
Functions to generate graphs that maximise the NODF (nestedness metric based
    on overlap and decreasing fill) metric for a given number of rows, columns
    and links. NODF was originally defined by Almeida-Neto et al. (2008)
    <doi:10.1111/j.0030-1299.2008.16644.x>. As nestedness in ecological networks
    depends on the size of the networks we require normalisation to make them
    comparable. We offer three highly optimised algorithms to find the
    optimising graphs so that users can choose an appropriate trade off between
    computation time and NODF value for the task at hand.
Generate maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018) "Designing Computer Experiments with Multiple Types of Factors: The MaxPro Approach", Journal of Quality Technology, to appear.
It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.
Maximally selected rank statistics with
 several p-value approximations.
Estimates a linear combination of predictors by maximizing a smooth approximation to the estimated true positive rate (TPR; sensitivity) while constraining a smooth approximation to the estimated false positive rate (FPR; 1-specificity) at a user-specified level.
A suite of utility functions providing functionality commonly
    needed for production level projects such as logging, error handling,
    cache management and date-time parsing. Functions for date-time parsing and 
    formatting require that time zones be specified explicitly, avoiding a common 
    source of error when working with environmental time series.
Utility functions for discovering and managing metadata 
    associated with spatially unique "known locations". Applications include
    all fields of environmental monitoring (e.g. air and water quality) where 
    data are collected at stationary sites.
A suite of compiled functions calculating rolling mins, means, 
    maxes and other statistics. This package is designed to meet the needs of
    data processing systems for environmental time series.
A suite of convenience functions for generating US state and county
    thematic maps using datasets from the MazamaSpatialUtils package.
A suite of conversion functions to create internally standardized
    spatial polygons data frames. Utility functions use these data sets to
    return values such as country, state, time zone, watershed, etc. associated
    with a set of longitude/latitude pairs. (They also make cool maps.)
Supports the generation of parallelogram, equilateral
    triangle, regular hexagon, isosceles trapezoid, Koch snowflake,
    'hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski
    trapezoid mazes via 'TurtleGraphics'. Mazes are generated by the recursive method:
    the domain is divided into sub-domains in which mazes are generated,
    then dividing lines with holes are drawn between them, see
    J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.
A maze generator that creates the Elithorn Maze (HTML file) and the functions to calculate the associated maze parameters (i.e. Difficulty and Ability).
Methods for calculating and testing the significance of
  pairwise monotonic association from and based on the work of
  Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one
  or multiple sets can be performed in parallel thanks to the
  packages 'foreach' and 'doMC'.
Functionality for generating and plotting random mazes. The mazes are based on matrices, so can only consist of vertical and horizontal lines along a regular grid. But there is no need to use every possible space, so they can take on many different shapes.
Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by Øyvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).
Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals. The methods included are: Multiblock Principal Components Analysis (MB-PCA), Common Dimensions analysis (ComDim), Multiblock Partial Least Squares (MB-PLS) regression and Multiblock Weighted Covariate analysis (MB-WCov). E. Tchandao Mangamana, V. Cariou, E. Vigneau, R. Glèlè Kakaï, E.M. Qannari (2019) <doi:10.1016/j.chemolab.2019.103856>;  E. Tchandao Mangamana, R. Glèlè Kakaï, E.M. Qannari (2021) <doi:10.1016/j.chemolab.2021.104388>.
Distributions that are typically used for exposure rating in
             general insurance, in particular to price reinsurance contracts.
             The vignette shows code snippets to fit the distribution to
             empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208>
             freely available on-line.
Calibrate and apply multivariate bias correction algorithms
    for climate model simulations of multiple climate variables. Three methods
    described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and 
    Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented —
    (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr),
    and (iii) MBC N-dimensional PDF transform (MBCn) — as is the Rank
    Resampling for Distributions and Dependences (R2D2) method.
The companion package provides all original data sets and functions that are used in the book "Model-Based Clustering and Classification for Data Science" by Charles Bouveyron, Gilles Celeux, T. Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181).
Cluster genes based on Poisson or Negative-Binomial model
        for RNA-Seq or other digital gene expression (DGE) data
Perform clusterwise multiblock analyses (clusterwise multiblock Partial Least Squares, clusterwise multiblock Redundancy Analysis or a regularized method between the two latter ones) associated with a F-fold cross-validation procedure to select the optimal number of clusters and dimensions.
Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods.
   Jorjani, H., et al. (2003) <doi:10.3168/jds.S0022-0302(03)73646-7>.
   Schaeffer, L. R. (2014) <http://animalbiosciences.uoguelph.ca/~lrs/ELARES/PDforce.pdf>.
Implements methods that are useful in designing research studies and analyzing data, with 
	particular emphasis on methods that are developed for or used within the behavioral, 
	educational, and social sciences (broadly defined). That being said, many of the methods 
	implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a 
	suite of functions for a variety of related topics, such as effect sizes, confidence intervals 
	for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size
	planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and 
	minimum-risk point estimation perspectives), mediation analysis, various properties of 
	distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally 
	an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became
	more general and now contains methods applicable and used in a wide variety of fields and is an 
	orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has 
	greatly benefited from others, see <https://www3.nd.edu/~kkelley/site/MBESS.html> for a detailed 
	list of those that have contributed and other details.
Fast moment-based hierarchical model fitting. Implements
    methods from the papers
    "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017)
    and
    "Fitting a Deeply Nested Hierarchical Model to a Large Book Review
    Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry
    (2018).
Provides spatially survey balanced designs using the quasi-random number method described Robinson et al. (2013) <doi:10.1111/biom.12059>. These designs can accommodate, without substantial detrimental effects on spatial balance, legacy sites (Foster et al., 2017 <doi:10.1111/2041-210X.12782>), and for both point samples and transect samples (Foster et al. 2020, <doi:10.1111/2041-210X.13321>).  Additional information about the package use itself is given in Foster (2021) <doi:10.1111/2041-210X.13535>.
Over 20 multiple-site diversity indices can be calculated.
        Later versions will include phylogenetic diversity
Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.
Provides linear models based on Theil-Sen
        single median and Siegel repeated medians. They are very robust
        (29 or 50 percent breakdown point, respectively), and if no
        outliers are present, the estimators are very similar to OLS.
Lightweight utilities for nucleic acid melting curve analysis are 
    important in life sciences and diagnostics. This software can be used for 
    the analysis and presentation of melting curve data from microbead-based 
    assays (surface melting curve analysis) and reactions in solution (e.g., 
    quantitative PCR (qPCR), real-time isothermal Amplification). Further 
    information are described in detail in two publications in The R Journal [
    <https://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf>; 
    <https://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf>].
Evaluate whether a microbiome sample is a mixture of two
    samples, by fitting a model for the number of read counts as a
    function of single nucleotide polymorphism (SNP) allele and the
    genotypes of two potential source samples.
    Lobo et al. (2019) <doi:10.1101/529040>.
Fits Bayesian dose-response model-based network meta-analysis (MBNMA) 
    that incorporate multiple doses within an agent by modelling different dose-response functions, as
    described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>. 
    By modelling dose-response relationships this can connect networks of evidence that might
    otherwise be disconnected, and can improve precision on treatment estimates. Several common 
    dose-response functions are provided; others may be added by the user. Various characteristics
    and assumptions can be flexibly added to the models, such as shared class effects. The consistency 
    of direct and indirect evidence in the network can be assessed using unrelated mean effects models 
    and/or by node-splitting at the treatment level.
Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple
  time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions,
  following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. 
  The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple 
  treatment comparisons. Several general time-course functions are provided; others may be added 
  by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared 
  class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects 
  models and/or by node-splitting.
Functional gradient descent algorithm
  (boosting) for optimizing general risk functions utilizing
  component-wise (penalised) least squares estimates or regression
  trees as base-learners for fitting generalized linear, additive
  and interaction models to potentially high-dimensional data.
  Models and algorithms are described in \doi{10.1214/07-STS242}, 
  a hands-on tutorial is available from \doi{10.1007/s00180-012-0382-5}.
  The package allows user-specified loss functions and base-learners.
Importing and converting an mbox file into a tibble object.
Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) <DOI:10.1002/essoar.10504791.1>.
Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses.  The response dimension reduction is theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.
Compute and visualize the ps-index, a new integrated index for
    multiple biomarker responses, as described in Pham & Sokolova
    (2021, unpublished).
Fit generalized linear models with binomial responses using  a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).
An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.
Implements a sparse Bayesian multivariate linear regression model using shrinkage priors from the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <arXiv:1711.07635>.
Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models.  Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component.
Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi: 10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).
Implements a Monte Carlo Based Heterogeneity Test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (OR) in Meta-Analysis Studies. Depending on the presence of moderators, this Monte Carlo Based Test can be implemented in the random or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihood, so installation of R package 'metafor' is required. This approach refers to the studies of Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).
A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous).
Post processes MC2 output, especially for use by Path or ST-Sim.  MC2 (short for "MC1 version 2") is a dynamic global vegetation model (en.wikipedia.org/wiki/DGVM).  Path (essa.com/tools/path) and ST-Sim (www.apexrms.com) are state-and-transition model (STM) engines. MC2 has a user website at sites.google.com/site/mc1dgvmusers.  Since 2001, MC1 has been used to simulate changes in natural vegetation due to climate change at scales from regional to global.  In 2012, MC1 was reimplemented in C++ to make it faster and to reduce storage requirements.  This newer version is referred to as MC2, an abbreviation of "MC1 version 2".  Beginning in 2011, output from MC1 and MC2 has been used to inform regional state-and-transition model simulations by the U.S. Forest Service and the Washington State Department of Natural Resources.  Projects to date have involved study areas in central Oregon, the Olympic Peninsula, the Blue Mountains ecoregion, southwestern Oregon, and southeastern Oregon.  In the first of this series of projects, the netCDF output files from MC2 were manually post-processed, mostly in Excel, to produce input .csv files for the STM engines. Beginning with the second project, R scripts were used to automate the post-processing work.  These R scripts have been collected into the MC2toPath R-package.
Provides two variants of multiple correspondence analysis (ca):
                     multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.
When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. <DOI:10.1111/biom.13024>. Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in <https://CRAN.R-project.org/package=flare> and the latest version is useful for 'mcb'.
Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous.
    Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile.
    You can find more information and validation in the Open Access paper.
    Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.
Monte Carol based tests for the Behrens Fisher Problem enhance the statistical power 
           and performs better than Welch's t-approximation, see Ullah et al. (2019).
Computes the prime implicants or a minimal disjunctive normal form for a
  logic expression presented by a truth table or a logic tree. Has been particularly 
  developed for logic expressions resulting from a logic regression analysis, i.e.
  logic expressions typically consisting of up to 16 literals, where the prime implicants 
  are typically composed of a maximum of 4 or 5 literals.
Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
    'Multi-Accuracy Boosting' (2019) <arXiv:1805.12317> for the multi-calibration of a machine learning model's prediction.
    'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups.
    Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are.
    This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
A number of biomedical problems involve performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared to the doubled p-value, due to asymmetry in the null distribution. Exact (permutation) testing approaches are attractive, but can be computationally intensive and cumbersome. MCC is an approximation to exact association testing of two vectors that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled p-values.
It contains six common multi-category classification accuracy evaluation measures.
 All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically,
 Hypervolume Under Manifold (HUM), described in
 Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>.
 Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in
 Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>.
 Polytomous Discrimination Index (PDI), described in
 Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>.
 Li et al. (2018) <doi:10.1177/0962280217692830>.
 We described all these above measures and our mcca package in
 Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.
The MCC-F1 analysis is a method to evaluate the performance of binary classifications. 
    The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth.
    The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.
Implements methods for processing a sample of (hard)
        clusterings, e.g. the MCMC output of a Bayesian clustering
        model. Among them are methods that find a single best
        clustering to represent the sample, which are based on the
        posterior similarity matrix or a relabelling algorithm.
Applying the methodology from Manuel et al. to estimate parameters using a matched 
    case control data with a mismeasured exposure variable that is accompanied by 
    instrumental variables (Submitted).
The Matthews correlation coefficient (MCC) score is calculated (Matthews BW  (1975) <DOI:10.1016/0005-2795(75)90109-9>).
Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with algorithms, 
    preference elicitation and data visualisation functions. Sébastien Bigaret, Richard Hodgett, Patrick Meyer, 
    Tatyana Mironova, Alexandru Olteanu (2017) Supporting the multi-criteria decision aiding process : 
    R and the MCDA package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4, 
    pages 169 - 194 <doi:10.1007/s40070-017-0064-1>.
Implementation of several MCDM methos for crisp data for decision
    making problems. The methods that are implemented in this package are RIM,
    TOPSIS (with two normalization procedures), VIKOR, Multi-MOORA and WASPAS.
    In addition, MetaRanking function calculates a new ranking from the sum 
    of the rankings calculated, as well as an aggregated ranking.
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
    For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005)
	<DOI:10.1111/j.1467-9868.2005.00499.x>.
Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.
Machine coded genetic algorithm (MCGA) is a fast tool for
    real-valued optimization problems. It uses the byte
    representation of variables rather than real-values. It
    performs the classical crossover operations (uniform) on these
    byte representations. Mutation operator is also similar to
    classical mutation operator, which is to say, it changes a
    randomly selected byte value of a chromosome by +1 or -1 with
    probability 1/2. In MCGAs there is no need for
    encoding-decoding process and the classical operators are
    directly applicable on real-values. It is fast and can handle a
    wide range of a search space with high precision. Using a
    256-unary alphabet is the main disadvantage of this algorithm
    but a moderate size population is convenient for many problems.
    Package also includes multi_mcga function for multi objective
    optimization problems. This function sorts the chromosomes
    using their ranks calculated from the non-dominated sorting
    algorithm.
'mcgibbsit' provides an implementation of Warnes & Raftery's
  MCGibbsit run-length diagnostic for a set of (not-necessarily
  independent) MCMC samplers.  It combines the estimate error-bounding
  approach of the Raftery and Lewis MCMC run length diagnostic with
  the between verses within chain approach of the Gelman and
  Rubin MCMC convergence diagnostic.
Fitting multivariate covariance generalized linear
     models (McGLMs) to data.  McGLM is a general framework for non-normal
     multivariate data analysis, designed to handle multivariate response
     variables, along with a wide range of temporal and spatial correlation
     structures defined in terms of a covariance link function combined
     with a matrix linear predictor involving known matrices.
     The models take non-normality into account in the conventional way
     by means of a variance function, and the mean structure is modelled
     by means of a link function and a linear predictor.
     The models are fitted using an efficient Newton scoring algorithm
     based on quasi-likelihood and Pearson estimating functions, using
     only second-moment assumptions. This provides a unified approach to
     a wide variety of different types of response variables and covariance
     structures, including multivariate extensions of repeated measures,
     time series, longitudinal, spatial and spatio-temporal structures.
     The package offers a user-friendly interface for fitting McGLMs
     similar to the glm() R function. 
     See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information 
     and examples.
The package performs global optimization combining Monte Carlo and Quasi Monte Carlo simulation with a local search. \n       The local searches can be easily speeded-up by using a network of local workstations.
mcheatmaps serves to visualize multiple different symmetric matrices and matrix clusters in a single figure using a dendogram, two half matrices and various color labels.
Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.
Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package implements two market area models (Huff Model, Multiplicative Competitive Interaction Model) into R, while the emphases lie on 1.) fitting these models based on empirical data via OLS regression and nonlinear techniques and 2.) data preparation and processing (esp. interaction matrices and data preparation for the MCI Model).
Apply the marginal classification method to achieve the purpose of providing 
    the point and interval estimates for the minimal clinically important difference 
    based on the classical anchor-based method. For more details of the methodology, please 
    see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.
Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.
The likelihood of direct CAR models and Binomial and Poisson GLM with latent CAR variables are approximated by the Monte Carlo likelihood. The Maximum Monte Carlo likelihood estimator is found either by an iterative procedure of directly maximising the Monte Carlo approximation or by a response surface design method.Reference for the method can be found in the DPhil thesis in Z. Sha (2016). For application a good reference is R.Bivand et.al (2017) <doi:10.1016/j.spasta.2017.01.002>.
Maximum likelihood estimation using a Monte Carlo local likelihood (MCLL) method
Provides estimators for multinomial logit models in their
    conditional logit and baseline logit variants, with or without random effects,
    with or without overdispersion. 
    Random effects models are estimated using the PQL technique (based on a Laplace approximation)
    or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated
    with caution if the group sizes are small.
Gaussian finite mixture models fitted via EM algorithm for
  model-based clustering, classification, and density estimation, 
  including Bayesian regularization, dimension reduction for 
  visualisation, and resampling-based inference.
Extend the functionality of the 'mclust' package for 
  Gaussian finite mixture modelling by including: 
  density estimation for data with bounded support 
  (Scrucca, 2019 <doi:10.1002/bimj.201800174>);
  modal clustering using MEM algorithm for Gaussian mixtures 
  (Scrucca, 2021 <doi:10.1002/sam.11527>).
Given a set of data points, a clustering is defined as a disjoint partition
    where each pair of sets in a partition has no overlapping elements. 
    This package provides 25 methods that play a role somewhat similar to 
    distance or metric that measures similarity of two clusterings - or partitions.
    For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.
Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data.
Simulates continuous distributions of random vectors using
    Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
    R function that evaluates the log unnormalized density.  Algorithms
    are random walk Metropolis algorithm (function metrop), simulated
    tempering (function temper), and morphometric random walk Metropolis
    (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>,
    function morph.metrop),
    which achieves geometric ergodicity by change of variable.
Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt.
Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.
Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.
Generates derived parameter(s) from Monte Carlo Markov Chain
    (MCMC) samples using R code. This allows Bayesian models to be fitted
    without the inclusion of derived parameters which add unnecessary
    clutter and slow model fitting. For more information on MCMC samples
    see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Provides ensemble samplers for
    affine-invariant Monte Carlo Markov Chain, which allow a faster
    convergence for badly scaled estimation problems. Two samplers are
    proposed: the 'differential.evolution' sampler from ter Braak and
    Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the 'stretch' sampler
    from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.
Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques.
Hybrid Markov chain Monte Carlo (MCMC) to simulate from a
        multimodal target distribution.  A Gaussian process
        approximation makes this possible when derivatives are unknown.
        The Package serves to minimize the number of function
        evaluations in Bayesian calibration of computer models using
        parallel tempering.  It allows replacement of the true target
        distribution in high temperature chains, or complete replacement
        of the target.  Methods used are described in, "Efficient MCMC
        schemes for computationally expensive posterior distributions",
        Fielding et al. (2011) <doi:10.1198/TECH.2010.09195>.
        The research presented in this work was carried out as part of
        the Singapore-Delft Water Alliance Multi-Objective
        Multi-Reservoir Management research programme (R-264-001-272).
Implements a class ('mcmcOutput') for efficiently storing and handling Markov chain Monte Carlo (MCMC) output, intended as an aid for those writing customized MCMC samplers. A range of constructor methods are provided covering common output formats. Functions are provided to generate summary and diagnostic statistics and to display histograms or density plots of posterior distributions, for the entire output, or subsets of draws, nodes, or parameters.
Contains functions to perform Bayesian
        inference using posterior simulation for a number of
        statistical models. Most simulation is done in compiled C++
        written in the Scythe Statistical Library Version 1.0.3. All
        models return 'coda' mcmc objects that can then be summarized
        using the 'coda' package. Some useful
        utility functions such as density functions,
	pseudo-random number generators for statistical
        distributions, a general purpose Metropolis sampling algorithm,
        and tools for visualization are provided.
Functions for convenient plotting and viewing of MCMC output.
Estimates the precision of transdimensional Markov chain Monte Carlo 
    (MCMC) output, which is often used for Bayesian analysis of models with different 
    dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible 
    jump MCMC) relies on sampling a discrete model-indicator variable to estimate 
    the posterior model probabilities. If only few switches occur between the models, 
    precision may be low and assessment based on the assumption of independent 
    samples misleading. Based on the observed transition matrix of the indicator 
    variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, 
    Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws 
    posterior samples of the stationary distribution to (a) assess the uncertainty 
    in the estimated posterior model probabilities and (b) estimate the effective 
    sample size of the MCMC output.
Functions and classes to store, manipulate and summarise
    Monte Carlo Markov Chain (MCMC) samples. For more information see
    Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Fit multi-level models with possibly correlated
    random effects using Markov Chain Monte Carlo simulation.
    Such models allow smoothing over space and time and are useful in,
    for example, small area estimation.
Provides tools for computing Monte Carlo standard
        errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
        computation for expectation and quantile estimators is
        supported as well as multivariate estimations. The package also provides 
	functions for computing effective sample size and for plotting
	Monte Carlo estimates versus sample size.
Provides functions to prepare time priors for 'MCMCtree' analyses in the 'PAML' software from Yang (2007)<doi:10.1093/molbev/msm088> and plot time-scaled phylogenies from any Bayesian divergence time analysis. Most time-calibrated node prior distributions require user-specified parameters. The package provides functions to refine these parameters, so that the resulting prior distributions accurately reflect confidence in known, usually fossil, time information. These functions also enable users to visualise distributions and write 'MCMCtree' ready input files. Additionally, the package supplies flexible functions to visualise age uncertainty on a plotted tree with using node bars, using branch widths proportional to the age uncertainty, or by plotting the full posterior distributions on nodes. Time-scaled phylogenetic plots can be visualised with absolute and geological timescales . All plotting functions are applicable with output from any Bayesian software, not just 'MCMCtree'.
Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with 'Stan', 'NIMBLE', 'JAGS', and other software.
Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems. Additionally, a modular toolbox for the generation of multi-objective benchmark graph problems is included.
A collection of function to solve multiple criteria optimization problems 
  using genetic algorithms (NSGA-II). Also included is a collection of test functions.
The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.
Provides a class for multi-companion matrices with methods for
    arithmetic and factorization.  A method for generation of
    multi-companion matrices with prespecified spectral properties is
    provided, as well as some utilities for periodically correlated and
    multivariate time series models. See Boshnakov (2002)
    <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>.
The package aids in creating a Conjoint Analysis design
        with extra cards.  Unlike traditional "holdout" cards these
        cards are used to create a set of "good" (balanced and low
        correlation) designs. Each of these designs is analyzed and the
        average calculated.
Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.
Multiple contrast tests and simultaneous confidence
 intervals based on normal approximation. With implementations for
 binomial proportions in a 2xk setting (risk difference and odds ratio),
 poly-3-adjusted tumour rates, biodiversity indices (multinomial data) 
 and expected values under lognormal assumption. Approximative power 
 calculation for multiple contrast tests of binomial and Gaussian data.
Provides a function that wraps 
    mcparallel() and mccollect() from 'parallel' with temporary variables and a 
    task handler.  Wrapped in this way the results of an mcparallel() call 
    can be returned to the R session when the fork is complete 
    without explicitly issuing a specific mccollect() to retrieve the value.
    Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with 
    a single call to mcparallelDoCheck().
Implements a methodology for the design and analysis of dose-response studies that
             combines aspects of multiple comparison procedures  and modeling approaches
	     (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>).
             The package provides tools for the analysis of dose finding trials as well as a variety
             of tools necessary to plan a trial to be conducted with the MCP-Mod methodology.
             Please note: The 'MCPMod' package will not be further developed, all future development of 
             the MCP-Mod methodology will be done in the 'DoseFinding' R-package.
Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available.
	Bretz, F., Pinheiro, J. C., and Branson, M. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>.
	Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) <doi:10.2307/2533961>.
	Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) <doi:10.1002/sim.6052>.
An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).
Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.
Performs the execution of the main procedures of multiple 
    comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, 
    Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations 
    and export to different extensions of its results. An additional 
    part of the package is the presence of the performance evaluation 
    of the tests (Type I error per experiment and the power). This will 
    assist the user in making the decision for the chosen test.
Regression methods to quantify the relation between two measurement methods. In particular it addresses regression problems with errors in both variables and without repeated measurements. The package provides implementations of Deming regression, weighted Deming regression, and Passing-Bablok regression following the CLSI EP09-A3 recommendations for analytical method comparison and bias estimation using patient samples.
Multi-core replication function to make it easier to do fast 
  Monte Carlo simulation. Based on the mcreplicate() function from the 
  'rethinking' package. The 'rethinking' package requires installing 'rstan', 
  which is onerous to install, while also not adding capabilities to this 
  function.
Perform the model confidence set procedure of Hansen et al (2011) <doi:10.3982/ECTA5771>.
Identifies the optimal number of clusters by calculating the similarity between
             two clustering methods at the same number of clusters using the corrected indices of Rand and
             Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at
             which the index attain its maximum more frequently is a candidate for being the optimal
             number of clusters.
mcsm contains a collection of functions that allows the
        reenactment of the R programs used in the book EnteR Monte
        Carlo Methods without further programming. Programs being
        available as well, they can be modified by the user to conduct
        one's own simulations.
Provides functionality to produce graphs of sampling distributions of test 	statistics from a variety of common statistical tests. With only a few keystrokes, 	the user can conduct a hypothesis test and visualize the test statistic and 		corresponding p-value through the shading of its sampling distribution. Initially       	created for statistics at Middlebury College.
Package computes popular and widely used multicollinearity diagnostic measures <doi:10.17576/jsm-2019-4809-26> and <doi:10.32614/RJ-2016-062> . Package also indicates which regressors may be the reason of collinearity among regressors.
Transition matrices (probabilities or counts) estimation for discrete Markov Chains of order n (1 <= n <= 5).
A complete and consistent toolkit to process the Munich ChronoType 
    Questionnaire (MCTQ) for its three versions (standard, micro, and shift). 
    MCTQ is a quantitative and validated tool to assess chronotypes using 
    peoples' sleep behavior, originally presented by Till Roenneberg, Anna 
    Wirz-Justice, and Martha Merrow (2003, <doi:10.1177/0748730402239679>).
Unit testing for Monte Carlo methods, particularly Markov Chain Monte Carlo (MCMC) methods, are implemented as extensions of the 'testthat' package. The MCMC methods check whether the MCMC chain has the correct invariant distribution. They do not check other properties of successful samplers such as whether the chain can reach all points, i.e. whether is recurrent. The tests require the ability to sample from the prior and to run steps of the MCMC chain. The methodology is described in Gandy and Scott (2020) <arXiv:2001.06465>.
Visualize the relationship between linear regression variables and causes of multi-collinearity. 
    Implements the method in Lin et. al. (2020) <doi:10.1080/10618600.2020.1779729>.
In the context of multistate models, which are popular in sociology,
    demography, and epidemiology, Markov chain with rewards calculations can
    help to refine transition timings and so obtain more accurate estimates. The
    package code accommodates up to nine transient states and irregular age (time)
    intervals. Traditional demographic life tables result as a special case.
    Formulas and methods involved are explained in detail in the accompanying
    article: Schneider / Myrskyla / van Raalte (2021): Flexible Transition Timing
    in Discrete-Time Multistate Life Tables Using Markov Chains with Rewards,
    MPIDR Working Paper WP-2021-002.
Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.
Produces clean and neat Markdown log file
    and also provide an argument to include the function call inside the Markdown log.
Mixture and flexible discriminant analysis, multivariate
        adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines.
	Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.
Learning a mixed directed acyclic graph based on both continuous and categorical data.
An implementation of functions for medical data analysis which perform basic data pre processing,
             univariate and bivariate analysis.Kirkwood et al. (2003) <doi:10.1002/sim.1961>.
Projection based methods for preprocessing,
    exploring and analysis of multivariate data used in chemometrics.
    S. Kucheryavskiy (2020) <doi: 10.1016/j.chemolab.2020.103937>.
Provides 3D plots of the Moran-Downton bivariate exponential distribution (MDBED), 
  generates bivariate random values, and also provides values of the joint and conditional PDFs and CDFs. 
  Nagao M, Kadoya M (1971) <http://hdl.handle.net/2433/124795>.
  Balakrishna N, Lai CD (2009) <doi:10.1007/b101765>.
Use the open source 'MDB Tools' utilities
    <https://github.com/mdbtools/mdbtools/>. Primarily used for converting
    proprietary Microsoft Access files to simple text files and then
    reading those as data frames.
Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data
             lies and therefore find the most appropriate action to take.
             Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.
Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
A comprehensive collection of linkage methods for agglomerative
  hierarchical clustering on a matrix of proximity data (distances or
  similarities), returning a multifurcated dendrogram or multidendrogram.
  Multidendrograms can group more than two clusters when ties in proximity data
  occur, and therefore they do not depend on the order of the input data.
  Descriptive measures to analyze the resulting dendrogram are additionally
  provided.
Functions for MultiDimensional Feature Selection (MDFS):
 calculating multidimensional information gains, scoring variables,
 finding important variables, plotting selection results.
 This package includes an optional CUDA implementation that speeds up
 information gain calculation using NVIDIA GPGPUs.
 R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.
'MTrackJ' is an 'ImageJ' plugin for motion tracking and analysis (see 
    <https://imagescience.org/meijering/software/mtrackj/>). This package reads 
    and writes 'MTrackJ Data Files' ('.mdf', see 
    <https://imagescience.org/meijering/software/mtrackj/format/>). It supports
    2D data and read/writes cluster, point, and channel information. If desired, 
    generates track identifiers that are unique over the clusters.
    See the project page for more information and examples.
Provides functions to impute missing values using Gaussian 
    copulas for mixed data types as described by Christoffersen et al. 
    (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) 
    <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> 
    but differs by making a direct approximation of the log marginal likelihood 
    using an extended version of the Fortran code created by Genz and Bretz 
    (2002) <doi:10.1198/106186002394> in addition to also support multinomial 
    variables.
Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.
Normalize data to minimize the difference between sample plates 
    (batch effects). For given data in a matrix and grouping variable (or
	plate), the function 'normn_MA' normalizes the data on MA coordinates. 
	More details are in the citation. The primary method is 'Multi-MA'. Other 
	fitting functions on MA coordinates can also be employed e.g. loess.
Implemented are the one-sided and two-sided 
  multiple-direction logrank test for two-sample right 
  censored data. In addition to the statistics p-values are calculated: 
  1. For the one-sided testing problem one p-value based on a
   wild bootstrap approach is determined. 2. In the two-sided case
   one p-value based on a chi-squared approximation and 
   a second p-values based on a permutation approach are calculated.
 Ditzhaus, M. and Friedrich, S. (2018) <arXiv:1807.05504>.
 Ditzhaus, M. and Pauly, M. (2018) <arXiv:1808.05627>.
Runs a Shiny web application that merges raw 'qPCR' fluorescence data with related 
    metadata to visualize species presence/absence detection patterns and assess data quality. 
    The application calculates threshold values from raw fluorescence data using a method based 
    on the second derivative method, Luu-The et al (2005) <doi:10.2144/05382RR05>,  and utilizes 
    the ‘chipPCR’ package by Rödiger, Burdukiewicz, & Schierack (2015) <doi:10.1093/bioinformatics/btv205> 
    to calculate Cq values. The application has the ability to connect to a custom developed MySQL 
    database to populate the applications interface. The application allows users to interact with 
    visualizations such as a dynamic map, amplification curves and standard curves, that allow for 
    zooming and/or filtering. It also enables the generation of customized exportable reports based
    on filtered mapping data.
Contains model-based treatment of missing data for regression 
    models with missing values in covariates or the dependent 
    variable using maximum likelihood or Bayesian estimation 
    (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>;
    Luedtke, Robitzsch, & West, 2020a, 2020b;
    <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>).
    The regression model can be nonlinear (e.g., interaction 
    effects, quadratic effects or B-spline functions). 
    Multilevel models with missing data in predictors are
    available for Bayesian estimation. Substantive-model compatible 
    multiple imputation can be also conducted.
Allows users to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size. For details on the method, see McArtor, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.
Statistical inference for quadratic functional of the moderate-dimensional linear model in Guo and Cheng (2021) <DOI:10.1080/01621459.2021.1893177>.
Provides graph-constrained regression methods in which
    regularization parameters are selected automatically via estimation of
    equivalent Linear Mixed Model formulation. 'riPEER' (ridgified Partially
    Empirical Eigenvectors for Regression) method employs a penalty term being
    a linear combination of graph-originated and ridge-originated penalty terms,
    whose two regularization parameters are ML estimators from corresponding
    Linear Mixed Model solution; a graph-originated penalty term allows imposing
    similarity between coefficients based on graph information given whereas
    additional ridge-originated penalty term facilitates parameters estimation:
    it reduces computational issues arising from singularity in a graph-originated
    penalty matrix and yields plausible results in situations when graph information
    is not informative. 'riPEERc' (ridgified Partially Empirical Eigenvectors
    for Regression with constant) method utilizes addition of a diagonal matrix
    multiplied by a predefined (small) scalar to handle the non-invertibility of
    a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs
    variable-reduction procedure to handle the non-invertibility of a graph
    Laplacian matrix.
Provides automatization for plot generation succeeding common molecular dynamics analyses.
             This includes straightforward plots, such as RMSD (Root-Mean-Square-Deviation) and
             RMSF (Root-Mean-Square-Fluctuation) but also more sophisticated ones such as
             dihedral angle maps, hydrogen bonds, cluster bar plots and
             DSSP (Definition of Secondary Structure of Proteins) analysis. Currently able to load
             GROMOS, GROMACS and AMBER formats, respectively.
The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.
A set of core functions for handling medical device event data in
    the context of post-market surveillance, pharmacovigilance, signal detection
    and trending, and regulatory reporting. Primary inputs are data on events by
    device and data on exposures by device. Outputs include: standardized
    device-event and exposure datasets, defined analyses, and time series.
A set of functions to obtain modified score test for generalized linear models.
Tools associated with General
    Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 2x2 and more
    general designs, associated plotting and model comparison tools,
    and tests of marginal response invariance and report independence.
A graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.
Estimate genetic linkage maps for markers on a single chromosome (or in a single linkage group) from pairwise recombination fractions or intermarker distances using weighted metric multidimensional scaling. The methods are suitable for autotetraploid as well as diploid populations. Options for assessing the fit to a known map are also provided. Methods are discussed in detail in Preedy and Hackett (2016) <doi:10.1007/s00122-016-2761-8>.
Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued data via metric MDS (ratio, interval, mspline).Selecting the optimal multidimensional scaling procedure for interval-valued data by varying all combinations of normalization and optimization methods.Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties.
 (Borg, I., Groenen, P.J.F., Mair, P. (2013) <doi:10.1007/978-3-642-31848-1>, 
 Groenen, P.J.F., Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
 Walesiak, M. (2016) <doi:10.15611/ekt.2016.2.01>, 
 Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).
An interactive document on  the topic of multidimensional scaling and principal component analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/MDS_PCAShiny/>.
A complement to *Modern Data
    Science with R*, both the first (ISBN: 978-1498724487, publisher URL: 
    <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9781498724487>)
    and second editions (ISBN: 978-0367191498, publisher URL: 
    <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>).
    This package contains data and code to complete exercises and 
    reproduce examples from the text. It also facilitates connections 
    to the SQL database server used in the book. Both editions of the book are 
    supported by this package.
Modeling microstructures of
	human tooth dentin and
	horizontal serial-sectioning of the dentin.
	Corresponding age range of dentin serial sections,
	that is used in stable isotope analyses,
	can be calculated by using this package.
A collection of 'ggplot2' themes that render text as markdown/HTML. This enables
    the creation of complex formatted plot labels, e.g. titles with individual words highlighted
    in different colors.
Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <DOI:10.1002/sim.8212>.
Sentiment analysis is a popular technique in text mining that
    attempts to determine the emotional state of some text. We provide a new
    implementation of a common method for computing sentiment, whereby words are
    scored as positive or negative according to a dictionary lookup. Then the
    sum of those scores is returned for the document. We use the 'Hu' and 'Liu'
    sentiment dictionary ('Hu' and 'Liu', 2004) <doi:10.1145/1014052.1014073>
    for determining sentiment. The scoring function is 'vectorized' by document,
    and scores for multiple documents are computed in parallel via 'OpenMP'.
Performs mean shift classification using linear and 
  k-d tree based nearest neighbor implementations for the Gaussian,
  Epanechnikov, and biweight product kernels.
Quickly make tables of descriptive statistics (i.e., counts, 
    means, confidence intervals) for continuous variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to 'Microsoft Word' ® with minimal pain.
Core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments, published by Gelfman et al (2017) at <https://github.com/igm-team/meaRtools/>.
Send server-side tracking data from R.
  The Measurement Protocol version 2 
  <https://developers.google.com/analytics/devguides/collection/protocol/ga4>
  allows sending HTTP tracking events from R code.
Collection of tools to make working with physical measurements
		easier. Convert between metric and imperial units, or calculate a dimension's
		unknown value from other dimensions' measurements.
Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.
Identification of ring borders on scanned image sections from dendrochronological samples. Processing of image reflectances to produce gray matrices and time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders or control of automatic detection. Routines to visually include/exclude ring borders on the R graphical devices, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to positive/negative extreme values in the smoothed time-series of gray values. Most of the in-package routines can be recursively implemented using the multiDetect() function.
Maximum entropy density based dependent data bootstrap. 
  An algorithm is provided to create a population of time series (ensemble) 
  without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains
  how the algorithm satisfies the ergodic theorem and the central limit theorem.
Functions to facilitate model-based clustering of nodes in a network in a mixture of experts setting, which incorporates covariate information on the nodes in the modelling process. Isobel Claire Gormley and Thomas Brendan Murphy (2010) <doi:10.1016/j.stamet.2010.01.002>.
Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).
Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.
Multi Calculator of different scores to measure adherence to Mediterranean Diet, to compute them in nutriepidemiological data. Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies.
Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.
Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition.
Functions to simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>; De Caceres et al. (2021) <doi:10.1016/j.agrformet.2020.108233>].
Run flexible mediation analyses using natural effect models as described in 
  Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, 
  Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> 
  and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.
API wrapper to gather news stories, media information and tags from the 'mediacloud.org' API, based on a multilevel query <https://mediacloud.org/>. A personal API key is required.
Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
Provides a general framework for clinical trial simulations based
    on the Clinical Scenario Evaluation (CSE) approach. The package supports a
    broad class of data models (including clinical trials with continuous, binary,
    survival-type and count-type endpoints as well as multivariate outcomes that are
    based on combinations of different endpoints), analysis strategies and commonly
    used evaluation criteria.
The following modules are included in the package: 
    Adaptive designs with data-driven sample size or event count re-estimation, 
    Adaptive designs with data-driven treatment selection, 
    Adaptive designs with data-driven population selection, 
    Optimal selection of a futility stopping rule, 
    Event prediction in event-driven trials,
    Adaptive trials with response-adaptive randomization (experimental module), 
    Traditional trials with multiple objectives (experimental module).
Extract textual data from different media channels 
    through its source based on users choice of keywords. 
    These data can be used to perform text analysis to 
    identify patterns in respective media reporting.
    The media channels used in this package are print media.
    The data (or news) used are publicly available to consumers.
Functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach. We perform the methods considered in Chao Cheng, Donna Spiegelman, and Fan Li. (2020+) <https://github.com/chaochengstat/chaochengstat.github.io/blob/master/manuscript_Epidemiologic_Methods.pdf>.
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results 
 to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. 
 First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as 
 sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the 
 strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured 
 pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in 
 different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based 
 methods. It is applicable to both randomized experiments and observational studies.
Provides access to well-documented medical datasets for teaching.
    Featuring several from the Teaching of Statistics in the Health Sciences 
    website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical
    research, some reformatted and extended from existing R packages, 
    and some data donations.
Generates risk estimates and comorbidity flags from ICD-9-CM
    codes available in administrative medical datasets. The package supports
    the Charlson Comorbidity Index, the Elixhauser Comorbidity
    classification, the Revised Cardiac Risk Index, and the Risk Stratification
    Index.  Methods are table-based, fast, and use the 'plyr' package, so
    parallelization is possible for large jobs. Also includes a sample of
    real ICD-9 data for 100 patients from a publicly available dataset.
Publicly available data from Medicare frequently requires extensive
    initial effort to extract desired variables and merge them; this package
    formalizes the techniques I've found work best. More information on the 
    Medicare program, as well as guidance for the publicly available data this package 
    targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.
A simple meditation timer that logs session information.
Prints a random quote from Marcus Aurelius' book Meditations.
Set of functions working with survey data in the format of the MEDITS project <https://www.sibm.it/SITO%20MEDITS/principaleprogramme.htm>. In this version, functions use TA, TB and TC tables respectively containing haul, catch and aggregated biological data.
Contains a dataset of morphological and structural features of 'Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (<http://www.instituteofayurveda.org/plants/>). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka.
This toolbox allows you to do simple mediation and moderation 
    analysis. It is also available as a module for 'jamovi' 
    (see <https://www.jamovi.org> for more information). 'Medmod' is based 
    on the 'lavaan' package by Yves Rosseel. You can find an in depth tutorial 
    on the 'lavaan' model syntax used for this package on 
    <http://lavaan.ugent.be/tutorial/index.html>.
This package contains the functions used to perform some
        confidence statistics based in population median.
An increasingly important source of health-related bibliographic 
    content are preprints - preliminary versions of research articles that have
    yet to undergo peer review. The two preprint repositories most relevant to 
    health-related sciences are medRxiv <https://www.medrxiv.org/> and
    bioRxiv <https://www.biorxiv.org/>, both of which are operated by the Cold 
    Spring Harbor Laboratory. 'medrxivr' provides programmatic access to the 
    'Cold Spring Harbour Laboratory (CSHL)' API <https://api.biorxiv.org/>,
    allowing users to easily download medRxiv and bioRxiv preprint metadata
    (e.g. title, abstract, publication date, author list, etc) into R. 
    'medrxivr' also provides functions to search the downloaded preprint records 
    using regular expressions and Boolean logic, as well as helper functions 
    that allow users to export their search results to a .BIB file for easy 
    import to a reference manager and to download the full-text PDFs of 
    preprints matching their search criteria.
Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics. Sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
It is a computer tool to conduct linear mediation analysis for complex surveys using multi-stage sampling and Balanced Repeated Replication (BRR). Specifically, the mediation analysis method using balanced repeated replications was proposed by Mai, Ha, and Soulakova (2019) <DOI:10.1080/10705511.2018.1559065>. The current version can only handle continuous mediators and outcomes. The development of 'MedSurvey' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of MedSurvey do not necessarily represent the policy of the ALSAC.
Allows management of 'Meetup' groups via the <https:www.meetup.com/meetup_api/>.
    Provided are a set of functions that enable fetching information of joined meetups, attendance,
    and members. This package requires the use of an API key.
A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
An S4 update of the 'mefa' package
  using sparse matrices for enhanced efficiency.
  Sparse array-like objects are supported via
  lists of sparse matrices.
Uses as input genetic data that have been reformatted and
     stored in a 'SQLite' database; this database is initially created
     by the standalone 'mega2' C++ program (available freely from
     <https://watson.hgen.pitt.edu/register/>). Loads and manipulates
     data frames containing genotype, phenotype, and family
     information from the input 'SQLite' database, and decompresses
     needed subsets of the genotype data, on the fly, in a memory
     efficient manner.  We have also created several more functions
     that illustrate how to use the data frames as well as perform
     useful tasks: these permit one to run the 'pedgene' package to
     carry out gene-based association tests on family data using
     selected marker subsets, to run the 'SKAT' package to carry out
     gene-based association tests using selected marker subsets, to
     run the 'famSKATRC' package to carry out gene-based association
     tests on families (optionally) and with rare or common variants
     using selected marker subsets, to output the 'Mega2R' data as a
     VCF file and related files (for phenotype and family data), and
     to convert the data frames into CoreArray Genomic Data Structure
     (GDS) format.
Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.
Exploratory model analysis.  Fit and graphical
    explore ensembles of linear models.
Create variable width bar charts i.e.
    "bar mekko" charts to include important quantitative context.
    Closely related to mosaic, spine (or spinogram), matrix,
    submarine, olympic, Mondrian or product plots and tree maps.
Multiple hypothesis testing via empirical likelihood in general 
    block designs. It provides asymptotic generalized family-wise 
    error rate control.
The goal of 'meltr' is to provide a fast and friendly way to
    read non-rectangular data, such as ragged forms of csv (comma-separated
    values), tsv (tab-separated values), and fwf (fixed-width format) files.
Framework for merging and disambiguating event data based on spatiotemporal co-occurrence and secondary event characteristics. It can account for intrinsic "fuzziness" in the coding of events, varying event taxonomies and different geo-precision codes.
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. 'memapp' is a web application created in the Shiny framework for the 'mem' R package.
The word 'Meme' was originated from the book, 'The Selfish Gene', authored by Richard Dawkins (1976).
             It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity.
             The internet memes are captioned photos that are intended to be funny, ridiculous.
             Memes behave like infectious viruses and travel from person to person quickly through social media.
             The 'meme' package allows users to make custom memes.
Generates internet memes that optionally include a superimposed inset plot and other atypical features, 
    combining the visual impact of an attention-grabbing meme with graphic results of data analysis.
    The package differs from related packages that focus on imitating and reproducing standard memes.
    Some packages do this by interfacing with online meme generators whereas others achieve this natively.
    This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites.
    It reads images directly from local files or via URL and meme generation is done by the package.
    While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on 
    allowing for non-standard meme layouts and hybrids of memes mixed with graphs.
    While this package can be used to make basic memes like an online meme generator would produce, 
    it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting 
    foreground graphs of data analysis results.
    The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes 
    rather than restricting to the standard top and/or bottom text placement. 
    This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels.
    The package offers a selection of templates for graph placement and appearance with respect to the underlying meme.
    Graph templates also permit additional template-specific customization.
    Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 
    'magick' is not required unless gif functionality is desired.
Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation.  Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.
A simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation.
An infrastructure for the management of survey data including
        value labels, definable missing values, recoding of variables,
        production of code books, and import of (subsets of) 'SPSS' and
        'Stata' files is provided. Further, the package allows to produce
        tables and data frames of arbitrary descriptive statistics and
        (almost) publication-ready tables of regression model
        estimates, which can be exported to 'LaTeX' and HTML.
A simple in-memory, LRU cache that can be wrapped
    around any function to memoize it. The cache can be keyed on a hash of the
    input data (using 'digest') or on pointer equivalence.
A simple way to memoize function results to improve performance by eliminating unnecessary computation or data retrieval activities.
Producing high-quality documents suitable for publication directly from R is made possible by the R Markdown ecosystem.
  'memoiR' makes it easy.
  It provides templates to knit memoirs, articles and slideshows with helpers to publish the documents on GitHub Pages and activate continuous integration.
Cache the results of a function so that when you
    call it again with the same arguments it returns the previously computed
    value.
A 'rmarkdown' template that supports company logo, contact info, 
    watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar 
    'HTML' theme will be added in the future.
Tools to quantify ecological memory in long time-series with Random Forest models (Breiman 2001 <doi:10.1023/A:1010933404324>) fitted with the 'ranger' library (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>). Particularly oriented to palaeoecological datasets and simulated pollen curves produced by the 'virtualPollen' package, but also applicable to other long time-series involving a set of environmental drivers and a biotic response.
Data sets and sample analyses from Pinheiro and Bates,
  "Mixed-effects Models in S and S-PLUS" (Springer, 2000).
How much ram do you need to store a 100,000 by 100,000 matrix?
    How much ram is your current R session using? How much ram do you even have?
    Learn the scintillating answer to these and many more such questions with
    the 'memuse' package.
Encodes several methods for performing Mendelian randomization
    analyses with summarized data. Summarized data on genetic associations with the
    exposure and with the outcome can be obtained from large consortia. These data
    can be used for obtaining causal estimates using instrumental variable methods.
Based on the input data an n-dimensional cube with sub cells of user specified side length is created.
    The number of sample points which fall in each sub cube is counted, and with the cell volume and overall sample
    size an empirical probability can be computed. A number of cubes of higher resolution can be superimposed. The
    basic method stems from J.L. Bentley in "Multidimensional Divide and Conquer".
    J. L. Bentley (1980) <doi:10.1145/358841.358850>.
    Furthermore a simple kernel density estimation method is made available, as well as an expansion of Bentleys
    method, which offers a kernel approach for the grid method.
Defines the classes used to explore, cluster and
  visualize distance matrices, especially those arising from binary
  data. See Abrams and colleagues, 2021, <doi:10.1093/bioinformatics/btab037>.
Compute case-wise and cluster-wise derivative for mixed effects models with respect to fixed effects parameter, random effect (co)variances, and residual variance. This material is partially based on work supported by the National Science Foundation under Grant Number 1460719.
Package to carry out merged block randomization (Van der Pas (2019), <doi:10.1177/1740774519827957>), a restricted randomization method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicentre trials. It can be used for more than two groups or unequal randomization ratios.
Aggregates a set of trees with the same leaves to create a consensus tree. The trees are typically 
  obtained via hierarchical clustering, hence the hclust format is used to encode both the aggregated trees and the final 
  consensus tree. The method is exact and proven to be O(nqlog(n)), n being the individuals and q being the number of trees to aggregate.
Fits linear, non-linear, and user-defined mixed effects regression models following 
  the framework developed by Crowther (2017) <arXiv:1710.02223>. 'merlin' can fit multivariate 
  outcome models of any type, each of which could be repeatedly measured (longitudinal), with 
  any number of levels, and with any number of random effects at each level. Standard 
  distributions/models available include the Bernoulli, Gaussian, Poisson, beta, 
  negative-binomial, and time-to-event/survival models include the exponential, 
  Gompertz, Royston-Parmar, Weibull and general hazard model. 'merlin' provides a 
  flexible predictor syntax, allowing the user to define variables, random effects, 
  spline and fractional polynomial functions, functions of 
  other outcome models, and any interaction between each of them. Non-linear and 
  time-dependent effects are seamlessly incorporated into the predictor. 'merlin' 
  allows multivariate normal random effects, which are integrated out using Gaussian 
  quadrature or Monte-Carlo integration. Note, 'merlin' is based on the 'Stata' package 
  of the same name, described in Crowther (2018) <arXiv:1806.01615>.
N>=3 methods are used to measure each of n items. 
 The data are used to estimate simultaneously systematic error (bias)
 and random error (imprecision). Observed measurements for each method
 or device are assumed to be linear functions of the unknown true values
 and the errors are assumed normally distributed. Maximum likelihood 
 estimation is used for the imprecision standard deviation estimates. 
 Pairwise calibration curves and plots can be easily generated.
Provides methods for extracting results from mixed-effect model
    objects fit with the 'lme4' package. Allows construction of prediction intervals
    efficiently from large scale linear and generalized linear mixed-effects models.
Fits Bayesian spatial or spatiotemporal multivariate regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889> and Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>. Funded by ERC grant 856506 and NIH grant R01ES028804.
A mixed collection of useful and semi-useful diverse
    statistical functions, some of which may even be referenced in
    The R Primer book.
Provides tools for creating and issuing nicely-formatted
    text within R diagnostic messages and those messages given during
    warnings and errors. The formatting of the messages can be
    customized using templating features. Issues with singular and
    plural forms can be handled through specialized syntax.
Contains a set of tools for constructing and coercing
  into and from the messydt class. 
  This date class implements ISO 8601-2:2019(E) and
  allows regular dates to be annotated 
  to express unspecified date components,
  approximate or uncertain date components, 
  date ranges, and sets of dates. 
  This is useful for describing and analysing temporal information,
  whether historical or recent, where date precision may vary.
Evaluate covariate balance of a matched sample, and suggest a new feature that the new match should balance if the current match is not satisfactory (only available with method being generalized Kolmogorov-Smirnov statistic on a finite set, GFKS for short).
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015):
 - fixed effect and random effects meta-analysis;
 - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
 - statistical tests and trim-and-fill method to evaluate bias in meta-analysis;
 - import data from 'RevMan 5';
 - prediction interval, Hartung-Knapp method for random effects model;
 - cumulative meta-analysis and leave-one-out meta-analysis;
 - meta-regression;
 - generalised linear mixed models;
 - produce forest plot summarising several (subgroup) meta-analyses.
Implement meta-analyses for simultaneously estimating individual means with shrinkage,
 isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm.
 This methodology is published in Taketomi et al.(2021) <doi:10.3390/axioms10040267>.
Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <https://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).
An interactive application to visualise meta-analysis data as a
    physical weighing machine. The interface is based on the Shiny web application
    framework, though can be run locally and with the user's own data.
A package for meta-analysis of genome-wide association
    scans between quantitative or binary traits and SNPs
The sample mean and standard deviation are two commonly used statistics in meta-analyses, 
    but some trials use other summary statistics such as the median and quartiles to report the results. 
    Therefore, researchers need to transform those information back to the sample mean and 
    standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.
Computes the posterior model probabilities for standard meta-analysis models 
    (null model vs. alternative model assuming either fixed- or random-effects, respectively).
    These posterior probabilities are used to estimate the overall mean effect size 
    as the weighted average of the mean effect size estimates of the random- and 
    fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & 
    Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define 
    a wide range of non-informative or informative priors for the mean effect size 
    and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, 
    meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) 
    priors can be fitted and tested. This allows to compute Bayes factors and 
    perform Bayesian model averaging across random- and fixed-effects meta-analysis 
    with and without moderators. For a primer on Bayesian model-averaged meta-analysis, 
    see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2020, <doi:10.31234/osf.io/97qup>).
Data sets from a variety of biological sample matrices, 
    analysed using a number of mass spectrometry based metabolomic analytical techniques.
    The example data sets are stored remotely using GitHub releases 
    <https://github.com/aberHRML/metaboData/releases> which can be accessed from R using the package.
    The package also includes the 'abr1' FIE-MS data set from the 'FIEmspro' package <https://users.aber.ac.uk/jhd/> <doi:10.1038/nprot.2007.511>.
Fits probabilistic principal components analysis,
        probabilistic principal components and covariates analysis and
        mixtures of probabilistic principal components models to
        metabolomic spectral data.
Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. 
    It contains all the data and functions to reproduce the analysis.
    "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis".
    Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) <doi:10.1249/MSS.0000000000002506>.
An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. 
 Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. 
 Several methods were implemented to reduce the metabolomics matrix such as the  principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , 
 the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the 
 elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. 
 Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. 
 Large scale cross validation can be performed in  order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
Automatic metabolite annotation from Liquid Chromatography-Mass Spectrometry (LC-MS and LC-MS/MS DIA) analysis.
A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification.
Takes QC signal for each day and normalize metabolomic
    data that has been acquired in a certain period of time. At least
    three QC per day are required.
Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) <doi:10.1037/hea0000018> and Li et al. (2017) <doi:10.1111/bmsp.12088>.
Utilizes 11 peak quality metrics and 8 diverse machine learning algorithms to build a classifier for the automatic
  assessment of peak integration quality of peaks from untargeted metabolomics analyses.
  The 12 peak quality metrics were adapted from those defined in the following references:
  Zhang, W., & Zhao, P.X. (2014) <doi:10.1186/1471-2105-15-S11-S5>
  Toghi Eshghi, S., Auger, P., & Mathews, W.R. (2018) <doi:10.1186/s12014-018-9209-x>.
Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.
A set of tools for parsing, manipulating, and graphing data
    classified by a hierarchy (e.g. a taxonomy).
Functions to analyze coherence, boundary clumping, and turnover
    following the pattern-based metacommunity analysis of Leibold and Mikkelson
    2002  <doi:10.1034/j.1600-0706.2002.970210.x>. The package also includes 
		functions to visualize ecological networks, and to calculate modularity 
		as a replacement to boundary clumping.
Implements routines for metagenome sample taxonomy assignments collection, 
    aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2, 
    BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots 
    comparing taxa abundances across projects.
Visualize 'confounder' control in meta-analysis.
    'metaconfoundr' is an approach to evaluating bias in studies used in
    meta-analyses based on the causal inference framework. Study groups
    create a causal diagram displaying their assumptions about the
    scientific question. From this, they develop a list of important
    'confounders'. Then, they evaluate whether studies controlled for
    these variables well. 'metaconfoundr' is a toolkit to facilitate this
    process and visualize the results as heat maps, traffic light plots,
    and more.
Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP)
        meta-analytical approaches with correlation coefficients as
        effect sizes.
There are two functions-meta2d and meta3d for
    detecting rhythmic signals from time-series datasets. For analyzing
    time-series datasets without individual information, 'meta2d' is 
    suggested, which could incorporates multiple methods from ARSER, 
    JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For 
    analyzing time-series datasets with individual information, 'meta3d' is 
    suggested, which takes use of any one of these three methods to analyze 
	time-series data individual by individual and gives out integrated values 
    based on analysis result of each individual.
A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.
Provides parsing functionality for over 30 metabolomics databases, with most available without having to create an account on given websites. 
    Once parsed, calculates given adducts and isotope patterns and inserts into one big database which can be used to annotate unknown m/z values.
    Furthermore, formulas can be predicted for a given m/z, and these can be matched to ChemSpider, PubChem, SUPERNATURAL II, KNApSAcK and ChemIDplus for further annotation.
    Current databases available: HMDB, ChEBI, LMDB, BMDB, MCDB, ECMDB, Wikidata, mVOC, VMH, T3DB, Exposome Explorer, FooDB, MetaCyc (requires account), DrugBank (requires account), ReSPECT, MaConDa, Blood Exposome DB, KEGG, SMPDB, LIPID MAPS, MetaboLights, DimeDB, Phenol Explorer, MassBank, YMDB, PAMDB, ANPDB, Metabolomics Workbench, PharmGKB, Reactome, mVOC and STOFF.
    Featured in the 'MetaboShiny' package (Wolthuis, J. (2019) <doi:10.1007/s11306-020-01717-8>).
High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms.
Metadynamics is a state of the art biomolecular simulation technique.
  'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes
  it possible to perform metadynamics using various simulation codes. The results of
  metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package
  'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package.
  It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037>
  to calculate a free energy surface from hills. Minima can be located and plotted on
  the free energy surface. Transition states can be analyzed by Nudged Elastic Band
  method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy
  surfaces, minima and transition paths can be plotted to produce publication quality
  images.
Metadynamics is a state of the art biomolecular simulation technique.
  'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes
  it possible to perform metadynamics using various simulation codes. The results of
  metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package
  'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package.
  As an addendum, 'metadynaminer3d' is used to visualize 3D hills. It uses
  a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037>
  to calculate a free energy surface from hills. Minima can be located and plotted on
  the free energy surface. Free energy surfaces and minima can be plotted to produce
  publication quality images.
Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
The metafolio R package is a tool to simulate salmon
    metapopulations and apply financial portfolio optimization concepts. The
    package accompanies the paper 'Portfolio conservation of metapopulations
    under climate change'. See citation("metafolio").
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
Fused lasso method to cluster and estimate regression coefficients
    of the same covariate across different data sets when a large number of
    independent data sets are combined. Package supports Gaussian, binomial,
    Poisson and Cox PH models.
Meta-analysis of generalized additive
    models and generalized additive mixed models. A typical use case is
    when data cannot be shared across locations, and an overall meta-analytic
    fit is sought. 'metagam' provides functionality for removing individual
    participant data from models computed using the 'mgcv' and 'gamm4' packages such
    that the model objects can be shared without exposing individual data.
    Furthermore, methods for meta-analysing these fits are provided. The implemented
    methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>,
    extending previous works by Schwartz and Zanobetti (2000)
    and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.
Functionalities for facilitating systematic reviews, data
    extractions, and meta-analyses. It includes a GUI (graphical user interface)
    to help screen the abstracts and titles of bibliographic data; tools to assign
    screening effort across multiple collaborators/reviewers and to assess inter-
    reviewer reliability; tools to help automate the download and retrieval of
    journal PDF articles from online databases; figure and image extractions 
    from PDFs; web scraping of citations; automated and manual data extraction 
    from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for
    Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools
    to fill gaps in incomplete or missing study parameters; generation of random
    effects sizes for Hedges' d, log response ratio, odds ratio, and correlation
    coefficients for Monte Carlo experiments; covariance equations for modelling
    dependencies among multiple effect sizes (e.g., effect sizes with a common
    control); and finally summaries that replicate analyses and outputs from 
    widely used but no longer updated meta-analysis software (i.e., metawin).
	Funding for this package was supported by National Science Foundation (NSF) 
	grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) 
	Facilitating systematic reviews, data extraction and meta-analysis with the 
	metagear package for R. Methods in Ecology and Evolution 7, 323-330 
	<doi:10.1111/2041-210X.12472>.
According to a phenomenon known as "the wisdom of the crowds," 
    combining point estimates from multiple judges often provides a 
    more accurate aggregate estimate than using a point estimate from
    a single judge. However, if the judges use shared information in 
    their estimates, the simple average will over-emphasize this common
    component at the expense of the judges’ private information. 
    Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds
    Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions"
    <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes
    a procedure for calculating a weighted average of the judges’ individual
    estimates such that resulting aggregate estimate appropriately combines
    the judges' collective information within a single estimation problem. 
    The authors use both simulation and data from six experimental studies
    to illustrate that the weighting procedure outperforms existing averaging-like
    methods, such as the equally weighted average, trimmed average, and median.
    This aggregate estimate -- know as "the knowledge-weighted estimate" --
    inputs a) judges' estimates of a continuous outcome (E) and 
    b) predictions of others' average estimate of this outcome (P).  
    In this R-package, the function knowledge_weighted_estimate(E,P) 
    implements the knowledge-weighted estimate. Its use is illustrated
    with a simple stylized example and on real-world experimental data.
A nature-inspired metaheuristics algorithm based on the pollination
    process of flowers. This R package makes it easy to implement the standard
    flower pollination algorithm for every user. The algorithm was first developed
    by Xin-She Yang in 2012 (<DOI:10.1007/978-3-642-32894-7_27>).
An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>),  whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).
An ensemble meta-prediction framework to integrate multiple regression 
    models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) 
    <arXiv:2010.09971>.
    A meta-analysis framework along with two weighted estimators as the ensemble 
    of empirical Bayes estimators, which combines the estimates from the different 
    external models. The proposed framework is flexible and robust in the ways 
    that (i) it is capable of incorporating external models that use a slightly 
    different set of covariates; (ii) it is able to identify the most relevant 
    external information and diminish the influence of information that is less 
    compatible with the internal data; and (iii) it nicely balances the bias-variance 
    trade-off while preserving the most efficiency gain. The proposed estimators 
    are more efficient than the naive analysis of the internal data and other 
    naive combinations of external estimators.
A pipeline for the meta-analysis  of gene expression data. We have
	assembled several analysis and plot functions to
    perform integrated multi-cohort analysis of gene expression data (meta-
    analysis). Methodology described in:
	<http://biorxiv.org/content/early/2016/08/25/071514>.
A set of tools to foster the development of reproducible analytical workflow by simplifying the download of data and 
    metadata from 'DataONE' (<https://www.dataone.org>) and easily importing this information into R.
Tools to generate random landscape graphs, evaluate species
    occurrence in dynamic landscapes, simulate future landscape occupation and
    evaluate range expansion when new empty patches are available (e.g. as a
    result of climate change). References: Mestre, F., Canovas, F., Pita, R., 
    Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., 
    Risk, B., Mira, A., Beja, P., Pita, R. (2017) 
    <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja,
    P. (2020) <doi:10.1186/s12898-019-0273-5>.
First- and higher-order likelihood inference in
        meta-analysis and meta-regression models.
Identify time intervals of differentially abundant metagenomic features in longitudinal studies (Metwally AA, et al., Microbiome, 2018 <doi:10.1186/s40168-018-0402-y>).
Combines either p-values or modified effect sizes from different
    studies to find differentially expressed genes
Implements several methods to meta-analyze studies that report the 
    sample median of the outcome. When the primary studies are one-group 
    studies, the methods of McGrath et al. (2019) <doi:10.1002/sim.8013> can 
    be applied to estimate the pooled median. In the two-group context, the 
    methods of McGrath et al. (2020) <doi:10.1002/bimj.201900036> can be 
    applied to estimate the pooled raw difference of medians across groups.
Creates data with identical statistics (metamers) using an iterative 
   algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.
Generalized Additive Model for Location, Scale and Shape (GAMLSS) 
    with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data 
    (with various options for data transformation/normalization to address compositional effects) and 
    random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies 
    are implemented. 
    Random Forest model to predict microbiome age based on relative abundances of  
    shared bacterial genera with the Bangladesh data (Subramanian et al 2014), 
    comparison of multiple diversity indexes using linear/linear mixed effect models 
    and some data display/visualization are also implemented.
    The reference paper is published by 
    Ho NT, Li F, Wang S, Kuhn L (2019) <doi:10.1186/s12859-019-2744-2> .
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Resolves complex metagenomic mixtures by analysing
    deep sequencing data, using a mixture model based approach.
    The use of parallel Monte Carlo Markov chains for the exploration
    of the species space enables the identification of the set
    of species more likely to contribute to the mixture.
Performs stability analysis of multi-environment trial data
    using parametric and non-parametric methods. Parametric methods
    includes Additive Main Effects and Multiplicative Interaction (AMMI)
    analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence
    by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot
    analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric
    adaptability index by Mohammadi & Amri (2008)
    <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart
    & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>,
    genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's
    (2004) method, power law residuals (POLAR) statistics by Doring et al.
    (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of
    variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>,
    stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>,
    weighted average of absolute scores by Olivoto et al. (2019a)
    <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by
    Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>.
    Non-parametric methods includes superiority index by Lin & Binns
    (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
    stability by Huehn (1990)
    <https://link.springer.com/article/10.1007/BF00024241>, TOP third
    statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for
    computing biometrical analysis such as path analysis, canonical
    correlation, partial correlation, clustering analysis, and tools for
    inspecting, manipulating, summarizing and plotting typical
    multi-environment trial data are also provided.
Novel method to unbiasedly include studies with Non-statistically Significant Unreported Effects (NSUEs) in a meta-analysis <doi:10.1001/jamapsychiatry.2015.2196> and <doi:10.1177/0962280218811349>. Briefly, the method first calculates the interval where the unreported effects (e.g. t-values) should be according to the threshold of statistical significance used in each study. Afterwards, maximum likelihood techniques are used to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance and statistical significance bounds. Finally, a restricted-maximum likelihood random-effects meta-analysis is separately conducted for each set of imputations, and estimations from these meta-analyses are pooled. Please read the reference in 'metansue' for details of the procedure.
The canonical way to perform meta-analysis involves using effect sizes.
   When they are not available this package provides a number of methods for
   meta-analysis of significance values including the methods of Edgington, Fisher,
   Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results;
   and a routine for graphical display.
Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.
Perform the Meta-analysis for Pathway Enrichment (MAPE) 
		methods introduced by Shen and Tseng (2010). It includes functions to
        automatically perform MAPE_G (integrating multiple studies at
        gene level), MAPE_P (integrating multiple studies at pathway
        level) and MAPE_I (a hybrid method integrating MAEP_G and
        MAPE_P methods). In the simulation and real data analyses in
        the paper, MAPE_G and MAPE_P have complementary advantages and
        detection power depending on the data structure. In general,
        the integrative form of MAPE_I is recommended to use. In the
        case that MAPE_G (or MAPE_P) detects almost none pathway, the
        integrative MAPE_I does not improve performance and MAPE_P (or
        MAPE_G) should be used. Reference: Shen, Kui, and George C
        Tseng. Meta-analysis for pathway enrichment analysis when
        combining multiple microarray studies.Bioinformatics (Oxford,
        England) 26, no. 10 (April 2010): 1316-1323.
        doi:10.1093/bioinformatics/btq148.
        http://www.ncbi.nlm.nih.gov/pubmed/20410053.
Designs plots in terms of core structure.  See 'example(metaplot)'.
 Primary arguments are (unquoted) column names; order and type (numeric or not)
 dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
 and any conditioning variables to metaplot() to generate density plots, boxplots, 
 mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() 
 to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' 
 are honored, producing fully annotated plots with minimal effort. Attribute 'guide' 
 is typically units, but may be encoded() to provide interpretations of categorical 
 values (see '?encode').  Utility unpack() transforms scalar column attributes to row 
 values and pack() does the reverse, supporting tool-neutral storage of metadata along 
 with primary data. The package supports customizable aesthetics such as such as reference 
 lines, unity lines, smooths, log transformation, and linear fits. The user may choose
 between trellis and ggplot output. Compact syntax and integrated metadata promote workflow 
 scalability.
Creates crosshairs plots to summarize and analyse
    meta-analysis results. In due time this package will contain code
    that will create other kind of meta-analysis graphs.
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).
Provides an interface to 'MetaPost' (Hobby, 1998) 
    <http://www.tug.org/docs/metapost/mpman.pdf>.
    There are functions to generate an R description of a 'MetaPost'
    curve, functions to generate 'MetaPost' code from an R description,
    functions to process 'MetaPost' code, and functions to read
    solved 'MetaPost' paths back into R.
A simple and effective tool for computing and visualizing statistical power for meta-analysis,
    including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, 
    test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, 
    and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.
Set of tools for descriptive analysis of metaproteomics data generated from high-throughput 
    mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, 
    expressed as spectral counts, and to manipulate them in groups of metaproteins. This information 
    can be represented using multiple visualization functions to portray the global metaproteome 
    landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, 
    taxonomic levels and/or functional annotation. The provided tools allow to implement flexible 
    analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.
User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings.
 - Replicability-analysis for fixed-effects and random-effect meta analysis: 
 - r(u)-value;
 - lower bounds on the number of studies with replicated positive and\or negative effect;
 - Allows detecting inconsistency of signals;
 - forest plots with the summary of replicability analysis results;
 - Allows Replicability-analysis with or without the common-effect assumption.
R implementation of a multivariate meta-analysis of randomized controlled trials (RCT) with the difference in restricted mean survival times (RMSTD). Use this package with individual patient level data from an RCT for a time-to-event outcome to determine combined effect estimates according to 4 methods: 1)  a univariate meta-analysis using observed treatment effects, 2) a univariate meta-analysis using effects predicted by fitted Royston-Parmar flexible parametric models, 3) multivariate meta-analysis with analytically derived covariance, 4) multivariate meta-analysis with bootstrap derived covariance. This package computes all combined effects and provides an RMSTD curve with combined effect estimates and their confidence intervals.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. <doi:10.1016/j.jmp.2019.102297>.
A collection of functions for conducting meta-analysis using a
             structural equation modeling (SEM) approach via the 'OpenMx' and
             'lavaan' packages. It also implements various procedures to
			 perform meta-analytic structural equation modeling on the
             correlation and covariance matrices,
			 see Cheung (2015) <doi:10.3389/fpsyg.2014.01521>.
The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis':
 - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>;
 - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>;
 - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>;
 - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>;
 - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.
Performs Bayesian meta-analysis and model-based meta-analysis using 'Stan'. 
             Includes binomial-normal hierarchical models and option to use weakly informative priors for the
             heterogeneity parameter and the treatment effect parameter which are described in 
             Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.
If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. 
      The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 
      'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. 
      It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. 
      It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. 
      See for application: Nolte I.M. et al. (2017); <doi: 10.1038/ejhg.2017.50>.
To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity.
Fits and tests meta regression models and generates a
 number of useful test statistics: next to t- and z-tests, the likelihood ratio,
 bartlett corrected likelihood ratio and permutation tests are performed on
 the model coefficients.
Create meta tags for 'R Markdown' HTML documents and 'Shiny'
    apps for customized social media cards, for accessibility, and quality
    search engine indexing. 'metathis' currently supports HTML documents
    created with 'rmarkdown', 'shiny', 'xaringan', 'pagedown', 'bookdown',
    and 'flexdashboard'.
This package allows doing meta-analysis for primary studies with classification outcomes in order to evaluate systematically the accuracies of classifiers, namely, the diagnostic tests. It provides functions to fit the bivariate model of Reitsma et al.(2005). Moreover, if the reference employed in the classification process isn't a gold standard, its deficit can be detected and its influence to the underestimation of the diagnostic test's accuracy can be corrected, as described in Botella et al.(2013).
Contains functions to estimate the proportion of effects stronger than a threshold
    of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval),
    to make effect size conversions (r_to_d, r_to_z, z_to_r, d_to_logRR), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from 
    existing meta-analyses for re-analysis (scrape_meta, parse_CI_string, ci_to_var).
Collection of functions to compute covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).
A compilation of functions to create visually appealing and information-rich 
    plots of meta-analytic data using 'ggplot2'. Currently allows to create forest plots, 
    funnel plots, and many of their variants, such as rainforest plots, thick forest plots, 
    additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities 
    for visual inference with the funnel plot in the context of meta-analysis are provided.
A tool for implementing so called 'deft' approach
    (see Fisher, David J., et al. (2017) <DOI:10.1136/bmj.j573>) and model
    visualization.
Spatio-temporal geostatistical mapping of meteorological data. Global spatio-temporal models calculated using publicly available data are stored in package.
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs>, MeteoGalicia <https://www.meteogalicia.gal/web/modelos/threddsIndex.action>, NAM <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-mesoscale-forecast-system-nam>, and RAP <https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/rapid-refresh-rap>.
meteogram is a collection of programs for plotting
        meteograms for meteorological data such as atmospheric cross
        section, temperatures plots.
Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].
A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modelling, particularly crop and crop disease modeling.
Provides a unified mixture-of-experts (ME) modeling and 
    estimation framework with several original and flexible ME models to 
    model, cluster and classify heterogeneous data in many complex 
    situations where the data are distributed according to non-normal, 
    possibly skewed distributions, and when they might be corrupted by 
    atypical observations. Mixtures-of-Experts models for complex and 
    non-normal distributions ('meteorits') are originally introduced and 
    written in 'Matlab' by Faicel Chamroukhi. The references are mainly the 
    following ones. The references are mainly the following ones.
    Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>.
    Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>.
    Chamroukhi F. (2015) <arXiv:1506.06707>.
    Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>.
    Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>.
    Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>.
    Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.
Access to different Spanish meteorological stations data services and APIs (AEMET, SMC, MG, 
  Meteoclimatic...).
Fit and plot macroecological patterns predicted by the Maximum
    Entropy Theory of Ecology (METE).
Provides a function me.test() to test equality of distributions when observations are subject to measurement errors.
Functions for selection of visual meteor data, calculations of Zenithal Hourly Rate (ZHR) and population index, graphics of population index, ZHR and magnitude distribution.
An adaptation of the multi-variable stochastic weather generator proposed in 'Rglimclim' to perform gap-filling and temporal extension at sub-daily resolution. Simulation is performed based on large scale variables and climatic observation data that could be generated from different gauged stations having geographical proximity. SWG relies on reanalyses. Multi-variable dependence is taking into account by using the decomposition of the product rule (in statistics) into conditional probabilities. See <https://hal.archives-ouvertes.fr/hal-02554676>.
Meth27QC is a tool for analyzing Illumina Infinium
        HumanMethylation27 BeadChip Data and generating QC reports.
        This package allows users quickly assess data quality of the
        Assay. Users can evaluate the data quality in the way that
        Illumina GenomeStudio/BeadStudio recommended based on the
        control probes. The package reads files exported from
        GenomeStudio/BeadStudio software, generating intensity and
        standard deviation plots grouped by the types of the control
        probes. Meth27 carries 40 control probes for staining,
        hybridization, target removal, extension, bisulfite conversion,
        specificity, negative and non-polymorphic controls. Details of
        those control probes can be found in the Infinium Assay for
        Methylation Protocol Guide from Illumina.We also used the other
        non-control probes to plot intensity of detected genes, signal
        average for green and red. Outliers can be identified.
Methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.
Identify and rank CpG DNA methylation conservation along the human 
    genome. Specifically it includes bootstrapping methods to provide ranking 
    which should adjust for the differences in length as without it short 
    regions tend to get higher conservation scores.
Implementation of the methodology from the paper entitled
    "Effective plots to assess bias and precision in method comparison studies"
    published in Statistical Methods in Medical Research, P. Taffé (2018) <doi:10.1177/0962280218759693>.
It plots the output from Methpup (https://github.com/XinYang6699/Methpup)
Provides a univariate and several high dimensional multivariate test statistics for detecting differentially methylated regions based on MethylCap-seq data.
Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package contains a collection of R functions which can be used to remove unwanted variation and obtain normalized metabolomics data.
Meteorological Tools following the FAO56 irrigation paper of Allen et al. (1998) [1].
    Functions for calculating:
    reference evapotranspiration (ETref),
    extraterrestrial radiation (Ra),
    net radiation (Rn),
    saturation vapor pressure (satVP),
    global radiation (Rs),
    soil heat flux (G),
    daylight hours,
    and more.
    [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9).
Provides a number of functions to facilitate the handling and production of reports using time series data.
    The package was developed to be understandable for beginners, so some functions aim to transform processes that would be
    complex into functions with a few lines. The main advantage of using the 'metools' package is the ease of producing reports and
    working with time series using a few lines of code, so the code is clean and easy to understand/maintain. 
    Learn more about the 'metools' at <https://metoolsr.wordpress.com>.
Split an untargeted metabolomics data set into a set of likely true 
    metabolites and a set of likely measurement artifacts. This process involves 
    comparing missing rates of pooled plasma samples and biological samples. The 
    functions assume a fixed injection order of samples where biological samples are 
    randomized and processed between intermittent pooled plasma samples. By comparing 
    patterns of missing data across injection order, metabolites that appear in blocks
    and are likely artifacts can be separated from metabolites that seem to have 
    random dispersion of missing data. The two main metrics used are: 1. the number of 
    consecutive blocks of samples with present data and 2. the correlation of missing rates 
    between biological samples and flanking pooled plasma samples.
Many useful functions and extensions for dealing
    with meteorological data in the tidy data framework. Extends 'ggplot2'
    for better plotting of scalar and vector fields and provides commonly
    used analysis methods in the atmospheric sciences.
An implementation of evaluation metrics in R that are commonly
             used in supervised machine learning. It implements metrics for
             regression, time series, binary classification, classification,
             and information retrieval problems. It has zero dependencies and
             a consistent, simple interface for all functions.
Provides an 'htmlwidgets' interface to the
    'MetricsGraphics.js' ('D3'-based) charting library which is geared towards
    displaying time-series data. Chart types include line charts, scatterplots,
    histograms and rudimentary bar charts. Support for laying out multiple charts
    into a grid layout is also provided. All charts are interactive and many
    have an option for line, label and region annotations.
Provides weighted versions of several metrics, scoring
    functions and performance measures used in machine learning, including
    average unit deviances of the Bernoulli, Tweedie, Poisson, and Gamma
    distributions, see Jorgensen B. (1997, ISBN: 978-0412997112). The
    package also contains a weighted version of generalized R-squared, see
    e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore,
    'dplyr' chains are supported.
The Washington Metropolitan Area Transit Authority
    is a government agency operating light rail and passenger buses in the
    Washington D.C. area. With a free developer account, access their
    'Metro Transparent Data Sets API' <https://developer.wmata.com/> to
    return data frames of transit data for easy analysis.
Provides classes and calculation and plotting functions 
 for metrology applications, including measurement uncertainty estimation
 and inter-laboratory metrology comparison studies.
Learning and using the Metropolis algorithm for
    Bayesian fitting of a generalized linear model. The package vignette
    includes examples of hand-coding a logistic model using several
    variants of the Metropolis algorithm. The package also contains R
    functions for simulating posterior distributions of Bayesian
    generalized linear model parameters using guided, adaptive,
    guided-adaptive and random walk Metropolis algorithms. The random walk
    Metropolis algorithm was originally described in Metropolis et al
    (1953); <doi:10.1063/1.1699114>.
Implementation of various statistical models for multivariate
    event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate
    cumulative incidence models <doi:10.1002/sim.6016>, and  bivariate random
    effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
    Also contains two-stage binomial modelling that can do pairwise odds-ratio
    dependence modelling based marginal logistic regression models. This is an
    alternative to the alternating logistic regression approach (ALR).
A tool for locating, mapping, and gathering environmental data and metadata, worldwide.  Users can search for and filter metadata from > 157,000 environmental monitoring stations among 219 countries/territories and >20 networks/organizations via elevation, location, active dates, elements measured (e.g., temperature, precipitation), country, network, and/or known identifier. Future updates to the package will allow the user to obtain datasets from stations within the database.
Provides a Shiny application to estimate the sample size required 
    for a metabolomic experiment to achieve a desired statistical power. 
    Estimation is possible with or without available data from a pilot study.
Provides an interface with the Meteo France Synop data API 
    (see <https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32> 
    for more information). 
    The Meteo France Synop data are made of meteorological data recorded 
    every three hours on 62 French meteorological stations.
Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
Computes the average of a sequence of random vectors
    in a moving expanding window using a fixed amount of storage
Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.
A color palette generator inspired by Mexican politics, with 
    colors ranging from red on the left to gray in the middle and green on the 
    right. Palette options range from only a few colors to several colors, but with
    discrete and continuous options to offer greatest flexibility to the user. 
    This package allows for a range of applications, from mapping brief discrete scales 
    (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays 
    including dozens of shades graded from red to green.
The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.
Calculate MF (mitigated fraction) with clustering and bootstrap options. See http://goo.gl/pcXYVr for definition of MF.
Multivariate functional principal component analysis via fast covariance estimation for
             multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi:10.1002/sta4.245>.
Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
Functions and tools for estimation of mixed-frequency Bayesian vector autoregressive (VAR) models. The package implements a state space-based VAR model that handles mixed frequencies of the data as proposed by Schorfheide and Song (2015) <doi:10.1080/07350015.2014.954707>, and extensions thereof developed by Ankargren, Unosson and Yang (2020) <doi:10.1515/jtse-2018-0034>, Ankargren and Joneus (2019) <arXiv:1912.02231>, and Ankargren and Joneus (2020) <doi:10.1016/j.ecosta.2020.05.007>. The models are estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution. Prior distributions that can be used include normal-inverse Wishart and normal-diffuse priors as well as steady-state priors. Stochastic volatility can be handled by common or factor stochastic volatility models.
Computing functional traits-based distances between pairs of 
    species for species gathered in assemblages allowing to build several
    functional spaces. The package allows to compute functional diversity
    indices assessing the distribution of species (and of their dominance) in a
    given functional space for each assemblage and the overlap between
    assemblages in a given functional space, see: Chao et al. (2018)
    <doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>,
    Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al.
    (2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009)
    <doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included.
    Visit the 'mFD' website for more information, documentation and examples.
Creates and manages a PostgreSQL database suitable for storing fisheries data
             and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model.
             See <https://mareframe.github.io/mfdb/> for more information.
Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), 
  MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale
  Multifractal Analysis (MMA). The MFDFA() function proposed in this package was 
  used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). 
  See references for more information. Interested users can find a parallel version of
  the MFDFA() function on GitHub.
Extracts meta-features from datasets to support the design of 
  recommendation systems based on Meta-Learning. The meta-features, also called 
  characterization measures, are able to characterize the complexity of datasets
  and to provide estimates of algorithm performance. The package contains not 
  only the standard characterization measures, but also more recent 
  characterization measures. By making available a large set of meta-feature 
  extraction functions, tasks like comprehensive data characterization, deep 
  data exploration and large number of Meta-Learning based data analysis can be
  performed. These concepts are described in the paper: Rivolli A., Garcia L., 
  Soares c., Vanschoren J. and Carvalho A. (2018) <arXiv:1808.10406>.
Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
Multivariate functional halfspace depth and median for two-dimensional functional data.
The mFilter package implements several time series filters useful
        for smoothing and extracting trend and cyclical components of a
        time series. The routines are commonly used in economics and
        finance, however they should also be interest to other areas.
        Currently, Christiano-Fitzgerald, Baxter-King,
        Hodrick-Prescott, Butterworth, and trigonometric regression
        filters are included in the package.
A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.
Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Calculate a multivariate functional principal component analysis
    for data observed on different dimensional domains. The estimation algorithm
    relies on univariate basis expansions for each element of the multivariate
    functional data  (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. 
    Multivariate and univariate functional data objects are
    represented by S4 classes for this type of data implemented in the package
    'funData'. For more details on the general concepts of both packages and a case 
    study, see Happ-Kurz (2020) <doi:10.18637/jss.v093.i05>.
Provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance <doi:10.1214/14-AOAS782>. Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points <doi:10.1111/jtsa.12254>. Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies <doi:10.1007/s10827-016-0635-3>. Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series <doi:10.1007/s00184-018-0672-1>. Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series.
Convenience functions for multivariate MCMC using univariate samplers including:
  slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>),
  adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>),
  adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and
  univariate Metropolis with Gaussian proposal.
Estimates probit, logit, Poisson, negative binomial, 
  and beta regression models, returning their marginal effects, odds ratios, 
  or incidence rate ratios as an output.
  Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
Procedures to simulate, estimate and diagnose MGARCH
    processes of BEKK and multivariate GJR (bivariate asymmetric GARCH
    model) specification.
Compute the multiple Grubbs-Beck low-outlier test on positively distributed
 data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow
 data processing discussed in Cohn et al. (2013) <doi:10.1002/wrcr.20392> and
 England et al. (2017) <doi:10.3133/tm4B5>.
Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.
Generalized additive (mixed) models, some of their extensions and 
             other generalized ridge regression with multiple smoothing 
             parameter estimation by (Restricted) Marginal Likelihood, 
             Generalized Cross Validation and similar, or using iterated 
             nested Laplace approximation for fully Bayesian inference. See 
             Wood (2017) <doi:10.1201/9781315370279> for an overview. 
             Includes a gam() function, a wide variety of smoothers, 'JAGS' 
             support and distributions beyond the exponential family.
Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
Provides a model designed to be a reliable testbed where various gene 
    drive interventions for mosquito-borne diseases control. It is being developed to 
    accommodate the use of various mosquito-specific gene drive systems within a 
    population dynamics framework that allows migration of individuals between patches 
    in landscape. Previous work developing the population dynamics can be found in Deredec et al. 
    (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, 
    and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) 
    <doi:10.1038/s41598-017-02744-7>.
A simulation modeling framework which significantly extends capabilities from the
    'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets.
    For more information about 'MGDrivE', see our publication: <https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13318>.
    Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations,
    epidemiological dynamics, time-varying parameters, and a continuous-time simulation
    framework with various sampling algorithms for both deterministic and stochastic interpretations.
    'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we
    suggest installing that package initially.
Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. 
 The naive analysis which is solely based on the observed data without adjustment may lead to bias.
 The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. 
 The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given
 validation set. This package is an implementation 
 of Chen (2013) <doi:10.1002/bimj.201200195>.
An aggressive dimensionality reduction and network estimation
    technique for a high-dimensional Gaussian graphical model (GGM). Please
    refer to: Efficient Dimensionality Reduction for High-Dimensional Network
    Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of
    The 31st International Conference on Machine Learning, 2014, p. 1953--1961.
Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.
Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimating is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "MGMM: an R package for fitting Gaussian Mixture Models on Incomplete Data." <doi:10.1101/2019.12.20.884551>.
A glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. 'MGMS2' (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) <doi:10.1021/acs.analchem.9b03340> "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) <doi:10.1093/bioinformatics/bts447> "MALDIquant: a versatile R package for the analysis of mass spectrometry data."
Extends distribution and density functions to parametric
        multivariate generalized Pareto distributions (MGPD of Type
        II), and provides fitting functions which calculate maximum
        likelihood estimates for bivariate and trivariate models. (Help
        is under progress)
Convenience Functions for R Language Access to the v.1 API of the MG-RAST Metagenome Annotation Server, part of the US Department of Energy (DOE) Systems Biology Knowledge Base (KBase).
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.
Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021).
Designed to enable simultaneous substitution in strings in a safe fashion.
    Safe means it does not rely on placeholders (which can cause errors in same length matches).
Performs gene set analysis based on GSZ scoring function and asymptotic p-value. It is different from GSZ in that it implements asymptotic p-values instead of empirical p-values. Asymptotic p-values are calculated by fitting suitable distribution model to the null distribution. Unlike empirical p-values, resolution of asymptotic p-values are independent of the number of permutations and hence requires considerably fewer permutations. In addition, this package allows gene set analysis with seven other popular gene set analysis methods.
Functions for computing (Mixed) Geographycally Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
Performs general Metropolis-Hastings Markov Chain Monte
        Carlo sampling of a user defined function which returns the
        un-normalized value (likelihood times prior) of a Bayesian
        model.  The proposal variance-covariance structure is updated
        adaptively for efficient mixing when the structure of the
        target distribution is unknown.  The package also provides some
        functions for Bayesian inference including Bayesian Credible
        Intervals (BCI) and Deviance Information Criterion (DIC)
        calculation.
Estimates the survival function and Cox regression parameters
    for the multivariate survival setting where there are multiple
    (right-censored) outcome variables. The Volterra, Dabrowska, and
    Prentice-Cai estimates of the bivariate survival function may be
    computed as well as the Dabrowska estimate of the trivariate survival
    function. Bivariate Cox regression estimates can also be computed.
    Functions are also provided to compute (bootstrap) confidence intervals
    and plot the estimates of the bivariate survival function. For details,
    see "The Statistical Analysis of Multivariate Failure Time Data: A
    Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN:
    978-1-4822-5657-4), CRC Press.
MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python
    tool to predict MHC class I and MHC class II epitopes.
    This package allows one to call MHCnuggets from R.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
Estimation of the latent states and partition by maximum likelihood. Model can be used for analyzing accelerometer data. In such a case, the latent states corresponds to activity levels and the partition permits to consider heterogeneity within the population. Emission laws are zero-inflated gamma distributions. Their parameters depends on the latent states but not on the partition, to compare the time spent by activity levels between classes. Model description is available in Du Roy de Chaumaray, M. and Marbac, M. and Navarro, F. (2019) <arXiv:1906.01547>.
An implementation of the multilevel (also known as mixed or random 
    effects) hidden Markov model using Bayesian estimation in R. The multilevel 
    hidden Markov model (HMM) is a generalization of the well-known hidden
    Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The 
    multilevel HMM is tailored to accommodate (intense) longitudinal data of 
    multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. 
    <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow 
    for heterogeneity in the model parameters (transition probability matrix and 
    conditional distribution), while estimating one overall HMM. The model can 
    be fitted on multivariate data with a categorical distribution, and include 
    individual level covariates (allowing for e.g., group comparisons on model 
    parameters). Parameters are estimated using Bayesian estimation utilizing 
    the forward-backward recursion within a hybrid Metropolis within Gibbs 
    sampler. The package also includes various visualization options, a function 
    to simulate data, and a function to obtain the most likely hidden state 
    sequence for each individual using the Viterbi algorithm.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
A framework for multiple hypothesis testing based on distribution
    of p values. It is well known that the p values come from different
    distribution for null and alternatives, in this package we provide
    functions to detect that change. We provide a method for using the change
    in distribution of p values as a way to detect the true signals in the
    data.
A comprehensive tool for almost all existing multiple testing
    methods for discrete data. The package also provides some novel multiple testing
    procedures controlling FWER/FDR for discrete data. Given discrete p-values
    and their domains, the [method].p.adjust function returns adjusted p-values,
    which can be used to compare with the nominal significant level alpha and make
    decisions. For users' convenience, the functions also provide the output option 
    for printing decision rules.
A Comprehensive tool for almost all existing multiple testing
    methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.
Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
Estimation of models with zero left-censored variables.
             Null values may be caused by a selection process (Cragg
             (1971) <doi:10.2307/1909582>), insufficient resources
             (Tobin (1958) <doi:10.2307/1907382>) or infrequency of
             purchase (Deaton and Irish (1984)
             <doi:10.1016/0047-2727(84)90067-7>).
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <arxiv:2108.07086>. It is dedicated to dealing with multiple imputation for proteomics.
Tools for training, selecting, and evaluating maximum entropy
    (and standard logistic regression) distribution models. This package 
    provides tools for user-controlled transformation of explanatory variables, 
    selection of variables by nested model comparison, and flexible model 
    evaluation and projection. It follows principles based on the maximum-
    likelihood interpretation of maximum entropy modeling, and uses infinitely-
    weighted logistic regression for model fitting.
'Mica' is a server application used to create data web portals for 
    large-scale epidemiological studies or multiple-study consortia. 'Mica' helps
    studies to provide scientifically robust data visibility and web presence 
    without significant information technology effort. 'Mica' provides a 
    structured description of consortia, studies, annotated and searchable data
    dictionaries, and data access request management. This 'Mica' client allows
    to perform data extraction for reporting purposes.
Multiple imputation using Fully Conditional Specification (FCS)
    implemented by the MICE algorithm as described in Van Buuren and
    Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
    its own imputation model. Built-in imputation models are provided for
    continuous data (predictive mean matching, normal), binary data (logistic
    regression), unordered categorical data (polytomous logistic regression)
    and ordered categorical data (proportional odds). MICE can also impute
    continuous two-level data (normal model, pan, second-level variables).
    Passive imputation can be used to maintain consistency between variables.
    Various diagnostic plots are available to inspect the quality of the
    imputations.
Contains functions for multiple imputation which
    complements existing functionality in R.
    In particular, several imputation methods for the
    mice package (van Buuren & Groothuis-Oudshoorn, 2011,
    <doi:10.18637/jss.v045.i03>) are included.
    Main features of the miceadds package include
    plausible value imputation (Mislevy, 1991,
    <doi:10.1007/BF02294457>), multilevel imputation for
    variables at any level or with any number of hierarchical
    and non-hierarchical levels (Grund, Luedtke & Robitzsch,
    2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, 
    Ch.7, <doi:10.1201/9780429492259>), imputation using 
    partial least squares (PLS) for high dimensional 
    predictors (Robitzsch, Pham & Yanagida, 2016), 
    nested multiple imputation (Rubin, 2003, 
    <doi:10.1111/1467-9574.00217>), substantive model
    compatible imputation (Bartlett et al., 2015,
    <doi:10.1177/0962280214521348>), and features
    for the generation of synthetic datasets
    (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
    Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
Various tools for microeconomic analysis and microeconomic modelling,
   e.g. estimating quadratic, Cobb-Douglas and Translog functions,
   calculating partial derivatives and elasticities of these functions,
   and calculating Hessian matrices, checking curvature
   and preparing restrictions for imposing monotonicity of Translog functions.
Functions and tools
   for analysing consumer demand
   with the Almost Ideal Demand System (AIDS)
   suggested by Deaton and Muellbauer (1980).
Tools for econometric analysis and economic modelling
   with the traditional two-input Constant Elasticity of Substitution (CES) function
   and with nested CES functions with three and four inputs.
   The econometric estimation can be done by the Kmenta approximation,
   or non-linear least-squares
   using various gradient-based or global optimisation algorithms.
   Some of these algorithms can constrain the parameters to certain ranges,
   e.g. economically meaningful values.
   Furthermore, the non-linear least-squares estimation
   can be combined with a grid-search for the rho-parameter(s).
Tools for calculating Laspeyres, Paasche, and Fisher
  price and quantity indices.
Tools for econometric production analysis
   with the Symmetric Normalized Quadratic (SNQ) profit function,
   e.g. estimation, imposing convexity in prices,
   and calculating elasticities and shadow prices.
Fast imputations under the object-oriented programming paradigm. 	
  Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
  The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used.
  A single evaluation of a quantitative model for the multiple imputations is another major enhancement.
  A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.
Multiple Imputation has been shown to 
  be a flexible method to impute missing values by 
  Van Buuren (2007) <doi:10.1177/0962280206074463>. 
  Expanding on this, random forests have been shown 
  to be an accurate model by Stekhoven and Buhlmann 
  <arXiv:1105.0828> to impute missing values in datasets. 
  They have the added benefits of returning out of bag 
  error and variable importance estimates, as well as 
  being simple to run in parallel.
Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. 
    His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuß am Frack an a Ma'nsgrëßt"),
    but he also wrote many more works, including Rodange, Tockert (1928) <https://www.autorenlexikon.lu/page/document/361/3614/1/FRE/index.html> 
    ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) <https://www.autorenlexikon.lu/page/document/361/3615/1/FRE/index.html>
    ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the
    plain text versions of his works available on 
    <https://data.public.lu/fr/datasets/the-works-in-luxembourguish-of-michel-rodange/>.
Implementation of a framework for cluster analysis with selection of the
    final number of clusters and an optional variable selection procedure. The package
    is designed to integrate the results of multiple imputed datasets while accounting
    for the uncertainty that the imputations introduce in the final results. In addition,
    the package can also be used for a cluster analysis of the complete cases of a single
    dataset. The package also includes specific methods to summarize and plot the results.
    The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.
A procedure for comparing multivariate samples associated with
    different groups. It uses principal component analysis to convert
    multivariate observations into a set of linearly uncorrelated statistical
    measures, which are then compared using a number of statistical methods. The
    procedure is independent of the distributional properties of samples and
    automatically selects features that best explain their differences, avoiding
    manual selection of specific points or summary statistics. It is appropriate
    for comparing samples of time series, images, spectrometric measures or
    similar multivariate observations.
Fits Semiparametric Promotion Time Cure Models, taking into account (using a 
			 corrected score approach or the SIMEX algorithm) or not the measurement error
			 in the covariates, using a backfitting approach to maximize the likelihood.
A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
Provides infrastructure to accurately measure and compare
        the execution time of R expressions.
Provides functions to enhance the available 
            statistical analysis procedures in R by providing simple functions to 
            analysis and visualize the 16S rRNA data.Here we present a tutorial 
            with minimum working examples to demonstrate usage and dependencies.
A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2021)<arXiv:2104.00242>).  The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.
Functions for assigning 16S sequence data to a
    taxonomic level in the tree-of-life for prokaryotes.
An implementation of the model in Betancourt, Zanella, Steorts (2020) <arXiv:2004.02008>, which performs microclustering models for categorical data. The package provides a vignette for two proposed methods in the paper as well as two standard Bayesian non-parametric clustering approaches for entity resolution. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.
The consensus taxonomy for prokaryotes is a set of data-sets for
    best possible taxonomic classification based on 16S rRNA sequence data.
Provides utilities for reading and processing microdata from Spanish official statistics with R.
The 'Microsoft Academic Knowledge' API provides programmatic access
	to scholarly articles in the 'Microsoft Academic Graph'
	(<https://academic.microsoft.com/>). Includes methods matching all 'Microsoft
	Academic' API routes, including search, graph search, text similarity, and
	interpret natural language query string.
A series of statistical and plotting approaches in microbial community ecology based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity statistics, beta diversity statistics, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis.
A downstream bioinformatics tool to construct and assist 
    curation of microhaplotypes from short read sequences.
Most multilevel methodologies can only model macro-micro
    multilevel situations in an unbiased way, wherein group-level predictors
    (e.g., city temperature) are used to predict an individual-level
    outcome variable (e.g., citizen personality). In contrast,
    this R package enables researchers to model micro-macro situations, wherein
    individual-level (micro) predictors (and other group-level predictors) are
    used to predict a group-level (macro) outcome variable in an unbiased way.
This group of functions simplifies the creation of linked micromap
    plots.
Provides the users with the ability to quickly create Linked 
     Micromap plots for a collection of geographic areas.  
     Linked Micromaps are visualizations of georeferenced data that link statistical
     graphics to an organized series of small maps or graphic images. 
     The Help description contains examples of how to use the micromapST function.
     Contained in this package are border group datasets to support creating micromaps for the 
     50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, 
     the 105 counties in the state of Kansas, the 62 counties of New York,
     the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas 
     in China, the 218 administrative areas in the UK and Ireland (for testing only), 
     the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa 
     continent.
     A border group dataset contains the boundaries related to the data level areas, 
     a second layer boundaries, a top or third layer boundary, a parameter list of 
     run options, and a cross indexing table between area names, abbreviations, 
     numeric identification and alias matching strings for the specific geographic
     area.  By specifying a border group, the package create micromaps for any
     geographic region.  The user can create and provide their own border group dataset 
     for any area beyond the areas contained within the package.
     Copyrighted 2013, 2014, 2015 and 2016 by Carr, Pearson and Pickle.
Measures niche breadth and overlap of microbial taxa from large matrices.
			Niche breadth measurements include Levins' niche breadth (Bn) index, Hurlbert's Bn 
			and Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E.,
			Poole, R.W. (1981) <doi:10.2307/1936664>). Niche overlap measurements include 
			Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a Jaccard 
			similarity index of Feinsinger's PS values between taxa pairs, as Proportional Overlap.
A collection of functions for computations and visualizations of microbial pan-genomes.
The microplot function writes a set of R graphics files to be used as
  microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word',
  or 'Excel' files.  For 'LaTeX', we provide methods for the
  Hmisc::latex() generic function to construct 'latex' tabular
  environments which include the graphs.  These can be used directly
  with the operating system 'pdflatex' or 'latex' command, or by using
  one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an
  intermediary.  For 'MS Word', the msWord() function uses the
  'flextable' package to construct 'Word' tables which include the
  graphs.  There are several distinct approaches for constructing HTML
  files.  The simplest is to use the msWord() function with argument
  filetype="html".  Alternatively, use either 'Emacs org-mode' or the
  htmlTable::htmlTable() function to construct an 'HTML' file
  containing tables which include the graphs.  See the documentation
  for our as.htmlimg() function.  For 'Excel' use on 'Windows', the
  file examples/irisExcel.xls includes 'VBA' code which brings the
  individual panels into individual cells in the spreadsheet.
  Examples in the examples and demo subdirectories are shown with
  'lattice' graphics, 'ggplot2' graphics, and 'base' graphics.
  Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and
  'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input
  files and their 'pdf' output files.  Examples for 'HTML' include
  'org-mode' and 'Rmd' input files and their webarchive 'HTML' output
  files.  In addition, the as.orgtable() function can display a
  data.frame in an 'org-mode' document.  The examples for 'MS Word'
  (with either filetype="docx" or filetype="html") work with all
  operating systems.  The package does not require the installation of
  'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files.
Modelling interacting microbial populations - example applications
    include human gut microbiota, rumen microbiota and phytoplankton. Solves a
    system of ordinary differential equations to simulate microbial growth and
    resource uptake over time.
Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a  general simulation methodology:
  Algorithm III of Barnett, Helen, Helena Geys, Tom Jacobs, and Thomas Jaki. (2017) "Optimal Designs for Non-Compartmental
  Analysis of Pharmacokinetic Studies. (currently unpublished)"
  This methodology consist of (1) specifying a pharmacokinetic model
  including variability among animals; (2) generating possible sampling times; (3)
  evaluating performance of each time point choice on simulated data; (4)
  generating possible schemes given a time point choice and additional constraints
  and finally (5) evaluating scheme performance on simulated data. The default
  settings differ from the article of Barnett and others, in the default pharmacokinetic model used and
  the parameterization of variability among animals. Details can be found in the package vignette. A 'shiny'
  web application is included, which guides users from model parametrization to
  optimal microsampling scheme.
Clinical sequencing of tumor is usually performed on formalin-fixed
    and paraffin-embedded (FFPE) samples and have many sequencing errors. We
    found that the majority of these errors are detected in chimeric read
    caused by single-strand DNA with microhomology. Our filtering pipeline
    focuses on the uneven distribution of the artifacts in each read and removes
    such errors in FFPE samples without over-eliminating the true mutations
    detected in fresh frozen samples.
Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.
'Microservice' architectural style is an approach to developing a 
    single application as a suite of small services, each running in its own 
    process and communicating with lightweight mechanisms, often an 'HTTP' 
    resource 'API'. These services are built around business capabilities and 
    independently deployable by fully automated deployment machinery. There 
    is a bare minimum of centralized management of these services, which may 
    be written in different programming languages and use different data storage 
    technologies.
Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for both event-oriented and process-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.
An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders.
The toolkit 'µSTASIS' has been developed for the stability analysis of microbiota in a temporal framework by leveraging on iterative clustering. Concretely, the core function uses Hartigan-Wong k-means algorithm as many times as possible for stressing out paired samples from the same individuals to test if they remain together for multiple numbers of clusters over a whole data set of individuals. Moreover, the package includes multiple functions to subset samples from paired times, validate the results or visualize the output.
A generalization of the 'Synth' package that is
    designed for data at a more granular level (e.g., micro-level).
    Provides functions to construct weights (including propensity
    score-type weights) and run analyses for synthetic control methods
    with micro- and meso-level data; see Robbins, Saunders, and Kilmer
    (2017) <doi:10.1080/01621459.2016.1213634> and Robbins and Davenport
    (2021) <doi:10.18637/jss.v097.i02>.
This entry-level toolkit allows performing continuous-time microsimulation for a wide range of demographic applications. Individual life-courses are specified by a continuous-time multi-state model.
Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) <doi: 10.1214/08-AOAS167>. The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative.
Set of functions designed to simplify transcriptome analysis and identification of marker molecules using microarrays data. The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR (False Discovery Rate) multiple comparison and the definition of potential biological markers.
Contains functions for converting existing HTML/JavaScript
   source into equivalent 'shiny' functions. Bootstraps the process of making
   new 'shiny' functions by allowing us to turn HTML snippets directly into 
   R functions.
The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.
Methods and tools for mixed frequency time series data analysis.
    Allows estimation, model selection and forecasting for MIDAS regressions.
Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's  MIDAS.
This is a wrapper around the C++ implementation of 'MIDAS' (Bhatia et al., 2020) <https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf> by Siddharth Bhatia for graph like data.
Implementation of the mid-n algorithms presented in 
             Wellek S (2015) <DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact 
             sample size calculation for superiority trials with binary outcome.
Apply tests of multiple comparisons based
    on studentized 'midrange' and 'range' distributions. 
    The tests are: Tukey Midrange ('TM' test),
    Student-Newman-Keuls Midrange ('SNKM' test), 
    Means Grouping Midrange ('MGM' test) and 
    Means Grouping Range ('MGR' test). The first two tests were published by 
    Batista and Ferreira (2020) <doi:10.1590/1413-7054202044008020>. 
    The last two are being published.
Impute the covariance matrix of incomplete data so that factor 
  analysis can be performed. Imputations are made using multiple imputation 
  by Multivariate Imputation with Chained Equations (MICE) and combined with 
  Rubin's rules. Parametric Fieller confidence intervals and nonparametric
  bootstrap confidence intervals can be obtained for the variance explained by 
  different numbers of principal components. The method is described in 
  Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.
Tools for estimating, measuring and working with migration data.
A set of tools that extend common social network analysis packages
   for analysing multimodal and multilevel networks.
   It includes functions for one- and two-mode (and sometimes three-mode) 
   centrality, centralization, clustering, and constraint,
   as well as for one- and two-mode network regression and block-modelling.
   All functions operate with matrices, edge lists, 
   and 'igraph', 'network'/'sna', and 'tidygraph' objects.
   The package is released as a complement to 
   'Multimodal Political Networks' (2021, ISBN:9781108985000),
   and includes various datasets used in the book.
Tools to help convert credit risk data at two time points 
    into traditional credit state migration (aka, "transition") matrices.
    At a higher level, 'migrate' is intended to help an analyst understand 
    how risk moved in their credit portfolio over a time interval. 
    References to this methodology include: 
    1. Schuermann, T. (2008) <doi:10.1002/9780470061596.risk0409>.
    2. Perederiy, V. (2017) <arXiv:1708.00062>.
This package provides various indices, like Crude Migration Rate,
    different Gini indices or the Coefficient of Variation among others, to
    show the (un)equality of migration.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
We report an information-theoretic method which learns a large
    class of causal or non-causal graphical models from purely observational
    data, while including the effects of unobserved latent variables, commonly
    found in many datasets. Starting from a complete graph, the method
    iteratively removes dispensable edges, by uncovering significant information
    contributions from indirect paths, and assesses edge-specific confidences
    from randomization of available data. The remaining edges are then oriented
    based on the signature of causality in observational data. This approach can
    be applied on a wide range of datasets and provide new biological insights
    on regulatory networks from single cell expression data, genomic alterations
    during tumor development and co-evolving residues in protein structures.
    For more information you can refer to:
    Cabeli et al. PLoS Comp. Bio. 2020 <doi:10.1371/journal.pcbi.1007866>,
    Verny et al. PLoS Comp. Bio. 2017 <doi:10.1371/journal.pcbi.1005662>.
Implements multiple imputation for proportional hazards regression
    with interval censored data or proportional sub-distribution hazards
    regression for interval censored competing risks data. The main functions
    allow to estimate survival function, cumulative incidence function, Cox
    and Fine & Gray regression coefficients and associated variance-covariance
    matrix. 'MIICD' functions call 'Surv', 'survfit' and 'coxph' from the
    'survival' package, 'crprep' from the 'mstate' package, and 'mvrnorm' from
    the 'MASS' package.
Contains functions for data analysis of Repeated measurement continuous,categorical data using MCMC. Data may contain missing value in response and covariates. Mean Score Method and Inverse Probability Weighted method for parameter estimation when there is missing value in covariates are also included. 
             Reference for mean score method, inverse probability weighted method is
             Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.
Functions for estimating structural equation models using 
    instrumental variables.
An interface to build machine learning models for classification 
    and regression problems. 'mikropml' implements the ML pipeline described
    by Topçuoğlu et al. (2020) <doi:10.1128/mBio.00434-20> with reasonable 
    default options for data preprocessing, hyperparameter tuning, 
    cross-validation, testing, model evaluation, and interpretation steps. 
    See the website <https://www.schlosslab.org/mikropml/> for more information, 
    documentation, and examples.
The MILC package is designed to predict individual trajectories using the continuous time microsimulation model MILC, that describes the natural history of lung cancer.
A variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted).
Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Milet et al 2020 <doi:10.1101/2020.01.17.910109>.
The multiple instance data set consists of many independent
    subjects (called bags) and each subject is composed of several components
    (called instances). The outcomes of such data set are binary or categorical responses,
    and, we can only observe the subject-level outcomes. For example, in manufacturing
    processes, a subject is labeled as "defective" if at least one of its own
    components is defective, and otherwise, is labeled as "non-defective". The
    'milr' package focuses on the predictive model for the multiple instance
    data set with binary outcomes and performs the maximum likelihood estimation
    with the Expectation-Maximization algorithm under the framework of logistic
    regression. Moreover, the LASSO penalty is attached to the likelihood function
    for simultaneous parameter estimation and variable selection.
Guesses the MIME type from a filename extension using the data
    derived from /etc/mime.types in UNIX-type systems.
Generalized low-rank models for mixed and incomplete data frames. The main function may be used for dimensionality reduction of imputation of numeric, binary and count data (simultaneously). Main effects such as column means, group effects, or effects of row-column side information (e.g. user/item attributes in recommendation system) may also be modelled in addition to the low-rank model. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) <arXiv:1806.09734>.
Tools to combine results for multiply-imputed data using
        mixture approximations
The MIMS-unit algorithm is developed to compute Monitor Independent
    Movement Summary Unit, a measurement to summarize raw accelerometer data 
    while ensuring harmonized results across different devices. It also includes
    scripts to reproduce results in the related publication 
    (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).
Calculate dissolved gas concentrations from raw MIMS 
	(Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on 
	a formatted CSV file to return dissolved gas concentrations 
	(mg and microMole) of N2, O2, Ar based on 
	gas solubility at temperature, pressure, and salinity. See references 
	Benson and Krause (1984) <DOI:10.4319/lo.1992.37.6.1307>, Garcia and 
	Gordon (1992) <DOI:10.4319/lo.1984.29.3.0620>, Stull (1947) 
	<DOI:10.1021/ie50448a022>, and Hamme and Emerson (2004) 
	<DOI:10.1016/j.dsr.2004.06.009> for more information. Easily save the 
	output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). 
	Supports dual-temperature standard calibration for dual-bath MIMS setups.
A versatile tool that aims at (1) defining the minimum background extent necessary to fit Species Distribution Models reliable enough to extract ecologically relevant conclusions from them and (2) optimizing the modelling process in terms of computation demands. See Rotllan-Puig, X. & Traveset, A. (2021) <https://www.sciencedirect.com/science/article/pii/S0304380020304191>.
Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) <doi:10.1016/S0378-3758(98)00147-5>.
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, and directory structures, into mind map widgets or files ('.mm'), and vice versa. "FreeMind" mind map ('.mm') files can be opened by or imported to common mindmap software such as 'FreeMind' (<http://freemind.sourceforge.net/wiki/index.php/Main_Page>).
Citrus is a computational technique developed for the analysis
    of high dimensional cytometry data sets. This package extracts, statistically
    analyzes, and visualizes marker expression from 'citrus' data. This code was used to 
    generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. 
    “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile 
    Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus,
    please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download
    the 'citrus' package, please see <https://github.com/nolanlab/citrus>.
The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials.
Wrapper for 'minepy' implementation of Maximal
        Information-based Nonparametric Exploration statistics (MIC and
        MINE family). Detailed information of the ANSI C implementation of
	'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.
This package implements the core idea of games known as
        'Minesweeper' on Microsoft Windows or 'KMines' for KDE on
        Unix-like operating systems.
Makes it possible to create an internally consistent
    repository consisting of selected packages from CRAN-like repositories.
    The user specifies a set of desired packages, and 'miniCRAN' recursively
    reads the dependency tree for these packages, then downloads only this
    subset. The user can then install packages from this repository directly,
    rather than from CRAN.  This is useful in production settings, e.g. server
    behind a firewall, or remote locations with slow (or zero) Internet access.
Create minimal, responsive, and style-agnostic HTML documents with
    the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'.
    Powerful features include table of contents floating as a sidebar,
    folding codes and results, and more.
Quick and simple Tcl/Tk Graphical User Interface 
  to call functions. Also comprises a very simple experimental 
  GUI framework.
Generate central composite designs (CCD)with full as well 
    as fractional factorial points (half replicate) and Box Behnken 
    designs (BBD) with minimally changed run sequence.
Create tile grid maps, which are like choropleth maps except each
  region is represented with equal visual space.
The minimax family of distributions is a two-parameter
        family like the beta family, but computationally a lot more
        tractible.
Provides two main functions, minimax() and miniMaxPro(), for computing minimax 
    and minimax projection designs using the minimax clustering algorithm in Mak and 
    Joseph (2018) <DOI:10.1080/10618600.2017.1302881>. Current design region options 
    include the unit hypercube ("hypercube"), the unit simplex ("simplex"), the unit ball
    ("ball"), as well as user-defined constraints on the unit hypercube ("custom"). Minimax
    designs can also be computed on user-provided images using the function minimax.map(). 
    Design quality can be assessed using the function mMdist(), which computes the minimax
    (fill) distance of a design.
Shiny web application to run meta-analyses. 
    Essentially a graphical front-end to package 'meta' for R. 
    Can be useful as an educational tool, and for quickly 
    analyzing and sharing meta-analyses. 
    Provides output to quickly fill in GRADE (Grading of 
    Recommendations, Assessment, Development and Evaluations)
    Summary-of-Findings tables. 
    Importantly, it allows further processing of the results 
    inside R, in case more specific analyses are needed.
A binding to the minimist JavaScript library. This module implements
    the guts of optimist's argument parser without all the fanciful decoration.
Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.
Provides UI widget and layout functions for writing Shiny apps
    that work well on small screens.
Performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) <DOI:10419/161931>.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Derivative-free optimization by quadratic approximation
        based on an interface to Fortran implementations by M. J. D.
        Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) <doi:10.1186/1471-2105-8-278> "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants."
Learns direct microbe-microbe interaction networks using a Poisson
    multivariate-normal hierarchical model with an L1 penalized precision
    matrix. Optimization is carried out using an iterative conditional modes
    algorithm.
It can be used to create/encode molecular "license-plates" from sequences and to also decode the "license-plates" back to sequences.  While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) <doi:10.1093/bioinformatics/btw194>. It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) <doi:10.1038/srep41184>.
For a given set of stoichiometric reactions, this package
    evaluates the mass and charge balance, extracts all reactants, products, orphan
    metabolites, metabolite names and compartments. Also are included some options
    to characterize and write models in TSV and SBML formats.
This package implements entropy optimization distribution
        under specified constraints. It also offers an R interface to
        the MinxEnt and MaxEnt distributions.
An implementation of the iterative proportional fitting (IPFP), 
    maximum likelihood, minimum chi-square and weighted least squares procedures
    for updating a N-dimensional array with respect to given target marginal 
    distributions (which, in turn can be multidimensional). The package also
    provides an application of the IPFP to simulate multivariate Bernoulli
    distributions.
This package contains functions to carry out processing of
        high throughput data analysis and detection of putative
        hits/mutants.  Contents include a function for post-hoc quality
        control for removal of outlier sample sets, a median-based
        normalization method for use in datasets where there are no
        explicit controls and where most of the responses are of the
        wildtype/no response class (see accompanying paper).  The
        package also includes a way to prioritize individuals of
        interest using am empirical cumulative distribution function.
        Methods for generating synthetic data as well as data from the
        Chloroplast 2010 project are included.
Generic functions to produce area/bar/box/line plots of data following IAMC (Integrated Assessment Modeling Consortium) submission format.
Calibration of generalized linear models and Cox regression models for prediction using 
    multiple imputation to account for missing values in the predictors as described in the paper by 
    "Mertens, Banzato and de Wreede" (2018) <arXiv:1810.05099>. The methodology and calculations 
    described in this paper are fully implemented in this package. The vignette describes all data analytic steps 
    which allow users to replicate results using the package functions on the data analyzed in the paper or 
    on their own data. 
    Imputations are generated using the package 'mice' without using the outcomes of observations for which the
    predictions are generated. Two options are provided to generate predictions. The first is prediction-averaging of 
    predictions calibrated from single models fitted on single imputed 
    datasets within a set of multiple imputations. The second is application of the Rubin's rules pooled model.
    For both implementations, unobserved values in the predictor data of new observations for 
    which the predictions are derived are automatically imputed. The package contains two basic functions.  
    The first, mipred() generates predictions of outcome on new observations. The second, mipred.cv() generates 
    cross-validated predictions with the methodology on existing data for which outcomes
    have already been observed. The present version is still in development and should support continuous, 
    binary and counting outcomes, but we have only thoroughly checked performance for binary outcome 
    logistic regression modeling. We will include the Cox regression extension later.
This package collects algorithms/functions developed for
        microRNA profiling data analyses.  Analytical platforms include
        traditional hybridization microarray, CGH, beads-based
        microarray, and qRT-PCR array.
Fitting recurrent events survival models for
    left-censored data with multiple imputation of the number of previous episodes.
    See Hernández-Herrera G, Moriña D, Navarro A. (2020) <arXiv:2007.15031>.
Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.
Providing tools for microRNA (miRNA) text mining. 
    miRetrieve summarizes miRNA literature by extracting, counting, and 
    analyzing miRNA names, thus aiming at gaining biological insights into a 
    large amount of text within a short period of time. To do so, miRetrieve 
    uses regular expressions to extract miRNAs and tokenization to 
    identify meaningful miRNA associations. In addition, miRetrieve uses 
    the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020) 
    "miRTarBase 2020: updates to the experimentally validated microRNA–target 
    interaction database" <doi:10.1093/nar/gkz896>) to display field-specific 
    miRNA-mRNA interactions. The most important functions are available as a 
    Shiny web application under <https://miretrieve.shinyapps.io/miRetrieve/>.
Test for overall association between microbiome composition data 
  and phenotypes via phylogenetic kernels. The phenotype can be univariate 
  continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), 
  survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), 
  multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and 
  structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). 
  The package can also use robust regression (unpublished work) and 
  integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). 
  In each case, the microbiome community effect is modeled nonparametrically 
  through a kernel function, which can incorporate phylogenetic tree information.
A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 <doi:10.1186/s12859-015-0715-9>. The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 <doi:10.1016/j.softx.2020.100569>. Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information.
Machine learning method specifically designed for
    pre-miRNA prediction. It takes advantage of unlabeled sequences to improve
    the prediction rates even when there are just a few positive examples, when
    the negative examples are unreliable or are not good representatives of
    its class. Furthermore, the method can automatically search for negative
    examples if the user is unable to provide them. MiRNAss can find a good
    boundary to divide the pre-miRNAs from other groups of sequences; it
    automatically optimizes the threshold that defines the classes boundaries,
    and thus, it is robust to high class imbalance. Each step of the method is
    scalable and can handle large volumes of data.
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of dichotomous and polytomous response data using
    unidimensional and multidimensional latent trait models under the Item
    Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). 
    Exploratory and confirmatory models can be estimated with quadrature (EM) 
    or stochastic (MHRM) methods. Confirmatory
    bi-factor and two-tier analyses are available for modeling item testlets.
    Multiple group analysis and mixed effects designs also are available for
    detecting differential item and test functioning as well as modeling
    item and person covariates. Finally, latent class models such as the DINA,
    DINO, multidimensional latent class, and several other discrete latent
    variable models, including mixture and zero-inflated response models, 
    are supported.
Provides tools to generate an HTML interface for creating adaptive
    and non-adaptive educational and psychological tests using the shiny
    package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). 
    Suitable for applying unidimensional and multidimensional
    computerized adaptive tests (CAT) using item response theory methodology and for
    creating simple questionnaires forms to collect response data directly in R.
    Additionally, optimal test designs (e.g., "shadow testing") are supported
    for tests which contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying the behavior of computerized adaptive test banks.
Provides constrained joint maximum likelihood estimation
    algorithms for item factor analysis (IFA) based on multidimensional item response theory
    models. So far, we provide functions for exploratory and confirmatory IFA based on the 
    multidimensional two parameter logistic (M2PL) model for binary response data. Comparing 
    with traditional estimation methods for IFA, the methods implemented in this package scale
    better to data with large numbers of respondents, items, and latent factors. The computation
    is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to:
    1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for 
    High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. 
    <doi:10.1007/s11336-018-9646-5>;
    2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: 
    Identifiability, Estimability, and Their Implications. Journal of the American Statistical 
    Association, <doi: 10.1080/01621459.2019.1635485>.
Provides singular value decomposition based estimation algorithms for exploratory item factor analysis (IFA) 
    based on multidimensional item response theory models. For more information, please refer to:
    Zhang, H., Chen, Y., & Li, X. (2020). A note on exploratory item factor analysis by 
    singular value decomposition. Psychometrika, 1-15, <DOI:10.1007/s11336-020-09704-7>.
Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.
A collection of miscellaneous 3d plots, including
    isosurfaces.
Various functions for random number generation, density 
             estimation, classification, curve fitting, and spatial 
             data analysis.
Implementing various things including functions for LaTeX tables,
    the Kalman filter, web scraping, development tools, relative risk and odds
    ratio.
Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) <doi:10.1007/s11222-016-9705-7>. Misclassification probabilities can either be specified as fixed or estimated. Models with time dependent misclassification may also be fitted.
Estimates models that extend the standard GLM to take
    misclassification into account. The models require side information from a secondary data set
    on the misclassification process, i.e. some sort of misclassification
    probabilities conditional on some common covariates.
    A detailed description of the algorithm can be found in
    Dlugosz, Mammen and Wilke (2015) <http://www.zew.de/PU70410>.
Statistical test for the product-moment correlation coefficient based on H0: rho = rho0 
             including sample size computation, statistical test for comparing the product-moment 
             correlation coefficient in independent ad dependent samples, sequential triangular
             test for the product-moment correlation coefficient, partial and semipartial correlation,
             simulation of bivariate normal and non-normal distribution with a specified correlation.
A collection of miscellaneous methods to simplify various tasks,
  including plotting, data.frame and matrix transformations, environment
  functions, regular expression methods, and string and logical operations, as
  well as numerical and statistical tools. Most of the methods are simple but
  useful wrappers of common base R functions, which extend S3 generics or
  provide default values for important parameters.
Miscellaneous small tools and utilities.
   Many of them facilitate the work with matrices,
   e.g. inserting rows or columns, creating symmetric matrices,
   or checking for semidefiniteness.
   Other tools facilitate the work with regression models,
   e.g. extracting the standard errors,
   obtaining the number of (estimated) parameters,
   or calculating R-squared values.
Clears the workspace. Useful for the beginnings of R scripts, to
    avoid potential problems with accidentally using information from variables
    or functions from previous script evaluations, too many figure windows open
    at the same time, packages that you don't need any more, or a cluttered
    console.  Uses code from various StackOverflow users.  See help(mise) for
    pointers to the relevant StackOverflow pages.
Penalized regression methods, such as lasso and elastic net, are used in
    many biomedical applications when simultaneous regression coefficient
    estimation and variable selection is desired. However, missing data
    complicates the implementation of these methods, particularly when
    missingness is handled using multiple imputation. Applying a variable
    selection algorithm on each imputed dataset will likely lead
    to different sets of selected predictors, making it difficult
    to ascertain a final active set without resorting to ad hoc
    combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet)
    and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes,
    developed by Du et al (2020), currently under review. They, by construction,
    force selection of the same variables across multiply imputed data.
    'miselect' also provides cross validated variants of these methods.
Open-source software for computing Likelihood ratios thresholds and error rates in DNA kinship testingi. Marsico FL. et al (2021) <doi:10.1016/j.fsigen.2021.102519>.
Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.
There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates.
Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <arXiv:2003.09202v2>.
Enables investigation of the predictors of misreporting on sensitive survey questions through a multivariate list experiment regression method. The method permits researchers to model whether a survey respondent's answer to the sensitive item in a list experiment is different from his or her answer to an analogous direct question.
Offers a convenient pipeline to test and compare various missing data
  imputation algorithms on simulated and real data. These include simpler methods, such as mean and median
  imputation and random replacement, but also include more sophisticated algorithms already implemented in popular 
  R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren 
  and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) 
  <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 
  'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 
  'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables 
  vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation 
  algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and 
  sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns.
  'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm
  in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you 
  assess imputation performance.
Implements two methods: a nonparametric risk adjustment and a
    data imputation method that use general population mortality tables to allow a
    correct analysis of time to disease recurrence. Also includes a powerful set of
    object oriented survival data simulation functions.
Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) <arXiv:2107.05427>.
The function 'missForest' in this package is used to
        impute missing values particularly in the case of mixed-type
        data. It uses a random forest trained on the observed values of
        a data matrix to predict the missing values. It can be used to
        impute continuous and/or categorical data including complex
        interactions and non-linear relations. It yields an out-of-bag
        (OOB) imputation error estimate without the need of a test set
        or elaborate cross-validation. It can be run in parallel to 
        save computation time.
Contains a suite of functions for health economic evaluations with missing outcome data. 
  The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). 
  Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models.
  In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. 
  The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
Supply functions for the creation and handling of
    missing data as well as tools to evaluate missing data methods. Nearly
    all possibilities of generating missing data discussed by Santos et.
    al (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
    implemented.  Functions are supplied to compare parameter estimates
    and imputed values to true values to evaluate missing data methods.
    Evaluations of these types are done, for example, by Cetin-Berber et
    al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
    <doi:10.1093/bioinformatics/bth499>.
Alternative implementation of the beautiful 'MissForest'
    algorithm used to impute mixed-type data sets by chaining random
    forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
    <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
    lightning fast random jungle package 'ranger'. Between the iterative
    model fitting, we offer the option of using predictive mean matching.
    This firstly avoids imputation with values not already present in the
    original data (like a value 0.3334 in 0-1 coded variable).  Secondly,
    predictive mean matching tries to raise the variance in the resulting
    conditional distributions to a realistic level. This would allow e.g.
    to do multiple imputation when repeating the call to missRanger().  A
    formula interface allows to control which variables should be imputed
    by which.
When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 
  due to missing information between node pairs), it is possible to account for the underlying process
  that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2021) <arXiv:1906.12201>,
  adjusts the popular stochastic block model from network data sampled under various missing data conditions, 
  as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.
The Self-Organizing Maps with Built-in Missing Data Imputation. Missing values are imputed and regularly updated during the online Kohonen algorithm. Our method can be used for data visualisation, clustering or imputation of missing data. It is an extension of the online algorithm of the 'kohonen' package.
Test for association between a set of SNPS/genes and
        continuous or binary outcomes by including variant
        characteristic information and using (weighted) score
        statistics.
Provide all the data sets and statistical analysis applications used in "Modern Industrial Statistics: with applications in R, MINITAB and JMP" by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2021, which is a third revised and expanded revision of "Modern Industrial Statistics: Design and Control of Quality and Reliability", R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
A flexible computational framework for mixture distributions with the focus on the composite models.
Various reliability analysis methods for rare event inference:1) computing failure probability (probability that the output of a numerical model exceeds a threshold), 2) computing quantile of low or high-order, 3) Wilks formula to compute quantile(s) from a sample or the size of the required i.i.d. sample.
Miscellaneous functions for descriptive statistics (e.g., frequency table, cross tabulation, multilevel descriptive statistics, within-group and between-group correlation matrix, coefficient alpha and omega, and various effect size measures), missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), data management (e.g., grand-mean and group-mean centering, recode variables and reverse code items, scale and group scores, reading and writing SPSS and Excel files), and statistical analysis (e.g., confidence intervals, collinearity diagnostics, Levene's test, t-test, z-test, and sample size determination).
Provides tools for multiple imputation of missing data in multilevel
 modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo',
 and several functions for visualization, data management and the analysis 
 of multiply imputed data sets.
Tools to perform analyses and combine results from
        multiple-imputation datasets.
Extract, transform and load MITRE standards.
    This package gives you an approach to cybersecurity data sets.
    All data sets are build on runtime downloading raw data from MITRE public services.
    MITRE <https://www.mitre.org/> is a government-funded research organization 
    based in Bedford and McLean. Current version includes most used standards as
    data frames. It also provide a list of nodes and edges with all relationships.
Implements the Mittag-Leffler function, distribution,
  random variate generation, and estimation. Based on the Laplace-Inversion
  algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.
Implements the Wilson confidence interval 
    for binomial proportions given multiple imputations of missing data (detailed 
    theory provided in "Wilson Confidence Intervals for Binomial
    Proportions With Multiple Imputation for Missing
    Data" (A. Lott & J. Reiter, 2018)). Our package also implements a Wald confidence
    interval and allows for both MIDs object and proportion vector arguments.
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem.  Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>). The imputation models are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>), univariate Bayesian imputation (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian regression imputation.
Estimation/multiple imputation programs for mixed categorical
    and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for model-based clustering and classification.
 It supports various types of data: continuous, categorical and counting
 and can handle mixed data of these types. It can fit Gaussian (with diagonal
 covariance structure), gamma, categorical and Poisson models. The algorithms
 also support missing values. This package can be used as an independent
 alternative to the (not free) 'mixtcomp' software available at
 <https://massiccc.lille.inria.fr/>.
Model time series using mixture autoregressive (MAR)
             models.  Implemented are frequentist (EM) and Bayesian
             methods for estimation, prediction and model
             evaluation. See Wong and Li (2002)
             <doi:10.1111/1467-9868.00222>, Boshnakov (2009)
             <doi:10.1016/j.spl.2009.04.009>), and the extensive
             references in the documentation.
Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects.
Deconvolution of thermal decay curves allows you to quantify proportions 
    of biomass components in plant litter. Thermal decay curves derived from 
    thermogravimetric analysis (TGA) are imported, modified, and then modelled in a 
    three- or four- part  mixture model using the Fraser-Suzuki function. The output 
    is estimates for weights of pseudo-components corresponding to hemicellulose, 
    cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, 
    H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. 
    (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and 
    Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.
Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.
Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet 
    (Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
    to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
    infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
    paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
    applicable to large datasets. It is described and tested in the accompanying paper by 
    Ahlmann-Eltze and Yau (2018) <doi:10.1109/DSAA.2018.00068>.
Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.
Semi-parametric approach for sparse canonical correlation analysis 
    which can handle mixed data types: continuous, binary and truncated continuous.
    Bridge functions are provided to connect Kendall's tau to latent correlation
    under the Gaussian copula model. The methods are described in 
    Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and 
    Yoon, Müller and Gaynanova (2020) <arXiv:2006.13875>.
Implementation of the co-clustering method for mixed type data proposed in M. Selosse, J. Jacques, C. Biernacki (2018) <https://hal.archives-ouvertes.fr/hal-01893457>. It consists in clustering simultaneously the rows (observations) and the columns (features) of a heterogeneous data set.
Functions for testing randomness for a univariate time series with arbitrary distribution  (discrete, continuous, mixture of both types) and for testing  independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in <doi:10.1093/biomet/asy059>.
Fits mixed membership models with discrete multivariate data (with
    or without repeated measures) following the general framework of Erosheva et al
    (2004). This package uses a Variational EM approach by approximating the
    posterior distribution of latent memberships and selecting hyperparameters
    through a pseudo-MLE procedure. Currently supported data types are
    Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with 
    fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al 
    (2014) for other examples of mixed membership models.
The estimation of the parameters in mixed Poisson models.
Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) 
    and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). 
    Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets.
    For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).
Inference on stochastic differential models Ornstein-Uhlenbeck or
    Cox-Ingersoll-Ross, with one or two random effects in the drift function.
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Contains functions for estimating a mixed-effects model for
             clustered data (or batch-processed data) with cluster-level (or batch-
             level) missing values in the outcome, i.e., the outcomes of some 
             clusters are either all observed or missing altogether. The model is 
             developed for analyzing incomplete data from labeling-based quantitative 
             proteomics experiments but is not limited to this type of data. 
             We used an expectation conditional maximization (ECM) algorithm for model 
             estimation. The cluster-level missingness may depend on the average 
             value of the outcome in the cluster (missing not at random).
Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.
Utilizing model-based clustering (unsupervised)
        for functional magnetic resonance imaging (fMRI) data.
        The developed methods (Chen and Maitra (2021) <arXiv:2102.03639>)
        include 2D and 3D clustering analyses
        (for p-values with voxel locations) and
        segmentation analyses (for p-values alone) for fMRI data where p-values
        indicate significant level of activation responding to stimulate
        of interesting. The analyses are mainly identifying active
        voxel/signal associated with normal brain behaviors.
        Analysis pipelines (R scripts) utilizing this package
        (see examples in 'inst/workflow/') is also implemented with high
        performance techniques.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of  multiple scaled generalized hyperbolic distributions, the 'cMSGHD'  is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.
Developed to deal with multi-locus genotype data, this package is especially designed for those panel which include different type of markers. Basic genetic parameters like allele frequency, genotype frequency, heterozygosity and Hardy-Weinberg test of mixed genetic data can be obtained.     In addition, a new test for mutual independence which is compatible for mixed genetic data is developed in this package.
Kernel-based methods are powerful methods for integrating 
    heterogeneous types of data. mixKernel aims at providing methods to combine
    kernel for unsupervised exploratory analysis. Different solutions are 
    provided to compute a meta-kernel, in a consensus way or in a way that 
    best preserves the original topology of the data. mixKernel also integrates
    kernel PCA to visualize similarities between samples in a non linear space
    and from the multiple source point of view. Functions to assess and display
    important variables are also provided in the package. Jerome Mariette and 
    Nathalie Villa-Vialaneix (2018) <doi:10.1093/bioinformatics/btx682>.
Specification and estimation of multinomial logit
    models.  Large datasets and complex models are supported, with an
    intuitive syntax.  Multinomial Logit Models, Mixed models, random
    coefficients and Hybrid Choice are all supported.  For more
    information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
The main functions perform mixed models analysis by least squares
    or REML by adding the function r() to formulas of lm() and glm(). A collection of
    text-book statistics for higher education is also included, e.g. modifications
    of the functions lm(), glm() and associated summaries from the package 'stats'.
A collection of functions to implement the MixMAP algorithm, which performs gene
    level tests of association using data from a previous GWAS or data from a
    meta-analysis of several GWAS.  Conceptually, genes are detected as
    significant if the collection of p-values within a gene are determined to
    be collectively smaller than would be observed by chance.
Provides sampling and density functions for matrix
    variate normal, t, and inverted t distributions;  ML estimation for matrix
    variate normal and t distributions using the EM algorithm,
    including some restrictions on the parameters; and classification by linear and
    quadratic discriminant analysis for matrix variate normal and t
    distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>.
    Performs clustering with matrix variate normal and t mixture models.
A collection of functions to perform various meta-analytical models
  through a unified mixed-effects framework, including standard univariate
  fixed and random-effects meta-analysis and meta-regression, and non-standard
  extensions such as multivariate, multilevel, longitudinal, and dose-response
  models.
Simple tools to perform mixture optimization based on the 'desirability' package by Max Kuhn.
    It also provides a plot routine using 'ggplot2' and 'patchwork'.
Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
Estimates Rasch models and mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model.
Provides supplemental functions for the 'mixRasch'
    package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a 
    plotting function to compare item parameters for multiple
    class models and a function that provides average theta values for each class in
    a mixture model.
Fits mixtures of (possibly multivariate) regressions
	(which has been described as doing ANCOVA when you don't
	know the levels). Turner (2000) <doi:10.1111/1467-9876.00198>.
It offers random-forest-based functions to impute clustered
    incomplete data. The package is tailored for but not limited to imputing
    multitissue expression data, in which a gene's expression is measured on the
    collected tissues of an individual but missing on the uncollected tissues.
The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).
Separates DNA mixtures using a statistical model within a
        greedy algorithm with a useful tcl/tk GUI.
Creates and runs Bayesian mixing models to analyze
    biological tracer data (i.e. stable isotopes, fatty acids), which estimate the
    proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR'
    is not one model, but a framework that allows a user to create a mixing model
    based on their data structure and research questions, via options for fixed/
    random effects, source data types, priors, and error terms. 'MixSIAR' incorporates
    several years of advances since 'MixSIR' and 'SIAR'.
The utility of this package is in simulating mixtures of Gaussian
        distributions with different levels of overlap between mixture
        components.  Pairwise overlap, defined as a sum of two
        misclassification probabilities, measures the degree of
        interaction between components and can be readily employed to
        control the clustering complexity of datasets simulated from
        mixtures. These datasets can then be used for systematic
        performance investigation of clustering and finite mixture
        modeling algorithms. Among other capabilities of 'MixSim', there
        are computing the exact overlap for Gaussian mixtures,
        simulating Gaussian and non-Gaussian data, simulating outliers
        and noise variables, calculating various measures of agreement
        between two partitionings, and constructing parallel
        distribution plots for the graphical display of finite mixture
        models.
Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.
Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew 
    power exponential and power exponential distributions, respectively. A generalized expectation-maximization 
    framework is used for parameter estimation. Methodology for mixtures of power exponential distributions is 
    from Dang et al. (2015) <doi: 10.1111/biom.12351>.
Provides an optimization method based on sequential
    quadratic programming (SQP) for maximum likelihood estimation of
    the mixture proportions in a finite mixture model where the
    component densities are known. The algorithm is expected to obtain
    solutions that are at least as accurate as the state-of-the-art
    MOSEK interior-point solver (called by function "KWDual" in the
    'REBayes' package), and they are expected to arrive at solutions
    more quickly when the number of samples is large and the number of
    mixture components is not too large. This implements the "mix-SQP"
    algorithm, with some improvements, described in Y. Kim,
    P. Carbonetto, M. Stephens & M. Anitescu (2020)
    <DOI:10.1080/10618600.2019.1689985>.
Differential expression analysis of RNA-Seq data when replicates under two conditions are available is performed. First, mixtures of Negative Binomial distributions are fitted on the data in order to estimate the dispersions, then the Wald test is computed.
Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
Curve Fitting for monotonic(sigmoidal) & non-monotonic(J-shaped) 
 concentration-response data. Prediction of mixture toxicity based on reference 
 models such as 'concentration addition', 'independent action', and 'generalized 
 concentration addition'.
A set of utility functions for analysing and modelling data from 
    continuous report short-term memory experiments using either the 2-component
    mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the 
    3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users 
    are also able to simulate from these models.
An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
Functions for computing the penalized maximum likelihood estimate (PMLE) or maximum likelihood estimate (MLE), testing the order of a finite mixture model using EM-test, drawing histogram of observations and the fitted density or probability mass function of the mixture model.
Implementation of robust model based cluster analysis with missing data. 
    The models used are: Multivariate Contaminated Normal Mixtures (MCNM),
    Multivariate Student's t  Mixtures (MtM), and Multivariate Normal Mixtures (MNM)
    for data sets with missing values at random. 
    "Cluster analysis and outlier detection with missing data"
    Hung Tong, Cristina Tortora (2020) <arXiv:2012.05394>.
Fit mixture regression models with nonsusceptibility/cure for left-truncated and interval-censored (LTIC) data (see Chen et al. (2013) <doi:10.1002/sim.5845>). This package also provides the nonparametric maximum likelihood estimator (NPMLE) for the survival/event curves with LTIC data.
Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2020) <arXiv:2011.07420>.
Optimization algorithms implemented in R, including
    conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the
    limited memory BFGS (L-BFGS) methods. Most internal parameters can be set 
    through the call interface. The solvers hold up quite well for 
    higher-dimensional problems.
A set of classes and methods to set up and run multi-species, trait
    based and community size spectrum ecological models, focused on the marine
    environment.
Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), <doi:10.1016/B978-1-55860-247-2.50035-8>) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), <doi:10.1093/oxfordjournals.aje.a113284>; Hosmer et al (1997), <doi:10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O>). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), <doi:10.1093%2Facref%2F9780199976720.001.0001>).
Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.
Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), <doi:10.2307/1268758>), robust coefficient of variation (CV) (Arachchige et al. (2019), <arXiv:1907.01110>), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4).
Calculation routines based on the FOCUS Kinetics Report (2006,
  2014).  Includes a function for conveniently defining differential equation
  models, model solution based on eigenvalues if possible or using numerical
  solvers.  If a C compiler (on windows: 'Rtools') is installed, differential
  equation models are solved using automatically generated C functions.  Please
  note that no warranty is implied for correctness of results or fitness for a
  particular purpose.
Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), <doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) and multiple imputation (Barnard and Rubin (1999), <doi:10.1093/biomet/86.4.948>) t-test. Graphical visualization by volcano plots.
Package for fast computation of the maximum kernel
        likelihood estimator (mkle)
It's a Modern K-Means clustering algorithm allowing data of any number of dimensions, any initial center, and any number of clusters to expect.
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.
Package solves multiple knapsack optimisation problem. 
    Given a set of items, each with volume and value, 
    it will allocate them to knapsacks of a given size in a way that
    value of top N knapsacks is as large as possible.
Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015), <doi:10.1093/nar/gkv007> ) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).
Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>) and comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>).
mkssd is a package that generates efficient balanced
        non-aliased multi-level k-circulant supersaturated designs by
        interchanging the elements of the generator vector. The package
        tries to generate a supersaturated design that has chisquare
        efficiency more than user specified efficiency level (mef). The
        package also displays the progress of generation of an
        efficient multi-level k-circulant design through a progress
        bar. The progress of 100% means that one full round of
        interchange is completed. More than one full round (typically
        4-5 rounds) of interchange may be required for larger designs.
Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.
Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.
Provides 'R6' abstract classes for building machine learning models 
    with 'scikit-learn' like API. <http://scikit-learn.org/> is a popular module 
    for 'Python' programming language which design became de facto a standard 
    in industry for machine learning tasks.
A collection of artificial and real-world machine learning
        benchmark problems, including, e.g., several
        data sets from the UCI repository.
Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.
Main use case is to gives users the ability to work with Major League Baseball data in a clean and detailed way.
      Which provides users with a variety of ways to improve visualizations.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.
An interactive document on  the topic of linear discriminant analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/DiscriminantAnalysisModelling/>.
Exploratory data analysis and manipulation functions for multi-
    label data sets along with an interactive Shiny application to ease their use.
Large collection of multilabel datasets along with the functions
    needed to export them to several formats, to make partitions, and to obtain
    bibliographic information.
Difference scaling is a method for scaling perceived 
  supra-threshold differences.  The package contains functions that
  allow the user to design and run a difference scaling experiment, 
  to fit the resulting data by maximum likelihood and test the
  internal validity of the estimated scale.
Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.
A 'sparklyr' <https://spark.rstudio.com> extension that provides
  an interface to 'MLeap' <https://github.com/combust/mleap>, an open source library
  that enables exporting and serving of 'Apache Spark' pipelines.
This package provides a unified interface to various
        machine learning algorithms. Confusion matrices are provided
        too.
This package contains functions to compute the
        nonparametric maximum likelihood estimator (MLE) for the
        bivariate distribution of (X,Y), when realizations of (X,Y)
        cannot be observed directly.  To be more precise, we consider
        the situation where we observe a set of rectangles that are
        known to contain the unobservable realizations of (X,Y). We
        compute the MLE based on such a set of rectangles.  The methods
        can also be used for univariate censored data (see data set
        'cosmesis'), and for censored data with competing risks (see
        data set 'menopause').  We also provide functions to visualize
        the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
        univariate and multi-dimensional outputs with diagnostic plots 
        following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>.
        Contact the maintainer for a package version that includes 
        sensitivity analysis.
Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.
Provides functions for unit root testing using MLE method
Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.
Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.
The Iterative Proportional Fitting (IPF) algorithm operates on count data. 
    This package offers implementations for several algorithms that extend this to 
    nested structures: 'parent' and 'child' items for both of which constraints can be provided.
    The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>,
    Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>,
    and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods
    is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.
R interface to 'MLflow', open source platform for
    the complete machine learning life cycle, see <https://mlflow.org/>.
    This package supports installing 'MLflow', tracking experiments,
    creating and running projects, and saving and serving models.
Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati".
It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2018) <https://hal.inria.fr/hal-01857242>).
Processing and analysis of high throughput (Roche 454)
        sequences generated from multiple loci and multiple biological
        samples. Sequences are assigned to their locus and sample of
        origin, aligned and trimmed. Where possible, genotypes are
        called and variants mapped to known alleles.
An R code implementation of the maximum likelihood (fixed
        point) algorithm of Hyvaerinen, Karhuna, and Oja for
        independent component analysis.
Tools and functions to fit a multilevel index of dissimilarity.
Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.
An implementation of Multi-level Monte Carlo for R.  This package
    builds on the original 'Matlab' and C++ implementations by Mike Giles to provide
    a full MLMC driver and example level samplers.  Multi-core parallel sampling
    of levels is provided built-in.
A collection of evaluation metrics, including loss, score and
    utility functions, that measure regression, classification and ranking performance.
Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.
Maximum likelihood estimates (MLE) of the proportions
    of 5-mC and 5-hmC in the DNA using information from BS-conversion,
    TAB-conversion, and oxBS-conversion methods. One can use information from all three methods 
    or any combination of two of them. Estimates are based on Binomial model by
    Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and
    Kiihl et al. (2019) <doi:10.1515/sagmb-2018-0031>.
Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) <doi:10.1038/ng.2314>. The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions.
Computational strategies for multivariate linear
        mixed-effects models with missing values, Schafer and Yucel
        (2002), Journal of Computational and Graphical Statistics, 11,
        421-442.
An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection.
The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.
Data and examples from a multilevel modelling software review
  as well as other well-known data sets from the multilevel modelling
  literature.
Maximum likelihood estimation of random utility discrete
             choice models. The software is described in Croissant (2020)
              <doi:10.18637/jss.v095.i11> and the underlying methods in
              Train (2009) <doi:10.1017/CBO9780511805271>.
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
A fast, flexible machine learning library, written in C++, that
             aims to provide fast, extensible implementations of cutting-edge
             machine learning algorithms.  See also Curtin et al. (2018)
             <doi:10.21105/joss.00726>.
Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.
An implementation of classifier chains (CC's) for multi-label
    prediction. Users can employ an external package (e.g. 'randomForest',
    'C50'), or supply their own. The package can train a single set of CC's or
    train an ensemble of CC's -- in parallel if running in a multi-core
    environment. New observations are classified using a Gibbs sampler since
    each unobserved label is conditioned on the others. The package includes
    methods for evaluating the predictions for accuracy and aggregating across
    iterations and models to produce binary or probabilistic classifications.
Quantification is a prominent machine learning task that has received an 
    increasing amount of attention in the last years. The objective is to predict the 
    class distribution of a data sample. This package is a collection of machine learning 
    algorithms for class distribution estimation. This package include algorithms from
    different paradigms of quantification. These methods are described in the paper: 
    A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set 
    size in quantification assessment. In Proceedings of the Twenty-Ninth International 
    Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020.
    <doi:10.24963/ijcai.2020/366>.
Interface to a large number of classification and
    regression techniques, including machine-readable parameter
    descriptions. There is also an experimental extension for survival
    analysis, clustering and general, example-specific cost-sensitive
    learning. Generic resampling, including cross-validation,
    bootstrapping and subsampling.  Hyperparameter tuning with modern
    optimization techniques, for single- and multi-objective problems.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning, also
    allowing for easy nested resampling.  Most operations can be
    parallelized.
Efficient, object-oriented programming on the
    building blocks of machine learning. Provides 'R6' objects for tasks,
    learners, resamplings, and measures. The package is geared towards
    scalability and larger datasets by supporting parallelization and
    out-of-memory data-backends like databases. While 'mlr3' focuses on
    the core computational operations, add-on packages provide additional
    functionality.
Implements methods for post-hoc analysis and
    visualisation of benchmark experiments, for 'mlr3' and beyond.
Extends the 'mlr3' package with cluster analysis.
A small collection of interesting and educational machine
    learning data sets which are used as examples in the 'mlr3' book
    (<https://mlr3book.mlr-org.com>), the use case gallery
    (<https://mlr3gallery.mlr-org.com>), or in other examples. All data
    sets are properly preprocessed and ready to be analyzed by most
    machine learning algorithms.  Data sets are automatically added to the
    dictionary of tasks if 'mlr3' is loaded.
Extends the 'mlr3' package with a backend to
    transparently work with databases. Includes two extra backends:
    One relies on relies on the abstraction of package 'dbplyr' to interact with
    one of the many supported database management systems (DBMS). The other one
    is specialized for package 'duckdb'.
Extends 'mlr3' with filter methods for feature selection.
    Besides standalone filter methods built-in methods of any
    machine-learning algorithm are supported.  Partial scoring of
    multivariate filter methods is supported.
Implements methods for feature selection with
    'mlr3', e.g.  random search and sequential selection. Various
    termination criteria can be set and combined. The class
    'AutoFSelector' provides a convenient way to perform nested resampling
    in combination with 'mlr3'.
Implements hyperband method for hyperparameter
    tuning.  Various termination criteria can be set and combined. The
    class 'AutoTuner' provides a convenient way to perform nested
    resampling in combination with 'mlr3'. The hyperband algorithm was
    proposed by Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin
    Rostamizadeh and Ameet Talwalkar (2018) <arXiv:1603.06560>.
Recommended Learners for 'mlr3'. Extends 'mlr3' and
    'mlr3proba' with interfaces to essential machine learning packages on
    CRAN.  This includes, but is not limited to: (penalized) linear and
    logistic regression, linear and quadratic discriminant analysis,
    k-nearest neighbors, naive Bayes, support vector machines, and
    gradient boosting.
Implements multiple performance measures for
    supervised learning.  Includes over 40 measures for regression and
    classification. Additionally, meta information about the performance
    measures can be queried, e.g. what the best and worst possible
    performances scores are.
Frequently used helper functions and assertions
    used in 'mlr3' and its companion packages. Comes with helper functions
    for functional programming, for printing, to work with 'data.table',
    as well as some generally useful 'R6' classes. This package also
    supersedes the package 'BBmisc'.
Provides an interface to 'OpenML.org' to list and
    download machine learning data and tasks. Data and tasks can be
    automatically converted to 'mlr3' tasks. For a more sophisticated
    interface which also allows uploading experiments, see the 'OpenML'
    package.
Dataflow programming toolkit that enriches 'mlr3' with a diverse
  set of pipelining operators ('PipeOps') that can be composed into graphs.
  Operations exist for data preprocessing, model fitting, and ensemble
  learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can
  therefore be resampled, benchmarked, and tuned.
Provides extensions for probabilistic supervised learning for
    'mlr3'.  This includes extending the regression task to probabilistic
    and interval regression, adding a survival task, and other specialized
    models, predictions, and measures.
A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.
Extends the mlr3 ML framework with methods for spatial
    objects. Data storage and prediction are supported for packages
    'terra', 'raster' and 'stars'.
Extends the mlr3 ML framework with spatio-temporal resampling
    methods to account for the presence of spatiotemporal autocorrelation
    (STAC) in predictor variables. STAC may cause highly biased
    performance estimates in cross-validation if ignored.
Implements methods for hyperparameter tuning with 'mlr3',
    e.g. grid search, random search, generalized simulated annealing and
    iterated racing.  Various termination criteria can be set and
    combined.  The class 'AutoTuner' provides a convenient way to perform
    nested resampling in combination with 'mlr3'.
Collection of search spaces for hyperparameter tuning.
    Includes various search spaces that can be directly applied on an
    `mlr3` learner. Additionally, meta information about the search space
    can be queried.
The 'mlr3' package family is a set of packages for
    machine-learning purposes built in a modular fashion. This wrapper
    package is aimed to simplify the installation and loading of the core
    'mlr3' packages. Get more information about the 'mlr3' project at
    <https://mlr3book.mlr-org.com/>.
Provides visualizations for 'mlr3' objects such as tasks,
    predictions, resample results or benchmark results via the autoplot()
    generic of 'ggplot2'. The returned 'ggplot' objects are intended to
    provide sensible defaults, yet can easily be customized to create
    camera-ready figures. Visualizations include barplots, boxplots,
    histograms, ROC curves, and Precision-Recall curves.
Toolset that enriches 'mlr' with a diverse set of preprocessing
    operators. Composable Preprocessing Operators ("CPO"s) are first-class
    R objects that can be applied to data.frames and 'mlr' "Task"s to modify
    data, can be attached to 'mlr' "Learner"s to add preprocessing to machine
    learning algorithms, and can be composed to form preprocessing pipelines.
The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of
  incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility
  interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine
  learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization
  of general objective functions using the 'bbotk' black box optimization framework. The
  control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'.
Flexible and comprehensive R toolbox for model-based optimization
    ('MBO'), also known as Bayesian optimization. It implements the Efficient
    Global Optimization Algorithm and is designed for both single- and multi-
    objective optimization with mixed continuous, categorical and conditional
    parameters. The machine learning toolbox 'mlr' provide dozens of regression
    learners to model the performance of the target algorithm with respect to
    the parameter settings. It provides many different infill criteria to guide
    the search process. Additional features include multi-point batch proposal,
    parallel execution as well as visualization and sophisticated logging
    mechanisms, which is especially useful for teaching and understanding of
    algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that
    single components can be easily replaced or adapted by the user for specific
    use cases.
An interactive application for working with multiple linear regression technique. The application has a template for solving problems on multiple linear regression. Runtime examples are provided in the package function as well as at  <https://kartikeyastat.shinyapps.io/MLR_WEB_K/>.
An interactive application for working with multiple linear regression technique. The application has a template for solving problems on multiple linear regression. Runtime examples are provided at  <https://kartikeyastat.shinyapps.io/MLR_WEB_K/>.
Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).
Generate a stream of pseudo-random numbers generated using the MLS 
    Junk Generator algorithm. Functions exist to generate single pseudo-random 
    numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Likelihood-based estimation of conditional transformation
  models via the most likely transformation approach described in
  Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020)
  <DOI:10.18637/jss.v092.i01>.
Additional documentation, a package vignette and 
  regression tests for package mlt.
A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.
A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
Estimates the multi-level vector autoregression model on time-series data.
             Three network structures are obtained: temporal networks, contemporaneous
             networks and between-subjects networks.
Simulation, inference and clustering of multilevel networks using a
    Stochastic Block Model framework as described in Chabert-Liddell, Barbillon,
    Donnet and Lazega (2021) <doi:10.1016/j.csda.2021.107179>. 
    A multilevel network is defined as the junction of two interaction networks, 
    the upper level or inter-organizational level and the lower level or 
    inter-individual level. The inter-level represents an affiliation 
    relationship.
Simulation and visualization of complex
    models for longitudinal data. The models are encoded using the model coding
    language 'Mlxtran' and automatically converted into C++ codes. 
    That allows one to implement very easily complex ODE-based models and complex 
    statistical models, including mixed effects models, for continuous, count, 
    categorical, and time-to-event data.
Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.
Various utilities for the Multiplicative Multinomial distribution.
A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human Medulloblastoma (MB) patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA (Principal Component Analysis) plots and heatmaps. Deena Gendoo and Benjamin Haibe-Kains (2016) <doi:10.1186/s13029-016-0053-y>.
Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Hunter, D. R., & Lange, K. (2004) <doi:10.1198/0003130042836>).
Used for general multiple mediation analysis. 
	The analysis method is described in Yu et al. (2014) <doi:10.4172/2155-6180.1000189> "General Multiple Mediation Analysis With an Application to Explore Racial Disparity in Breast Cancer Survival", published on Journal of Biometrics & Biostatistics, 5(2):189; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.
Used for general multiple mediation analysis with big data sets.
Contains the data sets for the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The book will be published by Oxford University Press in 2018 with ISBN-13: 978-019882472.
Provides tools for performing mathematical morphology operations,
    such as erosion and dilation, on data of arbitrary dimensionality. Can also
    be used for finding connected components, resampling, filtering, smoothing
    and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile.
Uses memory-mapping to enable the random access of elements of
  a text file of characters separated by characters as if it were a simple
  R(cpp) matrix.
Mobile-monitoring or "sensors on a mobile platform", is an increasingly 
    popular approach to measure high-resolution pollution data at the street level. 
    Coupled with location data, spatial visualisation of air-quality parameters 
    helps detect localized areas of high air-pollution, also called hotspots. 
    In this approach, portable sensors are mounted on a vehicle and driven on 
    predetermined routes to collect high frequency data (1 Hz). 
    'mmaqshiny' is for analysing, visualising and spatial mapping of 
    high-resolution air-quality data collected by specific devices installed on 
    a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate  
    matter with size less than 2.5 microns), Black carbon mass concentrations 
    (BC), ultra-fine particle number concentrations, carbon dioxide along with 
    GPS coordinates and relative humidity (RH) data collected by popular 
    portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, 
    LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It 
    incorporates device specific cleaning and correction algorithms. RH correction 
    is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) 
    <doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear 
    regression coefficients for correcting the PM2.5 data (if required). BC data
    will be cleaned for the vibration generated noise, by adopting the statistical 
    procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>, 
    followed by a loading correction as suggested by Ban-Weiss et al., (2009)  
    <doi:10.1021/es8021039>. For the number concentration data, provision is 
    given for dilution correction factor (if a diluter is used with CPC3007; 
    default value is 1). The package joins the raw, cleaned and corrected data 
    from the above said instruments and outputs as a downloadable csv file.
Supports Bayesian models with full and partial (hence
    arbitrary) dependencies between random variables. Discrete and continuous
    variables are supported, and conditional joint probabilities and probability
    densities are estimated using Kernel Density Estimation (KDE). The full
    general form, which implements an extension to Bayes' theorem, as well as
    the simple form, which is just a Bayesian network, both support regression
    through segmentation and KDE and estimation of probability or relative
    likelihood of discrete or continuous target random variables. This package
    also provides true statistical distance measures based on Bayesian models.
    Furthermore, these measures can be facilitated on neighborhood searches,
    and to estimate the similarity and distance between data points.
    Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053>
    and by Scutari (2010) <doi:10.18637/jss.v035.i03>.
Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
An implementation of modified maximum contrast methods (Sato et al. (2009) <doi:10.1038/tpj.2008.17>;
  Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997)
  <doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte
  Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.
The aim of the package is two-fold: (i) To implement 
    the MMD method for attribution of individuals to sources using 
    the Hamming distance between multilocus genotypes. (ii) To select 
    informative genetic markers based on information theory concepts 
    (entropy, mutual information and redundancy). The package implements the 
    functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., 
    Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to 
    efficiently attribute individuals to source populations. 
    Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>.
    See more details and examples in the README file.
A method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.
Provides functions for the robust estimation of 
	parametric families of copulas using minimization of 
	the Maximum Mean Discrepancy, following the article
	Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2020) <arXiv:2010.00408>.
Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one
    random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing
    independent time effect; Model 3, introducing correlated time effect.
    mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.
Fit multivariate mixture of normal distribution using
        covariance structure.
Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. 
  See Meyer (1985) <doi:10.2307/2530651> and Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.
A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space.    
    Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition.
    Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix.
    These models have significant differences in terms of the analytical approach.
    Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes).
    Research project No. 1.1.1.2/VIAA/1/16/075.
Fits multivariate marginal models with shared regression parameters for discrete and continuous responses
Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression
	coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
Provides functions for measuring
    population divergence from genotypic data.
To determine the number of quantitative assays needed for a sample 
    of data using pooled testing methods, which include mini-pooling (MP), MP 
    with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number 
    of assays needed, the package also provides a tool to conduct Monte Carlo (MC) 
    to simulate different orders in which the sample would be collected to form pools. 
    Using MC avoids the dependence of the estimated number of assays on any specific 
    ordering of the samples to form pools.
Marginalizes prediction functions using Monte-Carlo integration and computes permutation importance.
Compute similarities and distances between marked point processes.
Simple helpers for matrix multiplication on data.frames. These allow for more
    concise code during low level mathematical operations, and help ensure code is more
    easily read, understood, and serviced.
Researchers doing a mixed-methods analysis (nested analysis as
    developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can
    use the package for the classification of cases and case selection using
    results of a linear regression. One can designate cases 
    as typical, deviant, extreme and pathway case and use different case 
    selection strategies for the choice of a case belonging to one of
    these types.
Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985).
An implementation of multiple maps t-distributed stochastic
    neighbor embedding (t-SNE). Multiple maps t-SNE is a method for
    projecting high-dimensional data into several low-dimensional maps such that
    non-metric space properties are better preserved than they would be by a single
    map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE.
    When projecting onto more than one map, multiple maps t-SNE estimates a set
    of latent weights that allow each point to contribute to one or more maps
    depending on similarity relationships in the original data. This
    implementation is a port of the original 'Matlab' library by Laurens van der
    Maaten. 
    See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>.
    This material is based upon work supported by the United States Air Force 
    and Defense Advanced Research Project Agency (DARPA) under Contract No. 
    FA8750-17-C-0020.
    Any opinions, findings and conclusions or recommendations expressed in this
    material are those of the author(s) and do not necessarily reflect the views 
    of the United States Air Force and Defense Advanced Research Projects Agency.
    Distribution Statement A: Approved for Public Release; Distribution Unlimited.
A variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model.
    Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). 
    Users can also visualize the heatmap of the posterior mean of covariance matrix. 
    Kim, T. Nicolae, D. (2019) <https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>.
    Guan, Y. Stephens, M. (2011) <doi:10.1214/11-AOAS455>.
The first day of any MMWR week is Sunday.
    MMWR week numbering is sequential beginning with 1
    and incrementing with each week to a maximum of 52
    or 53. MMWR week #1 of an MMWR year is the first week
    of the year that has at least four days in the calendar
    year. This package provides functionality to convert
    Dates to MMWR day, week, and year and the reverse.
Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <arXiv:2009.07662>.
Diagnostic tools as residual analysis, global, 
    local and total-local influence for the multivariate model 
    from the random intercept Poisson generalized log gamma model 
    are available in this package. Including also, the estimation 
    process by maximum likelihood method, for details see 
    Fabio, L. F; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2021) 
    <arXiv:2102.07752>.
An API package for the Members' Name Information Service operated 
    by the UK parliament. Documentation for the API itself can be found 
    here: <http://data.parliament.uk/membersdataplatform/default.aspx>.
Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions.  Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra.  Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.
Perform FlexBoost in R. FlexBoost is a newly suggested algorithm based on AdaBoost by adjusting adaptive loss functions. Not only FlexBoost but also other machine learning algorithms (e.g. Support Vector Machines) will be added. For more details on FlexBoost see Jeon, Y. S., Yang, D. H., & Lim, D. J. (2019) <doi:10.1109/access.2019.2938356>.
Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014,
    <doi:10.1080/00273171.2014.889594>). 
    Regularization methods are implemented for assessing non-invariant items. 
    Currently, the package includes dichotomous items and unidimensional
    item response models. Extensions will be included in future package
    versions.
Functions to easily return simulated predicted probabilities and
    first differences for multinomial logit models. It takes a specified 
    scenario and a multinomial model to predict probabilities with a set of
    coefficients, drawn from a simulated sampling distribution. The simulated 
    predictions allow for meaningful plots with means and confidence intervals.
    The methodological approach is based on the principles laid out by King,
    Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan 
    (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.
An interactive presentation on  the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/MultinomPresentation>.
Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.
A data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis.
Functions are provided for computing the density and the
  distribution function of d-dimensional normal and "t" random variables,
  possibly truncated (on one side or two sides),  and for generating random 
  vectors sampled from these distributions, except sampling from the truncated
  "t". Moments of arbitrary order of a multivariate truncated normal are
  computed, and converted to cumulants up to order 4. 
  Probabilities are computed via non-Monte Carlo methods; different routines 
  are used in the case d=1, d=2, d=3, d>3, if d denotes the dimensionality.
Fits the Bayesian multinomial probit model via Markov chain
 Monte Carlo.  The multinomial probit model is often used to analyze 
 the discrete choices made by individuals recorded in survey data. 
 Examples where the multinomial probit model may be useful include the 
 analysis of product choice by consumers in market research and the 
 analysis of candidate or party choice by voters in electoral studies.  
 The MNP package can also fit the model with different choice sets for 
 each individual, and complete or partial individual choice orderings 
 of the available alternatives from the choice set. The estimation is
 based on the efficient marginal data augmentation algorithm that is 
 developed by Imai and van Dyk (2005). "A Bayesian Analysis of the 
 Multinomial Probit Model Using the Data Augmentation." Journal of 
 Econometrics, Vol. 124, No. 2 (February), pp. 311-334. 
 <doi:10.1016/j.jeconom.2004.02.002>  Detailed examples are given in 
 Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial 
 Probit Model."  Journal of Statistical Software, Vol. 14, No. 3 (May), 
 pp. 1-32. <doi:10.18637/jss.v014.i03>.
Allows to analyze the reading data obtained with the MNREAD Acuity 
    Chart, a continuous-text reading acuity chart for normal and low vision. 
    Provides the necessary functions to plot the MNREAD curve and estimate 
    automatically the four MNREAD parameters: Maximum Reading Speed, 
    Critical Print Size, Reading Acuity and Reading Accessibility Index.
    Parameters can be estimated either with the standard method 
    or with a nonlinear mixed-effects (NLME) modeling. 
    See Calabrese et al. 2018 for more details <doi:10.1167/18.1.8>.
An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) <arXiv:2004.07332> titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns).
Generate the monotonic binning and
    perform the woe (weight of evidence) transformation for the logistic regression
    used in the consumer credit scorecard development. The woe transformation is a piecewise
    transformation that is linear to the log odds. For a numeric variable, all of its monotonic 
    functional transformations will converge to the same woe transformation.
A framework for interacting with
  R modules such as Reports, Models, and Scripts from a mobile device. 
  The framework allows you to list available modules and select a
  module of interest using a basic e-mail interface. After 
  selecting a specific module, you can either run it as is or 
  provide input via the e-mail interface. After parsing your
  request, R will send the results back to your mobile device.
Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included.
Models the dependence between two variables in the extremes, identifies most 
    relevant models among six models: the conditional extremes model, the Jt-KDE model 
    and four copulae (Gumbel, Galambos, Normal, FGM). Bivariate return periods for 
    the six models and bivariate level curves can be created.
    Methods used in the package are described in the following reference:
    Tilloy, Malamud, Winter and Joly-Laugel (2020) <doi:10.5194/nhess-20-2091-2020>
    Supporting references for the conditional extremes model,
    Jt-KDE model and for copula modelling are the following: 
    Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>
    Cooley, Thibaud, Castillo and Wehner (2019) <doi:10.1007/s10687-019-00348-0>
    Nelsen (2006) <doi:10.1007/0-387-28678-0>.
Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.
Functions for calculating metrics for the measurement biodiversity
    and its changes across scales, treatments, and gradients. The methods 
    implemented in this package are described in:
    Chase, J.M., et al. (2018) <doi:10.1111/ele.13151>, 
    McGlinn, D.J., et al. (2019) <doi:10.1111/2041-210X.13102>, and
    McGlinn, D.J., et al. (2021) <doi:10.1002/ecy.3233>.
Tools for the simulation, analysis and sampling of spatial
    biodiversity data (May et al. 2017) <doi:10.1101/209502>.
    In the simulation tools user define the numbers of
    species and individuals, the species abundance distribution and species
    aggregation. Functions for analysis include species rarefaction 
    and accumulation curves, species-area relationships and the distance
    decay of similarity.
Fits and vizualize user defined finite mixture models for multivariate observations 
 using maximum likelihood. (McLachlan, G., Peel, D. (2000) Finite Mixture Models. Wiley-Interscience.)
Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.
The two main functionalities of this package are creating mock
    objects (functions) and selectively intercepting calls to a given
    function that originate in some other function. It can be used
    with any testing framework available for R. Mock objects can
    be injected with either this package's own stub() function or a
    similar with_mock() facility present in the 'testthat' package.
Provides a means to mock a package function, i.e., temporarily substitute it for testing. Designed as a drop-in replacement for 'testthat::with_mock()', which may break in R 3.4.0 and later.
With the deprecation of mocking capabilities shipped with
    'testthat' as of 'edition 3' it is left to third-party packages to replace
    this functionality, which in some test-scenarios is essential in order to
    run unit tests in limited environments (such as no Internet connection).
    Mocking in this setting means temporarily substituting a function with a
    stub that acts in some sense like the original function (for example by
    serving a HTTP response that has been cached as a file). The only exported
    function 'with_mock()' is modeled after the eponymous 'testthat' function
    with the intention of providing a drop-in replacement.
Creates modules inline or from a file. Modules can contain any R object and be nested. Each module have their own scope and package "search path" that does not interfere with one another or the user's working environment.
Package for processing downloaded MODIS Surface reflectance
    Product HDF files. Specifically, MOD09 surface reflectance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
This is a 'shiny' module that encapsulates various charting options available in 'htmlwidgets', and provides options for each type of chart, a 'crosstalk' like interface for aggregate reports between 'DT' and other chart types.
Fits a modified version of the Connor-Mosimann distribution (Connor & Mosimann (1969)<doi:10.2307/2283728>), 
    a Connor-Mosimann distribution or Dirichlet distribution (e.g. Gelman, Carlin, Stern & Rubin Chapter 3.5 (2004, <ISBN:1-58488-388-X>) to elicited
    quantiles of a multinomial distribution. Code is also provided to 
    sample from the distributions, generating inputs suitable for a 
    probabilistic sensitivity analysis / Monte Carlo simulation in a decision model.
Provides estimators of the mode of univariate
    data or univariate distributions.
Given independent and identically distributed observations X(1), ..., X(n) from a density f,
 provides five methods to perform a multiscale analysis about f as well as the necessary critical
 values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
 for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
 any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
 I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
 correction term Gamma in either method offers another two approaches which are more powerful on small scales and
 less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
 compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
 equivalent to the first and was introduced in Rufibach and Walther (2010).
Model-based trees for subgroup analyses in clinical trials and
  model-based forests for the estimation and prediction of personalised
  treatment effects (personalised models). Currently partitioning of linear
  models, lm(), generalised linear models, glm(), and Weibull models,
  survreg(), is supported.  Advanced plotting functionality is supported for
  the trees and a test for parameter heterogeneity is provided for the
  personalised models. For details on model-based trees for subgroup analyses
  see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for
  details on model-based forests for estimation of individual treatment effects
  see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.
Implements a general interface for model-based
    estimations for a wide variety of models (see support list of insight;
    Lüdecke, Waggoner & Makowski (2019) <doi:10.21105/joss.01412>), used
    in the computation of marginal means, contrast analysis and
    predictions.
This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.
Provides two important functions for producing Gain chart and Lift chart for any classification model.
Data sets used for demonstrating or testing model-related
    packages are contained in this package.
Uses 'dplyr' and 'tidyeval' to fit statistical models inside the database. It currently supports KMeans and linear regression models.
Website generator with HTML summaries for predictive models.
    This package uses 'DALEX' explainers to describe global model behavior. 
    We can see how well models behave (tabs: Model Performance, Auditor),
    how much each variable contributes to predictions (tabs: Variable Response) 
    and which variables are the most important for a given model (tabs: Variable Importance).
    We can also compare Concept Drift for pairs of models (tabs: Drifter).
    Additionally, data available on the website can be easily recreated in current R session.
    Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 
    at Warsaw University of Technology, Faculty of Mathematics and Information Science.
Defines classes and methods to learn models and use them
  to predict binary outcomes.  These are generic tools, but we also
  include specific examples for many common classifiers.
Local linear estimation of psychometric functions.
        Provides functions for nonparametric estimation of a
        psychometric function and for estimation of a derived threshold
        and slope, and their standard deviations and confidence
        intervals
Bootstrap cross-validation for ROC, AUC and Brier score to assess
    and compare predictions of binary status responses.
A minimalistic but flexible framework that facilitates the creation,
    management and training of multiple 'caret' models. A model grid consists of
    two components: (1) a set of settings that is shared by all models by 
    default, and (2) specifications that apply only to the individual models.
    When the model grid is trained, model and training specifications are first 
    consolidated from the shared and the model specific settings into complete
    'caret' model configurations. These models are then trained with the 'train' 
    function from the 'caret' package.
Calculate the financial impact of using a churn model in terms of cost, revenue, profit and return on investment.
An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.
Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
The model.matrix() function in R is  convenient for transforming training dataset for modeling. 
    But it does not save any parameter used in transformation, so it is hard to apply the same transformation 
    to test dataset or new dataset. This package is created to solve the problem.
Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.
Plots to assess the quality of predictive models from a business perspective. 
    Using these plots, it can be shown how implementation of the model will impact business 
    targets like response on a campaign or return on investment. Different scopes can be selected: 
    compare models, compare datasets or compare target class values and various plot customization 
    and highlighting options are available.
    targets like response on a campaign. Different scopes can be selected: compare models, compare 
    datasets or compare target class values and various plot customization and highlighting options 
    are available.
Functions for modelling that help you seamlessly
    integrate modelling into a pipeline of data manipulation and
    visualisation.
The ROC curve method is one of the most important and commonly used 
    methods for model accuracy assessment, which is one of the most important elements 
    of model evaluation.
      The 'modelROC' package is a model-based ROC assessment tool, which directly works 
    for ROC analysis of regression results for logistic regression of binary variables, 
    including the glm() and lrm() commands, and COX regression for survival analysis, 
    including the cph() and coxph() commands.
      The most important feature of 'modelROC' is that both the model and the independent 
    variables can be analysed simultaneously, and for survival analysis 
    multiple time points and area under the curve analysis are supported.
      Still, flexible visualisation is possible with the 'ggplot2' package.
      Reference are Kelly H. Zou (1998) <doi:10.1002/(sici)1097-0258(19971015)16:19%3C2143::aid-sim655%3E3.0.co;2-3> and
    P J Heagerty (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
Automate the explanatory analysis of machine learning predictive models.
    Generate advanced interactive model explanations in the form of a serverless HTML
    site with only one line of code. This tool is model-agnostic, therefore compatible
    with most of the black-box predictive models and frameworks. The main function
    computes various (instance and model-level) explanations and produces
    a customisable dashboard, which consists of multiple panels for plots with their
    short descriptions. Easily save the dashboard and share it with others. Tools
    for Explanatory Model Analysis unite with tools for Exploratory Data Analysis
    to give a broad overview of the model behavior.
Create beautiful and customizable tables to summarize several
    statistical models side-by-side. Draw coefficient plots, multi-level
    cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and
    correlation matrices. This package supports dozens of statistical models,
    and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint,
    Excel, RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 
    'knitr' dynamic documents.
Provides a number of testthat tests that can be
    used to verify that tidy(), glance() and augment() methods meet
    consistent specifications. This allows methods for the same generic to
    be spread across multiple packages, since all of those packages can
    make the same guarantees to users about returned objects.
The time series forecasting framework for use with the 'tidymodels' ecosystem. 
    Models include ARIMA, Exponential Smoothing, and additional time series models
    from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" 
    (<https://otexts.com/fpp2/>).
    Refer to "Prophet: forecasting at scale" 
    (<https://research.fb.com/blog/2017/02/prophet-forecasting-at-scale/>.).
A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, 
    weighted averaging, and stacking. These techniques are popular methods 
    to improve forecast accuracy and stability. Refer to papers such as 
    "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
Use the 'GluonTS' deep learning library inside of 'modeltime'.
    Available models include 'DeepAR', 'N-BEATS', and 'N-BEATS' Ensemble. 
    Refer to "GluonTS - Probabilistic Time Series Modeling" 
    (<https://ts.gluon.ai/index.html>).
Use the 'H2O' machine learning library inside of 'modeltime'.
    Available models include 'AutoML' for Automatic Machine Learning. 
    Please see H2O.ai for more information <https://github.com/h2oai/h2o-3>.
A 'modeltime' extension that implements forecast resampling tools
    that assess time-based model performance and stability for a single time series, 
    panel data, and cross-sectional time series analysis.
A collection of tools to deal with statistical models. 
  The functionality is experimental and the user interface is likely to
  change in the future. The documentation is rather terse, but packages `coin'
  and `party' have some working examples. However, if you find the
  implemented ideas interesting we would be very interested in a discussion
  of this proposal. Contributions are more than welcome!
Makes a word cloud of text, sized by the frequency of the word, and colored either by user-specified colors or colored by the strength of the coefficient of that text derived from a regression model.
Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.
Provides functions that fit two modern education-based value-added models.  
                 One of these models is the quantile value-added model.  This model 
                 permits estimating a school's value-added based on specific quantiles of 
                 the post-test distribution.  Estimating value-added based on quantiles 
                 of the post-test distribution provides a more complete picture of an 
                 education institution's contribution to learning for students of all 
                 abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017)
                 <doi:10.1111/rssa.12195> for more details.  The second model is a temporally 
                 dependent value-added model. This model takes into account the temporal 
                 dependence that may exist in school performance  between two cohorts in one 
                 of two ways.  The first is by modeling school  random effects with a 
                 non-stationary AR(1) process.  The second is by  modeling school effects 
                 based on previous cohort's post-test performance. In addition to more 
                 efficiently estimating value-added, this model permits making statements 
                 about the persistence of a schools effectiveness. The standard  value-added 
                 model is also an option.
User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.
Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.
Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.
This is a shiny module that presents a file picker user interface to get an Excel file name, and reads the Excel sheets using 'readxl' package and returns the resulting sheet(s) as a vector and data in dataframe(s).
Algorithms for multivariate outlier detection when missing values occur.
    Algorithms are based on Mahalanobis distance or data depth. Imputation is based
    on the multivariate normal model or uses nearest neighbour donors. The algorithms 
    take sample designs, in particular weighting, into account. The methods are 
    described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.
Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by  Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size.
    Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>.
    Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. 
    sen, P. K. (1968).<doi:10.2307/2285891>.
    Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>.
    Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>.
    Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>.
    Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>.
    Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>.
    Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.
Package for processing downloaded MODIS Cloud Product HDF
        files and derived files. Specifically, MOD35_L2 cloud product
        files, and the associated MOD03 geolocation files (for
        MODIS-TERRA); and MYD35_L2 cloud product files, and the
        associated MYD03 geolocation files (for MODIS-AQUA).  The
        package will be most effective if the user installs MRTSwath
        (MODIS Reprojection Tool for swath products;
        https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath),
        and adds the directory with the MRTSwath executable to the
        default R PATH by editing ~/.Rprofile.
Package for downloading Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover data. Global daily snow cover at 500 m resolution derived from MODIS is made available by the National Snow and Ice Center Data Center <http://nsidc.org/>.
Programmatic interface to the Oak Ridge National Laboratories
    'MODIS Land Products Subsets' web services 
    (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy
    downloads of 'MODIS' time series directly to your R workspace or
    your computer.
Allows automating the creation of time series of rasters derived
    from MODIS satellite land products data. It performs several typical
    preprocessing steps such as download, mosaicking, reprojecting and resizing
    data acquired on a specified time period. All processing parameters
    can be set using a user-friendly GUI. Users can select which layers of
    the original MODIS HDF files they want to process, which additional
    quality indicators should be extracted from aggregated MODIS quality
    assurance layers and, in the case of surface reflectance products,
    which spectral indexes should be computed from the original reflectance
    bands. For each output layer, outputs are saved as single-band raster
    files corresponding to each available acquisition date. Virtual files
    allowing access to the entire time series as a single file are also created.
    Command-line execution exploiting a previously saved processing options
    file is also possible, allowing users to automatically update time series
    related to a MODIS product whenever a new image is available.
    For additional documentation refer to the following article: 
    Busetto and Ranghetti (2016) <doi:10.1016/j.cageo.2016.08.020>.
Calculate predicted levels and marginal effects,
    using the delta method to calculate standard errors. This is an R-based
    version of the 'margins' command from Stata.
The algorithms implemented here are used to detect the community structure of a network. 
            These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) <https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.
'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent.
Contains basic tools for performing 
  multiple-output quantile regression and computing
  regression quantile contours by means of directional
  regression quantiles. In the location case, one can thus
  obtain halfspace depth contours in two to six dimensions.
  Hallin, M., Paindaveine, D. and Siman, M. (2010)
  Multivariate quantiles and multiple-output regression quantiles:
  from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669
  For more references about the method, see Help pages.
Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.
Collection of tools for regression and classification tasks. The package implements a consistent user interface to the most popular regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, and complements it with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453> to 
    estimate 50 percent effective dose (ED50) in the study of drug sensitivity. The method has 
    its own advantages for that it can provide robust ED50 estimation. This package contains 
    the modified function of Choi's turning point method.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
Provides modules as an organizational unit for source code. Modules
    enforce to be more rigorous when defining dependencies and have
    a local search path. They can be used as a sub unit within packages
    or in scripts.
RMSD and Internal Energy analysis of NAMD and YASARA Molecular Dynamics output files. Allows to comparison of different dynamics per different complexes. Input files have to be ASCII files tab separated.
Modular implementation of Multiobjective Evolutionary Algorithms 
              based on Decomposition (MOEA/D) [Zhang and Li (2007), 
              <DOI:10.1109/TEVC.2007.892759>] for quick assembling and 
              testing of new algorithmic components, as well as easy 
              replication of published MOEA/D proposals. The full framework is
              documented in a paper published in the Journal of Statistical 
              Software [<doi:10.18637/jss.v092.i06>].
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
This is a thin wrapper around the 'MOEX' 'ISS' REST interface, see
    <https://iss.moex.com>.  It allows to quickly fetch price candles for a 
    particular security, obtain its profile information and so on.
Statistical utilities for the analysis of data by means of the Marshall-Olkin Extended Zipf distribution are presented. The distribution is a two-parameter extension of the widely used Zipf model. By plotting the probabilities in log-log scale, this two-parameter extension allows a concave as well as a convex behavior of the function at the beginning of the distribution, maintaining the linearity, associated to the Zipf model, in the tail.
Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for data sets with large number of variables. For more information, see Sinha, Malo & Kuosmanen (2015) <doi:10.1080/10618600.2014.899236>.
Support JSON flattening in a long data frame way, where the nesting keys will be stored in the absolute path. It also
   provides an easy way to summarize the basic description of a JSON list. The idea of 'mojson' is to transform a JSON object in an 
   absolute serialization way, which means the early key-value pairs will appear in the heading rows of the resultant data frame. 
   'mojson' also provides an alternative way of comparing two different JSON lists, returning the left/inner/right-join style results.
Contains functions for performing Mokken
   scale analysis on test and questionnaire data.
   It includes an automated item selection algorithm, and various checks of model assumptions.
Multi-Objective optimization based on the Kriging metamodel.
  Important functions: mkm() (builder for the multiobjective models), MVPF() (sequential minimizator using variance reduction), 
  MEGO() (generalization of ParEgo) and HEGO() (minimizator using the expected hypervolume improvement).
  References are Passos and Luersen (2018) <doi:10.1590/1679-78254324>.
Surface topography calculations of Dirichlet's normal energy,
    relief index, surface slope, and orientation patch count for teeth using scans of
    enamel caps.
    Importantly, for the relief index and orientation patch count calculations to
    work, the scanned tooth files must be oriented with the occlusal plane parallel
    to the x and y axes, and perpendicular to the z axis. The files should also be
    simplified, and smoothed in some other software prior to uploading into R.
Model for simulating language evolution in terms of cultural evolution (Smith & Kirby (2008) <DOI:10.1098/rstb.2008.0145>; Deacon 1997). The focus is on the emergence of argument-marking systems (Dowty (1991) <DOI:10.1353/lan.1991.0021>, Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) <DOI:10.1075/sl.21.2.04ari>, Lestrade (2010) <DOI:10.7282/T3ZG6R4S>), person indexing (Ariel 1999, Dahl (2000) <DOI:10.1075/fol.7.1.03dah>, Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) <DOI:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015) <DOI:10.1002/9781118346136.ch27>) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) <DOI:10.3765/bls.v13i0.1834>, Givon (1995) <DOI:10.1075/z.74>, Croft (2000), Saffran (2001) <DOI:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) <DOI:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). In Lestrade (2015b) <DOI:10.15496/publikation-8640>, Lestrade (2015c) <DOI:10.1075/avt.32.08les>, and Lestrade (2016) <DOI:10.17617/2.2248195>), which reported on the results of preliminary versions, this package was announced as WDWTW (for who does what to whom), but for reasons of pronunciation and generalization the title was changed.
A set of functions to be able to manage data shared on a
  'MOLGENIS Armadillo' storage server ('MinIO').
Discover 'OpenID Connect' endpoints and authenticate
    using device flow. Used by 'MOLGENIS' packages.
Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.
Outlier detection in, possibly high-dimensional, categorical data following
	     Mads Lindskou et al. (2019) <doi:10.1111/sjos.12407>.
Networks provide a means to incorporate molecular interactions into
    reasoning, but on the omics-level, they are currently mainly used to combine
    genomic and proteomic information. We here present a novel network analysis
    pipeline that enables integrative analysis of multi-omics data including
    metabolomics. It allows for comparative conclusions between two different
    conditions, such as tumor subgroups, healthy vs. disease, or generally control
    vs. perturbed.
    Our approach focuses on interactions and their strength instead of on node
    properties and includes molecules with low abundance and unknown function. We
    use correlation-induced networks that are reduced and combined to form 
    heterogeneous, multi-omics molecular networks. Prior information such as
    metabolite-protein interactions are incorporated. A semi-local, path-based
    integration step denoises the network and ensures integrative conclusions. As
    case studies, we investigate differential drug response in breast cancer tumor
    datasets providing proteomics, transcriptomics, phospho-proteomics and
    metabolomics data and contrasting patients with different estrogen receptor
    status.
    Our proposed pipeline leverages multi-omics data for differential predictions,
    e.g. on drug response, and includes prior information on interactions.
    The case study presented in the vignette uses data published by
    Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only
    to the software and explicitly not to the included data.
Bayesian model selection and averaging for regression and mixtures for non-local and selected local priors.
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and  the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Functions to calculate: moments, Pearson's kurtosis,
        Geary's kurtosis and skewness; tests related to them
        (Anscombe-Glynn, D'Agostino, Bonett-Seier).
Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
The goal of 'Momocs' is to provide a complete, convenient, 
       reproducible and open-source toolkit for 2D morphometrics.
       It includes most common 2D morphometrics approaches on outlines, open outlines, 
       configurations of landmarks, traditional morphometrics, and facilities for data preparation, 
       manipulation and visualization with a consistent grammar throughout.
       It allows reproducible, complex morphometrics analyses and other morphometrics approaches 
       should be easy to plug in, or develop from, on top of this canvas.
Cluster any count data matrix with a fixed number of variables, such as document/term matrices. It integrates the dimension reduction aspect of topic models in the mixture models framework. Inference is done by means of a greedy Classification Variational Expectation Maximisation (C-VEM) algorithm. An Integrated Classication Likelihood (ICL) model selection is designed for selecting the latent dimension (number of topics) and the number of clusters. For more details, see the article of Jouvin et. al. (2020) <arxiv:1909.00721>.
'MetaOMineR' suite is a set of R packages that offers many functions and modules needed for the analyses 
    of quantitative metagenomics data. 'momr' is the core package and contains routines for biomarker identification and exploration.
    Developed since the beginning of field, 'momr' has evolved and is structured around the different modules 
    such as preprocessing, analysis, vizualisation, etc. See package help for more information.
It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.
   References used for this package:
   Arellano-Valle, R. B. & Genton, M. G. (2005). On fundamental skew distributions. Journal of Multivariate Analysis, 96, 93-116.
   Galarza C.E., Matos L.A., Dey D.K. & Lachos V.H. (2019) On Moments of Folded and Truncated Multivariate Extended Skew-Normal Distributions. Technical report. ID 19-14. University of Connecticut. <https://stat.uconn.edu/tech-reports-2019/>.
A HTML widget rendering the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the built-in features of the 'Monaco' editor, the widget allows to prettify multiple languages, to view the 'HTML' rendering of 'Markdown' code, and to view and resize 'SVG' images.
Keep track of dates in terms of fractional calendar months 
  per Damien Laker "Time Calculations for Annualizing Returns: the Need for Standardization", 
  The Journal of Performance Measurement, 2008.
  Model dates as of close of business.
  Perform date arithmetic in units of "months" and "years".
  Allow "infinite" dates to model "ultimate" time.
The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample. 
  As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). 
  We named the package after the famous painter as the graphical output recalls Mondrian’s paintings.
Allows to pull data from MonetDB into R.
High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'.
    Includes support for aggregation, indexing, map-reduce, streaming, encryption,
    enterprise authentication, and GridFS. The online user manual provides an overview 
    of the available methods in the package: <https://jeroen.github.io/mongolite/>.
Translate R code into MongoDB aggregation pipelines.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
Train and make predictions from a multi-layer perceptron neural
        network with optional partial monotonicity constraints.
Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) 
	development. All functions handle both binary and continuous target variable. 
	Functions that use isotonic regression in the first stage of binning process have an additional 
	feature for correction of minimum percentage of observations and minimum target rate per bin. 	
	Additionally, monotonic trend can be identified based on raw data or, if known in advance,
	forced by functions' argument. Missing values and other possible special values are treated 
	separately from so-called complete cases.
This is an add-on package to the 'monobin' package that simplifies its use. It provides shiny-based user interface (UI) 
	     that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning 
	     of numeric risk factors when building credit rating models. The additional functions implemented in 
	     'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. 
	     The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, 
	     while special case and outlier imputation functions are written to be used with shiny UI.
Implementation of the Monothetic Clustering
    algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on
    continuous data sets. A lot of extensions are included in the package,
    including applying Monothetic clustering on data sets with circular
    variables, visualizations with the results, and permutation and
    cross-validation based tests to support the decision on the number of
    clusters.
Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology charts and distributional and diversity maps are also available.
Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range.
Estimation of multivariate normal (MVN) and student-t data of 
 arbitrary dimension where the pattern of missing data is monotone.
 See Pantaleo and Gramacy (2010) <doi:10.1214/10-BA602>.
 Through the use of parsimonious/shrinkage regressions 
 (plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
 the package can handle a nearly arbitrary amount of missing data. 
 The current version supports maximum likelihood inference and 
 a full Bayesian approach employing scale-mixtures for Gibbs sampling.
 Monotone data augmentation extends this Bayesian approach to arbitrary 
 missingness patterns.  A fully functional standalone interface to the 
 Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown),
 Horseshoe (from Carvalho, Polson, & Scott), and ridge regression 
 with model selection via Reversible Jump, and student-t errors 
 (from Geweke) is also provided.
Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. 
 "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) <doi:10.7717/peerj-cs.56>.
Functions for fitting monotone polynomials to data.
             Detailed discussion of the methodologies used can be
             found in Murray, Mueller and Turlach (2013)
             <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and
             Turlach (2016) <doi:10.1080/00949655.2016.1139582>.
A fast up-and-down-blocks implementation for the pool-adjacent-violators algorithm 
  for simple linear ordered monotone regression, including two spin-off functions
  for unimodal and bivariate monotone regression.
Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) <doi:10.1016/j.jfineco.2010.06.006> and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.
Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.
Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.
Simplifies Monte Carlo simulation studies by automatically 
             setting up loops to run over parameter grids and parallelising
             the Monte Carlo repetitions. It also generates LaTeX tables.
Monte Carlo simulation allows to test different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model.
  First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists. 
  When data generation is completed (or when generated data sets are given) model test can also be run.
A collection of functions to connect to a 'Moodle' database, cache relevant tables locally and generate learning analytics. 
    'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit <https://moodle.org>.
Several analysis-related functions for the book entitled "R
    statistics and graph for medical articles" (written in Korean), version 1,
    by Keon-Woong Moon with Korean demographic data with several plot
    functions.
Provides real & simulated datasets containing time-series traffic observations
    and additional information pertaining to Loop 1 "Mopac" located in Austin, Texas.
A multi-objective optimization solver based on particle
        swarm optimization with crowding distance.
With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.
MOrder provide functions to check time homogeneity and order
    of markov chain by using chi-squared test, AIC value and BIC value.
Additions to 'party' package : tools for the interpretation of forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti et al (2017) <arXiv:1310.5726>, Hapfelmeier and Ulm (2013) <doi:10.1016/j.csda.2012.09.020>, Altmann et al (2010) <doi:10.1093/bioinformatics/btq134>) and parallelized versions of conditional forest and variable importance functions.
This package is a port of the MATLAB colourmap functions
    accompanying the paper M. Geissbuehler and T. Lasser, "How to display data
    by color schemes compatible with red-green color perception deficiencies,"
    Opt. Express 21, 9862-9874 (2013) to R.
Tokenize text into morphemes. The morphemepiece algorithm uses a 
  lookup table to determine the morpheme breakdown of words, and falls back on a 
  modified wordpiece tokenization algorithm for words not found in the lookup 
  table.
Provides data about morphemes, the smallest units of meaning in a 
    language.
Mixture of logistic regressions parameters (H)estimation with
    (U)spectral methods. The main methods take d-dimensional inputs and a vector
    of binary outputs, and return parameters according to the GLMs mixture model
    (General Linear Model). For more details see chapter 3 in the PhD thesis of
		Mor-Absa Loum: <http://www.theses.fr/s156435>, available here
		<https://tel.archives-ouvertes.fr/tel-01877796/document>.
A toolset for Geometric Morphometrics and mesh processing. This
    includes (among other stuff) mesh deformations based on reference points,
    permutation tests, detection of outliers, processing of sliding
    semi-landmarks and semi-automated surface landmark placement.
Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section.
Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package will be described in Šlenker et al. (in prep).
Tools for ecotoxicologists and regulators dedicated to the
    mathematical and statistical modelling of toxicity test data. They use advanced and
    innovative methods for a valuable quantitative environmental risk assessment. See also
    Delignette-Muller et al. (2017) <doi:10.1021/acs.est.6b05326>. and Baudrot et al. (2018) <doi:10.1021/acs.est.7b05464>.
MorseGen is a program for generating raw data based on
        user-specified summary (descriptive) statistics. Samples based
        on the supplied statistics are drawn from a normal distribution
        (or, in some cases, an exponential distribution) and scaled to
        match the desired descriptive statistics. Intended uses include
        creating raw data that fits desired characteristics or to
        replicate the results in a published study.
A collection of functions for the analysis of archaeological mortality
    data (on the topic see e.g. Chamberlain 2006 
    <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). 
    It takes demographic data in different formats and displays the result in a standard life table
    as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population).
Life expectancy is highly correlated over time among countries and 
  between males and females. These associations can be used to improve forecasts. 
  Here we have implemented a method for forecasting female life expectancy based on 
  analysis of the gap between female life expectancy in a country compared with
  the record level of female life expectancy in the world. Second, to forecast 
  male life expectancy, the gap between male life expectancy and female life 
  expectancy in a country is analysed. We named this method the Double-Gap model.
  For a detailed description of the method see Pascariu et al. (2017). 
  <doi:10.1016/j.insmatheco.2017.09.011>.
Fit the most popular human mortality 'laws', and construct 
  full and abridge life tables given various input indices. A mortality
  law is a parametric function that describes the dying-out process of 
  individuals in a population during a significant portion of their 
  life spans. For a comprehensive review of the most important mortality 
  laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. 
  Practical functions for downloading data from various human mortality 
  databases are provided as well.
Classes to implement and plot cohort life tables
    for actuarial calculations. In particular, birth-year dependent mortality
    tables using a yearly trend to extrapolate from a base year are implemented,
    as well as period life table, cohort life tables using an age shift, and
    merged life tables.
Age-specific mortality rates are estimated and projected using 
    the Kannisto, Lee-Carter and related methods as described in 
    Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.
Returns information about characters, locations, and episodes from
    'The Rick and Morty' API: <https://rickandmortyapi.com>.
Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Part of the Project MOSAIC (<http://mosaic-web.org/>)
    suite that provides utility functions for doing calculus 
    (differentiation and integration) in R. The main differentiation and 
    antidifferentiation operators are described using formulas and return functions 
    rather than numerical values. Numerical values can be obtained by evaluating
    these functions.
Common utilities used in other MOSAIC-family packages are 
    collected here.
Data sets from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Provides functions for evaluating, displaying, and interpreting statistical models. The goal is to abstract the operations on models from the particular architecture of the model. For instance, calculating effect sizes rather than looking at coefficients. The package includes interfaces to both regression and classification architectures, including lm(), glm(), rlm() in 'MASS', random forests and recursive partitioning, k-nearest neighbors, linear and quadratic discriminant analysis, and models produced by the 'caret' package's train(). It's straightforward to add in other other model architectures.
Performs simultaneous hyperparameter tuning and
  feature selection through both single-objective and multi-objective
  optimization as described in Binder, Moosbauer et al. (2019) <arXiv:1912.12912>.
  Uses the 'ecr'-package as basis but adds mixed integer
  evolutionary strategies and multi-fidelity functionality as well as operators
  specific for the problem of feature selection.
This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.
High dimensionality, noise and heterogeneity among
    samples and features challenge the omic integration task. Here we
    present an omic integration method based on sparse singular value
    decomposition (SVD) to deal with these limitations, by: a. obtaining
    the main axes of variation of the combined omics, b. imposing sparsity
    constraints at both subjects (rows) and features (columns) levels
    using Elastic Net type of shrinkage, and c. allowing both linear and
    non-linear projections (via t-Stochastic Neighbor Embedding) of the
    omic data to detect clusters in very convoluted data
    (Gonzalez-Reymundez & Vazquez, 2020) <doi:10.1038/s41598-020-65119-5>.
Provides functions similar to the 'SAS' macros previously provided
              to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826>
              and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers
              which outline practical benefits and challenges of factorial
              and fractional factorial experiments for scientists interested
              in developing biological and/or behavioral interventions, especially
              in the context of the multiphase optimization strategy
              (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
              currently contains three functions. First, RelativeCosts1() draws a graph
              of the relative cost of complete and reduced factorial designs versus
              other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
              which contains a list of random numbers that can be used to conveniently
              assign participants to conditions in an experiment with
              many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
              size, or required sample size of a factorial or fractional factorial
              experiment, for main effects or interactions, given several possible choices
              of effect size metric, and allowing pretests and clustering.
Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2021) <arXiv:1910.12486>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2021) <arXiv:2106.12844>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.
Learning, manipulation and  evaluation of mixtures of  truncated basis  functions 
  (MoTBFs),  which include mixtures of  polynomials (MOPs) and  mixtures of truncated 
  exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
  networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The  package provides  functionality for learning  univariate, multivariate and
  conditional  densities, with the  possibility of incorporating prior  knowledge. Structural
  learning of hybrid Bayesian  networks is also provided. A set of useful tools is provided,
  including  plotting, printing  and likelihood  evaluation. This  package  makes use of  S3 
  objects, with two new classes called 'motbf' and 'jointmotbf'.
Measure of the Effect ('MOTE') is an effect size calculator, including a 
    wide variety of effect sizes in the mean differences family (all versions of d) and 
    the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central 
    confidence intervals for each effect size, relevant test statistics, and output 
    for reporting in APA Style (American Psychological Association, 2010, 
    <ISBN:1433805618>) with 'LaTeX'. In research, an over-reliance on p-values 
    may conceal the fact that a study is under-powered (Halsey, Curran-Everett, 
    Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>). A test may be statistically 
    significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 
    <doi:10.1177/0959354312436870>). Although the American Psychological Association 
    has long advocated for the inclusion of effect sizes (Wilkinson & American 
    Psychological Association Task Force on Statistical Inference, 1999 
    <doi:10.1037/0003-066X.54.8.594>), the vast majority of peer-reviewed, 
    published academic studies stop short of reporting effect sizes and confidence 
    intervals (Cumming, 2013, <doi:10.1177/0956797613504966>). 'MOTE' simplifies 
    the use and interpretation of effect sizes and confidence intervals. For more 
    information, visit <https://www.aggieerin.com/shiny-server>.
Describes spatial patterns of categorical raster data for 
    any defined regular and irregular areas. 
    Patterns are described quantitatively using built-in signatures 
    based on co-occurrence matrices but also allows for 
    any user-defined functions. 
    It enables spatial analysis such as search, change detection,
    and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).
Tools for spectral clustering of weighted directed networks using motif
    adjacency matrices. Methods perform well on large and sparse networks, and
    random sampling methods for generating weighted directed networks are also
    provided. Based on methodology detailed in Underwood, Elliott and Cucuringu
    (2020) <arXiv:2004.01293>.
Tools for motif analysis in multi-level networks.
    Multi-level networks combine multiple networks in one, e.g.
    social-ecological networks. Motifs are small configurations of nodes
    and edges (subgraphs) occurring in networks. 'motifr' can visualize
    multi-level networks, count multi-level network motifs and compare
    motif occurrences to baseline models. It also identifies contributions
    of existing or potential edges to motifs to find critical or missing
    edges. The package is in many parts an R wrapper for the excellent
    'SESMotifAnalyser' 'Python' package written by Tim Seppelt.
Functions for fitting models of trait evolution
        on phylogenies for continuous traits. The majority of functions
        described in Thomas and Freckleton (2012) <doi:10.1111/j.2041-210X.2011.00132.x> 	and include functions that allow for tests of variation in the rates of trait evolution.
The temporal relationship between motor neurons can offer 
    explanations for neural strategies. We combined functions to reduce neuron 
    action potential discharge data and analyze it for short-term, time-domain 
    synchronization. Even more so, motoRneuron combines most available methods 
    for the determining cross correlation histogram peaks and most available 
    indices for calculating synchronization into simple functions. See 
    Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> 
    for a more thorough introduction.
Functions to estimate start and duration of moult from moult 
             data, based on models developed in Underhill 
             and Zucchini (1988, 1990).
Lattice functions for drawing folded empirical cumulative
    distribution plots, or mountain plots. A mountain plot is similar
    to an empirical CDF plot, except that the curve increases from
    0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
    the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.
Extract from two-dimensional x-y coordinates of an arm-reaching trajectory, several dependent measures such as area under the curve, latency to start the movement, x-flips, etc.; which characterize the action-dynamics of the response. Mainly developed to analyze data coming from mouse-tracking experiments.
Mouse-tracking, the analysis of mouse movements in computerized
    experiments, is a method that is becoming increasingly popular in the
    cognitive sciences. The mousetrap package offers functions for importing,
    preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
    An introduction into mouse-tracking analyses using mousetrap can be found
    in Kieslich, Henninger, Wulff, Haslbeck, & Schulte-Mecklenbeck (2019)
    <doi:10.4324/9781315160559-9> (preprint: <https://psyarxiv.com/zuvqa/>).
Contains functions to access movement data stored in 'movebank.org'
    as well as tools to visualize and statistically analyze animal movement data,
    among others functions to calculate dynamic Brownian Bridge Movement Models.
    Move helps addressing movement ecology questions.
Provides the facility to calculate non-isotropic accumulated cost surface, least-cost paths, least-cost corridors, least-cost networks using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) <doi:10.1016/j.softx.2019.100331>.
Provides tools for animal movement modelling using hidden Markov
    models. These include processing of tracking data, fitting hidden Markov models
    to movement data, visualization of data and fitted model, decoding of the state
    process...
Tools to visualize movement data (e.g. from GPS tracking) and temporal changes of environmental data (e.g. from remote sensing) by creating video animations.
Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.
Fit and simulate mixtures of von Mises-Fisher distributions.
Weekly notified dengue cases in 25 districts of Sri Lanka from 2008/ week-52 to 2014/ week-21.
Multidimensional projection techniques are used to create two
    dimensional representations of multidimensional data sets.
Preprocessing and analysis of genomic data. 'MPAgenomics'
    provides wrappers from commonly used packages to streamline their repeated
    manipulation, offering an easy-to-use pipeline. The segmentation of
    successive multiple profiles is performed with an automatic choice of
    parameters involved in the wrapped packages. Considering multiple profiles
    in the same time, 'MPAgenomics' wraps efficient penalized regression methods
    to select relevant markers associated with a given outcome.
    Grimonprez et al. (2014) <doi:10.1186/s12859-014-0394-y>.
Algorithms compute concave convex (CC) estimators including robust (penalized) generalized linear models and robust support vector machines via the COCO - composite optimization by conjugation operator. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>,
      Wang et al. (2015) <doi:10.1002/bimj.201400143>,
      Wang et al. (2016) <doi:10.1177/0962280214530608>,
      Wang (2021) <doi:10.1007/s11749-021-00770-2>,
      Wang (2020) <arXiv:2010.02848>.
Fits Multinomial Probit Bayesian Additive Regression Trees.
Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.
It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
A collection of functions for estimation, testing and diagnostic checking for the mean-parametrized Conway-Maxwell-Poisson (COM-Poisson) regression model of Huang (2017) <doi:10.1177/1471082X17697749>.
Multivariate process capability analysis using the multivariate process capability vector. Allows to analyze a multivariate process with both normally and non-normally distributed and also with dependent and independent quality characteristics.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
Functions for calculating sample size and power for clinical trials
    with multiple (co-)primary endpoints.
Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.
(1) Our system provides a network-based strategies for metabolite pathway identification.(2) The MPINet can support the identification of pathways using Hypergeometric test based on metabolite set. (3)MPINet can support pathways from multiple databases.
A matrix discrete model having the form
    'M[i+1] = (I + Q)*M[i]'.
    The calculation of the values of 'M[i]' only for
    pre-selected values of 'i'. The method of calculation
    is presented in the vignette 'Fundament' ('Base'). Maybe it`s own
    idea of the author of the package. A weakness is that the method
    gives information only in selected steps of the process.
    It mainly refers to cases with matrices that are not Markov chain.
    If 'Q' is Markov transition matrix, then MUPkL() may be
    used to calculate the steady-state distribution 'p' for
    'p = Q*p'.
    Matrix power of non integer (matrix.powerni()) gives
    the same results as a mpower() from package 'matlib'.
    References:
    "Markov chains",
    (<https://en.wikipedia.org/wiki/Markov_chain#Expected_number_of_visits>).
    Donald R. Burleson, Ph.D. (2005), 
    "ON NON-INTEGER POWERS OF A SQUARE MATRIX", 
    (<http://www.blackmesapress.com/Eigenvalues.htm>).
Computing modified profile likelihood estimates for Weibull Shape and Regression Parameters. Modified likelihood estimates are provided.
Model stability and variable inclusion plots [Mueller and Welsh
    (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
    (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
    (2008, <doi:10.1214/07-AOS517>); Jiang et al. 
    (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
Leverages the R language to automate latent variable model estimation
	and interpretation using 'Mplus', a powerful latent variable modeling program
	developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package
    provides routines for creating related groups of models, running batches of
    models, and extracting and tabulating model parameters and fit statistics.
Uses recursive partitioning to create homogeneous subgroups based on structural equation models 
             fit in 'Mplus', a stand-alone program developed by Muthen and Muthen.
Exploratory graphical analysis of multivariate data,
    specifically gene expression data with different projection
    methods: principal component analysis, correspondence analysis,
    spectral map analysis.
Functions to compute and plot multivariate (partial) Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
    for comparisons between all types of variables including continuous vs
    continuous, continuous vs discrete and discrete vs discrete. Uses a
    nonparametric bias correction giving Bias Corrected Mutual Information
    (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
    large genomic datasets.
Calculates the Most Probable Number (MPN) to quantify the
    concentration (density) of microbes in serial dilutions of a laboratory
    sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>).
    Also calculates the Aerobic Plate Count (APC) for similar microbial
    enumeration experiments.
Symbolic computing with multivariate polynomials in R.
A procedure to test for dependence between point processes on the real line, e.g. causal dependence, correlation, inhibition or anti-correlation. The package also provides a number of utilities for plotting simultaneous point processes, and combining p-values.
Analysis of experimental multi-parent populations to detect
             regions of the genome (called quantitative trait loci, QTLs)
             influencing phenotypic traits. The population must be composed of crosses
             between a set of at least three parents (e.g. factorial design,
             'diallel', or nested association mapping). The functions cover data
             processing, QTL detection, and results visualization. The implemented
             methodology is described by Garin, Wimmer, Mezmouk, Malosetti and
             van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>.
Infer parental genotypes based on low-coverage population sequencing data and 
		thus can genotype mapping populations and construct ultra-high density linkage map 
		in a parent-independent manner. Weibo Xie et al. (2010) <doi:10.1073/pnas.1005931107>.
Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
A low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>,
  which applies a mass-preserving spline to soil attributes. Splining soil data 
  is a safe way to make continuous down-profile estimates of attributes measured
  over discrete, often discontinuous depth intervals.
Supplementary materials and datasets for the book "Modern Psychometrics With R" (Mair, 2018, Springer useR! series).
Fitting and testing multinomial processing tree (MPT) models, a
  class of nonlinear models for categorical data.  The parameters are the
  link probabilities of a tree-like graph and represent the latent cognitive
  processing steps executed to arrive at observable response categories
  (Batchelder & Riefer, 1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009
  <doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988
  <doi:10.1037/0033-295x.95.3.318>).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
Statistical or cognitive modeling usually requires a number of more or less 
    arbitrary choices creating one specific path through a 'garden of forking paths'. 
    The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, 
    <doi:10.1177/1745691616658637>) offers a principled alternative in which results 
    for all possible combinations of reasonable modeling choices are reported. 
    MPTmultiverse performs a multiverse analysis for multinomial processing tree 
    (MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models combining 
    maximum-likelihood/frequentist and Bayesian estimation approaches with 
    different levels of pooling (i.e., data aggregation). For the 
    frequentist approaches, no pooling (with and without parametric or nonparametric 
    bootstrap) and complete pooling are implemented using 
    MPTinR <https://cran.r-project.org/package=MPTinR>. 
    For the Bayesian approaches, no pooling, complete pooling, and three different 
    variants of partial pooling are implemented using 
    TreeBUGS <https://cran.r-project.org/package=TreeBUGS>. The main function is 
    fit_mpt() who performs the multiverse analysis in one call.
'RAMAS Metapop' <https://www.ramas.com/ramas.htm#metapop> is a 
    popular software package for performing spatially-explicit population 
    viability analysis. It is primarily GUI-driven, but can benefit from 
    integration into an R workflow, wherein model results can be subjected to 
    further analysis. 'RAMAS Metapop' stores metapopulation model parameter 
    settings and population dynamics simulation results in plain text files 
    (.mp files). This package facilitates access, summary and visualisation of 
    'RAMAS Metapop 5' outputs in order to better integrate 'RAMAS' analyses into 
    an R workflow.
Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis (3rd ed), by Montgomery, Peck
             and Vining.
             Some additional data sets and functions are also included.
Complements the book "Using R for Modelling and  
    Quantitative Methods in Fisheries" ISBN: 9780367469894, being 
    published in September 2020 by Chapman & Hall in their "Using R series". 
    There are numerous functions and data-sets that are used in the 
    book's many practical examples.
Parametric modeling of M-quantile regression coefficient functions.
mQTL provides a complete QTL analysis pipeline for metabolomic data. 
        Distinctive features include normalisation using PQN approach, peak alignment  
        using RSPA approach, dimensionality reduction using SRV approach and finally 
        QTL mapping using R/qtl package.
Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.
Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) <doi:10.2307/2556151>; Jolly (1965) <doi:10.2307/2333826>; Seber (1965) <doi:10.2307/2333827>) and Huggin's (1989) <doi:10.2307/2336377> closed population. 
	Link functions include logit, sine, and hazard.  Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) <doi:10.2307/2280784> estimator.
Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.
Bayesian estimation of inverse variance weighted (IVW), Burgess 
    et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et 
    al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian 
    randomization analyses.
Nuclear Magnetic Resonance is widely used in Life Science
    research. The package (<doi:10.1021/acs.jproteome.0c00684>) converts 1D 
    or 2D data into a matrix of values suitable for further data analysis and
    performs basic processing steps in a reproducible way. Negative values, a
    common issue in such data, are replaced by positive values. All used
    parameters are stored in a readable text file and can be restored from that
    file to enable exact reproduction of the data at a later time.
A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
    mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
    To infer which of the captured features are credible, Bayesian analysis is used.
    The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
    (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
    (3) Posterior credibility analysis of the differences of smooths created.
    The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011>.
    Matlab code is available under <http://cc.oulu.fi/~lpasanen/MRBSiZer/>.
The MRCV package provides functions for analyzing the association between
    one single response categorical variable (SRCV) and one multiple response
    categorical variable (MRCV), or between two or three MRCVs.  A modified Pearson
    chi-square statistic can be used to test for marginal independence for the one or
    two MRCV case, or a more general loglinear modeling approach can be used to examine
    various other structures of association for the two or three MRCV case.  Bootstrap-
    and asymptotic-based standardized residuals and model-predicted odds ratios are
    available, in addition to other descriptive information.
Animal abundance estimation via conventional, multiple covariate
    and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
    fitting is performed via maximum likelihood. Also included are diagnostics
    and plotting for fitted detection functions. Abundance estimation is via a
    Horvitz-Thompson-like estimator.
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics
Tools to get marine regions data from
    <http://www.marineregions.org/>. Includes tools to get region metadata,
    as well as data in 'GeoJSON' format, as well as Shape files. Use cases
    include using data downstream to visualize 'geospatial' data by marine
    region, mapping variation among different regions, and more.
Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications, in press, Processes 2021.
Model fitting, sampling and visualization
    for the (Hidden) Markov Random Field model with pairwise interactions and 
    general interaction structure from 
    Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>,
    which has many popular models used in 2-dimensional lattices
    as particular cases, like the Ising Model and Potts Model.
Performs the MRFA approach proposed by Sung et al. (2019+) <arXiv:1709.07064> to fit
    and predict nonlinear regression problems, particularly for large-scale and
    high-dimensional problems. The application includes deterministic or stochastic
    computer experiments, spatial datasets, and so on.
Approximate node interaction parameters of Markov Random Fields 
    graphical networks. Models can incorporate additional covariates, allowing users to estimate
    how interactions between nodes in the graph are predicted to change across
    covariate gradients. The general methods implemented in this package are described 
    in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Tools to compute depth measures and implementations of related 
             tasks such as outlier detection, data exploration and 
            classification of multivariate, regression and functional data.
A Markov random field structure estimator that uses a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion (Frondana, 2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.
Simulation from an 'mrgsolve' 
    <https://cran.r-project.org/package=mrgsolve> model using a parallel backend.  
    Input data sets are split (chunked) and simulated in parallel using 
    mclapply() or future_lapply() 
    <https://cran.r-project.org/package=future.apply>.
Perform sensitivity analysis on ordinary differential equation 
    based models, including ad-hoc graphical analyses based on structured 
    sequences of parameters as well as local sensitivity analysis. Functions 
    are provided for creating inputs, simulating scenarios and plotting outputs.
Fast simulation from ordinary differential equation
    (ODE) based models typically employed in quantitative pharmacology and
    systems biology.
Used on survival data to jointly estimate the hazard rate and the effects of covariates on failure times. Can accommodate covariates under the proportional and non-proportional hazards setting, and is ideal for analysis of survival data with long-term follow-up.
Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) 
    self-exciting process, with given immigrant hazard rate functions and 
    offspring density function. Calculate the likelihood of a MRHawkes process 
    with given hazard rate functions and offspring density function for an 
    (increasing) sequence of event times. Calculate the Rosenblatt residuals of the 
    event times. Predict future event times based on observed event times up to a 
    given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.
It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices.  The indices are proposed in Cugmas and Ferligoj (2018) <http://ibmi.mf.uni-lj.si/mz/2018/no-1/Cugmas2018.pdf>.
Various methods for MRI tissue classification.
A causal mediation approach under the counterfactual framework 
    to test the significance of total, direct and indirect effects. In this approach, 
    a group of methylated sites from a predefined region are utilized as the mediator, 
    and the functional transformation is used to reduce the possible high dimension 
    in the region-based methylated sites and account for their location information.
Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.
Computes mutual information matrices from continuous, categorical 
  and survival variables, as well as feature selection with minimum redundancy, 
  maximum relevance (mRMR) and a new ensemble mRMR technique. Published in
  De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.
Computes multiple correlation coefficient when the data matrix is given and tests its significance.
A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC 
            (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also 
            contains functions to simulate data under a certain topology, to visualize a graph in different 
            ways, and to compare graphs and quantify the differences. 
            See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2018) <arXiv:1806.01899>.
An easy-to-use IMAP client that provides tools for message searching,
    selective fetching of message attributes, mailbox management, attachment extraction, 
    and several other IMAP features, paving the way for e-mail data analysis.
We can calculate directly used this package the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.
We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q).   We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from  independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals  in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) <doi:10.1145/3424670>.
Frequentist and Bayesian linear regression for large data sets. Useful 
    when the data does not fit into memory (for both frequentist and Bayesian regression), 
    to make running time manageable (mainly for Bayesian regression), and to reduce 
    the total running time because of reduced or less severe memory-spillover into 
    the virtual memory. This is an implementation of Merge & Reduce for linear regression
    as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020).
    'Streaming statistical models via Merge & Reduce'. International Journal of
    Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.
An implementation of the MRS algorithm for comparison across distributions,
  as described in Jacopo Soriano, Li Ma (2016) <doi:10.1111/rssb.12180>. 
  The model is based on a nonparametric process taking the form of a Markov model 
  that transitions between a "null" and an "alternative" state 
  on a multi-resolution partition tree of the sample space. 
  MRS effectively detects and characterizes a variety of underlying differences. 
  These differences can be visualized using several plotting functions.
An R implementation of 'GUIDE' style algorithm focusing on subgroup identification problem 
            under multiple responses of Loh et al. (2019) <doi:10.1002/widm.1326>. This package is intended for use 
            for randomized trials and observational studies.
This package contains functions, data and examples to compute a multiplicative-regression model for relative survival.
Provide a sample size calculator for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.
Provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on methodology developed in Qian et al. (2020) <doi:10.1093/biomet/asaa070>. Also provides a power calculator when the sample size is input by the user.
Calculates ARMSS (age related multiple sclerosis severity), and both local and global MSSS (multiple sclerosis severity score).
Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.
Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among response variables and borrowing the strength from related areas and auxiliary variables to increase the effectiveness of sample size, the multivariate model in this package is based on multivariate model 1 proposed by Roberto Benavent and Domingo Morales (2016) <doi:10.1016/j.csda.2015.07.013>. Benchmarking in Small Area Estimation is a modification of Small Area Estimation model to guarantee that the aggregate weighted mean of the county predictors equals the corresponding weighted mean of survey estimates. Difference Benchmarking is the simplest benchmarking method but widely used by multiplying empirical best linear unbiased prediction (EBLUP) estimator by the common adjustment factors (J.N.K Rao and Isabel Molina, 2015).
Multi-step adaptive elastic-net (MSAENet) algorithm for
    feature selection in high-dimensional regressions proposed in
    Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
    with support for multi-step adaptive MCP-net (MSAMNet) and
    multi-step adaptive SCAD-net (MSASNet) methods.
Implements multivariate ratio benchmarking small area estimation. This package provides ratio benchmarking estimation for univariate and multivariate small area estimation and its MSE. In fact, MSE estimators for ratio benchmark are not readily available, so resampling method that called parametric bootstrap is applied. The ratio benchmark model and parametric bootstrap in this package are based on the model proposed in small area estimation. J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7).
The new methodology "moving subset analysis" provides functions to investigate the effect of environmental conditions on the CO2 fertilization effect within longterm free air carbon enrichment (FACE)  experiments. In general, the functionality is applicable to derive the influence of a third variable (forcing experiment-support variable) on the relation between a dependent and an independent variable.
Statistical Analyses of Methylation-sensitive Amplification Polymorphism (MSAP) assays.
Visualizes multiple sequence alignments dynamically within the
    Shiny web application framework.
Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element
 isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks,
 examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI)
 ion sources.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).
Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters. Numerical solutions are provided for its extensions.
Create native charts for 'Microsoft PowerPoint' and 'Microsoft Word' documents. 
 These can then be edited and annotated. Functions are provided to let users create charts, modify 
 and format their content. The chart's underlying data is automatically saved within the 
 'Word' document or 'PowerPoint' presentation. It extends package 'officer' that does 
 not contain any feature for 'Microsoft' native charts production.
Three generalizations of the synthetic control method (which has 
    already an implementation in package 'Synth') are implemented: first, 
    'MSCMT' allows for using multiple outcome variables, second, time series 
    can be supplied as economic predictors, and third, a well-defined 
    cross-validation approach can be used.
    Much effort has been taken to make the implementation as stable as possible 
    (including edge cases) without losing computational efficiency.
    A detailed description of the main algorithms is given in 
    Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
Find common entities detected in both positive and negative
    ionization mode, delete this entity in the less sensible mode and combine both
    matrices.
Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.
Methods for analyzing and using quartets displayed on a collection of gene trees, 
      primarily to make inferences about the species tree or network under the multi-species 
      coalescent model. These include quartet hypothesis tests for the model, as developed by 
      Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, simplex plots of quartet concordance factors
      as presented by Allman et al. (2020) <doi:10.1101/2020.02.13.948083>, species tree inference methods 
      based on quartet distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and 
      Yourdkhani and Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, and the NANUQ algorithm for inference of level-1 
      species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>. Software announcement by 
      Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.
Statistical tests for validating multispecies coalescent gene tree simulators, using pairwise distances and rooted triple counts. Background is given by Allman, Banos, and Rhodes (2019) <arXiv:1908.01424>.
R Client for the Microsoft Cognitive Services Text Analytics
    REST API, including Sentiment Analysis, Topic Detection, Language Detection,
    and Key Phrase Extraction. An account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
R Client for the Microsoft Cognitive Services 
  'Text-to-Speech' REST API, including voice synthesis. A valid account 
  must be registered at the Microsoft Cognitive Services website 
  <https://azure.microsoft.com/services/cognitive-services/> in order to 
  obtain a (free) API key. Without an API key, this package will not 
  work properly.
R Client for the Microsoft Cognitive Services Web Language Model
    REST API, including Break Into Words, Calculate Conditional
    Probability, Calculate Joint Probability, Generate Next Words, and List
    Available Models. A valid account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
Implements the method of successive dichotomizations by
    Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, 
    which estimates item measures, person measures and ordered rating
    category thresholds given ordinal rating scale data.
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.
Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components.  The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
Parametric estimation in stochastic differential equations with random effects in the drift, or in the diffusion or both. Approximate maximum likelihood methods are used. M. Delattre, V. Genon-Catalot and A. Samson (2012) <doi:10.1111/j.1467-9469.2012.00813.x> M. Delattre, V. Genon-Catalot and A. Samson (2015) <doi:10.1051/ps/2015006> M. Delattre, V. Genon-Catalot and A. Samson (2016) <doi:10.1016/j.jspi.2015.12.003>.
Development, simulation testing, and implementation of management procedures for fisheries 
    (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).
A companion to the Chinese book ``Modern Statistical Graphics''.
Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2019) <doi:10.18637/jss.v091.i04>.
Implements the three parallel forecast combinations of Markov Switching GARCH and extreme learning machine model along with the selection of appropriate model for volatility forecasting. For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>.
Multinomial logistic regression with sparse group lasso
        penalty. Simultaneous feature selection and parameter
        estimation for classification. Suitable for high dimensional
        multiclass classification with many classes. The algorithm
        computes the sparse group lasso penalized maximum likelihood
        estimate. Use of parallel computing for cross validation and
        subsampling is supported through the 'foreach' and 'doParallel'
        packages. Development version is on GitHub, please report
        package issues on GitHub.
For fitting multivariate response and multiple predictor linear regressions with an arbitrary group structure assigned on the regression coefficient matrix, using the multivariate sparse group lasso and the mixed coordinate descent algorithm.
This is the library that can serialize or unserialize MessagePack format data.
Computes the degrees of freedom of the lasso,
        elastic net, generalized elastic net and adaptive lasso based
        on the generalized path seeking algorithm.  The optimal model
        can be selected by model selection criteria including Mallows'
        Cp, bias-corrected AIC (AICc), generalized cross validation
        (GCV) and BIC.
Provides new functions info(), warn() and error(), similar to message(),
    warning() and stop() respectively. However, the new functions can have a 'level'
    associated with them, so that when executed the global level option determines whether
    they are shown or not. This allows debug modes, outputting more information. The can also
    output all messages to a log file.
Allows for the computation of mSHAP values on
    two-part models as proposed  by Matthews, S. and Hartman, B. 
    (2021) <arXiv:2106.08990>.
    Also contains functions for simple plotting of
    the results (or any SHAP values). For information about the
    TreeSHAP algorithm that mSHAP builds on, see Lundberg, S.M.,
    Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz,
    R., Himmelfarb, J., Bansal, N., Lee, S.I. (2020)
    <doi:10.1038/s42256-019-0138-9>.
The Molecular Signatures Database ('MSigDB') is one of the most widely 
    used and comprehensive databases of gene sets for performing gene set 
    enrichment analysis <doi:10.1016/j.cels.2015.12.004>. The 'msig' package provides 
    you with powerful, easy-to-use and flexible query functions for the 'MsigDB' 
    database.
        There are 2 query modes in the 'msig' package: online query and local query. 
    Both queries contain 2 steps: gene set name and gene.
        The online search is divided into 2 modes: registered search and 
    non-registered browse. For registered search, email that you registered should 
    be provided.
        Local queries can be made from local database, which can be updated by msig_update() function.
Provides the 'Molecular Signatures Database' (MSigDB) gene sets
    typically used with the 'Gene Set Enrichment Analysis' (GSEA) software
    (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>, Liberzon et al. 2015
    <doi:10.1016/j.cels.2015.12.004>) in a standard R data frame with key-value
    pairs. The package includes the human genes as listed in MSigDB as well as
    the corresponding symbols and IDs for frequently studied model organisms
    such as mouse, rat, pig, fly, and yeast.
Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.
The 'MSiP' is a computational approach to predict protein-protein interactions from large-scale affinity purification mass 'spectrometry' (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.
An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
Analysis of annual average ocean water level time series
    from long (minimum length 80 years) individual records, providing improved
    estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on Singular Spectrum Analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also contains a forecasting module to consider the
    implication of user defined quantum of sea level rise between the end of the
    available historical record and the year 2100. A wide range of screen and pdf
    plotting options are available in the package.
Functions for fitting continuous-time Markov and hidden
    Markov multi-state models to longitudinal data.  Designed for
    processes observed at arbitrary times in continuous time (panel data)
    but some other observation schemes are supported. Both Markov
    transition rates and the hidden Markov output process can be modelled
    in terms of covariates, which may be constant or piecewise-constant
    in time.
Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.
A fast and general method for restructuring classical longitudinal data into
    augmented ones. The reason for this is to facilitate the modeling of longitudinal data under
    a multi-state framework using the 'msm' package.
Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). 
             The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".
Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This also serves as a companion package for the 
    STAT 571: Multivariate Analysis course offered by the Department of Statistics
    at the University of Illinois at Urbana-Champaign ('UIUC').
Given the maximum available sample size (N) for an experiment, and the target levels of Type I 
             and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT 
             the package can also obtain its operating characteristics and implement the test for a given 
             sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. 
             The proposed test has shown evidence of reducing the average sample size required to perform 
             statistical hypothesis tests at specified levels of significance and power. Currently, the package
             implements one-sample proportion tests, one and two-sample z tests, and one and two-sample 
             t tests. A brief user guidance for this package is provided below. One can also refer to the 
             supplemental information for the same.
This is a toolkit for multivariate process monitoring. It computes several multivariate control charts e.g. Hotelling, Chi-squared, MEWMA, MCUSUM and Generalized Variance. Ten didactic datasets are included. It includes some techniques for assessing multivariate normality e.g. Mardia's, Royston's and Henze-Zirkler's tests. Please, see the NEWS file for the latest changes in the package.
This is a implementation of design methods for multi-state reliability demonstration tests (MSRDT) with failure count data, 
    which is associated with the work from the published paper "Multi-state Reliability Demonstration Tests" by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>.
    It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). 
    For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively.
    It also provides the implementation of conventional design method, namely binomial tests for failure count data.
Provides methods to perform parameter estimation and 
  make analysis of multivariate observed outcomes through time which depends 
  on a latent state variable. All methods scale well in the dimension 
  of the observed outcomes at each time point. The package contains an 
  implementation of a Laplace approximation, particle filters like 
  suggested by Lin, Zhang, Cheng, & Chen (2005)
  <doi:10.1198/016214505000000349>, and the gradient and observed information
  matrix approximation suggested by Poyiadjis, Doucet, & Singh (2011) 
  <doi:10.1093/biomet/asq062>.
A collection of helper functions for analyzing Second Primary Cancer data, 
    including functions to reshape data, to calculate patient states and analyze cancer incidence.
Tools that extend the functionality of the 'RODBC' package to work
  with Microsoft SQL Server databases. Makes it easier to browse the database
  and examine individual tables and views.
Nonparametric estimation for right censored, left truncated time to event data in multistate models.
Constructs trees for multivariate survival data using marginal and frailty models.
    Grows, prunes, and selects the best-sized tree.
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.
This is a tool for epidemiologist, medical data analyst, 
    medical or public health professionals. It contains three domains of functions:
    1) data management, 2) statistical analysis and 3) calculating 
    epidemiological measures.
A collection of statistical tests for the detection of differential
    item functioning (DIF) in multistage tests. Methods entail logistic regression,
    an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests.
    The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform 
    DIF effects are available depending on which method is used.
A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.
Software to aid in modeling and analyzing mass-spectrometry-based
    proteome melting data. Quantitative data is imported and normalized and
    thermal behavior is modeled at the protein level. Methods exist for
    normalization, modeling, visualization, and export of results. For a
    general introduction to MS-based thermal profiling, see Savitski et al.
    (2014) <doi:10.1126/science.1255784>.
Implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.
Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. It holds various item response theory (IRT) and score-based methods to select the next module and estimate ability levels (Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).
Interface for creating data sets and extracting data through the 'MicroStrategy' REST API. Access the demo API at <https://demo.microstrategy.com/MicroStrategyLibrary/api-docs/index.html>.
Estimators for multivariate symmetrical uncertainty based
    on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>,
    total correlation, information gain and symmetrical uncertainty of
    categorical variables.
Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).
Functions for metabolomics data analysis: data preprocessing, 
  orthogonal signal correction, PCA analysis, PCA-DA analysis, 
	PLS-DA analysis, classification, feature selection, correlation 
	analysis, data visualisation and re-sampling strategies.
Build multiscalar territorial analysis based on various contexts.
Perform multi-trait rare-variant association tests using the summary statistics and adjust for possible sample overlap. Package is based on "Multi-Trait Analysis of Rare-Variant Association Summary Statistics using MTAR" by Luo, L., Shen, J., Zhang, H., Chhibber, A. Mehrotra, D.V., Tang, Z., 2019 (submitted).
The purpose of this package is to provide
    a collection of functions for 
    (1) aiding summarizing time related data; 
    (2) generating axis transformation from data; and
    (3) aiding Markdown (with html output) and Shiny file editing.
Read data in the 'MTConnect'<http://www.mtconnect.org/> standard.
    You can use the package to read data from historical 'MTConnect logs' along
    with the 'devices.xml' describing
    the device. The data is organised into a 'MTConnectDevice' S4 data structure
    and some convenience methods are also provided for basic read/view operations.
    The package also includes some functions for analysis of 'MTConnect' data. This includes
    functions to simulate data (primarily position data, feed rate and velocities) 
    based on the G code and visualisation functions to compare the actual and simulated data.
The Mass Transportation Distance rank histogram was developed to assess the reliability of scenarios with equal or different probabilities of occurrence <doi:10.1002/we.1872>.
Several robust estimators for linear regression and variable selection are provided. 
              Included are Maximum tangent likelihood estimator (Qin, et al., 2017), 
              least absolute deviance estimator and Huber regression. The penalized version of each of these 
              estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to 
              produce consistent estimates for both fixed and high-dimensional settings.
Calculate Sample Size and Power for
    Association Studies Involving Mitochondrial DNA Haplogroups.
    Based on formulae by Samuels et al. AJHG, 2006. 78(4):713-720. <DOI:10.1086/502682>.
MTK (Mexico ToolKit) is a generic platform for the sensitivity and uncertainty analysis of complex models. It provides functions and facilities for experimental design, model simulation, sensitivity and uncertainty analysis, methods integration and data reporting, etc.
An implementation of Multi-Task Logistic Regression (MTLR) for R. 
  This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves
  by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.
Simultaneous multiple outcomes prediction based on revised stacking algorithms, which enables the integration of information from predictions of individual models. An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019)  Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" <doi:10.1093/bioinformatics/btz531>.
Use morphological image processing and edge detection algorithms to automatically measure tree ring widths on digital images. Users can also manually mark tree rings on species with complex anatomical structures. The arcs of inner-rings and angles of successive inclined ring boundaries are used to correct ring-width series. The package provides a Shiny-based application, allowing R beginners to easily analyze tree ring images and export ring-width series in standard file formats.
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
Mahalanobis-Taguchi (MT) system is a collection of multivariate
    analysis methods developed for the field of quality engineering. MT system
    consists of two families depending on their purpose. One is a family of
    Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see
    Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and
    Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a
    family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y.
    (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods
    for the family of MT methods and one basic method for the family of T
    methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi
    Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the
    MT method and the two-sided Taguchi (T1) method is for the family of T
    methods. In addition, the Ta and Tb methods, which are the improved versions
    of the T1 method, are included.
Generates mid upper arm circumference (MUAC) and body mass index (BMI) 
            for age z-scores and percentiles based on LMS method for children and 
            adolescents up to 19 years that can be used to assess nutritional and health status and
            define risk of adverse health events.
Nonparametric approach to estimate the location of block boundaries (change-points) of 
    non-overlapping blocks in a random symmetric matrix which consists of random variables whose 
    distribution changes from block to block.
    BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) <doi:10.1016/j.jmva.2017.12.005>.
A data package containing public domain information on requests made by the
             'MuckRock' (https://www.muckrock.com/) project under the United States
             Freedom of Information Act.
Formatting and structuring multi-parameter spatiotemporal data
  is often a time-consuming task. This package offers functions and data structures 
  designed to easily organize and visualize these data for applications in geology, 
  paleolimnology, dendrochronology, and paleoclimate. See Dunnington and Spooner (2018)
  <doi:10.1139/facets-2017-0026>.
Compute a density estimate from a vector of right-censored survival time using kernel functions.
Nonparametric unfolding item response theory (IRT) model for dichotomous data (see W.H. Van Schuur (1984). Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method.
Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.
This package builds multi-fidelity cokriging models from
        responses with different levels of fidelity.  Important
        functions : MuFicokm, predict.MuFicokm, summary.MuFicokm.
Produces a smooth estimate of the hazard
  function for censored data.
Constructing hierarchical voronoi tessellations for a given data set and overlay heatmap for variables at various levels of the tessellations for in-depth data analysis. See <https://en.wikipedia.org/wiki/Voronoi_diagram> for more information. Credits to Mu Sigma for their continuous support throughout the development of the package.
A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.
Generates Muller plot from parental/genealogy/phylogeny information and population/abundance/frequency dynamics data.
    Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist).
    A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process.
    To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time.
    MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs.
Allows the user to create graph with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized.
    Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>.
    More about multilayered graphs and their usage can be found in our review paper:
    Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.
Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, 'mulset' can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.
Integer Programming Formulations Applied to Univariate and Multivariate Allocation Problems.
Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.
Contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.
Simultaneous tests and confidence intervals
  for general linear hypotheses in parametric models, including 
  linear, generalized linear, linear mixed effects, and survival models.
  The package includes demos reproducing analyzes presented
  in the book "Multiple Comparisons Using R" (Bretz, Hothorn, 
  Westfall, 2010, CRC Press).
Convert a logical vector or a vector of
    p-values or a correlation, difference, or distance
    matrix into a display identifying the pairs for
    which the differences were not significantly
    different.  Designed for use in conjunction with
    the output of functions like TukeyHSD, dist{stats},
    simint, simtest, csimint, csimtest{multcomp},
    friedmanmc, kruskalmc{pgirmess}.
Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.
Equivalence tests and related confidence intervals for the
        comparison of two treatments, simultaneously for one or many
        normally distributed, primary response variables (endpoints).
        The step-up procedure of Quan et al. (2001) is both applied for
        differences and extended to ratios of means. A related
        single-step procedure is also available.
Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints.
    The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative
    hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate
    permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated 
    that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal
    significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved
    by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple
    testing procedures.
GEE solver for correlated nominal or ordinal multinomial responses
    using a local odds ratios parameterization.
The base apply function and its variants, as well as the related
    functions in the 'plyr' package, typically apply user-defined functions to a
    single argument (or a list of vectorized arguments in the case of mapply). The
    'multiApply' package extends this paradigm with its only function, Apply, which
    efficiently applies functions taking one or a list of multiple unidimensional
    or multidimensional arrays (or combinations thereof) as input. The input
    arrays can have different numbers of dimensions as well as different dimension
    lengths, and the applied function can return one or a list of unidimensional or
    multidimensional arrays as output. This saves development time by preventing the
    R user from writing often error-prone and memory-inefficient loops dealing with
    multiple complex arrays. Also, a remarkable feature of Apply is the transparent
    use of multi-core through its parameter 'ncores'. In contrast to the base apply
    function, this package suggests the use of 'target dimensions' as opposite
    to the 'margins' for specifying the dimensions relevant to the function to be
    applied.
Efficient finite difference method for valuing European and American multi-asset options.
Estimation routines for several classes of affine term structure of interest rates models. All the models are based on the single-country unspanned macroeconomic risk framework from Joslin, Priebisch, and Singleton (2014) <doi:10.1111/jofi.12131>. Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015) <doi:10.1016/j.jfineco.2014.09.004> and Candelon and Moura (2021) <http://hdl.handle.net/2078.1/249985> are also available.
Algorithms for performing joint parameter estimation in
    astronomical survey data acquired in multiple bands.
Computationally efficient functions to provide direct likelihood-based
    inference for partially-observed multivariate birth-death processes.  Such processes
    range from a simple Yule model to the complex susceptible-infectious-removed model
    in disease dynamics.  Efficient likelihood evaluation facilitates maximum likelihood
    estimation and Bayesian inference.
A GUI with which users can construct and interact
        with Multibiplot Analysis and provides inferential results by using Bootstrap Methods.
Functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) 
   "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". 
   This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting 
   functions, several real data sets and six vignettes covering a range different applications.
Evaluate hypotheses concerning the distribution of multinomial
  proportions using bridge sampling. The bridge sampling routine is able to
  compute Bayes factors for hypotheses that entail inequality constraints,
  equality constraints, free parameters, and mixtures of all three. These
  hypotheses are tested against the encompassing hypothesis, that all parameters
  vary freely or against the null hypothesis that all category proportions are equal.
  For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.
Implements a generalization of the Cochran-Armitage trend test to
    multinomial data. In addition to an overall test, multiple testing adjusted
    p-values for trend in individual outcomes and power calculation is
    available.
Provides a basic interface for accessing annotation data from
   the Multi-CAST collection, a database of spoken natural language texts
   edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
   diverse set of languages and has been annotated across multiple levels.
   Annotation data is downloaded on request from the servers of the
   University of Bamberg. See the Multi-CAST website
   <https://multicast.aspra.uni-bamberg.de/> for more information and a list
   of related publications.
Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) <doi:10.1348/000711005X64817>, determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values.
A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret.  For complex problems RF performs better.  Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...).  Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model.  We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) <doi:10.1093/bioinformatics/btab088>.
A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.
This package provides a tool for analysis of multiple CNV.
The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.
    D.A. Belsley (1982) <doi:10.1016/0304-4076(82)90020-3>.
    D. A. Belsley (1991, ISBN: 978-0471528890).
    C. Garcia, R. Salmeron and C.B. Garcia (2019) <doi:10.1080/00949655.2018.1543423>.
    R. Salmeron, C.B. Garcia and J. Garcia (2018) <doi:10.1080/00949655.2018.1463376>.
    G.W. Stewart (1987) <doi:10.1214/ss/1177013444>.
Add multiple colors to text that is printed to the console.
Includes functions designed to examine relationships among multivariate constructs (e.g., personality). This includes functions for profile (within-person) analysis, dealing with large numbers of analyses, lens model analyses, and structural summary methods for data with circumplex structure. The package also includes functions for graphically comparing and displaying group means.
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from <http://tamivox.org/dave/multinomial/index.html> and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here <https://jeromekelleher.net/category/combinatorics.html>. The C++ code and Python code are distributed without conditions.
We introduce a nonparametric, graphical test based on optimal matching for assessing whether multiple unknown multivariate probability distributions are equal. This method is consistent, and does not make any distributional assumptions on the data. Our procedure combines data that belong to different classes or groups to create a graph on the pooled data, and then utilizes the number of edges connecting data points from different classes to examine equality of distributions among the classes. The functions available through this package implement the work described here: <arXiv:1906.04776>.
Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Lönnqvist & Ilmarinen (2021) <doi:10.1007/s11109-021-09681-2> and Ilmarinen et al. (2021) <doi:10.31234/osf.io/j59bs>.
Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.
Partition a data frame across multiple worker
    processes to provide simple multicore parallelism.
An implementation for multivariate functional additive mixed
    models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse 
    functional regression models and multivariate functional principal component
    analysis. This package contains the function to run a multiFAMM and some
    convenience functions useful when working with large models. An additional 
    package on GitHub contains more convenience functions to reproduce the 
    analyses of the corresponding paper 
    (<https://github.com/alexvolkmann/multifammPaper>).
A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos <doi:10.31234/osf.io/2z6pd>.
Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky and Ma (2018) <arXiv:1806.06777>.
Deals with several images of a same object, constituted of different zones. Each image constitutes a variable for a given pixel. The user can interactively select different zones of an image. Then, multivariate analysis (PCA) can be run in order to characterize the different selected zones, according to the different images. Hotelling (Hotelling, 1931, <doi:10.1214/aoms/1177732979>) and Srivastava (Srivastava, 2009, <doi:10.1016/j.jmva.2006.11.002>) tests can be run to detect multivariate differences between the zones.
Read a table of fixed width formatted data of different types into
    a data.frame for each type.
Uses a transformed, rotated and optionally adapted n-dimensional
    grid of quadrature points to calculate the numerical integral of n multivariate
    normal distributed parameters.
Functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options. Please note that this package still under a devel version.
Methods and models for analysing multigraphs as introduced by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit tests.
Multivariate analysis methods including principal component analysis,
    partial least square regression, and multiblock analysis to describe,
    summarize, and visualize data with a group structure.
Run the Multi Horizon Superior Predictive Ability test to compare the predictive performance of two distinct models when jointly considering all horizons of a forecast path.
Wrapper around the Unix join facility which is more efficient than the built-in R routine merge().
 The package enables the joining of multiple files on disk at once. 
 The files can be compressed and various filters can be deployed before joining.
 Compiles only under Unix.
Estimation and inference for multiple kink quantile regression. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, "Wei Zhong, Chuang Wan and Wenyang Zhang (2020). Estimation and inference for multi-kink quantile regression, submitted".
Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
The functions in this package are designed to be used in the analysis of multilevel data by applied psychologists.  The package includes functions for estimating common within-group agreement and reliability indices. The package also contains basic data manipulation functions that facilitate the analysis of multilevel and longitudinal data.
Conducts and visualizes propensity score analysis for multilevel, 
  or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.
Effect sizes, diagnostics and performance metrics for
  multilevel and mixed effects models.
  Includes marginal and conditional 'R2' estimates for linear mixed effects models
  based on Johnson (2014) <doi:10.1111/2041-210X.12225>.
Traditional and spatial capture-mark-recapture analysis with
    multiple non-invasive marks. The models implemented in 'multimark' combine
    encounter history data arising from two different non-invasive "marks",
    such as images of left-sided and right-sided pelage patterns of bilaterally
    asymmetrical species, to estimate abundance and related demographic
    parameters while accounting for imperfect detection. Bayesian models are
    specified using simple formulae and fitted using Markov chain Monte Carlo.
    Addressing deficiencies in currently available software, 'multimark' also
    provides a user-friendly interface for performing Bayesian multimodel
    inference using non-spatial or spatial capture-recapture data consisting of a single
    conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.
A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates inference on food intake from biomarker data alone, along with the associated uncertainty. Details are in D'Angelo, et al. (2020) <arXiv:2006.02995>.
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
Identifying comorbidities, frailty, and multimorbidity in claims 
    and administrative data is often a duplicative process.
    The functions contained in this package are meant to first prepare the data to a format
    acceptable by all other packages, then provide a uniform and simple approach to
    generate comorbidity and multimorbidity metrics based on these claims data. The package
    is ever evolving to include new metrics, and is always looking for new measures to include.
    The citations used in this package include the following publications: 
    Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>,
    Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>,
    Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>,
    Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>,
    Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>,
    Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>,
    Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>,
    Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>,
    Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.
This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
Functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.
Analyze multilevel networks as described in Lazega et al (2008)
    <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders 
    (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an 
    extension to 'igraph'.
Network meta-analysis and network meta-regression models for 
    aggregate data, individual patient data, and mixtures of both individual 
    and aggregate data using multilevel network meta-regression as described by
    Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a
    Bayesian framework using 'Stan'.
An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial distribution given a set of observations, proposed by Sison and Glaz in their paper:
        Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). 
        The method is an R translation of the SAS code implemented by May and Johnson in their paper:
        May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). 
        Paper and code available at <DOI:10.18637/jss.v005.i06>.
Implements Gibbs sampling and Bayes factors for multinomial models with
    linear inequality constraints on the vector of probability parameters. As
    special cases, the model class includes models that predict a linear order 
    of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models 
    assuming that the parameter vector p must be inside the convex hull of a 
    finite number of predicted patterns (i.e., vertices). A formal definition of 
    inequality-constrained multinomial models and the implemented computational
    methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). 
    Multinomial models with linear inequality constraints: Overview and improvements 
    of computational methods for Bayesian inference. Journal of Mathematical 
    Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>.
    Inequality-constrained multinomial models have applications in the area of 
    judgment and decision making to fit and test random utility models  
    (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of 
    preferences. Psychological Review, 118, 42–56, <doi:10.1037/a0021150>) or to 
    perform outcome-based strategy classification to select the decision strategy 
    that provides the best account for a vector of observed choice frequencies 
    (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information 
    processing to decisions: Formalizing and comparing probabilistic choice models. 
    Cognitive Psychology, 96, 26–40. <doi:10.1016/j.cogpsych.2017.05.003>).
MNL and overdispersed multinomial regression using robust
        (LQD and tanh) estimation
Provides implementations of functions that can be used to test multivariate integration routines. The package covers five different integration domains (unit hypercube, unit ball, unit sphere, standard simplex and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.
Tools to create a layout for figures made of multiple panels, and
    to fill the panels with base, 'lattice', 'ggplot2' and 'ComplexHeatmap'
    plots, grobs, as well as content from all image formats supported by
    'ImageMagick' (accessed through 'magick').
Performs genetic association tests between SNPs
        (one-at-a-time) and multiple phenotypes (separately or in joint
        model).
Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.
The iterative procedure estimates structural changes in the success probability of Bernoulli variables. It estimates the number and location of the breakpoints as well as the success probability of the different sequences between the breakpoints. In addition, it provides a graphical illustration of the result.
Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.
Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints.
Tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.
An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) 
    and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for 
    multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes 
    are able to be calculated for either null model, and subsequently classified into one of 
    four different interaction classifications (e.g., antagonistic or synergistic interactions). 
    Analyses can be conducted on data for single experiments through to large meta-analytical datasets. 
    Minimal input (or statistical knowledge) is required, with any output easily understood. 
    Summary figures are also able to be easily generated.
Algebraic procedures for the analysis of multiple social networks are delivered with this 
	    package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. Among other things, it 
	    makes it possible to create and manipulate multiplex, multimode, and multilevel network data 
	    with different formats. There are effective ways available to treat multiple networks with 
	    routines that combine algebraic systems like the partially ordered semigroup or the semiring 
	    structure with the relational bundles occurring in different types of multivariate network 
	    data sets. It also provides an algebraic approach for affiliation networks through Galois 
	    derivations between families of the pairs of subsets in the two domains.
Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.
Various utilities to manipulate multivariate polynomials.
Fits the Multiple Random Dot Product Graph Model and performs a test
	 for whether two networks come from the same distribution. Both methods are 
	 proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph
	 Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational
	 and Graphical Statistics).
Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap. See Keyel and Wiegand
    (2016) <doi:10.1111/2041-210X.12558> for more details.
Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.
Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.
Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.
Tools to solve real-world problems with multiple classes classifications by computing the areas under ROC and PR curve via micro-averaging and macro-averaging. The vignettes of this package can be found via <https://github.com/WandeRum/multiROC>. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Tools necessary to reconstruct clonal affiliations from
    temporally and/or spatially separated measurements of viral
    integration sites. For this means it utilizes correlations present
    in the relative readouts of the integration sites. Furthermore,
    facilities for filtering of the data and visualization of different
    steps in the pipeline are provided with the package.
Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.)
Functions to perform sensitivity analysis on a model with multivariate output.
The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and when applying the model in the 'test set'. This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly as described in (Solanes et al, Psychiatry Research: Neuroimaging 2021, in Press). It currently supports the estimation of sensitivity, specificity, balanced accuracy, the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, gaussian, and survival (time-to-event) outcomes.
Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879>, Qin, Hong, Deutsch, and Bein (2019) <doi:10.1111/rssa.12446>, and Qin, Deutsch, and Hong (2021) <doi:10.1002/pam.22268>. It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias.
Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.
Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
MultiSV is an R package for identification of structural
    variations in multiple populations based on whole genome resequencing. It
    fits linear mixed model and identifies structural variations in multiple
    populations using whole genome sequencing data. It could also be
    manipulated to use on RNA-seq data for differential gene expression
    (implementation in future releases). Main steps for analysis include
    generating read depth in bins using ComputeBinCounts. conversion of bins to
    MultiSV format using Bin2MultiSV. Finally, identification of structural
    variations using CallMultiSV.
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates. This package is an implementation of the method described in D.J. Thomson (1982) "Spectrum estimation and harmonic analysis" <doi:10.1109/PROC.1982.12433>.
Functions for simulating, estimating and forecasting stationary Vector Autoregressive (VAR) models for multiple subject data using the penalized multi-VAR framework in Fisher, Kim and Pipiras (2020) <arXiv:2007.05052>.
Distance multivariance is a measure of dependence which can be used to detect 
    and quantify dependence of arbitrarily many random vectors. The necessary functions are
    implemented in this packages and examples are given. It includes: distance multivariance, 
    distance multicorrelation, dependence structure detection, tests of independence and
    copula versions of distance multivariance based on the Monte Carlo empirical transform.
    Detailed references are given in the package description, as starting point for the 
    theoretic background we refer to:
    B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using 
    the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), 
    <doi:10.1515/stat-2020-0001>.
Package with multivariate analysis methodologies for experiment evaluation.
	 The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA,
	 principal components, canonical variables, etc. (Pacote com metodologias de analise
	 multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade,
	 construi de dendogramas, obtem a MANOVA,  componentes principais, variáveis canônicas, etc.)
Models and predicts multiple output features in single random forest considering the 
    linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.
Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.
It performs variable selection in a multivariate linear model by estimating the covariance matrix 
 of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion.
 The method is described in the paper Perrot-Dockès et al. (2017) <arXiv:1704.00076>.
A multivariate generalization of the emulator package.
Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016)
    <doi:10.1177/1745691616658637>, (Dragicevic P., Jansen Y., Sarma A., Kay M., Chevalier F., 2019) <doi:10.1145/3290605.3300295> 
    to show the robustness of statistical inference. 'Multiverse analysis' is a philosophy of 
    statistical reporting where paper authors report the outcomes of many different statistical 
    analyses in order to show how fragile or robust their findings are. 
    The 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>
    allows users to concisely and flexibly implement 'multiverse-style' 
    analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.
Tests for association between subgroups in two multivariate data views, two network data views, or a multivariate data view and a network data view. (Reference 1: Gao, L.L., Bien, J., and Witten, D. (2020) "Are Clusterings of Multiple Data Views Independent?", <doi:10.1093/biostatistics/kxz001> and Reference 2:  Gao, L.L., Witten, D., Bien, J. (2021) Testing for Association in Multi-View Network Data, <doi:10.1111/biom.13464>.)
Computation of an estimation of the long-memory parameters and
     the long-run covariance matrix using a multivariate model
	 (Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
	 implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
	 approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>).
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>].  Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.
Exports two functions implementing
    multi-way clustering using the method suggested by Cameron, Gelbach, &
    Miller (2011) and cluster (or block)
    bootstrapping for estimating variance-covariance matrices. Normal one and
    two-way clustering matches the results of other common statistical
    packages.  Missing values are handled transparently and rudimentary
    parallelization support is provided.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics.  Supported by grant NSF DMS 1712839.
In the case of multivariate ordinal responses, parameter estimates can be severely biased if personal response styles are ignored. This packages provides methods to account for personal response styles and to explain the effects of covariates on the response style, as proposed by Schauberger and Tutz 2021 <doi:10.1177/1471082X20978034>. The method is implemented both for the multivariate cumulative model and the multivariate adjacent categories model.
Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).
Implementation of commonly used p-value-based and
        parametric multiple testing procedures (computation of adjusted
        p-values and simultaneous confidence intervals) and parallel
        gatekeeping procedures based on the methodology presented in
        the book "Multiple Testing Problems in Pharmaceutical
        Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
        Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Calculates post-hoc variable importance measures for multivariate random forests. 
  These are given by split improvement for splits defined by feature j as measured using user-defined (i.e. training or test) examples.
  Importance measures can also be calculated on a per-outcome variable basis using the change in predictions for each split. Both measures can be
  optionally thresholded to include only splits that produce statistically significant changes as measured by an F-test.
Preprocessing of high-throughput data (normalization and
        scalings); Principal Component Analysis with help tool for
        choosing best-separating principal components and automatic
        testing for outliers; automatic univariate analysis for
        parametric and non-parametric data, with generation of specific
        reports (volcano and box plots); partial least square
        discriminant analysis (PLS-DA); orthogonal partial least square
        discriminant analysis (OPLS-DA); Statistical Total Correlation
        Spectroscopy (STOCSY); Ratio Analysis Nuclear Magnetic
        Resonance (NMR) Spectroscopy (RANSY).
Tools for performing model selection and model averaging. Automated
             model selection through subsetting the maximum model, with optional
             constraints for model inclusion. Model parameter and prediction
             averaging based on model weights derived from information criteria
             (AICc and alike) or custom model weighting schemes.
Fit multiplicative mixed models using maximum likelihood estimation via the Template
  Model Builder (TMB), Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM (2016) <doi:10.18637/jss.v070.i05>.
  One version of the multiplicative mixed model is applied in Piepho (1999) <doi:10.1111/j.0006-341X.1999.01120.x>.
  The package provides functions for calculating confidence intervals for the model parameters
  and for performing likelihood ratio tests.
Multidimensional unfolding using Schoenemann's algorithm for metric
   and Procrustes rotation of unfolding results.
Provides easy access to, and manipulation of, the Munsell 
    colours. Provides a mapping between Munsell's 
    original notation (e.g. "5R 5/10") and hexadecimal strings suitable 
    for use directly in R graphics. Also provides utilities 
    to explore slices through the Munsell colour tree, to transform 
    Munsell colours and display colour palettes.
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB.  Includes ISCC-NBS color block lookup.  Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
Data and code for the paper by Ehm, Gneiting, Jordan and
    Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet
    Representations, and Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).
Calculates k-best solutions and costs for an assignment problem following the method outlined in Murty (1968) <doi:10.1287/opre.16.3.682>.
Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.
An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package.
Provides functions allowing for (1) easy aggregation of multivariate time series into custom time scales, (2) comparison of statistical summaries between different data sets at multiple time scales (e.g. observed and bias-corrected data), (3) comparison of relations between variables and/or different data sets at multiple time scales (e.g. correlation of precipitation and temperature in control and scenario simulation) and (4) transformation of time series at custom time scales.
This package is a collection of function for converting and modifying mono dimensional nuclear magnetic resonance spectra.
A simple system for saving and loading objects in
    R. Long running computations can be stashed after the first run and
    then reloaded the next time. Dependencies can be added to ensure that
    a computation is re-run if any of its dependencies or inputs have
    changed.
Performs Wilcox rank sum test, Kruskal rank sum test,
        Friedman rank sum test and McNemar test.
Main function LR2 calculates likelihood ratio for non-inbred relationships accounting for mutation, silent alleles and theta correction. Egeland, Pinto and Amorim (2017) <DOI:10.1016/j.fsigen.2017.04.018>.
Designed to ease the application and comparison of multiple
    hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are 
    standardized and usable by the accompanying 'mutossGUI'.
The mutossGUI package provides a graphical user interface for the MuToss Project.
Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The framework includes modules that support raw data import and processing, mutational signature extraction, and results interpretation and visualization. The framework accepts widely used file formats storing information about DNA variants, such as Variant Call Format files. The framework performs Non-Negative Matrix Factorization to extract mutational signatures explaining the observed set of DNA mutations. Bootstrapping is performed as part of the analysis. The framework supports parallelization and is optimized for use on multi-core systems. The software was described by Fantini D et al (2020) <doi:10.1038/s41598-020-75062-0> and is based on a custom R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <doi:10.1016/j.celrep.2012.12.008>.
The Mutual Information Index (M) introduced to social science literature by
    Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup
    segregation measure that is highly decomposable and that according to Frankel
    and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo
    (2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit
    Decomposability and Strong Group Decomposability properties. This package allows
    computing and decomposing the total index value into its "between" and
    "within" terms. These last terms can also be decomposed into their
    contributions, either by group or unit characteristics. The factors that produce
    each "within" term can also be displayed at the user's request. The results can
    be computed considering a variable or sets of variables that define separate
    clusters.
An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.
Functions, data sets, analyses and examples from the book 
  `An Introduction to Applied Multivariate Analysis with R' 
  (Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (<http://www.gnu.org/software/gsl/>) and 'Rcpp'
        (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.
Package for multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.
Pacote para analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.
Fit log-linear model for multivariate Bernoulli
        distribution with mixed effect models and LASSO
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.
An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.
Implementation of multi-view bi-clustering algorithms. When a sample is characterized by two or more sets of input features, it creates multiple data matrices for the same set of examples, each corresponding to a view. For instance, individuals who are diagnosed with a disorder can be described by their clinical symptoms (one view) and their genomic markers (another view). Rows of a data matrix correspond to examples and columns correspond to features. A multi-view bi-clustering algorithm groups examples (rows) consistently across the views and simultaneously identifies the subset of features (columns) in each view that are associated with the row groups. This mvcluster package includes three such methods. (1) MVSVDL1: multi-view bi-clustering based on singular value decomposition where the left singular vectors are used to identify row clusters and the right singular vectors are used to identify features (columns) for each row cluster. Each singular vector is regularized by the L1 vector norm.  (2) MVLRRL0: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the so-called L0 vector norm (which is not really a vector norm). (3) MVLRRL1: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the L1 vector norm.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.
Computes regression deletion diagnostics for multivariate linear models and provides some associated
	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
	provided.
Pearson and Spearman correlation coefficients are commonly used to quantify the strength
	of bivariate associations of genomic variables.  For example, correlations of gene-level 
	DNA copy number and gene expression measurements may be used to assess the impact of 
	DNA copy number changes on gene expression in tumor tissue.  'MVisAGe' enables users to 
	quickly compute and visualize the correlations in order to assess the effect of regional 
	genomic events such as changes in DNA copy number or DNA methylation level.  Please see
	Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research 
	<doi:10.1158/0008-5472.CAN-17-3464>.
Allows a user to conduct multivariate multiple regression using analytic p-values rather than classic approximate F-tests.
Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
 Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Offers a general framework of multivariate mixed-effects
        models for the joint analysis of multiple correlated outcomes with clustered 
        data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may 
        depend on the values themselves (missing not at random and non-ignorable), 
        or may depend on only the covariates (missing at random and ignorable), or both.
        This package provides functions for two models: 1) mvMISE_b() 
        allows correlated outcome-specific random intercepts with a factor-analytic 
        structure, and 2) mvMISE_e() allows the correlated outcome-specific 
        error terms with a graphical lasso penalty on the error precision matrix. Both functions 
        are motivated by the multivariate data analysis on data with clustered structures 
        from labelling-based quantitative proteomic studies. These models and functions 
        can also be applied to univariate and multivariate analyses of clustered data 
        with balanced or unbalanced design and no missingness.