Estimation, simulation, and forecasting using the HAR model from Corsi(2009) <DOI:10.1093/jjfinec/nbp001> and extensions.
The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended.
Fits the first harmonics in a Fourier expansion to one or more time series. Trend elimination can be performed.  Computed values include estimates of amplitudes and phases, as well as confidence intervals and p-values for the null hypothesis of Gaussian noise.
Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr'
    implements some function to manipulate distance matrices and phylogenetic trees
    to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse'
    tools.
Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise.
The goal of 'HARtools' is to provide a simple set of functions
    to read/parse, write and visualise HTTP Archive ('HAR') files in R.
Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information.
Implements a data structure similar to hashes
        in Perl and dictionaries in Python but with a purposefully R
        flavor.  For objects of appreciable size, access using hashes
        outperforms native named lists and vectors.
An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.
Apply an adaptation of the SuperFastHash algorithm to any R
    object. Hash whole R objects or, for vectors or lists, hash R objects to obtain
    a set of hash values that is stored in a structure equivalent to the input. See
    <http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash
    algorithm.
Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set.
Import foreign statistical formats into R via the embedded
    'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.
Provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.
Damage Functions (DFs), also known as
    Vulnerability Functions, associate the physical damage
    to a building or a structure (and also its contents and
    inventory) from natural disasters to financial damage.
    The Federal Emergency Management Agency (FEMA) in USA
    developed several thousand DFs and these serve as a
    benchmark in natural catastrophe modeling, both in
    academia and industry. However, these DFs and their
    documentation are buried within the HAZUS software are
    not easily accessible for analysis and visualization.
    This package provides more than 1300 raw DFs used by FEMA's
    HAZUS software and also functionality to extract and
    visualize DFs specific to the flood hazard. The vignette
    included with this package demonstrates its use.
Fit an array of decision-making tasks with computational models in
    a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of
    various computational models with a single line of coding
    (Ahn et al., 2017) <doi:10.1162/CPSY_a_00002>.
Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.
Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
A package for building hierarchical block matrices from association matrices and for performing multi-scale analysis. It specifically targets chromatin contact maps, generated from high-throughput chromosome conformation capture data, such as 5C and Hi-C, and provides methods for detecting movements and for computing chain hierarchy and region communicability across scales.
Contains functions for fitting hierarchical versions of
        EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
        our gamma signal detection model to recognition memory
        confidence-ratings data.
Functions to compute small area estimates based on a basic
        area or unit-level model. The model is fit using restricted
        maximum likelihood, or in a hierarchical Bayesian way. In the
        latter case numerical integration is used to average over the
        posterior density for the between-area variance.  The output
        includes the model fit, small area estimates and corresponding
        MSEs, as well as some model selection measures. Additional
        functions provide means to compute aggregate estimates and
        MSEs, to minimally adjust the small area estimates to
        benchmarks at a higher aggregation level, and to graphically
        compare different sets of small area estimates.
This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.
Texts for H.C. Andersens fairy tales, ready for
    text analysis. Fairy tales in German, Danish, English, Spanish and
    French.
A new diagnostic check for model adequacy in regression
        and generalized linear models is implemented.
This package calculates the interval estimates for the parameters of 
 linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
 bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
 the percentile bootstrap and percentile bootstrap double. It is possible to calculate
 consistent estimates of the covariance matrix of the parameters of linear regression models
 with heteroskedasticity of unknown form. The package also provides function to calculate
 consistently the covariance matrix of the parameters of linear models with heteroskedasticity
 of unknown form.
Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) <arXiv:1810.01509>. The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities.
By binding R functions and the 'Highmaps' <https://www.highcharts.com.cn/products/highmaps> chart library, 'hchinamap' package provides a simple way to map China and its provinces. The map of China drawn by this package contains complete Chinese territory, especially the Nine-dotted line, South Tibet, Hong Kong, Macao and Taiwan.
Estimation of parameters in 3-segment (i.e. 2 change-point)
    regression models with heteroscedastic variances is provided based on both
    likelihood and hybrid Bayesian approaches, with and without continuity
    constraints at the change points.
This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. 
    We implement an effective solution to recover the above hidden compact representation under the likelihood framework. 
    Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.
A novel searching scheme for tuning parameter in high-dimensional 
             penalized regression. We propose a new estimate of the regularization
             parameter based on an estimated lower bound of the proportion of false 
             null hypotheses (Meinshausen and Rice (2006) <doi:10.1214/009053605000000741>).
             The bound is estimated by applying the empirical null distribution of the higher 
             criticism statistic, a second-level significance testing, which is constructed
             by dependent p-values from a multi-split regression and aggregation method
             (Jeng, Zhang and Tzeng (2019) <doi:10.1080/01621459.2018.1518236>). An estimate 
             of tuning parameter in penalized regression is decided corresponding to the lower 
             bound of the proportion of false null hypotheses. Different penalized 
             regression methods are provided in the multi-split algorithm.
Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Binary segmentation methods for detecting and estimating 
	multiple change-points in the mean or second-order structure 
	of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.
Perform mediation analysis in the presence of high-dimensional
    mediators based on the potential outcome framework. High dimensional
    Bayesian mediation (HDBM), developed by Song et al (2018)
    <doi:10.1101/467399>, relies on two Bayesian sparse linear mixed models to
    simultaneously analyze a relatively large number of mediators for a
    continuous exposure and outcome assuming a small number of mediators are
    truly active. This sparsity assumption also allows the extension of
    univariate mediator analysis by casting the identification of active
    mediators as a variable selection problem and applying Bayesian methods
    with continuous shrinkage priors on the effects.
The svd(singular value decomposition) or qr decomposition was using for the implementation, this avoid the recursion optimizing the time in the compute 
              <https://drive.google.com/drive/folders/1xJw7gM5_XiJipQ3grTZkfc6q4K0hzuCx?usp=sharing>.
Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.
Discriminant analysis and data clustering methods for high
    dimensional data, based on the assumption that high-dimensional data live in
    different subspaces with low dimensionality proposing a new parametrization of
    the Gaussian mixture model which combines the ideas of dimension reduction and
    constraints on the model.
Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <http://jmlr.org/papers/v18/16-342.html>.
Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations.
A procedure that fits derivative curves based on a sequence of quotient differences.  In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves.  In a non-hierarchical setting the package produces a single derivative curve.
Hard drive data: Class of data allowing the easy importation/manipulation of out of memory data sets. The data sets are located on disk but look like in-memory, the syntax for manipulation is similar to 'data.table'. Operations are performed "chunk-wise" behind the scene.
Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features.  The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods.
Cross-validated linear discriminant calculations determine
  the optimum number of features. Test and training scores from
  successive cross-validation steps determine, via a principal
  components calculation, a low-dimensional global space onto which test
  scores are projected, in order to plot them. Further functions are
  included that are intended for didactic use. The  package implements,
  and extends, methods described in J.H. Maindonald and C.J. Burden (2005)
  <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.
Tools to discover hydrological data, accessing catalogues and databases from various data providers. The package is described in Vitolo (2017) "hddtools: Hydrological Data Discovery Tools" <doi:10.21105/joss.00056>.
'HDF5' is a data model, library and file format for storing 
    and managing large amounts of data. This package provides a nearly
    feature complete, object oriented  wrapper for the 'HDF5' API
    <https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html> using R6 classes. 
    Additionally, functionality is added so that 'HDF5' objects behave very 
    similar to their corresponding R counterparts.
Provides an interface to 'HDFql' <http://www.hdfql.com/> 
    and helper functions for reading data from and writing data to 'HDF5' files. 'HDFql' 
    provides a high-level language for managing 'HDF5' data that is platform independent.
    For more information, see the reference manual 
    <http://www.hdfql.com/resources/HDFqlReferenceManual.pdf>.
Test the significance of coefficients in high dimensional generalized linear models.
Implementation of multiple approaches to perform inference in high-dimensional models.
A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.
We provide an R tool for nonparametric plug-in estimation of Highest Density Regions (HDRs) in the directional setting. Concretely, circular and spherical regions can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2020) <arXiv:2009.08915>. This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences.
The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2020). "Statistical inference for high-dimensional vector autoregression with measurement error", arXiv preprint <arXiv:2009.08011>.
Mimics the lm() function found in the package stats to fit
        high dimensional regression models with point estimates,
        standard errors, and p-values. Methods for printing and
        summarizing the results are given.
Testing homogeneity of k multivariate distributions is a classical and challenging problem in
             statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size.
             We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. 
             These tests are easy to implement and distribution-free in finite sample situations. Under appropriate 
             regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the 
             dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale 
             approach, where the results for different number of partitions are aggregated judiciously. Details are in 
             Biplab Paul, Shyamal K De and Anil K Ghosh (2020); Soham Sarkar and Anil K Ghosh (2019) 
             <doi:10.1109/TPAMI.2019.2912599>; William M Rand (1971) <doi:10.1080/01621459.1971.10482356>;  
             Cyrus R Mehta and Nitin R Patel (1983) <doi:10.2307/2288652>; Joseph C Dunn (1973) 
             <doi:10.1080/01969727308546046>; Sture Holm (1979) <doi:10.2307/4615733>; 
             Yoav Benjamini and Yosef Hochberg (1995) <doi: 10.2307/2346101>.
Implementation of selected high-dimensional statistical and
    econometric methods for estimation and inference. Efficient estimators and
    uniformly valid confidence intervals for various low-dimensional causal/
    structural parameters are provided which appear in high-dimensional
    approximately sparse models. Including functions for fitting heteroscedastic
    robust Lasso regressions with non-Gaussian errors and for instrumental variable
    (IV) and treatment effect estimation in a high-dimensional setting. Moreover,
    the methods enable valid post-selection inference and rely on a theoretically
    grounded, data-driven choice of the penalty.
    Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
Penalized regression for generalized linear models for
  measurement error problems (aka. errors-in-variables). The package
  contains a version of the lasso (L1-penalization) which corrects
  for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). 
  It also contains an implementation of the Generalized Matrix Uncertainty 
  Selector, which is a version the (Generalized) Dantzig Selector for the 
  case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
A multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package 'HDMT' we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) <doi:10.1080/01621459.2020.1765785>.
Creates nomogram visualizations for penalized Cox regression
    models, with the support of reproducible survival model building,
    validation, calibration, and comparison for high-dimensional data.
An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers.
In high-dimensional settings:
	Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model.
	Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors.
	Adjust the shrinkage bias in the predicted PC scores.
	Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.
Algorithms for lasso and fused-lasso problems: implementation of
    the lars algorithm for lasso and fusion penalization and EM-based
    algorithms for (logistic) lasso  and fused-lasso penalization.
Implementation of MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model (hdpGLM) presented in the paper Ferrari (2020) Modeling Context-Dependent Latent Heterogeneity, Political Analysis <DOI:10.1017/pan.2019.13>.
Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.
Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.
High dimensional testing procedures on mean, covariance and white noises.
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
A machine learning toolbox tailored to healthcare data.
Provides a shiny interface for a free, open-source managerial
    accounting-like system for health care practices. This package allows
    health care administrators to project revenue with monthly adjustments
    and procedure-specific boosts up to a 3-year period. Granular data
    (patient-level) to aggregated data (department- or hospital-level) can
    all be used as valid inputs provided historical volume and revenue
    data is available. For more details on managerial accounting
    techniques, see Brewer et al. (2015, ISBN:9780078025792).
Scrape data from Patient Forum <https://patient.info/forums> by entering urls. It will return a data frame containing text, user names, like counts, reply counts, etc.
Hospital data analysis workflow tools, modeling, and automations. This library
    provides many useful tools to review common administrative hospital data. Some 
    of these include average length of stay, readmission rates, average net pay
    amounts by service lines just to name a few. The aim is to provide a simple
    and consistent verb framework that takes the guesswork out of everything.
Provides data for functions typically used in the 'healthyR' package.
Hospital time series data analysis workflow tools, modeling, and automations. 
    This library provides many useful tools to review common administrative time 
    series hospital data. Some of these include average length of stay, and 
    readmission rates. The aim is to provide a simple and consistent verb 
    framework that takes the guesswork out of everything.
The 'healthyverse' is a set of packages that work in
    harmony because they share common data representations and 'API'
    design. This package is designed to make it easy to install and load
    multiple 'healthyverse' packages in a single step.
The heatex package calculates heat storage in the body and
        the components of heat exchange (conductive, convective,
        radiative, and evaporative) between the body and the
        environment during physical activity based on the principles of
        partitional calorimetry. The program enables heat exchange
        calculations for a range of environmental conditions when
        wearing various clothing ensembles.
An improved heatmap package. Completely
    compatible with the original R function 'heatmap',
    and provides more powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
    binary dependent variables, and calculates the related heatmap fit
    statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.
Create interactive cluster 'heatmaps' that can be saved as a stand-
    alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
    available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to
    show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical
    method for visualizing high-dimensional data, in which a table of numbers
    are encoded as a grid of colored cells. The rows and columns of the matrix
    are ordered to highlight patterns and are often accompanied by 'dendrograms'.
    'Heatmaps' are used in many fields for visualizing observations, correlations,
    missing values patterns, and more. Interactive 'heatmaps' allow the inspection
    of specific value by hovering the mouse over a cell, as well as zooming into
    a region of the 'heatmap' by dragging a rectangle around the relevant area.
    This work is based on the 'ggplot2' and 'plotly.js' engine. It produces
    similar 'heatmaps' to 'heatmap.2' with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), the ability to zoom from
    the 'dendrogram' panes, and the placing of factor variables in the sides of the
    'heatmap'.
The different methods of defining and detecting extreme events, known as heatwaves or cold-spells in both air and water temperature data are encompassed within this package. These detection algorithms may be used on non-temperature data as well however, this is not catered for explicitly here as no use of this technique in the literature currently exists.
Functions to perform robust estimation considering heavy-tailed distributions.
    Currently, the package includes routines for linear regression, linear mixed-effect
    models, Grubbs' model, multivariate location and scatter estimation, multivariate
    regression, penalized splines, random variate generation and some support functions.
Maximum likelihood estimation by an EM algorithm of Heckman-type sample selection Normal or Student-t models. The reference is Lachos, Prates and Dey (2020) <arXiv:2006.08036>.
Helper functions designed to make
    dynamically generating R Markdown documents easier by providing a
    simple and tidy way to create report pieces, shape them to your data,
    and combine them for exporting into a single R Markdown document.
Hedgehog will eat all your bugs.
  'Hedgehog' is a property-based testing package in the spirit
  of 'QuickCheck'. With 'Hedgehog', one can test properties
  of their programs against randomly generated input, providing
  far superior test coverage compared to unit testing. One of the
  key benefits of 'Hedgehog' is integrated shrinking of
  counterexamples, which allows one to quickly find the cause of
  bugs, given salient examples when incorrect behaviour occurs.
An implementation of the modelling and reporting features described 
    in reference textbook and guidelines (Briggs, Andrew, et al. Decision 
    Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011;
    Siebert, U. et al. State-Transition Modeling. Medical Decision Making 
    32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, 
    heterogeneity analysis, time dependency on state-time and model-time 
    (semi-Markov and non-homogeneous Markov models), etc.
Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.
The main focus is on preprocessing and data visualization of machine learning models performances. 
  Some functions allow to fill in gaps in time series using linear interpolation on panel data, some functions 
  permit to draw lift effect and lift curve in order to benchmark machine learning models or you can even find 
  the optimal number of clusters in agglomerative clustering algorithm.
Create publication-quality, 2-dimensional visualizations of alpha-helical peptide
  sequences. Specifically, allows the user to programmatically generate
  helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of
  alpha-helical oligopeptides. See Wadhwa RR, et al. (2018)
  <doi:10.21105/joss.01008> for more information.
Empirical value of the Hellinger correlation, a measure of dependence between
	     two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) <arXiv:1810.10276v4>.
Base R's default setting for 'stringsAsFactors' within
    'data.frame()' and 'as.data.frame()' is supposedly the most often complained
    about piece of code in the R infrastructure. The 'hellno' package provides
    an explicit solution without changing R itself or having to mess around with
    options. It tries to solve this problem by providing alternative
    'data.frame()' and 'as.data.frame()' functions that are in fact simple
    wrappers around base R's 'data.frame()' and 'as.data.frame()' with
    'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE )
    by default.
A dummy package to demonstrate how to interface to a jar
        file that resides inside an R package.
Access to large host-parasite data is often hampered by the 
  availability of data and difficulty in obtaining it in a programmatic way
  to encourage analyses. 'helminthR' provides a programmatic interface to the 
  London Natural History Museum's host-parasite database, one of the largest 
  host-parasite databases existing currently <https://www.nhm.ac.uk/research-curation/scientific-resources/taxonomy-systematics/host-parasites/>. The package allows the user
  to query by host species, parasite species, and geographic location.
Contains miscellaneous functions useful for managing 'NetCDF' files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Tools for accessing various open data sources in the Helsinki
    region in Finland. Current data sources include
    the Real Estate Department (<http://ptp.hel.fi/avoindata/>),
    Service Map API (<http://api.hel.fi/servicemap/v2/>),
    Linked Events API (<http://api.hel.fi/linkedevents/v1/>),
    Helsinki Region Infoshare statistics API (<https://dev.hel.fi/stats/>).
An implementation of several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs). 'HEMDAG' package: 1) reconciles flat predictions with the topology of the ontology; 2) can enhance the predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; 3) provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; 4) is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (as FunCat), since trees are DAGs; 5) scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; 6) provides several utility functions to process and analyze graphs; 7) provides several performance metrics to evaluate HEMs algorithms. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
Provides HE plot and other functions for visualizing hypothesis
    tests in multivariate linear models. HE plots represent sums-of-squares-and-
    products matrices for linear hypotheses and for error using ellipses (in two
    dimensions) and ellipsoids (in three dimensions). The related 'candisc' package
    provides visualizations in a reduced-rank canonical discriminant space when
    there are more than a few response variables.
Constructs paths to your project's files.
    Declare the relative path of a file within your project with 'i_am()'.
    Use the 'here()' function as a drop-in replacement for 'file.path()',
    it will always locate the files relative to your project root.
Interface to the 'HERE' REST APIs <https://developer.here.com/develop/rest-apis>:
  (1) geocode and autosuggest addresses or reverse geocode POIs using the 'Geocoder' API;
  (2) route directions, travel distance or time matrices and isolines using the 'Routing', 'Matrix Routing' and 'Isoline Routing' APIs;
  (3) request real-time traffic flow and incident information from the 'Traffic' API;
  (4) find request public transport connections and nearby stations from the 'Public Transit' API;
  (5) request intermodal routes using the 'Intermodal Routing' API;
  (6) get weather forecasts, reports on current weather conditions, astronomical
  information and alerts at a specific location from the 'Destination Weather' API.
  Locations, routes and isolines are returned as 'sf' objects.
Hierarchical exponential-family random graph models with local dependence.
Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
A novel statistical method based on expectation maximisation (EM)
  algorithm and genetic segregation analysis to identify heritable DNA
  methylation marks. Details about the method can be found in
  Joo et al. (2018) <doi:10.1038/s41467-018-03058-6>.
Statistical framework to analyze heritability of gene expression 
    based on next-generation sequencing data and simulating sequencing reads. 
    Variance partition coefficients (VPC) are computed using linear mixed effects 
    and generalized linear mixed effects models. Compound Poisson and negative 
    binomial models are included. Reference: Rudra, Pratyaydipta, et al. "Model based heritability scores for high-throughput sequencing data." BMC bioinformatics 18.1 (2017): 143.
Probability functions and other utilities for the generalized Hermite distribution.
Facilitates estimation of full univariate and bivariate 
  probability density functions and cumulative distribution functions along with
  full quantile functions (univariate) and Spearman's rank correlation 
  (bivariate) using Hermite series based estimators. These estimators are 
  particularly useful in the sequential setting (both stationary and 
  non-stationary) and one-pass batch estimation setting for large data sets. 
  Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, 
  Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential Estimation of Nonparametric Correlation using Hermite Series Estimators." arXiv Preprint (2020) <arXiv:2012.06287>.
An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) <doi:10.1111/rssb.12007>.    A hero is a specific type of sandwich.  Dictionary.com (2018) <https://www.dictionary.com> describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes.
A modular and computationally efficient R package for  
  parameterizing, simulating, and analyzing health economic simulation 
  models. The package supports cohort discrete time state transition models 
  (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>,
  N-state partitioned survival models (Glasziou et al. 1990)
  <doi:10.1002/sim.4780091106>, and individual-level continuous 
  time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>,
  encompassing both Markov (time-homogeneous and time-inhomogeneous) and 
  semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is 
  quantified with standard graphical and tabular summaries of a probabilistic 
  sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, 
  <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table
  make individual-patient simulation, probabilistic sensitivity analysis, 
  and incorporation of patient heterogeneity fast.
Distribution free heteroscedastic tests for functional data. 
    The following tests are included in this package: test of no main treatment or contrast
    effect and no simple treatment effect given in 
    Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>,
    no main time effect, and no interaction effect based on 
    original observations given in Wang and Akritas (2010a) 
    <doi:10.1080/10485250903171621>
    and tests based on ranks given in Wang and Akritas (2010b)
    <doi:10.1016/j.jmva.2010.03.012>.
The goal of this package is to user-friendly realizing Gaussian 
             graphical model-based heterogeneity analysis.
             Recently, several Gaussian graphical model-based heterogeneity 
             analysis techniques have been developed. A common methodological limitation 
             is that the number of subgroups is assumed to be known a priori, which 
             is not realistic. In a very recent study (Ren et al., 2021), a novel approach 
             based on the penalized fusion technique is developed to fully 
             data-dependently determine the number and structure of subgroups in 
             Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing 
             the Gaussian graphical model technique in more practical settings. Beyond 
             Ren et al. (2021), more estimations and functions are added, so 
             that the package is self-contained and more comprehensive and can 
             provide "more direct" insights to practitioners (with the 
             visualization function). Reference: 
             Ren, M., Zhang S., Zhang Q. and Ma S. (2021). Gaussian Graphical 
             Model-based Heterogeneity Analysis via Penalized Fusion. 
             Biometrics, <doi:10.1111/biom.13426>.
Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Provides functions for maximum likelihood and Bayesian estimation of the Heteroskedastic Ordered Probit (HETOP) model, using methods described in Lockwood, Castellano and Shear (2018) <doi:10.3102/1076998618795124> and Reardon, Shear, Castellano and Ho (2017) <doi:10.3102/1076998616666279>.  It also provides a general function to compute the triple-goal estimators of Shen and Louis (1998) <doi:10.1111/1467-9868.00135>.
Functions for the fitting and summarizing of heteroscedastic t-regression.
Computes diagnostics for linear regression when treatment effects are heterogeneous.
    The output of 'hettreatreg' represents ordinary least squares (OLS) 
    estimates of the effect of a binary treatment as a weighted average of the average treatment effect 
    on the treated (ATT) and the average treatment effect on the untreated (ATU). 
    The program estimates the OLS weights on these parameters, computes the associated model diagnostics, 
    and reports the implicit OLS estimate of the average treatment effect (ATE). 
    See Sloczynski (2019), <http://people.brandeis.edu/~tslocz/Sloczynski_paper_regression.pdf>.
Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <arXiv:1412.5000>,
    and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <arXiv:1605.06566>
    for testing whether there is unexplained variation in treatment effects across observations, and for characterizing
    the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions
    implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.
Structural handling of Finnish identity numbers (persons and companies); extract information, check ID validity and diagnostics.
Implements various heuristics like Take The Best and
    unit-weight linear, which do two-alternative choice: which of
    two objects will have a higher criterion?  Also offers functions
    to assess performance, e.g. percent correct across all row pairs
    in a data set and finding row pairs where models disagree.
    New models can be added by implementing a fit and predict function--
    see vignette.
Provides the heuristics miner algorithm for process discovery 
   as proposed by Weijters et al. (2011) <doi:10.1109/CIDM.2011.5949453>. The
   algorithm builds a causal net from an event log created with the 'bupaR' 
   package. Event logs are a set of ordered sequences of events for which 
   'bupaR' provides the S3 class eventlog(). The discovered causal nets 
   can be visualised as 'htmlwidgets' and it is possible to annotate them with
   the occurrence frequency or processing and waiting time of process 
   activities.
Binning and plotting functions for hexagonal bins.
Helper functions for creating reproducible hexagon sticker purely in R.
Simple and integrated tool that automatically extracts and folds all hairpin sequences from raw genome-wide data. It predicts the secondary structure of several overlapped segments, with longer length than the mean length of sequences of interest for the species under processing, ensuring that no one is lost nor inappropriately cut.
Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.
Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.
A data only package containing commercial domestic flights that
    departed Houston (IAH and HOU) in 2011.
Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) <https://www.jstor.org/stable/2346105?seq=1>. We provide an implementation (Ronnegard, Alam and Shen 2010) <https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf> following Lee, Nelder and Pawitan (2006) <ISBN: 9781420011340> with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) <https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf>.
This data-only package was created for distributing data used in the examples of the 'hglm' package.
The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization
  constants of unnormalized probability distributions by utilizing holonomic 
  systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method
  to find maximal likelihood estimates by utilizing the HGM.
Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.
Contains functions for
 identifying and correcting HGNC human gene symbols and MGI mouse gene symbols 
 which have been converted to date format by Excel, withdrawn, or aliased.
 Also contains functions for reversibly converting between HGNC
 symbols and valid R names.
Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.
A handy collection of utility functions designed to aid in package development, plotting and scientific research.
    Package development functionalities includes among others tools such as cross-referencing package imports with the description file,
    analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub.
    Some of the other functionalities include automatic package installation and loading, plotting points without overlap,
    creating nice breaks for plots, overview tables and many more handy utility functions.
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Heller-Heller-Gorfine tests are a set of powerful statistical
    tests of multivariate k-sample homogeneity and independence (Heller et. al., 2013, <doi:10.1093/biomet/ass070>). For the univariate
    case, the package also offers implementations of the 'MinP DDP' and 'MinP ADP'
    tests by Heller et. al. (2016), which are consistent against all continuous alternatives but are
    distribution-free, and are thus much faster to apply.
Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
    age-structured spatio-temporal model for infectious disease counts. The
    approach is illustrated in a case study on norovirus gastroenteritis in
    Berlin, 2011-2015, by age group, city district and week, using additional
    contact data from the POLYMOD survey. This package contains the data and
    code to reproduce the results from the paper, see 'demo("hhh4contacts")'.
Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in a space. This package allows for intuitive and straightforward computation of HHI scores, requiring placement of objects of interest directly into the function. The package also includes a plot function for quick visual display of an HHI time series using any measure of time (year, quarter, month, etc.). For usage, please cite the Journal of Open Source Software paper associated with the package: Waggoner, Philip D. (2018) <doi:10.21105/joss.00828>.
Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.
Simulation from distributions supported by nested
        hyperplanes, using the algorithm described in Petris &
        Tardella, "A geometric approach to transdimensional Markov
        chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4,
        (2003).  Also random direction multivariate Adaptive Rejection
        Metropolis Sampling.
We propose a multiple logistic regression model to assess the influences of genomic features such as DNA-binding proteins and functional elements on topological domain borders.
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
             It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical
             clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
             geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance
             thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
             with preliminary big data support, different clustering methods, hybrid hierarchical clustering,
             multivariate clustering (MVC), cluster validation, visualization of regionalization results, and
             exporting region map and mean timeseries into NetCDF-4 file.
             The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.
Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies.  Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Performs linear discriminant analysis in high dimensional
        problems based on reliable covariance estimators for problems
        with (many) more variables than observations. Includes routines
        for classifier training, prediction, cross-validation and
        variable selection.
Partitioning of the independent and joint contributions of each
    variable in a multivariate data set, to a linear regression by hierarchical
    decomposition of goodness-of-fit measures of regressions using all subsets 
    of predictors in the data set. (i.e., model (1), (2), ..., (N), (1,2), ...,
    (1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of
    each predictor is provided by using a randomisation test.
Functions for performing hierarchical analysis of distance
    sampling data, with ability to use an areal spatial ICAR model on
    top of user supplied covariates to get at variation in abundance
    intensity.  The detection model can be specified as a function of
    observer and individual covariates, where a parametric model is
    supposed for the population level distribution of covariate values.
    The model uses data augmentation and a reversible jump MCMC
    algorithm to sample animals that were never observed.  Also
    included is the ability to include point independence (increasing
    correlation multiple observer's observations as a function of
    distance, with independence assumed for distance=0 or first
    distance bin), as well as the ability to model species
    misclassification rates using a multinomial logit formulation on data
    from double observers.  There is also the the ability to
    include zero inflation, but this is only recommended for cases where
    sample sizes and spatial coverage of the survey are high.
Pure set data visualization approaches are often limited in
    scalability due to the combinatorial explosion of distinct set families as
    the number of sets under investigation increases. hierarchicalSets applies
    a set centric hierarchical clustering of the sets under investigation and
    uses this hierarchy as a basis for a range of scalable visual
    representations. hierarchicalSets is especially well suited for collections
    of sets that describe comparable comparable entities as it relies on the
    sets to have a meaningful relational structure.
Implementation of the convex banding procedure (using a
    hierarchical group lasso penalty) for covariance estimation that is
    introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
    Matrix. Accepted for publication in JASA.
Bipartite graph-based hierarchical clustering, developed for pharmacogenomic 
    datasets and datasets sharing the same data structure. The goal is to 
    construct a hierarchical clustering of groups of samples based on 
    association patterns between two sets of variables. In the context of
    pharmacogenomic datasets, the samples are cell lines, and the two sets of 
    variables are typically expression levels and drug sensitivity values. 
    For this method, sparse canonical correlation analysis from 
    Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> 
    is first applied to extract association patterns for each group of samples. 
    Then, a nuclear norm-based dissimilarity measure is used to construct a 
    dissimilarity matrix between groups based on the extracted associations. 
    Finally, hierarchical clustering is applied.
Hierarchical group-wise partitioning of phenotypic diversity into 
    within-group (alpha), among-group (beta), and pooled-total (gamma) 
    components using Hill numbers.
    Turnover and overlap are also calculated as standardized alternatives to
    beta diversity. Hierarchical bootstrapping is used to approximate 
    uncertainty around each diversity component.
Miscellaneous R functions for calculating and decomposing hierarchical diversity metrics, including hierarchical allele richness, hierarchical exponential Shannon entropy (true diversity of order q=1), hierarchical heterozygosity and genetic differentiation (Jaccard dissimilarity, Delta D, Fst and Jost's D). In addition,a new approach to identify population structure based on the homogeneity of multivariate variances of Shannon differentiation is presented. This package allows users to analyse spatial structured genetic data or species data under a unifying framework (Gaggiotti, O. E. et al, 2018, Evol Appl, 11:1176-1193; <DOI:10.1111/eva.12593>), which partitions diversity and differentiation into any hierarchical levels. It helps you easily structure and format your data. In summary,it implements the analyses of true diversity profiles (q=0, 1, 2), hierarchical diversities and differentiation decomposition, visualization of population structure, as well as the estimation of correlation between geographic distance and genetic differentiation.
Estimates hierarchical F-statistics from haploid or
    diploid genetic data with any numbers of levels in the hierarchy, following the
    algorithm of Yang (Evolution(1998), 52:950).
    Tests via randomisations the significance
    of each F and variance components, using the likelihood-ratio statistics G
    (Goudet et al. (1996) <https://www.genetics.org/content/144/4/1933>).
    Estimates genetic diversity statistics
    for haploid and diploid genetic datasets in various formats, including inbreeding and
    coancestry coefficients, and population specific F-statistics following
    Weir and Goudet (2017) <https://www.genetics.org/content/206/4/2085>.
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
Implementation of the convex hierarchical testing (CHT) procedure
    introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing
    of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.
Uses likelihood to estimate ancestry and heterozygosity.
        Evaluates simple hybrid classifications (parentals, F1, F2,
        backcrosses). Estimates genomic clines.
A wrapper for the 'Highcharts' library including
    shortcut functions to plot R objects. 'Highcharts'
    <http://www.highcharts.com/> is a charting library offering
    numerous chart types with a simple configuration syntax.
Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.
Implements the high-dimensional two-sample test 
    proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>. 
    It also implements the test proposed by Srivastava, Katayama, 
    and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests 
    are particularly suitable to high dimensional data from two populations 
    for which the classical multivariate Hotelling's T-square test fails due 
    to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm 
    tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>, 
    referred to as zwl_test() in this package, provide a reliable and powerful test.
Computes the scores and ranks candidates according to voting rules electing the highest median grade.
    Based on "Tie-breaking the highest median: alternatives to the majority judgment", A. Fabre, Social Choice & Welfare (forthcoming as of 2020). The paper is available here: <https://github.com/bixiou/highest_median/raw/master/Tie-breaking%20Highest%20Median%20-%20Fabre%202019.pdf>.
    Functions to plot the voting profiles can be found on github: <https://github.com/bixiou/highest_median/blob/master/packages_functions_data.R>.
Provide functionality to manage, clean and match highfrequency
    trades and quotes data, calculate various liquidity measures, estimate and
    forecast volatility, detect price jumps and investigate microstructure noise and intraday
    periodicity.
Syntax highlighter for R code based on the results
    of the R parser. Rendering in HTML and latex markup. Custom Sweave
    driver performing syntax highlighting of R code chunks.
A tool to format R markdown with CSS ids for HTML output. 
    The tool may be most helpful for those using markdown to create reproducible
    documents. The biggest limitations in formatting is the knowledge of CSS
    by the document authors.
Provides various tests for comparing high-dimensional mean vectors in two sample populations.
Perform high dimensional Feature Selection in the presence of survival outcome.
            Based on Feature Selection method and different survival analysis, it will obtain the best 
            markers with optimal threshold levels according to their effect on disease progression
            and produce the most consistent level according to those threshold values. 
            The functions' methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039>  and Bhattacharjee  et al (2021) <arXiv:2012.02102>.
Provides syntax highlighting for R source code. Currently it
    supports LaTeX and HTML output. Source code of other languages is supported
    via Andre Simon's highlight package (<http://www.andre-simon.de>).
Can be used to carry out extraction, normalization, quality control (QC), candidate hits identification and visualization for plate based assays, in drug discovery. The package methods were applied in H. W. Choi et al. "Identification of Novel Mast Cell Activators Using Cell-Based High-Throughput Screening", SLAS Discovery 24(6), 2019. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
Environmental health studies are increasingly measuring multiple pollutants
    to characterize the joint health effects attributable to exposure mixtures.
    However, the underlying dose-response relationship between toxicants and
    health outcomes of interest may be highly nonlinear, with possible nonlinear
    interaction effects. Hierarchical integrative group least absolute shrinkage
    and selection operator (HiGLASSO), developed by Boss et al (2020)
    <arXiv:2003.12844>, is a general framework to identify noteworthy nonlinear 
    main and interaction effects in the presence of group structures among a set
    of exposures.
Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm,
    a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD).
    In addition, this method attaches a confidence interval to assess the uncertainty of its predictions.
    See Su and Zhu (2018) <arXiv:1802.04876> for details.
Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires
    some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid,
    the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve
    each sample can be visualized as a simple density plot, and the distance between samples can be calculated from
    the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences
    between samples can identified using a simple bootstrap procedure.
Makes it easy to extract and combine variables from the HILDA (Household, Income and Labour Dynamics in Australia) survey maintained by the Melbourne Institute <https://melbourneinstitute.unimelb.edu.au/hilda>.
Tools for analysing, comparing, visualising and partitioning diversity based on Hill numbers.
  'hilldiv' is an R package that provides a set of functions to assist analysis of diversity for
  diet reconstruction, microbial community profiling or more general ecosystem characterisation
  analyses based on Hill numbers, using OTU/ASV tables and associated phylogenetic trees as
  inputs. The package includes functions for (phylo)diversity measurement, (phylo)diversity
  profile plotting, (phylo)diversity comparison between samples and groups, (phylo)diversity
  partitioning and (dis)similarity measurement. All of these grounded in abundance-based and
  incidence-based Hill numbers.
  The statistical framework developed around Hill numbers encompasses many of the most
  broadly employed diversity (e.g. richness, Shannon index, Simpson index),
  phylogenetic diversity (e.g. Faith's PD, Allen's H, Rao's quadratic entropy) and
  dissimilarity (e.g. Sorensen index, Unifrac distances) metrics. This enables the most
  common analyses of diversity to be performed while grounded in a single statistical
  framework. The methods are described in Jost et al. (2007) <DOI:10.1890/06-1736.1>,
  Chao et al. (2010) <DOI:10.1098/rstb.2010.0272> and Chiu et al. (2014)
  <DOI:10.1890/12-0960.1>; and reviewed in the framework of molecularly characterised
  biological systems in Alberdi & Gilbert (2019) <DOI:10.1111/1755-0998.13014>.
Generate occupancy patterns based on arrival and departure timestamps
Calculate taxonomic, functional and phylogenetic diversity measures 
    through Hill Numbers proposed by Chao, Chiu and Jost (2014) 
    <doi:10.1146/annurev-ecolsys-120213-091540>.
Estimation of heritability with confidence intervals in linear mixed models.
Allows to estimate and test high-dimensional mediation effects based on advanced mediator screening and penalized regression techniques. Methods used in the package refer to Haixiang Zhang, Yinan Zheng, Zhou Zhang, Tao Gao, Brian Joyce, Grace Yoon, Wei Zhang, Joel Schwartz, Allan Just, Elena Colicino, Pantel Vokonas, Lihui Zhao, Jinchi Lv, Andrea Baccarelli, Lifang Hou, Lei Liu (2016) <doi:10.1093/bioinformatics/btw351>.
For supersonic aircraft, flying subsonic over land,
    High Mach finds the best route between airports. Allows for coastal buffer and
    potentially closed regions. Uses minimal model of aircraft
    performance: focus is on time saved versus subsonic flight, rather
    than vertical flight profile. For modelling and forecasting, not for planning your
    flight!
H-index and h-alpha are a bibliometric indicators. This package 
  provides functions to simulate how these indicators may develop over time for
  a given set of researchers and to visualize the simulation data. 
  The implementation is based on the 'STATA' ado h-index and is described in 
  more detail in Bornmann et al. (2019) <arXiv:1905.11052>.
H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories.
Can be used for paternity and maternity assignment and outperforms 
  conventional methods where closely related individuals occur in the pool of 
  possible parents. The method compares the genotypes of offspring with any 
  combination of potentials parents and scores the number of mismatches of these 
  individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). 
  It elaborates on a prior exclusion method based on the Homozygous Opposite Test 
  (HOT; Huisman 2017 <doi:10.1111/1755-0998.12665>) by introducing the 
  additional exclusion criterion HIPHOP (Homozygous Identical Parents, 
  Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential 
  parents are excluded if they have more mismatches than can 
  be expected due to genotyping error and mutation, and thereby one can identify the 
  true genetic parents and detect situations where one (or both) of the true 
  parents is not sampled. Package 'hiphop' can deal with (a) the case where there 
  is contextual information about parentage of the mother (i.e. a female has been 
  seen to be involved in reproductive tasks such as nest building), but paternity 
  is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, 
  because there is no contextual information on which female laid eggs and which 
  male fertilized them (e.g. polygynandrous mating system where multiple females 
  and males deposit young in a common nest, or organisms with external fertilisation 
  that breed in aggregations).
    For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L.,
       Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. 
       (in revision). HIPHOP: improved paternity assignment among close relatives 
       using a simple exclusion method for bi-allelic markers. 
       Molecular Ecology Resources, DOI to be added upon acceptance.
Based on hierarchical modeling, this package provides a
        few practical functions to find and present the optimal designs
        for a pooled NGS design.
Read hierarchical fixed width files like those commonly used by 
    many census data providers. Also allows for reading of data in chunks,
    and reading 'gzipped' files without storing the full file in memory.
Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses
    by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion.
    The main idea is to use changing intensity ratios of ion pairs from peak list generated with 'xcms' as candidates and evaluate those against
    base peak chromatograms and spectra information within the raw measurement data automatically.
    The functionality is described in Hoffmann et al. (2018) <doi:10.1021/acs.analchem.8b00356>.
Implementation of a class of hierarchical item response
  theory (IRT) models where both the mean and the variance of latent preferences
  (ability parameters) may depend on observed covariates. The current
  implementation includes both the two-parameter latent trait model for binary data and the
  graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM)
  algorithm. Asymptotic standard errors are derived from the observed information
  matrix.
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
In some cases you will have data in a histogram format, where
  you have a vector of all possible observations, and a vector of how many
  times each observation appeared. You could expand this into a single 1D
  vector, but this may not be advisable if the counts are extremely large.
  'HistDat' allows for the calculation of summary statistics without the need
  for expanding your data.
The 'HistData' package provides a collection of small data sets
    that are interesting and important in the history of statistics and data
    visualization. The goal of the package is to make these available, both for
    instructional use and for historical research. Some of these present interesting
    challenges for graphics or analysis in R.
In the framework of Symbolic Data Analysis, a relatively new
    approach to the statistical analysis of multi-valued data, we consider
    histogram-valued data, i.e., data described by univariate histograms. The
    methods and the basic statistics for histogram-valued data are mainly based
    on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
    between quantile functions. The package contains unsupervised classification
    techniques, least square regression and tools for histogram-valued data and for
    histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi:10.1007/s11634-014-0176-4>.
Using the MDL principle, it is possible to estimate
	parameters for a histogram-like model. The package contains
	the implementation of such an estimation method.
Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).
Provides a number of utility functions useful for manipulating large histograms.  This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning.  It also provides a protocol buffer representations of the default R histogram class to allow histograms over large data sets to be computed and manipulated in a MapReduce environment.
These sample data sets are intended for historians
    learning R. They include population, institutional, religious,
    military, and prosopographical data suitable for mapping,
    quantitative analysis, and network analysis.
Automatically tracks and makes programmatically available code
    evaluation history in R sessions and dynamic documents.
Hierarchical inference testing (HIT) for (generalized) linear models with 
    correlated covariates applicable to high-dimensional settings.
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
The functions in this package produce a complete set of mortality rates as a function of a combination of HIV prevalence and either life expectancy at birth (e0), child mortality (5q0), or child mortality with adult mortality (45q15)
Hadoop InteractiVE facilitates distributed 
             computing via the MapReduce paradigm through R and Hadoop. An easy to use 
             interface to Hadoop, the Hadoop Distributed File System (HDFS), 
	     and Hadoop Streaming is provided.
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>.
This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.
Datasets related to Hong Kong, including information on the 2019 elected District Councillors (<https://www.districtcouncils.gov.hk> and <https://dce2019.hk01.com/>) and traffic collision data from the Hong Kong Department of Transport (<https://www.td.gov.hk/>). All
    of the data in this package is available in the public domain.
Several procedures around a particular hierarchical model for extreme value: the HKEVP of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>. Simulation, estimation and spatial extrapolation of this model are available for extreme value data. A special case of this process is also handled: the Latent Variable Model of Davison et al. (2012) <DOI:10.1214/11-STS376>.
A set of functions helps to retrieve data from HKEX (Hong Kong Stock Exchange), see <https://www.hkex.com.hk/> for more information. In addition, a function generates insert SQL statements from a dataframe.
Methods to make inference about the Hurst-Kolmogorov and the AR(1) process.
A streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database <https://haplostats.org/haplostats>. High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting 'HLAMatchMaker' <http://www.epitopes.net> versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality.
Provides access to datasets published by 'Hlídač státu' <https://www.hlidacstatu.cz/>, 
    a Czech watchdog, via their API.
A suite of diagnostic tools for hierarchical
    (multilevel) linear models. The tools include
    not only leverage and traditional deletion diagnostics (Cook's
    distance, covratio, covtrace, and MDFFITS) but also 
    convenience functions and graphics for residual analysis. Models
    can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package.
Implements Hierarchical Latent Space Network Model (HLSM) for ensemble of networks as described in Sweet, Thomas & Junker (2013). <DOI:10.3102/1076998612458702>.
For estimation of a variable of interest using two sources of auxiliary information available in a nested structure. For reference see Saarela et al. (2016)<doi:10.1007/s13595-016-0590-1> and Saarela et al. (2018) <doi:10.3390/rs10111832>.
Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models.  All of the code is written in R.  Theoretical references are listed below:.
    Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, 
    Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>, 
    Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>,
    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, 
    Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, 
    Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.
Utilities for reading data from the Human Mortality Database (<https://www.mortality.org>), Human Fertility Database (<https://www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<http://www.fertilitydata.org/>), The Japanese Mortality Database (<http://www.ipss.go.jp/p-toukei/JMD>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
Classification performance metrics that are derived from the ROC
    curve of a classifier. The package includes the H-measure performance metric
    as described in <http://link.springer.com/article/10.1007/s10994-009-5119-5>,
    which computes the minimum total misclassification cost, integrating over any
    uncertainty about the relative misclassification costs, as per a user-defined
    prior. It also offers a one-stop-shop for other scalar metrics of performance,
    including sensitivity, specificity and many others, and also offers plotting
    tools for ROC curves and related statistics.
Provides weighted lasso framework for high-dimensional mixed data graph estimation.  
    In the graph estimation stage, the graph structure is estimated by maximizing the conditional 
    likelihood of one variable given the rest. We focus on the conditional loglikelihood of each variable 
    and fit separate regressions to estimate the parameters, much in the spirit of the neighborhood 
    selection approach proposed by Meinshausen-Buhlmann for the Gaussian Graphical Model and by Ravikumar
    for the Ising Model. Currently, the discrete variables can only take two values. In the future, method 
    for general discrete data and for visualizing the estimated graph will be added. 
    For more details, see the linked paper.
Runs single level and multilevel imputation models as described in Speidel, Drechsler and Jolani (2020) <doi:10.18637/jss.v095.i09>. The user just has to pass the data to the main function and, optionally, his analysis model. Basically the package then translates this analysis model into commands to impute the data according to it with functions from 'mice', 'MCMCglmm' or routines build for this package.
Contains many functions useful for data
	analysis, high-level graphics, utility operations, functions for
	computing sample size and power, simulation, importing and annotating datasets,
	imputing missing values, advanced table making, variable clustering,
	character string manipulation, conversion of R objects to LaTeX and html code,
	and recoding variables.
A simple implementation of HMLasso (Lasso with High Missing rate).
  Takada, M., Fujisawa, H., & Nishikawa, T. (2019) <arXiv:1811.00255>.
Easy to use library to setup, apply and make inference
        with discrete time and discrete space Hidden Markov Models
Fits hidden Markov models with discrete non-parametric 
	observation distributions to data sets.  The observations may
	be univariate or bivariate. Simulates data from such models.
	Finds most probable underlying hidden states, the most
	probable sequences of such states, and the log likelihood
	of a collection of observations given the parameters of
	the model.  Auxiliary predictors are accommodated in the
        univariate setting.
Asymmetric hidden Markov model (HMM-A) learning.
   HMM-As are similar to regular HMMs, but use Bayesian Networks (BNs) in 
   their emission distribution. The HMM-As can therefore offer more problem 
   insight [Bueno et al. 2017, <doi:10.1016/j.ijar.2017.05.011>].
The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
R functions to estimate and perform goodness of fit test for several
    Markov regime switching and mixture bivariate copula models. 
    The goodness of fit test is based on a Cramer von Mises statistic and 
    uses the Rosenblatt transform and parametric bootstrap to estimate the p-value. 
    The estimation of the copula parameters are based on the pseudo-maximum likelihood
    method using pseudo-observations defined as normalized ranks.
Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) <arXiv:1710.09663> for more details.
Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte.
Functions for specifying and fitting marginal models for contingency tables proposed 
	by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al. 
	(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
 	tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables 
	is described by a marginal model. 
	Inequality constraints on the parameters are allowed and can be tested.
Provide functions to make estimate the number of states for a hidden Markov 
    model (HMM) using marginal likelihood method proposed by the authors.
    See the Manual.pdf file a detail description of all functions, and a detail tutorial.
Analysing time-series accelerometer data to quantify length and 
             intensity of physical activity using hidden Markov models. 
             It also contains the traditional cut-off point method.
             Witowski V, Foraita R, Pitsiladis Y, Pigeot I, 
             Wirsik N (2014)<doi:10.1371/journal.pone.0114089>.
Using Dirichlet-Multinomial distribution to provide several functions for formal hypothesis testing, power and sample size calculations for human microbiome experiments.
Tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.
Hierarchical Modelling of Species Communities (HMSC) is
   a model-based approach for analyzing community ecological data. 
   This package implements it in the Bayesian framework with Gibbs
   Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020)
   <doi:10.1111/2041-210X.13345>).
Tracks elapsed clock time using a `hms::hms()` scalar, 
    which if running has an attribute named start 
    that specifies the system time when the timer was started.
    The elapsed time is the value of the scalar 
    plus the difference between the current system time 
    and the system time when the timer was started.
Perform association test between a group of variable and the outcome.
Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.
Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions  of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).
Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.
Suite of tools for managing cached files, targeting
    use in other R packages. Uses 'rappdirs' for cross-platform paths.
    Provides utilities to manage cache directories, including targeting
    files by path or by key; cached directories can be compressed and
    uncompressed easily to save disk space.
Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://gml.noaa.gov/ccgg/trends/data.html>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/data/seaice_index/archives>, CSIRO <https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data: <https://cdiac.ess-dive.lbl.gov/trends/co2/vostok.html>.
Simulates hands and tournaments of Texas Holdem, 
	the most popular form of poker.
	For examples of probability problems involving Texas Holdem 
	and a brief explanation of the game see Schoenberg, F. (2011).
	An Introduction to Probability with Texas Hold'em Examples.
	Taylor and Francis, New York, ISBN-13: 978-1439827680.
Contains trading holiday and half-day data that is automatically registered when loaded.
Provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets
    with groups of variables with different degrees of variance, covariance, and effect size.
Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
A collection of functions useful for the analysis of
    building physics experiments.
A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.
Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (2016) <arXiv:1611.06739v2>.
A wrapper for the homologene database by the National Center for
    Biotechnology Information ('NCBI'). It allows searching for gene homologs across 
    species. Data in this package can be found at <ftp://ftp.ncbi.nih.gov/pub/HomoloGene/build68/>.
    The package also includes an updated version of the homologene database where 
    gene identifiers and symbols are replaced with their latest (at the time of
    submission) version and functions to fetch latest annotation data to keep updated.
Homomorphic computations in R for privacy-preserving applications. Currently only
             the Paillier Scheme is implemented.
A collection of homogeneity tests described in: Viglione
        A., Laio F., Claps P. (2007) ``A comparison of homogeneity
        tests for regional frequency analysis'', Water Resources
        Research, 43, W03428, doi:10.1029/2006WR005095.  More on
        Regional Frequency Analysis can be found in package nsRFA.
Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
Calculates horizon elevation angle and sky view factor from a digital terrain model (Dozier and Frew, 1990).
Supplement for the book "Handbook of Regression Methods" by D. S. Young.  Some datasets used in the book are included and documented.  Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. 
    Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.
Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression described in Nalenz & Villani (2017) <arXiv:1702.05008>.
Contains functions for applying the horseshoe prior to high-
    dimensional linear regression, yielding the posterior mean and credible
    intervals, amongst other things. The key parameter tau can be equipped with
    a prior or estimated via maximum marginal likelihood estimation (MMLE).
    The main function, horseshoe, is for linear regression. In addition, there
    are functions specifically for the sparse normal means problem, allowing
    for faster computation of for example the posterior mean and posterior
    variance. Finally, there is a function available to perform variable
    selection, using either a form of thresholding, or credible intervals.
Provides the posterior estimates of the regression coefficients when horseshoe prior is specified. 
             The regression models considered here are logistic model for binary response and 
             log normal accelerated failure time model for right censored survival response. 
             The linear model analysis is also available for completeness. 
             All models provide deviance information criterion and widely applicable information criterion. 
             See <doi:10.1111/rssc.12377> Maity et. al. (2019) <doi:10.1111/biom.13132> Maity et. al. (2020).
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
A set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.
Functions to compute the semi-axes lengths and coordinate points of Hotelling ellipse. Bro and Smilde (2014) <DOI:10.1039/c3ay41907j>. Brereton (2016) <DOI:10.1002/cem.2763>.
Contains data for software hotspot analysis, along with a function performing the analysis itself.
The hotspots package is designed to look within a set of measured values of a variable and identify values that are disproportionately high based on both the deviance of any given value from a statistical distribution and its similarity to other values. Because this relative magnitude of each value is taken into account, a value that is a statistical outlier may not always be a hot spot if other values are similarly large.
Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016.  Additional data sets containing geographical information and links to Wikipedia are also included.
A wrapper around a CSS library called 'Hover.css', intended for use 
  in 'shiny' applications.
When testing multiple hypotheses simultaneously, this
        package provides functionality to calculate a lower bound for
        the number of correct rejections (as a function of the number
        of rejected hypotheses), which holds simultaneously -with high
        probability- for all possible number of rejections.  As a
        special case, a lower bound for the total number of false null
        hypotheses can be inferred.  Dependent test statistics can be
        handled for multiple tests of associations. For independent
        test statistics, it is sufficient to provide a list of
        p-values.
Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.
By binding R functions and the 'Highcharts' <http://www.highcharts.com/> charting library, 'hpackedbubble' package provides a simple way to draw split packed bubble charts.
This package provides all the functions necessary to
        estimate the 8 parameters of the Heligman Pollard mortality
        model using a Bayesian Melding procedure with IMIS as well as
        to convert those parameters into age-specifc probabilities of
        death and a corresponding life table
Probabilistic models describing the behavior 
	of workload and queue on a High Performance Cluster and computing GRID 
	under FIFO service discipline basing on modified Kiefer-Wolfowitz 
	recursion. Also sample data for inter-arrival times, service times, 
	number of cores per task and waiting times of HPC of Karelian 
	Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
	Functions provided to import/export workload traces in Standard Workload Format (swf).
	Stability condition of the model may be verified either exactly, or approximately.
Compute house price indexes and series using a variety of different methods and
    models common through the real estate literature.  Evaluate index 'goodness' based
    on accuracy, volatility and revision statistics. Background on basic model construction
    for repeat sales models can be found at: Case and Quigley (1991) 
    <https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html> and for hedonic pricing models at: 
    Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>. The package author's working paper on the 
    random forest approach to house price indexes can be found at: <http://www.github.com/andykrause/hpi_research>.
An implementation of high-probability lower bounds for the total variance distance 
             as introduced in Michel & Naef & Meinshausen (2020) <arXiv:2005.06006>. An estimated 
             lower-bound (with high-probability) on the total variation distance between two probability distributions from which
             samples are observed can be obtained with the function HPLB.
Collection of functions for manipulating sets of HPO terms and
    plotting them with a various options.
This package contains some miscellaneous convenience functions, 
	to create a matrix of dummy columns from a factor, 
	to determine whether x lies in range [a,b], 
	to add a rectangular bracket to an existing plot, 
	and to convert frequencies between Hz, semitones, mel and Bark. 
	This package also contains an example data set of a stratified sample
	of 80 talkers of Dutch.
Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.
Transform and analyze workforce data in meaningful ways for human resources (HR) analytics. Get started with workforce planning using a simple Shiny app.
A compilation of extra 'ggplot2' themes, scales and utilities, including a 
    spell check function for plot label fields and an overall emphasis on typography. 
    A copy of the 'Google' font 'Roboto Condensed' <https://github.com/google/roboto/> 
    is also included along with a copy of the 'IBM' 'Plex Sans' <https://github.com/IBM/type>,
    'Titillium Web' <https://fonts.google.com/specimen/Titillium+Web>, and
    'Public Sans' <https://github.com/uswds/public-sans/> fonts
    are also included to support their respective typography-oriented themes.
Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).
Estimates the log hazard ratio associated with a binary exposure using a Cox PH model weighted by the propensity score. 
  Propensity model is estimated using a simple logistic regression. 
  Variance estimation takes into account the propensity score estimation step with the method proposed by Hajage et al. (2018) <doi:10.1002/bimj.201700330>. 
  Both the average treatment effect on the overall (ATE) or the treated (ATT) population can be estimated. 
  For the ATE estimation, both unstabilized and stabilized weights can be used.
Methods for testing main and interaction effects in possibly
    high-dimensional parametric or nonparametric repeated measures in factorial designs for univariate or multivariate data.
    The observations of the subjects are assumed to be multivariate normal if using the parametric test.
    The nonparametric version tests with regard to nonparametric relative effects (based on pseudo-ranks).
    It is possible to use up to 2 whole- and 3 subplot factors.
The analysis of high-resolution melting (HRM) curves provides cost-efficient post-PCR distinction among amplicons differing by as little as a single base pair, based on their thermal denaturation (melting) properties. HRM curve analysis has been used mainly for genotyping and detection of mutations. Beside genotyping, the method has been used to differentiate viral, bacterial and fungal pathogens, and it has a potential for plant genotyping in breeding and as an alternative to DNA barcoding for taxonomic assignment of species. This package offers functions that facilitate drawing denaturation, melting, and difference curves in HRM curve analysis in R. Lukas Beule was supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the Bonares-SIGNAL project (funding codes: 031A562A and 031B0510A). Please cite Schiwek et al. (2020) <doi:10.3390/pathogens9040270> if you use this package in your work.
Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" <arXiv:2104.12597>.
The book "Semiparametric Regression with R" by J. Harezlak, D. Ruppert & M.P. Wand (2018, Springer; ISBN: 978-1-4939-8851-8) makes use of datasets and scripts to explain semiparametric regression concepts. Each of the book's scripts are contained in this package as well as datasets that are not within other R packages. Functions that aid semiparametric regression analysis are also included.
Methods of homogenous segmentation for spatial lines data, such as pavement 
             performance indicators and traffic volumes. Three methods are available for 
             homogenous segmentation, including cumulative difference approach, minimization 
             coefficient of variation, and spatial heterogeneity based method.
A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
Functions, data sets, analyses and examples from the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2006). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the 
  second edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2008). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  the package contains Sweave code for producing slides for selected
  chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the 
  third edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Torsten Hothorn and Brian S.
  Everitt, Chapman & Hall/CRC, 2014). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  Sweave source code for slides of selected chapters is included in 
  this package (see HSAUR3/inst/slides). The publishers web page is 
  '<http://www.crcpress.com/product/isbn/9781482204582>'.
The theoretical covariance between pairs of markers is calculated
    from either paternal haplotypes and maternal linkage disequilibrium (LD) or 
    vise versa. A genetic map is required. Grouping of markers is based on the 
    correlation matrix and a representative marker is suggested for each group.
    Employing the correlation matrix, optimal sample size can be derived for 
    association studies based on a SNP-BLUP approach.
    The implementation relies on paternal half-sib families and biallelic 
    markers. If maternal half-sib families are used, the roles of sire/dam are 
    swapped. Multiple families can be considered.
    Wittenburg, Bonk, Doschoris, Reyer (2020) "Design of Experiments for 
    Fine-Mapping Quantitative Trait Loci in Livestock Populations" 
    <doi:10.1186/s12863-020-00871-1>.
    Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a 
    maximally informative set of single-nucleotide polymorphisms for association
    analyses using linkage disequilibrium" <doi:10.1086/381000>.
Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.
We explore sparsity and homogeneity of regression coefficients incorporating prior constraint information. A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints. We develop an modified alternating direction method of multipliers algorithm (ADMM) to obtain the estimators.
User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer et al. 2006 <doi:10.1890/04-0609>). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
To test the homogeneity of stratum effects in stratified paired binary data.
Canonical correlation analysis that extracts nonlinear
        correlation through the use of Hilbert Schmidt Independence
        Criterion and Centered Kernel Target Alignment.
Implementation of the block coordinate descent procedure for
    solving the proximal function of latent group Lasso, highlighted by
    decomposing a DAG into several non-overlapping path graphs, and getting
    closed-form solution for each path graph. The procedure was introduced
    as Algorithm 4 in Yan and Bien (2017) <doi:10.1214/17-STS622>
    "Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations", and the
    closed-form solution for each path graph is solved in Algorithm 3 of
    that paper.
A package for computation of hidden semi markov models
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data.
Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.
Paternal recombination rate and maternal linkage disequilibrium 
    (LD) are estimated for pairs of biallelic markers such as single nucleotide 
    polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The 
    implementation relies on paternal half-sib families. If maternal half-sib 
    families are used, the roles of sire/dam are swapped. Multiple families can 
    be considered. For parameter estimation, at least one sire has to be double 
    heterozygous at the investigated pairs of SNPs.
    Based on recombination rates, genetic distances between markers can be 
    estimated. Markers with unusually large recombination rate to markers in 
    close proximity can be discarded in this derivation.
    *A pipeline is available at github*
    <https://github.com/wittenburg/hsrecombi>
    Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of 
    recombination rate and maternal linkage disequilibrium in half-sibs" 
    <doi:10.3389/fgene.2018.00186>.
    Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium 
    in half-sib families" <doi:10.1534/genetics.111.137521>.
Linear and logistic regression models penalized with hierarchical
  shrinkage priors for selection of biomarkers (or more general variable
  selection), which can be fitted using Stan (Carpenter et al. (2017)
  <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized
  horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>),
  as well as the projection predictive selection approach to recover a sparse
  set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
  <doi:10.1214/20-EJS1711>).
Provides bindings to the National Geodetic Survey (NGS) Horizontal
 Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay,
 Chris Pearson, and Jarir Saleh of NGS. HTDP is a utility that allows users to
 transform positional coordinates across time and between spatial reference
 frames. See <https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml> for more
 information.
A collection of reweighted marginal hypothesis tests for clustered data, based
    on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) <doi:10.1111/1541-0420.00005>.
    The tests in this collection are clustered analogs to well-known hypothesis tests
    in the classical setting, and are appropriate for data with cluster- and/or group-size 
    informativeness. The syntax and output of functions are modeled after common, 
    recognizable functions native to R. Methods used in the package refer to 
    Gregg, M., Datta, S., and Lorenz, D. (2020) <doi:10.1177/0962280220928572>, 
    Nevalainen, J., Oja, H., and Datta, S. (2017) <doi:10.1002/sim.7288>
    Dutta, S. and Datta, S. (2015) <doi:10.1111/biom.12447>, 
    Lorenz, D., Datta, S., and Harkema, S. (2011) <doi:10.1002/sim.4368>, 
    Datta, S. and Satten, G. (2008) <doi:10.1111/j.1541-0420.2007.00923.x>, 
    Datta, S. and Satten, G. (2005) <doi:10.1198/016214504000001583>.
Efficient Bayesian multinomial logistic regression based on heavy-tailed
  (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters
  is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and
  Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed
  description of the method: Li and Yao (2018), 
  Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <arXiv:1405.3319>.
Convert a html document to simple plain texts by removing all html tags.  This package utilizes regular expressions to strip off html tags. It also offers gettxt() and browse() function, which enables you to get or browse texts at a certain web page.
Provides a 'Shiny' app allowing to convert 'HTML' code to 'R' code (e.g. '<span>Hello</span>' to 'tags$span("Hello")'), for usage in a 'Shiny' UI.
Generates HTML tag strings for HTML5 elements included in Mozilla's documentation of HTML5. 
    Attributes are passed as parameters. If the attribute name is a reserved R word, the attribute is suffixed with _attr (ex: for_attr). 
    To declare a DOCTYPE, wrap html with function html_doc().
    Mozilla's documentation for HTML5 is available here: <https://developer.mozilla.org/en-US/docs/Web/HTML/Element>.
    Elements marked as obsolete are not included.
Simple tools for scraping webpages, extracting common html tags and parsing contents to a tidy, tabular format.  Tools help with extraction of page titles, links, images, rss feeds, social media handles and page metadata.
HTML tables are a valuable data source but extracting and recasting
    these data into a useful format can be tedious. This package allows to collect
    structured information from HTML tables. It is similar to `readHTMLTable()`
    of the XML package but provides three major advantages. First, the function
    automatically expands row and column spans in the header and body cells.
    Second, users are given more control over the identification of header and body
    rows which will end up in the R table, including semantic header information
    that appear throughout the body. Third, the function preprocesses table code,
    corrects common types of malformations, removes unneeded parts and so helps to
    alleviate the need for tedious post-processing.
Tables with state-of-the-art layout elements such as row spanners,
    column spanners, table spanners, zebra striping, and more. While allowing
    advanced layout, the underlying css-structure is simple in order to maximize
    compatibility with word processors such as 'MS Word' or 'LibreOffice'. The package
    also contains a few text formatting functions that help outputting text
    compatible with HTML/LaTeX.
HTML documents can be beautiful and pristine. They can also be
    wretched, evil, malformed demon-spawn. Now, you can tidy up that HTML and XHTML
    before processing it with your favorite angle-bracket crunching tools, going beyond
    the limited tidying that 'libxml2' affords in the 'XML' and 'xml2' packages and
    taming even the ugliest HTML code generated by the likes of Google Docs and Microsoft
    Word. It's also possible to use the functions provided to format or "pretty print"
    HTML content as it is being tidied. Utilities are also included that make it  
    possible to view formatted and "pretty printed" HTML/XML
    content from HTML/XML document objects, nodes, node sets and plain character HTML/XML
    using 'vkbeautify' (by Vadim Kiryukhin) and 'highlight.js' (by Ivan Sagalaev).
    Also (optionally) enables filtering of nodes via XPath or viewing an HTML/XML document
    in "tree" view using 'XMLDisplay' (by Lev Muchnik). See
    <https://github.com/vkiryukhin/vkBeautify> and 
    <http://www.levmuchnik.net/Content/ProgrammingTips/WEB/XMLDisplay/DisplayXMLFileWithJavascript.html> 
    for more information about 'vkbeautify' and 'XMLDisplay', respectively.
Facilitates automated HTML report creation, in particular
        framed HTML pages and dynamically sortable tables.
A framework for creating HTML widgets that render in various
    contexts including the R console, 'R Markdown' documents, and 'Shiny'
    web applications.
Historical regression trees are an extension of standard trees, 
	producing a non-parametric estimate of how the response depends on 
	all of its prior realizations as well as that of any time-varying predictor 
	variables. The method applies equally to regularly as well as irregularly 
	sampled data. The package implements random forest and boosting ensembles 
	based on historical regression trees, suitable for longitudinal data. 
	Standard error estimation and Z-score variable importance is also implemented.
Provides methods for analysing and forecasting hierarchical and 
    grouped time series. The available forecast methods include bottom-up,
    top-down, optimal combination reconciliation (Hyndman et al. 2011) 
    <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation
    (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.
A Poisson mixture model is implemented to cluster genes from high-
    throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is
    performed using either the EM or CEM algorithm, and the slope heuristics are
    used for model selection (i.e., to choose the number of clusters).
Functions for the management and treatment of hydrology and 
  meteorology time-series stored in a 'Sqlite' data base.
Functions for analyzing high throughput sequencing 
    stable isotope probing (HTS-SIP) data.
    Analyses include high resolution stable isotope probing (HR-SIP),
    multi-window high resolution stable isotope probing (MW-HR-SIP), 
    and quantitative stable isotope probing (q-SIP).
Generic models and chemical-specific data for simulation and
             statistical analysis of chemical toxicokinetics ("TK") as
             described by Pearce et al. (2017) <doi:10.18637/jss.v079.i04>.
             Chemical-specific in vitro data have been obtained from relatively
             high throughput experiments. Both physiologically-based ("PBTK")
             and empirical (for example, one compartment) "TK" models can be
             parameterized with the data provided for thousands of chemicals,
             multiple exposure routes, and various species. The models consist
             of systems of ordinary differential equations which are solved
             using compiled (C-based) code for speed. A Monte Carlo sampler is
             included, which allows for simulating human biological variability
             (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>)
             and propagating parameter uncertainty. Calibrated methods are
             included for predicting tissue:plasma partition coefficients and
             volume of distribution
             (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>).
             These functions and data provide a set of tools for
             in vitro-in vivo extrapolation ("IVIVE") of high throughput
             screening data (for example, Tox21, ToxCast) to real-world
             exposures via reverse dosimetry (also known as "RTK")
             (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).
In order to improve performance for HTTP API clients, 'httpcache'
    provides simple tools for caching and invalidating cache. It includes the
    HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in
    replacements for those in the 'httr' package. These functions are cache-aware
    and provide default settings for cache invalidation suitable for RESTful
    APIs; the package also enables custom cache-management strategies.
    Finally, 'httpcache' includes a basic logging framework to facilitate the
    measurement of HTTP request time and cache performance.
Find and explain the meaning of 'HTTP' status codes.
    Functions included for searching for codes by full or partial number,
    by message, and get appropriate dog and cat images for many
    status codes.
A graphics device for R that is accessible via network protocols.
    This package was created to make it easier to embed live R graphics in 
    integrated development environments and other applications.
    The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics.
    The device asynchronously serves 'SVG' graphics via 'HTTP' and 'WebSockets'.
A suite of functions to ping 'URLs' and to time
    'HTTP' 'requests'. Designed to work with 'httr'.
Tools for emitting the 'Problem Details' structure defined in
  'RFC' 7807 <https://tools.ietf.org/html/rfc7807> for reporting errors from
  'HTTP' servers in a standard way.
HTTP Request protocols. Implements the GET, POST and multipart POST request.
Testing and documenting code that communicates with remote servers
    can be painful. Dealing with authentication, server state,
    and other complications can make testing seem too costly to
    bother with. But it doesn't need to be that hard. This package enables one
    to test all of the logic on the R sides of the API in your package without
    requiring access to the remote service. Importantly, it provides three
    contexts that mock the network connection in different ways, as well as
    testing functions to assert that HTTP requests were---or were
    not---made. It also allows one to safely record real API responses to use as
    test fixtures. The ability to save responses and load them offline also
    enables one to write vignettes and other dynamic documents that can be
    distributed without access to a live server.
Provides low-level socket and protocol support for handling
    HTTP and WebSocket requests directly from within R. It is primarily
    intended as a building block for other packages, rather than making it
    particularly easy to create complete web applications using httpuv alone.
    httpuv is built on top of the libuv and http-parser C libraries, both of
    which were developed by Joyent, Inc. (See LICENSE file for libuv and
    http-parser license information.)
Useful tools for working with HTTP organised by
    HTTP verbs (GET(), POST(), etc). Configuration functions make it easy
    to control additional request components (authenticate(),
    add_headers() and so on).
Creating effective colour palettes for figures is 
    challenging. This package generates and plot palettes of optimally 
    distinct colours in perceptually uniform colour space, based on 
    'iwanthue' <http://tools.medialab.sciences-po.fr/iwanthue/>. 
    This is done through k-means clustering of CIE Lab colour space, 
    according to user-selected constraints on hue, chroma, and 
    lightness.
Provides a general framework for
        high-dimensional undirected graph estimation. It integrates
        data preprocessing, neighborhood screening, graph estimation,
        and model selection techniques into a pipeline. In
        preprocessing stage, the nonparanormal(npn) transformation is
        applied to help relax the normality assumption. In the graph
        estimation stage, the graph structure is estimated by
        Meinshausen-Buhlmann graph estimation or the graphical lasso,
        and both methods can be further accelerated by the lossy
        screening rule preselecting the neighborhood of each variable
        by correlation thresholding. We target on high-dimensional data
        analysis usually d >> n, and the computation is
        memory-optimized using the sparse matrix output. We also
        provide a computationally efficient approach, correlation
        thresholding graph estimation. Three
        regularization/thresholding parameter selection methods are
        included in this package: (1)stability approach for
        regularization selection (2) rotation information criterion (3)
        extended Bayesian information criterion which is only available
        for the graphical lasso.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
             to discriminate the class labels, visualizing the ROC curve for two or three class labels.
Format quantities of time or bytes into human-friendly strings.
Human names are complicated and nonstandard things. Humaniformat,
    which is based on Anthony Ettinger's 'humanparser' project (https://github.com/
    chovy/humanparser) provides functions for parsing human names, making a best-
    guess attempt to distinguish sub-components such as prefixes, suffixes, middle
    names and salutations.
An almost direct port of the 'python' 'humanize' package <https://github.com/jmoiron/humanize>.
  This package contains utilities to convert values into human readable forms.
Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling,
  in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>).
  The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).
Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.
Low level spell checker and morphological analyzer based on the 
    famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
    or check individual words as well as parse text, latex, html or xml documents.
    For a more user-friendly interface use the 'spelling' package which builds on
    this package to automate checking of files, documentation and vignettes in all
    common formats.
Scraped dataset of the Hurricane Research Division's Hurricane 
    Re-Analysis Project known as HURDAT. Storm details are available for most 
    known hurricanes and tropical storms for the Atlantic and northeastern 
    Pacific ocean (northwestern hemisphere). See <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html> 
    for more information.
When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.
Allows users to create time series of tropical storm
    exposure histories for chosen counties for a number of hazard metrics
    (wind, rain, distance from the storm, etc.). This package interacts
    with data available through the 'hurricaneexposuredata' package, which
    is available in a 'drat' repository. To access this data package, see the 
    instructions at <https://github.com/geanders/hurricaneexposure>. 
    The size of the 'hurricaneexposuredata' package is
    approximately 20 MB. This work was supported in part by grants from the National
    Institute of Environmental Health Sciences (R00ES022631), the National Science
    Foundation (1331399), and a NASA Applied Sciences Program/Public Health Program
    Grant (NNX09AV81G).
Provides utility functions for, and drawing on, the 'data.table' package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of 'utils' and the author.
Provides utility functions that are simply, frequently used, 
    but may require higher performance that what can be obtained from base R.
    Incidentally provides support for 'reverse geocoding', such as matching a point
    with its nearest neighbour in another array. Used as a complement to package
    'hutils' by sacrificing compilation or installation time for higher running 
    speeds. The name is a portmanteau of the author and 'Rcpp'.
Creates styled tables for data presentation. Export to HTML, LaTeX,
  RTF, 'Word', 'Excel', and 'PowerPoint'. Simple, modern interface to manipulate 
  borders, size, position, captions, colours, text styles and number formatting.
  Table cells can span multiple rows and/or columns.
  Includes  a 'huxreg' function for creation of regression tables, and 'quick_*' 
  one-liners to print data to a new document.
HW.pval calculates plain and fully conditional
        root-mean-square, chi-square, and log likelihood-ratio P-values
        for the user-provided genotypic counts to be consistent with
        the Hardy-Weinberg equilibrium model.  For further information
        on the Hardy-Weinberg equilibrium model and the pseudocode,
        refer to the paper "Testing Hardy-Weinberg equilibrium with a
        simple root-mean-square statistic" by Rachel Ward.
Fits models for genotypic disequilibria, as described in
  Huttley and Wilson (2000), Weir (1996) and Weir and Wilson (1986).
  Contrast terms are available that account for first order interactions
  between loci.  Also implements, for a single locus in a single
  population, a conditional exact test for Hardy-Weinberg equilibrium.
Estimation and testing of HWE using Bayesian methods.
        Three models are currently considered: HWE, a model
        parameterized in terms of the allele frequencies and a single
        inbreeding coefficient f, and the saturated model. Testing is
        based on Bayes factors.
General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.
Provides a way to display word clouds in R. The word cloud is a html widget, so you can use it in interactive documents and 'shiny' applications.
Easy-to-use and versatile functions to output R objects in
        HTML format
Provides methods to test whether time series is consistent
	with white noise.
Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. Functions to cross-validate the hybrid ensemble and plot and summarize the results are also provided. There is also a function to assess the importance of the predictors.
This package is an R implementation of the Hybrid Monte
        Carlo and Multipoint Hybrid Monte Carlo sampling techniques
        described in Liu (2001): "Monte Carlo Strategies in Computing".
Simulates stochastic hybrid models for transmission of infectious
    diseases in dynamic networks. It is a metapopulation model in which each
    node in the network is a sub-population and disease spreads within nodes
    and among them, combining two approaches: stochastic simulation algorithm
    (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based
    approach, respectively. Equations that models spread within nodes are
    customizable and there are two link types among nodes: migration and
    influence (commuting). More information in Fernando S. Marques,
    Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.
Using hybrid data, this package created a vividly colored hybrid heat map.
    The input is two files which are auto-selected.
    The first file has three columns, the first two for pairs of species,
    with the third column for the hybrid experiment code (an integer).
    The second file is a list of code and their descriptions in two 
    columns. The output is a figure showing the hybrid heat map with a color legend.
Facilities for easy implementation of hybrid Bayesian networks
    using R. Bayesian networks are directed acyclic graphs representing joint
    probability distributions, where each node represents a random variable and
    each edge represents conditionality. The full joint distribution is therefore
    factorized as a product of conditional densities, where each node is assumed
    to be independent of its non-descendents given information on its parent nodes.
    Since exact, closed-form algorithms are computationally burdensome for inference
    within hybrid networks that contain a combination of continuous and discrete
    nodes, particle-based approximation techniques like Markov Chain Monte Carlo
    are popular. We provide a user-friendly interface to constructing these networks
    and running inference using the 'rjags' package. Econometric analyses (maximum
    expected utility under competing policies, value of information) involving
    decision and utility nodes are also supported.
Calculate an optimal embedding of a set of data points into low-dimensional hyperbolic space. This uses the strain-minimizing hyperbolic embedding of Keller-Ressel and Nargang (2019), see <arXiv:1903.08977>.
Functions for basic hydraulic calculations related to 
    water flow in circular pipes both flowing full (under pressure), and 
    partially full (gravity flow), and trapezoidal open channels. For 
    pressure flow this includes friction loss calculations by solving 
    the Darcy-Weisbach equation for head loss, flow or diameter, and 
    plotting a Moody diagram. The Darcy-Weisbach friction factor is 
    calculated using the Colebrook (or Colebrook-White equation), 
    the basis of the Moody diagram, the original citation being 
    Colebrook (1939) <doi:10.1680/ijoti.1939.13150>. For gravity flow, the
    Manning equation is used, again solving for missing parameters. The 
    derivation of and solutions using the Darcy-Weisbach equation and the
    Manning equation are outlined in many fluid mechanics texts such as 
    Finnemore and Franzini (2002, ISBN:978-0072432022). For the Manning equation
    solutions, this package uses modifications of original code from the 'iemisc' 
    package by Irucka Embry.
Package providing tools for hydrological applications and models developed for regional analysis in Northwestern Italy.
Pfafstetter Hydrological Codes 
    as cited in Verdin and Verdin (1999) <doi: 10.1016/S0022-1694(99)00011-6> 
    are decoded for upstream or downstream queries.
Contains one function for drawing Piper diagrams (also
    called Piper-Hill diagrams) of water analyses for major ions.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
This version 2 of the HydroMe v.1 package estimates the parameters
  in infiltration and water retention models by curve-fitting methods <doi:10.1016/j.cageo.2008.08.011>. The
  models considered are those commonly used in soil science.  It has new models
  for water retention and characteristic curves.
State-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable). hydroPSO can be used as a replacement of the 'optim' R function for (global) optimization of non-smooth and non-linear functions. However, the main focus of hydroPSO is the calibration of environmental and other real-world models that need to be executed from the system console. hydroPSO is model-independent, allowing the user to easily interface any computer simulation model with the calibration engine (PSO). hydroPSO  communicates with the model through the model's own input and output files, without requiring access to the model's source code. Several PSO variants and controlling options are included to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. hydroPSO is parallel-capable, to alleviate the computational burden of complex models with "long" execution time. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian). See Zambrano-Bigiarini and Rojas (2013) <doi:10.1016/j.envsoft.2013.01.004> for more details.
R interface to the Greek National Data Bank for Hydrological and 
    Meteorological Information. It covers 
    Hydroscope's data sources and provides functions to transliterate, 
    translate and download them into tidy dataframes.
Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.
Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling,
    data resampling, gap filler of precipitation, bias correction of forecasting
    data, flexible time series plot, and spatial map generation. It is a good pre-
    processing and post-processing tool for hydrological and hydraulic modellers.
Includes two main high level codes for hyperplane fitting (hyper.fit) and visualising (hyper.plot2d / hyper.plot3d). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
A suite of routines for the hyperdirichlet distribution; supersedes the 'hyperdirichlet' package.
This package provides functions for the hyperbolic and
        related distributions. Density, distribution and quantile
        functions and random number generation are provided for the
        hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, including fitting of
        the hyperbolic to data.
Provides functions and methods for organizing data in hypercubes
  (i.e., a multi-dimensional cube). Cubes are generated from molten data frames.
  Each cube can be manipulated with five operations: rotation (change.dimensionOrder()),
  dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()),
  and rolling up (remove.aggregation()).
Implements various tools for storing and analyzing hypergraphs.
  Handles basic undirected, unweighted hypergraphs, and various ways of 
  creating hypergraphs from a number of representations, and
  converting between graphs and hypergraphs.
Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs.
The Gaussian hypergeometric function for complex numbers.
Evaluates the hypergeometric functions of a matrix argument, which appear in random matrix theory. This is an implementation of Koev & Edelman's algorithm (2006) <doi:10.1090/S0025-5718-06-01824-2>.
Uses support vector machines to identify a perfectly separating hyperplane (linear or curvilinear) between two entities in high-dimensional space. If this plane exists, the entities do not overlap. Applications include overlap detection in morphological, resource or environmental dimensions. More details can be found in: Brown et al. (2020) <doi:10.1111/2041-210X.13363> .
Implements an algorithm for efficient and exact calculation of hypergeometric 
    and binomial probabilities using Chebyshev polynomials, while other algorithm use an 
    approximation when N is large. A useful applications is also considered in this package 
    for the construction of attribute sampling plans which is an important field of statistical
    quality control. The quantile, and the confidence limit for the attribute sampling plan are
    also implemented in this package. The hypergeometric distribution can be represented in 
    terms of Chebyshev polynomials. This representation is particularly useful in the calculation
    of exact values of hypergeometric variables.
Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data.
Comfortable ways to work with hyperspectral data sets.
    I.e. spatially or time-resolved spectra, or spectra with any other kind
    of information associated with each of the spectra. The spectra can be data
    as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
    etc. More generally, any data that is recorded over a discretized variable,
    e.g. absorbance = f (wavelength), stored as a vector of absorbance values
    for discrete wavelengths is suitable.
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
A Bay Area high level phylogenetic analysis package mostly
        using the birth-death process. Analysis of species tree
        branching times and simulation of species trees under a number
        of different time variable birth-death processes.  Analysis of
        gene tree species tree reconciliations and simulations of gene
        trees in species trees.
An implementation of Random Forest-based two-sample tests as introduced in Hediger & Michel & Naef (2020) <arXiv:1903.06287>.
Compute the confidence interval of the population mean
        with one sample or of the difference of population means of two
        samples from normal distributions or t-distributions.Compute
        the confidence interval of population variance with one sample
        or of the difference of population variances of two samples by
        chi-square tests.Test for population mean or the differences of
        two normal samples under normality with the given null
        hypothesis H0,which depends on the user,so that he can know if
        he can reject H0 or not at the significance level alpha.Do the
        chi-square tests with one or two samples which have multinomial
        distributions by using an approximate chi-square distribution
        when n is large enough.
Interact with the application programming interface for the web
    annotation service 'Hypothes.is' (See <http://hypothes.is> for more
    information.) Allows users to download data about public annotations, and
    create, retrieve, update, and delete their own annotations.
Translation between experimental null hypotheses, hypothesis matrices, and contrast matrices as used in linear regression models. The package is based on the method described in Schad, Vasishth, Hohenstein, and Kliegl (2019) <doi:10.1016/j.jml.2019.104038> and Rabe, Vasishth, Hohenstein, Kliegl, and Schad (2020) <doi:10.21105/joss.02134>.
Construction and analysis of multivalued zero-sum matrix games over the abstract space of probability distributions, which describe the losses in each scenario of defense vs. attack action. The distributions can be compiled directly from expert opinions or other empirical data (insofar available). The package implements the methods put forth in the EU project HyRiM (Hybrid Risk Management for Utility Networks), FP7 EU Project Number 608090. The method has been published in Rass, S., König, S., Schauer, S., 2016. Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions. PLoS ONE 11, e0168583. <doi:10.1371/journal.pone.0168583>, and applied for advanced persistent thread modeling in Rass, S., König, S., Schauer, S., 2017. Defending Against Advanced Persistent Threats Using Game-Theory. PLoS ONE 12, e0168675. <doi:10.1371/journal.pone.0168675>. A volume covering the wider range of aspects of risk management, partially based on the theory implemented in the package is the book edited by S. Rass and S. Schauer, 2018. Game Theory for Security and Risk Management: From Theory to Practice. Springer, <doi:10.1007/978-3-319-75268-6>, ISBN 978-3-319-75267-9.
Methods for addressing uncertainty in risk assessments using hybrid representations of uncertainty (probability distributions, fuzzy numbers, intervals, probability distributions with imprecise parameters). The uncertainty propagation procedure combines random sampling using Monte Carlo method with fuzzy interval analysis of Baudrit et al. (2007) <doi:10.1109/TFUZZ.2006.876720>. The sensitivity analysis is based on the pinching method of Ferson and Tucker (2006) <doi:10.1016/j.ress.2005.11.052>.
Enhance package 'testthat' by allowing tests to be attached to the function/object they test. 
    This allows to keep functional and unit test code together.
Fit, summarize and plot sinusoidal hysteretic processes using:
    two-step simple harmonic least squares, ellipse-specific non-linear least
    squares, the direct method, geometric least squares or linear least squares.
An R API wrapper for the 'Hystreet' project <https://hystreet.com>. 'Hystreet' provides pedestrian counts in different cities in Germany.
A collection of tools for modeling the shape of 1 dimensional clines.
Create customized, web-based dashboards for data presentation, exploration and sharing. 'i2dash' integrates easily into existing data analysis pipelines and can organize scientific findings thematically across different pages and layouts.
Provides functions to work with 'incidence2' objects, including a
  simplified interface for trend fitting and peak estimation. This package is
  part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for 
  outbreak analysis (<https://www.reconverse.org/).
In randomized-controlled trials, interim analyses are often planned for possible early 
   trial termination to claim superiority or futility of a new therapy. Blinded data also have 
   information about the potential treatment difference between the groups. We developed a blinded 
   data monitoring tool that enables investigators to predict whether they observe such an unblinded 
   interim analysis results that supports early termination of the trial. Investigators may skip 
   some of the planned interim analyses if an early termination is unlikely. 
   This tool will provide reference information about N: Sample size at interim analysis, and T: Total 
   number of responders at interim analysis for decision on performing an interim analysis.
Contains data frames and functions used in the book "An Introduction to Acceptance Sampling and SPC with R". This book is available electronically at <https://bookdown.org/>. A physical copy will be published by CRC Press.
Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading <DOI:10.1080/19466315.2018.1462727>.
Calculate false ring proportions from data frames of intra annual 
  density fluctuations.
An interface to the algorithms of 'Interpretable AI'
    <https://www.interpretable.ai> from the R programming language.
    'Interpretable AI' provides various modules, including 'Optimal Trees' for
    classification, regression, prescription and survival analysis, 'Optimal
    Imputation' for missing data imputation and outlier detection, and 'Optimal
    Feature Selection' for exact sparse regression. The 'iai' package is an
    open-source project. The 'Interpretable AI' software modules are proprietary
    products, but free academic and evaluation licenses are available.
Provides a monthly summary of Iowa liquor (class E) sales from January 2015
    to October 2020. See the package website for more information, 
    documentation and examples. Data source: 
    Iowa Data portal <https://data.iowa.gov/resource/m3tr-qhgy.csv>.
Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model.
Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.
Implements the standard D-Scoring algorithm
    (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT)
    data and includes plotting capabilities for exploring raw IAT data.
Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>.    The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample.    The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent).    Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds).    A sample dataset (titled 'sampledata') is included in this package to practice with.
This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with.
Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).
An implementation of the iterative bootstrap procedure of 
    Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This
    procedure has better bias correction properties than the 
    bootstrap bias correction technique.
Bayesian integrative models of gene expression and comparative genomic hybridization data. The package provides inference on copy number variations and their association with gene expression.
A collection of useful functions and datasets for the Data Science
  Course at IBAW in Lucerne.
Call wrappers for Istanbul Metropolitan
    Municipality's Open Data Portal (Turkish: İstanbul Büyükşehir
    Belediyesi Açık Veri Portalı) at <https://data.ibb.gov.tr/en/>.
Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) <doi:10.1534/g3.117.300309>.
A collection of several utility functions related to binary incomplete block designs. The package contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. The package also contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Package also allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.
Functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Method to test genetic linkage with covariates by
        regression methods with response IBD sharing for relative
        pairs.  Account for correlations of IBD statistics and
        covariates for relative pairs within the same pedigree.
Simulation of segments shared identical-by-descent (IBD) by 
    pedigree members. Using sex specific recombination rates along the human
    genome (Kong et. al (2010) <doi:10.1038/nature09525>), phased chromosomes
    are simulated for all pedigree members, either by unconditional gene 
    dropping or conditional on a specified IBD pattern. Additional functions 
    provide summaries and further analysis of the simulated genomes.
Simulation of segments shared identical-by-descent (IBD) by 
    pedigree members. Using sex specific recombination rates along the human
    genome (Halldorsson et al. (2019) <doi:10.1126/science.aau1043>), phased chromosomes
    are simulated for all pedigree members. Additional features include calculation
    of realised IBD coefficients and plots of IBD segment distributions.
A data-driven and adaptive hierarchical-scale decomposition method for irregular-lattice field (represented by polygons).
Some basic functions to implement belief functions including:
    transformation between belief functions using the method introduced by
    Philippe Smets <arXiv:1304.1122>, evidence combination, evidence
    discounting, decision-making, and constructing masses. Currently, thirteen
    combination rules and six decision rules are supported. It can also be
    used to generate different types of random masses when working on belief
    combination and conflict management.
Implementation of some (simple) Individual Based Models and methods
  to create new ones, particularly for population dynamics models (reproduction, 
  mortality and movement). The basic operations for the simulations are 
  implemented in Rcpp for speed.
Authentication can be the most difficult part about
  working with a new API. 'ibmAcousticR' facilitates making a
  connection to the 'IBM Acoustic' email campaign management API
  and executing various queries. The 'IBM Acoustic' API 
  documentation is available at
  <https://developer.ibm.com/customer-engagement/docs/>. This
  package is not supported by 'IBM'.
It provides a generic set of tools for initializing a synthetic
         population with each individual in specific disease states, and
         making transitions between those disease states according to the rates
         calculated on each timestep. The new version 1.0.0 has C++ code 
         integration to make the functions run faster. It has also a higher level
         function to actually run the transitions for the number of timesteps
         that users specify. Additional functions will follow for changing
         attributes on demographic, health belief and movement.
Functionality required to efficiently use R with IBM(R) Db2(R) 
        Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in 
        conjunction with IBM Db2 Analytics Accelerator for z/OS. 
        Many basic and complex R operations are pushed down into the database, 
        which removes the main memory boundary of R and allows to make full 
        use of parallel processing in the underlying database.
Simulation of the random evolution of structured population dynamics, called stochastic Individual Based Models (IBMs) (see e.g. Ferrière and Tran (2009) <doi:10.1051/proc/2009033>, Bansaye and Méléard (2015) <doi:10.1007/978-3-319-21711-6>, Boumezoued (2016)).
    The package allows users to simulate the random evolution of a population in which individuals are characterised by their date of birth, a set of attributes, and their potential date of death.
Generates Personality Insights sunburst diagrams
    based on 'IBM Watson' Personality Insights service output.
The package implements a general algorithm to obtain
        iterated bootstrap tests and confidence sets for a
        p-dimensional parameter based on the unstudentized version of
        the Rao statistic.
Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
Model agnostic tool for decomposition of predictions from black boxes.
    Supports additive attributions and attributions with interactions.
    The Break Down Table shows contributions of every variable to a final prediction. 
    The Break Down Plot presents variable contributions in a concise graphical way. 
    This package works for classification and regression models. 
    It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>,
    with new and faster strategies for orderings. 
    It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). 
    The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420>
    This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Provides native R access to Interactive Brokers Trader Workstation API.
Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function.
Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible)
             using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is 
             evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance
             are computed for variable selection. The thresholds values for variable selection are 
             computed using a nonparametric permutation test.
Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.
Implementation of the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.
Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.
Lorenz and concentration curves; Atkinson, Generalized
        entropy and SGini indices (with decomposition)
Calculation of IC50 values, automatic drawing of
        dose-response curves and validation of compound screens on 96-
        and 384-well plates.
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
Imperialist Competitive Algorithm (ICA)
         <http://en.wikipedia.org/wiki/Imperialist_competitive_algorithm>
         is a computational method that is used to solve optimization
	 problems of different types and it is the mathematical model
	 and the computer simulation of human social evolution.  
         The package provides a minimum value for the cost function
	 and the best value for the optimization variables by
	 Imperialist Competitive Algorithm. 
         Users can easily define their own objective function
	 depending on the problem at hand. 
         This version has been successfully applied to solve
	 optimization problems, for continuous functions.
A simple wrapper around the 'ical.js' library executing 
    'Javascript' code via 'V8' (the 'Javascript' engine driving the 'Chrome' 
    browser and 'Node.js' and accessible via the 'V8' R package). 
    This package enables users to parse 'iCalendar' files ('.ics', '.ifb', 
    '.iCal', '.iFBf') into lists and 'data.frames' to ultimately do statistics
    on events, meetings, schedules, birthdays, and the like.
To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as 'iCAMP' (Ning et al 2020) <doi:10.1038/s41467-020-18560-z>. It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) <doi:10.1038/ismej.2015.142> based on neutral theory model (Sloan et al 2006) <doi:10.1111/j.1462-2920.2005.00956.x> and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) <doi:10.1038/ismej.2013.93>. It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for 'QPEN' and added function 'icamp.cate()' to summarize 'iCAMP' results for different categories of taxa (e.g. core versus rare taxa).
Analysis and visualization of experimentally elucidated mutational
    signatures -- the kind of analysis and visualization in Boot et al.,
    "In-depth characterization of the cisplatin mutational signature in 
    human cell lines and in esophageal and liver tumors", Genome Research 2018, 
    <doi:10.1101/gr.230219.117> and
    "Characterization of colibactin-associated mutational signature in an 
    Asian oral squamous cell carcinoma and in other mucosal tumor types",
    Genome Research 2020 <doi:10.1101/gr.255620.119>.
    'ICAMS' stands for In-depth Characterization 
    and Analysis of Mutational Signatures. 'ICAMS' has functions to read in 
    variant call files (VCFs) and to collate the corresponding catalogs of 
    mutational spectra and to analyze and plot catalogs of mutational spectra
    and signatures. Handles both "counts-based" and "density-based" catalogs
    of mutational spectra or signatures.
Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.
Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources
Implements the integrative conditional autoregressive horseshoe model 
    discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. 
    BMC Bioinformatics 21, 11 (2020) <doi:10.1186/s12859-019-3333-0>.
    The model consists in three levels: Metabolic pathways level modeling interdependencies between
    variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify
    potential associations between heterogeneous omic variables via a Horseshoe prior and experimental
    design level to capture experimental design conditions through a mixed-effects model.
    The package also provides functions to simulate data from the model, construct pathway matrices,
    post process and plot model parameters.
Provides user-friendly tools for calibration in survey sampling.
    The package is production-oriented, and its interface is inspired by the famous
    popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to
    'icarus'. In addition to calibration (with linear, raking and logit methods),
    'icarus' features functions for calibration on tight bounds and penalized
    calibration.
Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds model, and probit model) to interval-censored time-to-event data.
Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.
Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.
Provides functions to calculate the requisite sample size for studies where ICC is 
    the primary outcome. Can also be used for calculation of power. In both cases it
    allows the user to test the impact of changing input variables by calculating the outcome
    for several different values of input variables. Based off the work of Zou.
    Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with
    precision and assurance. Statistics in medicine, 31(29), 3972-3981.
Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.
A function and vignettes for computing an intraclass correlation
    described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>.
    This package quantifies the share of variance in a dependent variable that
    is attributed to group heterogeneity in slopes.
Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals.
The function computes the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). The function transforms profiles comprising correlations according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score before entering into the function (McCrae, 2008). In case of missing values, the function will automatically use pairwise deletion. See Furr (2010) <doi:10.1080/00223890903379134> or McCrae (2008) <doi:10.1080/00223890701845104> for details.
Implements the conditional inference forest approach 
  to modeling interval-censored survival data. It also provides 
  functions to tune the parameters and evaluate the model fit. See 
  Yao et al. (2019) <arXiv:1901.04599>.
Data from the United States Center for Medicare and Medicaid
    Services (CMS) is included in this package. There are ICD-9 and ICD-10
    diagnostic and procedure codes, and lists of the chapter and sub-chapter 
    headings and the ranges of ICD codes they encompass. There are also two 
    sample datasets. These data are used by the 'icd' package for finding
    comorbidities.
Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information <https://www.dimdi.de>, and are redistributed in accordance with their license.
Provides an estimator for generalized linear models with incomplete
    data for discrete covariates. The estimation is based on the EM algorithm by the
    method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.
Categorization and scoring of injury severity typically involves
    trained personnel with access to injured persons or their medical records. 'icdpicr' contains a function
    that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS')
    from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury 
    severity scoring. 'ICDPIC' was originally developed in 'Stata', and 'icdpicr' is an open-access update 
    that accepts both 'ICD-9' and 'ICD-10' codes.
Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression.  2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.
Kernel Estimators for Interval-Censored Data
Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
Tools for correcting biases and calibrating sea ice predictions obtained from dynamic ensemble models. Implements and extends Director et al. (2017) <doi:10.1175/JCLI-D-17-0185.1> This package depends on the 'ncdf4' and 'rgeos' R packages. These packages require installing externally from R Unidata's 'NetCDF' library and Geometry Engine - Open Source ('GEOS'). (See the 'rgeos' and 'ncdf4' packages for details.)
Given two unbiased samples of patient level data on cost and effectiveness
  for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
  bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
  the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
  specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
  changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
  with economic preferences from an ICE map with specified values of lambda, beta and gamma
  parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation
  in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves
  from an ICE map with specified values of lambda, beta and either gamma or eta parameters.
A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq and CITE-Seq. Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550>  and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Regression models for interval censored data. Currently supports
    Cox-PH, proportional odds, and accelerated failure time models. Allows for
    semi and fully parametric models (parametric only for accelerated failure
    time models) and Bayesian parametric models. Includes functions for easy visual
    diagnostics of model fits and imputation of censored data.
Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017). "Survival Analysis with Interval-Censored Data: A Practical Approach." ISBN 978-1-4200-7747-6, published by Chapman and Hall/CRC. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.
We consider studies in which information from error-prone
    diagnostic tests or self-reports are gathered sequentially to determine the
    occurrence of a silent event. Using a likelihood-based approach
    incorporating the proportional hazards assumption, we provide functions to
    estimate the survival distribution and covariate effects. We also provide 
    functions for power and sample size calculations for this setting.
    Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) 
    <doi: 10.1214/15-AOAS810>, Xiangdong Gu and Raji Balasubramanian (2016) 
    <doi: 10.1002/sim.6962>, Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes,
    Yunsheng Ma, and Raji Balasubramanian (2020) <doi: 10.1186/s12911-020-01223-w>.
A collection of functions that facilitate computational steps
  related to advice for fisheries management, according to ICES guidelines.
  These include methods for calculating reference points and model diagnostics.
R interface to access the web services of the ICES (International
             Council for the Exploration of the Sea) DATRAS trawl survey
             database <https://datras.ices.dk/WebServices/Webservices.aspx>.
Functions to support the ICES Transparent Assessment Framework
  <https://taf.ices.dk> to organize data, methods, and results used in ICES
  assessments. ICES is an organization facilitating international collaboration
  in marine science.
R interface to access the RECO POX web services of the ICES
  (International Council for the Exploration of the Sea) Vocabularies database
  <https://vocab.ices.dk/services/POX.aspx>.
ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.
Generalized Odds Rate Hazards (GORH) model is a flexible model of fitting survival data, including the Proportional Hazards (PH) model and the Proportional Odds (PO) Model as special cases. This package fit the GORH model with interval censored data.
A minimal wrapper around the NIH's 'iCite' API (<https://icite.od.nih.gov/api>). 
  Given a vector of pubmed IDs, this package returns a dataframe of the information yielded by
  the 'iCite' API. The primary metrics yielded by 'iCite' are measurements of a paper's 
  scientific influence and translational potential, but the API also returns other meta-data 
  from the paper, including author names, publication journal, publication year, paper title, 
  doi, and a number of other citation metrics.
A GUI designed to support the analysis of financial-economic time
    series data.
Integrative clustering of multiple genomic data types
        using a joint latent variable model.
Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression (Pungpapong et. al. (2015) <doi:10.1214/15-EJS1034>, Pungpapong et. al. (2017) <arXiv:1707.08298>).
Sieve semiparametric likelihood methods for analyzing 
 interval-censored failure time data from an outcome-dependent sampling (ODS) 
 design and from a case-cohort design. 
 Zhou, Q., Cai, J., and Zhou, H. (2018) <doi:10.1111/biom.12744>; 
 Zhou, Q., Zhou, H., and Cai, J. (2017)  <doi:10.1093/biomet/asw067>.
Provides easy-to-use and easy-to-access datasets from the
    Index of COmplex Networks (ICON) database available on the University
    of Colorado website. All datasets can be loaded with a single function
    call and new datasets are being slowly added from ICON at
    <https://icon.colorado.edu>. Specific ICON datasets of interest can
    be requested at the BugReports URL <https://github.com/rrrlw/ICON/issues>.
Set of formal methods for studying archaeological iconographic datasets (rock-art, pottery decoration, stelae, etc.) using network and spatial analysis (Alexander 2008 <doi:10.11588/propylaeumdok.00000512>; Huet 2018 <https://hal.archives-ouvertes.fr/hal-02913656>).
Employs triangular tessellation to refine icosahedra
    defined in 3d space. The procedures can be set to provide a grid with a
    custom resolution. Both the primary triangular and their inverted penta-
    hexagonal grids are available for implementation. Additional functions
    are provided to position points (latitude-longitude data) on the grids,
    to allow 2D and 3D plotting, use raster data and shapefiles.
Reproducible, programmatic retrieval of datasets from the
    Inter-university Consortium for Political and Social Research archive.
Provides functions to compute and plot Krippendorff's inter-coder 
    reliability coefficient alpha and bootstrapped uncertainty estimates 
    (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to
    make use of parallel threads where supported.
Algorithms to construct simultaneous confidence intervals for the ranks of means mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.
Implements interval censored recursive forests (ICRF) based on Cho, Jewell, and Kosorok (2021+). 
            ICRF is a variant of random forests where the outcome variable is interval censored survival data.
            It can be used for usual right censored data and current status data as well.
            A recursion technique is used to improve accuracy and smoothed survival curves are provided.
Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests.  The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.
Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009,
            <doi:10.1111/j.1467-9868.2009.00706.x>) and Oja, Sirkia and Eriksson's
            (AJS, 2006, <http://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) method of two different
            scatter matrices to obtain an invariant coordinate system or independent
            components, depending on the underlying assumptions.
Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide.
Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components.
The Iterative Cumulative Sum of Squares (ICSS) algorithm by Inclan/Tiao (1994) <https://www.jstor.org/stable/2290916> detects multiple change points, i.e. structural break points, in the variance of a sequence of independent observations. For series of moderate size (i.e. 200 observations and beyond), the ICSS algorithm offers results comparable to those obtained by a Bayesian approach or by likelihood ration tests, without the heavy computational burden required by these approaches.
Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2016) <arXiv:1612.06118>) using a shiny app.
We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing. This package is based on a diploma thesis Hudak (2017) <https://opac.crzp.sk/?fn=detailBiblioForm&sid=7FE4E0810F66E41DF9A9CA23557F&seo=CRZP-detail-kniha>.
Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.
Provides the necessary tools to estimate average treatment effects with an instrumental variable by re-weighting observations using a model of compliance.
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
Functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.
Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.
Use R to make requests to the US Census Bureau's International Data Base API.
             Results are returned as R data frames.  For more information about the IDB API, visit
             <https://www.census.gov/data/developers/data-sets/international-database.html>.
The digits of the old version (before 2000 year) of 'Chinese ID Card Number' is 15, this package aims to update to the current version of 18 digits. Besides, this package can help check whether the given 'ID' is right or not.
The Integro-Difference Equation model is a linear, dynamical model used to model
   phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011,
   ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the
   heart of the model is the kernel, which dictates how the process evolves from one time
   point to the next. Both process and parameter reduction are used to facilitate computation,
   and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed
   to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted
   by maximum likelihood, and estimation is carried out using an evolution algorithm.
Time series plain text conversion and data visualization. It allows
    to transform IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales)
    daily series from plain text to CSV files or data frames in R. Additionally,
    it is possible to obtain exploratory graphs from times series. IDEAM’s data
    is freely delivered under formal request through the official web page
    <http://www.ideam.gov.co/solicitud-de-informacion>.
Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.
In randomized studies involving severely ill patients, functional
    outcomes are often unobserved due to missed clinic visits, premature
    withdrawal or death. It is well known that if these unobserved functional
    outcomes are not handled properly, biased treatment comparisons can be
    produced. In this package, we implement a procedure for comparing treatments
    that is based on the composite endpoint of both the functional outcome and
    survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>
    and Wang et al. (2020) <DOI:10.18637/jss.v093.i12>. It considers missing data
    imputation with different sensitivity
    analysis strategies to handle the unobserved functional outcomes not due to
    death.
Interactive dendrogram that enables the user to select and
    color clusters, to zoom and pan the dendrogram, and to visualize
    the clustered data not only in a built-in heat map, but also in
    'GGobi' interactive plots and user-supplied plots. 
    This is a backport of Qt-based 'idendro' 
    (<https://github.com/tsieger/idendro>) to base R graphics and 
    Tcl/Tk GUI.
Calculate identity coefficients, based on Mark Abney's C
        code.
In contrast to other methods of modeling spatio-temporal data,
  dynamic spatio-temporal models (DSTMs) directly model the dynamic
  data-generating process.
  'ideq' supports two main classes of DSTMs:
  (1) empirical orthogonal function (EOF) models and
  (2) integrodifference equation (IDE) models.
  EOF models do not directly use any spatial information;
  instead, they make use of observed relationships in the data
  (the principal components) to model the underlying process.
  In contrast, IDE models are based on diffusion dynamics and the process
  evolution is governed by a (typically Gaussian) redistribution kernel.
  Both types have a variety of options for specifying the model components,
  including the process matrix, process error, and observation error.
  The classic reference for DSTMs is
  Noel Cressie and Christopher K. Wikle (2011, ISBN:978-0471692744).
  For IDE models specifically, see
  Christopher K. Wikle and Noel Cressie (1999, <https://www.jstor.org/stable/2673587>)
  and 
  Christopher K. Wikle (2002, <doi:10.1191/1471082x02st036oa>).
An implementation of various methods for estimating intrinsic
    dimension of vector-valued dataset or distance matrix. Most methods implemented
    are based on different notion of fractal dimension such as the capacity
    dimension, the box-counting dimension, and the information dimension.
Provides efficient implementation of the Isolate-Detect
    methodology for the consistent estimation of the number and location of multiple 
    change-points in one-dimensional data sequences from the "deterministic 
    + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and
    Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>.
    Currently implemented scenarios are: piecewise-constant signal with Gaussian
    noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear 
    signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.
Intensity-duration-frequency (IDF) curves are a widely used analysis-tool
              in hydrology to assess extreme values of precipitation
              [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>].
              The package 'IDF' provides functions to estimate IDF parameters for given
              precipitation time series on the basis of a duration-dependent
              generalized extreme value distribution
              [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.
Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids (when not circular). Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangular, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).
Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.
Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".
Contains techniques for mining large and high-dimensional data
    sets by using the concept of Intrinsic Dimension (ID). Here the ID is
    not necessarily an integer. It is extended to fractal dimensions. And
    the Morisita estimator is used for the ID estimation, but other
    tools are included as well.
Explore a range of infectious disease models in a consistent framework. 
    The primary aim of 'idmodelr' is to provide a library of infectious disease models
    for researchers, students, and other interested individuals. These models can be 
    used to understand the underlying dynamics and as a reference point when developing
    models for research. 'idmodelr' also provides a range of utilities. These include: plotting 
    functionality; a simulation wrapper; scenario analysis tooling; an interactive 
    dashboard; tools for handling mult-dimensional models; and both model and parameter
    look up tables. Unlike other modelling packages such as 'pomp' (<https://kingaa.github.io/pomp/>), 'libbi' (<http://libbi.org>)
    and 'EpiModel' (<http://www.epimodel.org>), 'idmodelr' serves primarily as an educational 
    resource. It is most comparable to epirecipes (<http://epirecip.es/epicookbook/chapters/simple>)
    but provides a more consistent framework, an R based workflow, and additional utility tooling. 
    After users have explored model dynamics with 'idmodelr' they may then 
    implement their model using one of these packages in order to utilise the model
    fitting tools they provide.  For newer modellers, this package reduces the barrier
    to entry by containing multiple infectious disease models, providing a consistent
    framework for simulation and visualisation, and signposting towards other, more 
    research focussed, resources.
Modeling of regression effects for transition probabilities in a 
   progressive illness-death model. Azarang, Scheike, and de Una-Alvarez (2017) <doi:10.1002/sim.7245>.
Integrate molecular profiles to discover candidate oncogenic drivers.
Different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region.  In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra, converting humidity measures.
Functions to perform robust
		 nonparametric survival analysis with right censored 
		 data using a prior near-ignorant Dirichlet Process.
		 Mangili, F., Benavoli, A., de Campos, C.P., Zaffalon, M. (2015) <doi:10.1002/bimj.201500062>.
This is a package for estimating the copula mixture model and plotting correspondence curves in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel
Generate random or human readable and pronounceable identifiers.
Method of interactive detector for spatial associations (IDSA) 
             as described in Yongze Song (2021) <doi:10.1080/13658816.2021.1882680>. 
             IDSA is used to quantify the power of interactive determinant (PID) 
             between a spatial response variable and explanatory variables. 
             IDSA is developed based on methods of spatial heterogeneity.
Convert files to and from IDX format to vectors, matrices and arrays.
    IDX is a very simple file format designed for storing vectors and multidimensional matrices in
    binary format. The format is described on the website from Yann LeCun
    <http://yann.lecun.com/exdb/mnist/>.
iDynoMiCS is a computer program, developed by an international team of researchers, whose purpose is to model and simulate microbial communities in an individual-based way. It is described in detail in the paper "iDynoMiCS: next-generation individual-based modelling of biofilms" by Lardon et al, published in Environmental Microbiology in 2011. The simulation produces results in XML file format, describing the state of each species in each timestep (agent_State), a summary of the species statistics for a timepoint (agent_Sum), the state of each solute grid in each timestep (env_State) and a summary of the solutes for a timestep (env_Sum). This R package provides a means of reading this XML data into R such that the simulation response can be statistically analysed. iDynoMiCS is available from the website iDynoMiCS.org, where a full tutorial on using both the simulation and this R package is provided.
A collection of Irucka Embry's miscellaneous USGS functions
    (processing .exp and .psf files, statistical error functions,
    "+" dyadic operator for use with NA, creating ADAPS and QW
    spreadsheet files, calculating saturated enthalpy). Irucka created these
    functions while a Cherokee Nation Technology Solutions (CNTS) United States
    Geological Survey (USGS) Contractor and/or USGS employee.
A pair of functions for getting and setting the IEEE
        rounding mode for floating point computations.
A collection of Irucka Embry's miscellaneous functions
    (Engineering Economics, Civil & Environmental/Water Resources Engineering,
    Geometry, Statistics, GNU Octave length functions, Trigonometric functions
    in degrees, etc.).
Miscellaneous data sets [Engineering Economics, Environmental/
    Water Resources Engineering, US Presidential Elections].
The eclectic collection includes the following written pieces:
    "The War Prayer" by Mark Twain, "War Is A Racket" by Major General
    Smedley Butler, "The Mask of Anarchy: Written on the Occasion of the
    Massacre at Manchester" by Percy Bysshe Shelley, "Connect the D.O.T.S." by
    Obiora Embry, "Untitled: Climate Strange" by Irucka Ajani Embry, and
    "Untitled: Us versus Them or People Screwing over Other People (as we all
    live on one Earth and there is no "us versus them" in the actual Ultimate
    Reality}" by Irucka Ajani Embry.
Computes characteristics of independent rainfall events (duration, total rainfall depth, and intensity) 
  extracted from a sub-daily rainfall time series based on the inter-event time definition (IETD) method. To have a 
  reference value of IETD, it also analyzes/computes IETD values through three methods: autocorrelation analysis, the 
  average annual number of events analysis, and coefficient of variation analysis. Ideal for analyzing the sensitivity 
  of IETD to characteristics of independent rainfall events.
  Adams B, Papa F (2000) <ISBN: 978-0-471-33217-6>.
  Joo J et al. (2014) <doi:10.3390/w6010045>.
  Restrepo-Posada P, Eagleson P (1982) <doi:10.1016/0022-1694(82)90136-6>.
The package performes Independent Factor Analysis
A novel approach to make inference on the association of covariates with the absolute abundance (AA) of 'microbiome' in an ecosystem. It can be also directly applied to relative abundance (RA) data to make inference on AA (even if AA data is not available) because the ratio of two RA is equal ratio of their AA. This algorithm can estimate and test the associations of interest while adjusting for potential 'confounders'. The estimates of this method have easy interpretation like a typical regression analysis. High-dimensional covariates are handled with regularization and it is implemented by parallel computing. This algorithm finds optimal reference 'taxa/OTU (Operational Taxonomic Unit)/ASV (Amplicon Sequence Bariant)' and uses permutation to control FDR (False Discovery Rate).
This package implements a Bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.
Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'.
    This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
Contains several tools to treat imaging flow cytometry data from 'ImageStream®' and 'FlowSight®' cytometers ('Amnis®', part of 'Luminex®'). Provides an easy and simple way to read, write and subset .rif, .cif and .daf files. Information such as masks, features, regions and populations set within these files can be retrieved. In addition, raw data such as images stored can also be accessed. Users, may hopefully increase their productivity thanks to dedicated functions to extract, visualize and export 'IFC' data. Toy data example can be installed through the 'IFCdata' package of approximately 32 MB, which is available in a 'drat' repository <https://gitdemont.github.io/IFCdata/>. See file 'COPYRIGHTS' and file 'AUTHORS' for a list of copyright holders and authors.
Provides utility functions to deal with Italian fiscal
    code ('codice fiscale').
Compute both static and recursive time series forecasts of machine learning models.
A suite for identifying causal models using relative concordances and identifying causal polymorphisms in case-control genetic association data, especially with large controls re-sequenced data.
Insightful Research Tools is a collection of signal processing,
  image processing, and time series modeling routines written in the C
  language for speed and efficency. The routines were originally developed at
  Insightful for use in S-PLUS.
A dataset of the top colours of photos from Instagram 
 taken in 2014 in the city of Vancouver, British Columbia, Canada.
 It consists of: top colour and counts data. This data was
 obtained using the Instagram API. Instagram is a web photo 
 sharing service. It can be found at: <https://instagram.com>.
 The Instagram API is documented at: <https://instagram.com/developer/>.
Functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments.
  This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>.
  Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.
A collection of statistical tests for genetic association studies.
An implementation of the initial guided analytics for parameter testing and
    controlband extraction framework. Functions are available for continuous and 
    categorical target variables as well as for generating standardized reports of the
    conducted analysis. See <https://github.com/stefan-stein/igate> for more information
    on the technology.
Implements Bayesian linear regression, normal means estimation, and variable selection using the inverse gamma-gamma prior, as introduced by Bai and Ghosh (2018) <arXiv:1710.04369>.
Implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series and lasagna plots.
Tools to extract information from the
    Intergovernmental Organizations ('IGO') Database , version 3, provided
    by the Correlates of War Project <https://correlatesofwar.org/>. See
    also Pevehouse, J. C. et al. (2020), <doi:10.1177/0022343319881175>. 
    Version 3 includes information from 1815 to 2014.
Provides function to read data from the 'Igor Pro' data analysis
    program by Wavemetrics. The data formats supported are 'Igor' packed 
    experiment format (pxp) and 'Igor' binary wave (ibw). See: 
    http://www.wavemetrics.com/ for details. Also includes functions to load 
    special pxp files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp' 
    packages for recording and analysing neuronal data. See 
    http://www.neuromatic.thinkrandom.com/ for details.
Creates a Gaussian process model using the specified package. 
    Makes it easy to try different packages in same code, only the
    package argument needs to be changed.
    It is essentially a wrapper for the other Gaussian process
    software packages.
Routines for simple graphs and network analysis. It can
  handle large graphs very well and provides functions for generating random
  and regular graphs, graph visualization, centrality methods and much more.
A small collection of various network data sets,
    to use with the 'igraph' package: the Enron email network, various food webs,
    interactions in the immunoglobulin protein, the karate club network,
    Koenigsberg's bridges, visuotactile brain areas of the macaque monkey,
    UK faculty friendship network, domestic US flights network, etc.
Using 'shiny' to demo 'igraph' package makes learning graph theory easy and fun.
Graph matching methods and analysis. The package
  works for both 'igraph' objects and matrix objects. You provide the adjacency
  matrices of two graphs and some other information you might know, choose the
  graph matching method, and it returns the graph matching results. 'iGraphMatch'
  also provides a bunch of useful functions you might need related to graph
  matching.
This program facilitates exporting igraph graphs to the
        SoNIA file format
To integrate multiple GSEA studies, we propose a hybrid strategy,
    iGSEA-AT, for choosing random effects (RE) versus fixed effect (FE) models,
    with an attempt to achieve the potential maximum statistical efficiency as 
    well as stability in performance in various practical situations. In addition
    to iGSEA-AT, this package also provides options to perform integrative GSEA
    with testing based on a FE model (iGSEA-FE) and testing based on a RE model
    (iGSEA-RE). The approaches account for different set sizes when testing a
    database of gene sets. The function is easy to use, and the three approaches
    can be applied to both binary and continuous phenotypes.
Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.
Make complex, interactive heatmaps. 'iheatmapr' includes a modular 
    system for iteratively building up complex heatmaps, as well as the 
    iheatmap() function for making relatively standard heatmaps.
Web scraping the <https://www.dallasfed.org> for
    up-to-date data on international house prices and exuberance
    indicators. Download data in tidy format.
Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.
Efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso.
  Takada, M., Suzuki, T., & Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. AISTATS.
  <http://proceedings.mlr.press/v84/takada18a/takada18a.pdf>.
Computes individual contributions to the overall Gini and 
    Theil's T and Theil's L measures and their decompositions by groups 
    such as race, gender, national origin, with the three functions of 
    iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2019)
    <doi:10.1177/0049124119875961>.
Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.
General purpose TIFF file I/O for R users.
    Currently the only such package with read and write support for TIFF
    files with floating point (real-numbered) pixels, and the only package
    that can correctly import TIFF files that were saved from 'ImageJ' and
    write TIFF files than can be correctly read by 'ImageJ'
    <https://imagej.nih.gov/ij/>.  Also supports text image I/O.
Fitting a class of Lee-Carter mortality models using iterative fitting algorithm.
It performs interlaboratory studies (ILS) to detect those
    laboratories that provide non-consistent results when comparing to others. It
    permits to work simultaneously with various testing materials, from standard
    univariate, and functional data analysis (FDA) perspectives. The univariate
    approach based on ASTM E691-08 consist of estimating the Mandel's h and
    k statistics to identify those laboratories that provide more significant
    different results, testing also the presence of outliers by Cochran and Grubbs
    tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey
    tests) to test differences in means corresponding to different laboratories per
    each material. Taking into account the functional nature of data retrieved in
    analytical chemistry, applied physics and engineering (spectra, thermograms,
    etc.). ILS package provides a FDA approach for finding the Mandel's k and h
    statistics distribution by smoothing bootstrap resampling.
Provides functionality to perform a likelihood-free method for estimating the parameters of complex models
    that results in a simulated sample from the posterior distribution of model parameters given targets. The method begins
    with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution
    of model parameters. Accepted points result in model predictions that are within the initially specified tolerance
    intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of
    multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the
    acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the
    adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) <arXiv:1804.02090>.
Performs mQTL (methylation quantitative-trait locus) mapping in 
    bisulfite sequencing studies by fitting a binomial mixed model and then 
    incorporating the allelic-specific methylation pattern. Based on Fan, Yue; 
    Vilgalys, Tauras P.; Sun, Shiquan; Peng, Qinke; Tung, Jenny; Zhou, Xiang 
    (2019) <doi:10.1101/615039>.
Improve optical character recognition by binarizing images. The package focuses primarily on local adaptive thresholding algorithms. 
    In English, this means that it has the ability to turn a color or gray scale image into a black and white image. This is particularly useful
    as a preprocessing step for optical character recognition or handwritten text recognition.
An implementation of the Canny Edge Detector for detecting edges in images. The package provides an interface to the algorithm available at <https://github.com/Neseb/canny>.
An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). 
    The algorithm is explained at <doi:10.5201/ipol.2016.175>.
An implementation of the "FAST-9" corner detection algorithm explained in the paper 'FASTER and better: A machine learning approach to corner detection' by Rosten E., Porter R. and Drummond T. (2008), available at <arXiv:0810.2434>.
    The package allows to detect corners in digital images.
An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at <doi:10.5201/ipol.2018.229>. 
    The package allows to detect relevant points in images which are characteristic to the digital image.
Facility wrappers around the image processing functionality of
    'dlib'. 'Dlib' <http://dlib.net> is a 'C++' toolkit containing machine learning
    algorithms and computer vision tools. Currently the package allows to find feature
    descriptors of digital images, in particular 'SURF' and 'HOG' descriptors.
An open source library for face detection in images. 
    Provides a pretrained convolutional neural network based on <https://github.com/ShiqiYu/libfacedetection> which can be used to detect faces which have size greater than 10x10 pixels.
An implementation of the Line Segment Detector on digital images described in the paper: "LSD: A Fast Line Segment Detector with a False Detection Control" by Rafael Grompone von Gioi et al (2012). 
    The algorithm is explained at <doi:10.5201/ipol.2012.gjmr-lsd>.
An implementation of the Otsu's Image Segmentation Method described in the paper: "A C++ Implementation of Otsu's Image Segmentation Method". The algorithm is explained at <doi:10.5201/ipol.2016.158>.
Find text lines in scanned images and segment the lines into words.
    Includes implementations of the paper 'Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents' by Surinta O. et al (2014) <doi:10.1109/ICFHR.2014.37> available at <https://github.com/smeucci/LineSegm>,
    an implementation of 'A Statistical approach to line segmentation in handwritten documents' by Arivazhagan M. et al (2007) <doi:10.1117/12.704538>, 
    and a wrapper for an image segmentation technique to detect words in text lines as described in the paper 'Scale Space Technique for Word Segmentation in Handwritten Documents' by Manmatha R. and Srimal N. (1999) paper at <doi:10.1007/3-540-48236-9_3>, wrapper for code available at <https://github.com/arthurflor23/text-segmentation>.
Note that 'imageData' has been superseded by 'growthPheno'. 
    The package 'growthPheno' incorporates all the functionality of 
    'imageData' and has functionality not available in 'imageData', 
    but some 'imageData' functions have been renamed.  
    The 'imageData' package is no longer maintained, but is retained 
    for legacy purposes.
Get image statistics based on processing fluency theory. The
    functions provide scores for several basic aesthetic principles that
    facilitate fluent cognitive processing of images: contrast,
    complexity / simplicity, self-similarity, symmetry, and typicality.
    See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr
    (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.
Synthesize images into characteristic features for time-series analysis or machine learning applications.  The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity.  However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.
Fast image processing for images in up to 4 dimensions (two spatial
    dimensions, one time/depth dimension, one colour dimension). Provides most
    traditional image processing tools (filtering, morphology, transformations,
    etc.) as well as various functions for easily analysing image data using R. The
    package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image
    processing.
Provides advanced functions for image processing based on the package 'imager'.
Functions for the implementation of Independent Multiple-sample
  Greedy Equivalence Search (IMaGES), a causal inference algorithm for creating
  aggregate graphs and structural equation modeling data for one or more
  datasets. This package is useful for time series data with specific regions of
  interest. This implementation is inspired by the paper "Six problems
  for causal inference from fMRI" by Ramsey, Hanson, Hanson, Halchenko, Poldrack,
  and Glymour (2010) <DOI:10.1016/j.neuroimage.2009.08.065>. The IMaGES algorithm uses a modified BIC score to compute goodness
  of fit of edge additions, subtractions, and turns across all datasets and
  returns a representative graph, along with structural equation modeling data for
  the global graph and individual datasets, means, and standard errors. Functions
  for plotting the resulting graph(s) are provided. This package is built upon the
  'pcalg' package.
Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.
Provides fast application of image filters to data matrices,
    using R and C++ algorithms.
The pearl oyster, Pinctada margaritifera (Linnaeus, 1758), represents the second economic resource of French Polynesia. It is one of the only bivalves expressing a large varied range of inner shell color, & by correlation, of pearl color. This phenotypic variability is partly under genetic control, but also under environmental influence. With ImaginR, it's now possible to delimit the color phenotype of the pearl oyster's inner shell and to characterize their color variations (by the HSV color code system) with pictures.
This is an Automatic Item Generator for Psychological Assessment. Items created with the 'IMak' package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.
Zoom in and out of maps or any supplied lines or points,
        with control for color, poly fill, and aspect.
Class imbalance usually damages the performance of classifiers. Thus, it is
             important to treat data before applying a classifier algorithm. This package
             includes recent resampling algorithms in the literature: (Barua et al. 2014)
             <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>,
             (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014)
             <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014)
             <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to
             perform oversampling.
Provides a set of fast, chainable image-processing operations
             which are applicable to images of two, three or four dimensions,
             particularly medical images.
Provides API access to the <http://imdbapi.net> which maintains metadata
             about movies, games and television shows through a public API.
Theories are one of the most important tools of science. Although psychologists discussed problems of theory in their discipline for a long time, weak theories are still widespread in most subfields. 
    One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. 
    Previously a computational model for formal theory evaluation based on the concept of explanatory coherence was developed (Thagard, 1989, <doi:10.1017/S0140525X00057046>). 
    However, there are possible improvements to this model and it is not available in software that psychologists typically use. 
    Therefore, a new implementation of explanatory coherence based on the Ising model is available in this R-package.
Implements likelihood based methods for mediation analysis.
Search, extract and formulate IMF's datasets.
Explore and download data from the International Monetary Fund's
    data API <http://data.imf.org/>.
Provides ability to create color palettes from image files. 
    It offers control over the type of color palette to derive from an image (qualitative, sequential or divergent) and other palette properties.
    Quantiles of an image color distribution can be trimmed. 
    Near-black or near-white colors can be trimmed in RGB color space independent of trimming brightness or saturation distributions in HSV color space.
    Creating sequential palettes also offers control over the order of HSV color dimensions to sort by.
    This package differs from other related packages like 'RImagePalette' in approaches to quantizing and extracting colors in images to assemble color palettes 
    and the level of user control over palettes construction.
Provides an interface for image recognition using the 'Google Vision API' <https://cloud.google.com/vision/> .  Converts API data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.
A complete API client for the image hosting service Imgur.com, including the an imgur graphics device, enabling the easy upload and sharing of plots.
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.
Interpretability methods to analyze the behavior
    and predictions of any machine learning model.  Implemented methods
    are: Feature importance described by Fisher et al. (2018)
    <arXiv:1801.01489>, accumulated local effects plots described by Apley
    (2018) <arXiv:1612.08468>, partial dependence plots described by
    Friedman (2001) <www.jstor.org/stable/2699986>, individual
    conditional expectation ('ice') plots described by Goldstein et al.
    (2013) <doi:10.1080/10618600.2014.907095>, local models (variant of
    'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>, the
    Shapley Value described by Strumbelj et. al (2014)
    <doi:10.1007/s10115-013-0679-x>, feature interactions described by
    Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
Send emails using the 'mailgun' api. To use this package you will need an account from <https://www.mailgun.com> .
The pathway fingerprint is a method to indicate the profile of significant pathways being influenced by drugs, which may hint drug functions. Through the similarity of pathway fingerprints, the potential relationship between disease and prescription can be found. Ye (2012) <doi: 10.1007/s13238-012-2011-z>.
Implements some item response models for multiple
    ratings, including the hierarchical rater model, 
    conditional maximum likelihood estimation of linear 
    logistic partial credit model and a wrapper function
    to the commercial FACETS program. See Robitzsch and
    Steinfeld (2018) for a description of the functionality
    of the package. 
    See Wang, Su & Qiu (2014; <doi:10.1111/jedm.12045>)
    for an overview of modeling alternatives.
Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods.
Simulate full B-cell and T-cell receptor repertoires using an in silico 
    recombination process that includes a wide variety of tunable parameters to introduce noise and biases. 
    Additional post-simulation modification functions allow the user to implant motifs or codon biases as 
    well as remodeling sequence similarity architecture. The output repertoires contain records of all 
    relevant repertoire dimensions and can be analyzed using provided repertoire analysis functions.
    Preprint is available at bioRxiv (Weber et al., 2019 <doi:10.1101/759795>).
Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).
Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.
A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.
Implements the compartment model from Tokars (2018) 
  <doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of 
  population-wide impact of vaccination against vaccine-preventable 
  diseases such as influenza.
Imputing blockwise missing data by imprecise imputation,
    featuring a domain-based, variable-wise, and case-wise strategy. 
    Furthermore, the estimation of lower and upper bounds for 
    unconditional and conditional probabilities based on the obtained
    imprecise data is implemented.
    Additionally, two utility functions are supplied: one to check 
    whether variables in a data set contain set-valued observations;
    and another to merge two already imprecisely imputed data. 
    The method is described in a technical report by Endres, Fink and
    Augustin (2018, <doi:10.5282/ubm/epub.42423>).
A tool for computing the scores for the Implicit Association Test 
    (IAT; Greenwald, McGhee & Schwartz (1998) <doi:10.1037/0022-3514.74.6.1464>) 
    and the Single Category-IAT (SC-IAT: Karpinski & Steinman 
    (2006) <doi:10.1037/0022-3514.91.1.16>). Functions for preparing the data 
    (both for the IAT and the SC-IAT), plotting the results, and obtaining a 
    table with the scores of implicit measures descriptive statistics are 
    provided.
Convert bookmaker odds into proper probabilities. Seven different
    algorithms are available, including basic normalization, Shin's method 
    (Hyun Song Shin, (1992) <doi:10.2307/2234526>), and others.
'SQL' back-end to 'dplyr' for Apache Impala, the massively
    parallel processing query engine for Apache 'Hadoop'. Impala enables
    low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed
    File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple 
    Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', 
    and Dell 'EMC' 'Isilon'. See <https://impala.apache.org> for more
    information about Impala.
Alternative mechanism for importing objects from packages
    and R modules. The syntax allows for importing multiple objects with a single
    command in an expressive way. The import package bridges some of the gap
    between using library (or require) and direct (single-object) imports.
    Furthermore the imported objects are not placed in the current environment.
Enables 'Python'-like importing/loading of packages or functions
    with aliasing to prevent namespace conflicts.
Import and export data from the most common statistical formats by using 
	     R functions that guarantee the least loss of the data information, giving special
	     attention to the date variables and the labelled ones.
Download and manage data sets of statistical projects and geographic data created by Instituto Nacional de Estadistica y Geografia (INEGI). See <https://www.inegi.org.mx/>.
Provides color palettes from Impressionism and post-Impressionism artworks. This package allows to select colors combinations while looking at the original paintings where colors were sampled from.
Creation of imprecise classification trees. They rely on
    probability estimation within each node by means of either the
    imprecise Dirichlet model or the nonparametric predictive
    inference approach. The splitting variable is selected by the
    strategy presented in Fink and Crossman (2013)
    <http://www.sipta.org/isipta13/index.php?id=paper&paper=014.html>,
    but also the original imprecise information gain of Abellan and
    Moral (2003) <doi:10.1002/int.10143> is covered.
Missing values often occur in financial data due to a variety 
    of reasons (errors in the collection process or in the processing stage, 
    lack of asset liquidity, lack of reporting of funds, etc.). However, 
    most data analysis methods expect complete data and cannot be employed 
    with missing values. One convenient way to deal with this issue without 
    having to redesign the data analysis method is to impute the missing 
    values. This package provides an efficient way to impute the missing 
    values based on modeling the time series with a random walk or an 
    autoregressive (AR) model, convenient to model log-prices and log-volumes 
    in financial data. In the current version, the imputation is 
    univariate-based (so no asset correlation is used). In addition,
    outliers can be detected and removed.
    The package is based on the paper:
    J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of 
    Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on 
    Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.
The package contains a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
This package provides various approaches to handling
        missing values for the MDR analysis to identify gene-gene
        interactions using biallelic marker data in genetic association
        studies
Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.
Implements imputation methods using EM and Data Augmentation for
    multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
Imputes the missing values in time series data with PSF algorithm based approach.
    The details about PSF algorithm are available at:
    <https://cran.r-project.org/package=PSF>.
Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.
Provides a test bench for the comparison of missing data imputation 
    methods in uni-variate time series. Imputation methods are compared using 
    different error metrics. Proposed imputation methods and alternative error 
    metrics can be used.
Imputation (replacement) of missing values 
             in univariate time series. 
             Offers several imputation functions
             and missing data plots. 
             Available imputation algorithms include: 
            'Mean', 'LOCF', 'Interpolation', 
            'Moving Average', 'Seasonal Decomposition', 
            'Kalman Smoothing on Structural Time Series models',
            'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) 
            <doi: 10.32614/RJ-2017-009>.
Method brings less bias and more efficient estimates for AFT models.
Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers)
  evaluate patient images (cases). What does this mean? ... Many imaging studies are designed
  so that every reader reads every case in all modalities, a fully-crossed study. In this case, the
  data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects.
  An MRMC analysis accounts for the variability and correlations from the readers and cases when
  estimating variances, confidence intervals, and p-values. The functions in this package can treat
  arbitrary study designs and studies with missing data, not just fully-crossed study designs.
  The initial package analyzes the reader-average area under the receiver operating characteristic (ROC)
  curve with U-statistics according to Gallas, Bandos, Samuelson,
  and Wagner 2009 <doi:10.1080/03610920802610084>.
  Additional functions analyze other endpoints with U-statistics (binary performance
  and score differences) following the work by Gallas, Pennello, and Myers 2007
  <doi:10.1364/JOSAA.24.000B70>.
  Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.
Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.
Implementation of the information matrix test for generalized partial credit models.
Graphical User Interface (GUI) to some of the functions in the package extRemes version >= 2.0 are included.
Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting the algorithms. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the centers of clusters, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.
A framework for analysing inbreeding and heterozygosity-fitness
    correlations (HFCs) based on microsatellite and SNP markers.
Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. They are useful for census-weights adjustments, or for performing linear regression with integer parameters. This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy.
Handle data in formats used by cancer centers in Sweden, both from 'INCA' 
  (<https://rcc.incanet.se>) and by the older register platform 'Rockan'.
  All variables are coerced to suitable classes based on their format. 
  Dates (from various formats such as with missing month or day, with or 
  without century prefix or with just a week number) are all recognized as
  dates and coerced to the ISO 8601 standard (Y-m-d).
  Boolean variables (internally stored either as 0/1 or "True"/"False"/blanks 
  when exported) are coerced to logical. 
  Variable names ending in '_Beskrivning' and '_Varde' will be character, 
  and 'PERSNR' will be coerced (if possible) to a valid personal identification 
  number 'pin' (by the 'sweidnumbr' package).
  The package also allow the user to interactively choose if a variable should 
  be coerced into a potential format even though not all of its values might 
  conform to the recognized pattern.
  It also contain a caching mechanism in order to temporarily store data sets 
  with its newly decided formats in order to not rerun the identification 
  process each time. 
  The package also include a mechanism to aid the documentation process 
  connected to projects build on data from 'INCA'.
  From version 0.7, some general help functions are also included, 
  as previously found in the 'rccmisc' package.
Offers a pipe-friendly alternative to the 'dplyr' functions
    case_when() and if_else(), as well as a number of user-friendly
    simplifications for common use cases.  These functions accept a vector
    as an optional first argument, allowing conditional statements to be
    built using the 'magrittr' dot operator.  The functions also coerce
    all outputs to the same type, meaning you no longer have to worry
    about using specific typed variants of NA or explicitly declaring
    integer outputs, and evaluate outputs somewhat lazily, so you don't
    waste time on long operations that won't be used.
Data analysis, normalisation and differential expression for Translatome studies by microarrays (T Sbarrato et al. RNA. 2017 Aug 25; <DOI:10.1261/rna.060525.116>).
The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>.
An efficient and incremental approach for calculating 
    the differences in orbit counts when performing single edge modifications 
    in a network. Calculating the differences in orbit counts is much more efficient than
    recalculating all orbit counts from scratch for each time point.
Provides functions and classes to compute, handle and visualise
  incidence from dated events for a defined time interval. Dates can be provided
  in various standard formats. The class 'incidence' is used to store computed
  incidence and can be easily manipulated, subsetted, and plotted. In addition,
  log-linear models can be fitted to 'incidence' objects using 'fit'. This
  package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit
  for outbreak analysis.
Provides functions and classes to compute, handle and visualise 
  incidence from dated events for a defined time interval. Dates can be 
  provided in various standard formats. The class 'incidence2' is used to store
  computed incidence and can be easily manipulated, subsetted, and plotted.
  This package is part of the RECON (<https://www.repidemicsconsortium.org/>) 
  toolkit for outbreak analysis (<https://www.reconverse.org>).
Make empirical Bayes incidence curves from reported case data using a specified delay distribution.
Implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values.
    Bhoj, D. S. (1991) <doi:10.1002/bimj.4710330108> "Testing equality of means in the presence of correlation and missing data".
    Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) <doi:10.22237/jmasm/1020254460> "Rank-based procedures for mixed paired and two-sample designs".
    Einsporn, R. L. and Habtzghi, D. (2013) <https://pdfs.semanticscholar.org/89a3/90bafeb2bc41ed4414533cfd5ab84a6b54b6.pdf> "Combining paired and two-sample data using a permutation test".
    Ekbohm, G. (1976) <doi:10.1093/biomet/63.2.299> "On comparing means in the paired case with incomplete data on both responses".
    Lin, P. E. and Stivers, L. E. (1974) <doi:10.1093/biomet/61.2.325> On difference of means with incomplete data".
    Maritz, J. S. (1995) <doi:10.1111/j.1467-842x.1995.tb00649.x> "A permutation paired test allowing for missing values".
Suite of functions to study animal incubation.
    At the core of incR 
    lies an algorithm that allows for the scoring of 
    incubation behaviour. Additionally, several functions 
    extract biologically relevant metrics of incubation such as off-bout number 
    and off-bout duration - for a review of avian incubation studies, 
    see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) 
    edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.
Tools for estimating incidence from biomarker data in cross-
    sectional surveys, and for calibrating tests for recent infection. 
    Implements and extends the method of Kassanjee et al. (2012)
    <doi:10.1097/EDE.0b013e3182576c07>.
Genome-wide gene insertion and deletion rates can be modelled in a maximum 
    likelihood framework with the additional flexibility of modelling potential missing 
    data using the models included within. These models simultaneously estimate insertion 
    and deletion (indel) rates of gene families and proportions of "missing" data for 
    (multiple) taxa of interest. The likelihood framework is utilized for parameter 
    estimation. A phylogenetic tree of the taxa and gene presence/absence patterns 
    (with data ordered by the tips of the tree) are required. See Dang et al.
    (2016) <doi:10.1534/genetics.116.191973> for more details.
Performs three ranking-based nonparametric tests for the
                independence of two continuous variables:
                (1) the classical Hoeffding's D test;
                (2) a refined variant of it, named R;
                (3) the Bergsma-Dassios T* sign covariance.
                The first test is consistent assuming an
                absolutely continuous bivariate distribution,
                i.e., the population coefficient D=0
                iff the variables are independent.
                The latter two are consistent
                under no restriction on the distribution.
                All three statistics are computed
                in time O(n log n) given n iid paired samples.
                The computation of R and T* uses a new algorithm,
                following work of Even-Zohar and Leng (2019),
                see <arXiv:2010.09712>, <arXiv:1911.01414>.
Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://mi.mathnet.ru/eng/ppi797>). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.
Derivation of indexes for benchmarking purposes. A methodology with flexible number of constituents is implemented. Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) <doi:10.1016/j.jempfin.2018.08.004>.
We provide an R tool for teaching in Social Sciences. It allows the computation of index numbers. It is a measure of the evolution of a fixed magnitude for only a product of for several products. It is very useful in Social Sciences. Among others, we obtain simple index numbers (in chain or in serie), index numbers for not only a product or weighted index numbers as the Laspeyres index (Laspeyres, 1864), the Paasche index (Paasche, 1874) or the Fisher index (Lapedes, 1978).
Computes bilateral and multilateral index numbers. 
    It has support for many standard bilateral indexes as well as
    multilateral index number methods such as GEKS, GEKS-Tornqvist 
    (or CCDI), Geary-Khamis and the weighted time product dummy
    (for details on these methods see Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>). 
    It also supports updating of multilateral indexes using 
    several splicing methods.
Calculate Indian Income Tax liability for Financial years of Individual resident aged below 60 years,Senior Citizen,Super Senior Citizen, Firm, Local Authority, Any Non Resident Individual / Hindu Undivided Family / Association of Persons /Body of Individuals / Artificial Judicial Person, Co-operative Society.
Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].
Simplifies the generation of customized R Markdown PDF templates.
    A template may include an individual logo, typography, geometry or color
    scheme. The package provides a skeleton with detailed instructions for
    customizations. The skeleton can be modified by changing defaults in the
    'YAML' header, by adding additional 'LaTeX' commands or by applying dynamic
    adjustments in R. Individual corporate design elements, such as a title page, can be added as R functions that produce 'LaTeX' code.
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
Performs a unified approach for obtaining and estimating
    unidimensional Item Response Theory (IRT) Dual Models (DMs), proposed by
    Ferrando (2019 <doi:10.1177/0146621618817779>).
It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence,  and functions for generating and analysing  different types of dependence.
Provides functions to download and parse information from INEGI
    (Official Mexican statistics agency). To learn more about the API, see <https://www.inegi.org.mx/servicios/api_indicadores.html>.
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) <doi:10.1400/209575> Zenga M. (2015) <doi:10.1400/246627> Zenga M., Valli I. (2017) <doi:10.26350/999999_000005> Zenga M., Valli I. (2018) <doi:10.26350/999999_000011>.
Provides simple functions to compute and plot two
    types (sample-size- and coverage-based) rarefaction and extrapolation of species
    diversity (Hill numbers) for individual-based (abundance) data or sampling-unit-
    based (incidence) data. (Hsieh, Ma and Chao 2014) <doi: 10.1111/2041-210X.12613>.
This package contains functions to perform calculations of
        the infine-dimensional model (IDM) and to produce 95%
        confidence intervals around the model elements through
        bootstrapping.
The objective of this package is to perform
    inference using an expressive statistical grammar that coheres with
    the tidy design framework.
Collection of functions to extract inferential values
        (point estimates, confidence intervals, p-values, etc) of a
        fitted model object into a matrix-like object that can be used
        for table/report generation; transform point estimates via the
        delta method.
The InferenceSMR package provides functions to make
        inference about the standardized mortality ratio (SMR) when
        evaluating the effect of a screening program. The package is
        based on methods described in Sasieni (2003) and Talbot et al.
        (2011).
Provides methods for estimating causal effects in the presence of interference described in  B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of
    statistical tests provided in 'stats' package by including additional data types as inputs, expanding and
    restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.
Sampler and post-processing functions for semi-parametric Bayesian infinite factor models, motivated by the Multiplicative Gamma Shrinkage Prior of Bhattacharya and Dunson (2011) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419391/>. Contains component C++ functions for building samplers for linear and 2-way interaction factor models using the multiplicative gamma and Dirichlet-Laplace shrinkage priors. The package also contains post processing functions to return matrices that display rotational ambiguity to identifiability through successive application of orthogonalization procedures and resolution of column label and sign switching. This package was developed with the support of the National Institute of Environmental Health Sciences grant 1R01ES028804-01.
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
Contains a number of infix binary operators that may be useful in day to day practices.
Provides access to core inflation functions. Four different core inflation 
 functions are provided. The well known trimmed means, exclusion and double weighing methods, 
 alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.
This program analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. Reference to software development can be found at <doi:10.1021/acs.jproteome.0c00872>.
Implementation of methods Extremum Surface Estimator (ESE) and 
   Extremum Distance Estimator (EDE) to identify the inflection point of a curve .
   Christopoulos, DT (2014) <arXiv:1206.5478v2 [math.NA]> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
Provides a collection of tools for
        detecting influential cases in generalized mixed effects
        models. It analyses models that were estimated using 'lme4'. The
        basic rationale behind identifying influential data is that
        when single units are omitted from the data, models
        based on these data should not produce substantially different
        estimates. To standardize the assessment of how influential a
        (single group of) observation(s) is, several measures of
        influence are common practice, such as Cook's Distance. 
        In addition, we provide a measure of percentage change of the fixed point 
        estimates and a simple procedure to detect changing levels of significance.
A set of tools for evaluating several measures of case influence for structural equation models.
Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.
Provides functionality to compute various node centrality measures on networks.
    Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
    SNAP library), implementations of Burt's constraint and effective
    network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's
    bridging metric. On Unix systems, the betweenness, Key Players, and
    bridging implementations are parallelized with OpenMP, which may run
    faster on systems which have OpenMP configured.
Contains functions for the classification and ranking of top candidate features, reconstruction of networks from
    adjacency matrices and data frames, analysis of the topology of the network 
    and calculation of centrality measures, and identification of the most
    influential nodes. Also, a function is provided for running SIRIR model, which 
    is the combination of leave-one-out cross validation technique and the conventional SIR model, on a network to unsupervisedly rank the true influence of vertices. Additionally, some functions have been provided for the assessment 
    of dependence and correlation of two network centrality measures as well as 
    the conditional probability of deviation from their corresponding means in opposite direction.
    Fred Viole and David Nawrocki (2013, ISBN:1490523995).
    Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695.
    Adopted algorithms and sources are referenced in function document.
An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.
The main purpose of this package is to generate the structure of the analysis of variance 
            (ANOVA) table of the two-phase experiments. The user only need to input the design and the 
              relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, 
              this package can then quickly generate the structure of the ANOVA table with the 
              coefficients of the variance components for the expected mean squares.
              Thus, the balanced incomplete block design and provides the efficiency
              factors of the fixed effects can also be studied and compared much easily.
Performs exploratory data analysis and variable screening for
    binary classification models using weight-of-evidence (WOE) and information
    value (IV). In order to make the package as efficient as possible, aggregations
    are done in data.table and creation of WOE vectors can be distributed across
    multiple cores. The package also supports exploration for uplift models (NWOE
    and NIV).
Provides companion function for analysing the performance of
    classification models. Also, provides function to optimise probability cut-
    off score based on used specified objectives, Plot 'ROC' Curve in 'ggplot2',
    'AUROC', 'IV', 'WOE' Calculation, 'KS Statistic' etc to aid accuracy improvement
    in binary classification models.
Multiple Imputation for Informative Censoring.
    This package implements two methods. Gamma Imputation
    described in <DOI:10.1002/sim.6274> and Risk Score Imputation
    described in <DOI:10.1002/sim.3480>.
Aids in creating sequence statistics for Butts's 'relevent' software.
This package implements various measures of information theory based on several entropy estimators.
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
Takes a data frame containing latitude and longitude coordinates and downloads images from map servers to determine their file size as a proxy of infrastructure
Facilitates the generation of input files for infraFDTD and processes snapshot output. infraFDTD is a finite-difference model written by Keehoon Kim for simulating infrasound that considers topography and a 1-D atmosphere (see Kim et al., 2015 <doi:10.1002/2015GL064466>).
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated (Rousset et al. 2017 <doi:10.1111/1755-0998.12627>).
Calculation of information utility (i.e., Lindley
        information) quantities for item response models.
Collection of tools for assessment of feature importance and feature effects.
    Key functions are:
    feature_importance() for assessment of global level feature importance,
    ceteris_paribus() for calculation of the what-if plots,
    partial_dependence() for partial dependence plots,
    conditional_dependence() for conditional dependence plots,
    accumulated_dependence() for accumulated local effects plots,
    aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,
    generic print() and plot() for better usability of selected explainers,
    generic plotD3() for interactive, D3 based explanations, and
    generic describe() for explanations in natural language.
    The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Parse simple '.ini' configuration files to an structured list. Users
  can manipulate this resulting list with lapply() functions. This same
  structured list can be used to write back to file after modifications.
R dependency injection framework. Dependency injection allows
    a program design to follow the dependency inversion principle. The user
    delegates to external code (the injector) the responsibility of providing its
    dependencies. This separates the responsibilities of use and construction.
Calculate the injury severity score (ISS)
    based on the dictionary in 'ICDPIC' from <https://ideas.repec.org/c/boc/bocode/s457028.html>. The original code was written in 
    'STATA 11'. The original 'STATA' code was written by David Clark, 
    Turner Osler and David Hahn. I implement the same logic for easier access.
    Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. 
    "ICDPIC: Stata module to provide methods for translating International 
    Classification of Diseases (Ninth Revision) diagnosis codes into standard injury 
    categories and/or scores," Statistical Software Components S457028, 
    Boston College Department of Economics, revised 29 Oct 2010.
Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <http://www.r-inla.org>.
Facilitates spatial and general latent Gaussian modeling using
  integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>).
  Additionally, extends the GAM-like model class to more general nonlinear predictor
  expressions, and implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data.
  Model components are specified with general inputs and mapping methods to the
  latent variables, and the predictors are specified via general R expressions,
  with separate expressions for each observation likelihood model in
  multi-likelihood models. A prediction method based on fast Monte Carlo sampling
  allows posterior prediction of general expressions of the latent variables.
  Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019)
  <doi:10.1111/2041-210X.13168>.
Implementation of several multivariate areal latent effects for
    'INLA' using the 'rgeneric' latent effect (Palmí-Perales et al., 2019,
    <arXiv:1909.10804>). The 'INLA' package can be downloaded from 
    <https://www.r-inla.org>. In particular, the package includes latent 
    effects ready to use for several multivariate spatial models:
    intrinsic CAR, proper CAR and the M-model (Botella-Rocamora
    et al., 2015, <doi:10.1002/sim.6423>).
Contains data for the U.S. Geological Survey-Idaho National
   Laboratory (USGS-INL) aquifer monitoring networks administrated by the
   Idaho National Laboratory Project Office in cooperation with the U.S.
   Department of Energy.
Functionality to dynamically define R functions and S4 methods
 with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling
 conventions.
Generates Rd files from R source code with comments.
 The main features of the default syntax are that
 (1) docs are defined in comments near the relevant code,
 (2) function argument names are not repeated in comments, and
 (3) examples are defined in R code, not comments.
 It is also easy to define a new syntax.
A collection of functions for creating high-level graphics,
    performing raster-based analysis, processing MODFLOW-based models,
    selecting subsets using a genetic algorithm, creating interactive web maps,
    accessing color palettes, etc. Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory (INL) Project Office.
Infix operators to detect, subset, and replace the elements matched by a given condition.
  The functions have several variants of operator types, including subsets, ranges, regular expressions and others.
  Implemented operators work on vectors, matrices, and lists.
A set of functions to analyse and compare texts, using classical 
  text mining	functions, as well as those from theoretical ecology.
It provides in-place operators for R 
    that are equivalent to '+=', '-=', '*=', '/=' in C++. 
    Those can be applied on integer|double vectors|matrices.
    You have also access to sweep operations (in-place).
Non-parametric resampling-based inference tests for ExPosition.
Collection of functions for quality control (QC) of climatological daily time series (e.g. the ECA&D station data).
A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) 
    assays in pancreatic beta cells or islets.
    The package allows the user to import several sets of experiments from different spreadsheets 
    and to perform subsequent steps: summarise in a tidy format, visualise data quality 
    and compare experimental conditions without omitting to account for technical confounders 
    such as the date of the experiment or the technician.
    Together, insane is a comprehensive method that optimises pre-processing and analyses of 
    GSIS experiments in a friendly-user interface.
    The Shiny App was initially designed for EndoC-betaH1 cell line following method described 
    in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).
Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.
Using embedded sdmx queries, get the data of more than 150 000 insee series from bdm database. Have a look at the detailed sdmx web service page with the following link : <https://www.insee.fr/en/information/2868055>.
A tool to provide an easy, intuitive and consistent
    access to information contained in various R models, like model
    formulas, model terms, information about random effects, data that was
    used to fit the model or data from response variables. 'insight'
    mainly revolves around two types of functions: Functions that find
    (the names of) information, starting with 'find_', and functions that
    get the underlying data, starting with 'get_'.  The package has a
    consistent syntax and works with many different model objects, where
    otherwise functions to access these information are missing.
Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA4' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Functions to compute insolation on complex terrain.
Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.
A collection of utilities for columnwise summary, comparison and visualisation of data frames.  Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage.
Utility functions that implement and automate common sets of validation tasks. 
    These functions are particularly useful to validate inputs, intermediate objects and output 
    values in user-defined functions, resulting in tidier and less verbose functions.
A method to infer modules of co-expressed genes and the
    dependencies among the modules from multiple expression datasets that may
    contain different sets of genes. Please refer to: Extracting a low-dimensional
    description of multiple gene expression datasets reveals a potential driver for
    tumor-associated stroma in ovarian cancer, Safiye Celik, Benjamin A. Logsdon,
    Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins and Su-In
    Lee (2016) <DOI:10.1186/s13073-016-0319-7>.
The function `install_load` checks the local R library(ies) to see
    if the required package(s) is/are installed or not. If the package(s)
    is/are not installed, then the package(s) will be installed along with
    the required dependency(ies). This function pulls source or
    binary packages from the Rstudio-sponsored CRAN mirror and/or
    the USGS GRAN Repository. Lastly, the chosen package(s)
    is/are loaded. The function `load_package` simply loads the provided
    packages. If this package does not fit your needs, then you may want to
    consider these other R packages: 'needs', 'easypackages', 'pacman', 'pak',
    'anyLib', and/or 'librarian'.
R is great for installing software.  Through the 'installr'
    package you can automate the updating of R (on Windows, using updateR())
    and install new software. Software installation is initiated through a
    GUI (just run installr()), or through functions such as: install.Rtools(),
    install.pandoc(), install.git(), and many more. The updateR() command
    performs the following: finding the latest R version, downloading it,
    running the installer, deleting the installation file, copy and updating
    old packages to the new R installation.
Provides an interface to the Instagram API <https://instagram.com/
    developer/>, which allows R users to download public pictures filtered by
    hashtag, popularity, user or location, and to access public users' profile data.
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
Methods for insurance rating. It helps actuaries to implement GLMs within all relevant steps needed to construct
 a risk premium from raw data. It provides a data driven strategy for the construction of insurance tariff classes.
 This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. It also provides recipes
 on how to easily perform one-way, or univariate, analyses on an insurance portfolio. In addition it adds functionality
 to include reference categories in the levels of the coefficients in the output of a generalized linear regression analysis.
Provides classes and methods for automated
    spatial interpolation.
Includes additional functionality for spatial interpolation in the intamap package, such as bias correction and network optimization.
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves.
Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) <doi:10.1002/sim.7350>. In addition, missing event types or left truncation are considered. We provide the proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
The kernel of this 'Rcpp' based package is an efficient implementation of the generalized gradient projection method for spline function based constrained maximum likelihood estimator for interval censored survival data (Wu, Yuan; Zhang, Ying. Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 <doi:10.1214/12-AOS1016>). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC).
Provides intdag() for a constrained maximum likelihood estimate of a directed acyclic graph with intervention data. Also available is obsdag() for an estimate with observation data only, based on the method in the paper by Yuan, Shen and Pan (2018) <doi:10.1093/biomet/asy057>.
Provides a systematic framework for
 integrating multiple modalities of assays profiled on the same set of
 samples. The goal is to identify genes that are altered in cancer
 either marginally or consistently across different assays. The
 heterogeneity among different platforms and different samples are
 automatically adjusted so that the overall alteration magnitude can
 be accurately inferred. See Tong and Coombes (2012) 
 <doi:10.1093/bioinformatics/bts561>.
Generates a 'Graphviz' graph of the most significant 3-way 
    interaction gains (i.e. conditional information gains) based on a provided 
    discrete data frame. Various output formats are supported ('Graphviz', SVG, 
    PNG, PDF, PS). For references, see the webpage of Aleks Jakulin 
    <http://stat.columbia.edu/~jakulin/Int/>.
Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.
Electricity is not made equal and it vary in its carbon footprint (or carbon intensity) 
    depending on its source. This package enables to access and query data provided by the 
    Carbon Intensity API (<https://carbonintensity.org.uk/>). National Grid’s Carbon Intensity API 
    provides an indicative trend of regional carbon intensity of the electricity system in Great Britain.
Calculate metrics of change intensity for category, transition and interval levels in categorical maps from sequential intervals. For more information please consult: Aldwaik,Safaa Zakaria and Robert Gilmore Pontius Jr. (2012). "Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition". Landscape and Urban Planning. 106, 103-114. <doi:10.1016/j.landurbplan.2012.02.010>.
Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework.
This package searches for marginal interactions in a
        binary response model. Interact uses permutation methods to
        estimate false discovery rates for these marginal interactions
        and has some, limited visualization capabilities
Produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>].
    It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).
A suite of functions for conducting and interpreting analysis 
  of statistical interaction in regression models that was formerly part of the 
  'jtools' package. Functionality includes visualization of two- and three-way
  interactions among continuous and/or categorical variables as well as 
  calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., 
  Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These
  capabilities are implemented for generalized linear models in addition to the 
  standard linear regression context.
Implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.
An extension of the package 'igraph'. This package create
        possibly to work with 'igraph' objects interactively.
Chi-square tests are computed with corrections.
Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.
Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.
Computes large sample confidence intervals of Liu and Hudgens (2014), exact confidence intervals of Tchetgen Tchetgen and VanderWeele (2012), and exact confidence intervals of Rigdon and Hudgens (2014) for treatment effects on a binary outcome in two-stage randomized experiments with interference.
Performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator.
Computes interference color tables and plots customized Michel-Levy or Raith-Sorensen charts. Automatic interpretation of polarized-light microscopy images is still under development and will come soon.
Functions implemented in this package allow to coerce (i.e.
	convert) network data between classes provided by other R packages.
	Currently supported classes are those defined in packages: network and
	igraph.
Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.
Allows an interactive assessment of the timing of interim analyses. The algorithm simulates both the recruitment and treatment/event phase of a clinical trial based on the package 'interim'.
Converts matrices and lists of matrices into a single vector by interleaving 
    their values. That is, each element of the result vector is filled from the input 
    matrices one row at a time. This is the same as transposing a matrix, then removing the 
    dimension attribute, but is designed to operate on matrices in nested list structures.
Interlinearized glossed texts (IGT) are used in descriptive linguistics for
 representing a morphological analysis of a text through a morpheme-by-morpheme gloss.
 'InterlineaR' provide a set of functions that targets several popular formats of IGT
 ('SIL Toolbox', 'EMELD XML') and that turns an IGT into a set of data frames following
 a relational model (the tables represent the different linguistic units: texts,
 sentences, word, morphems).
 The same pieces of software ('SIL FLEX', 'SIL Toolbox') typically produce dictionaries
 of the morphemes used in the glosses. 'InterlineaR' provide a function for turning
 the LIFT XML dictionary format into a set of data frames following a relational model
 in order to represent the dictionary entries, the sense(s) attached to the entries,
 the example(s) attached to senses, etc.
Bivariate data interpolation on regular and irregular
  grids, either linear or using splines are the main part of this
  package.  It is intended to provide FOSS replacement functions for
  the ACM licensed akima::interp and tripack::tri.mesh functions.
  Currently the piecewise linear interpolation part of akima::interp
  (and also akima::interpp) is implemented in interp::interp, this
  corresponds to the call akima::interp(..., linear=TRUE) which is the
  default setting and covers most of akima::interp use cases in
  depending packages.  A re-implementation of Akimas spline
  interpolation (akima::interp(..., linear=FALSE)) is currently under
  development and will complete this package in a later
  version. Estimators for partial derivatives are already available,
  these are a prerequisite for the spline interpolation.  The basic
  part is currently a GPLed triangulation algorithm (sweep hull
  algorithm by David Sinclair) providing the starting point for the
  piecewise linear interpolator. As side effect this algorithm is also
  used to provide replacements for the basic functions of the tripack
  package which also suffer from the ACM restrictions.  All functions
  are designed to be backward compatible with their akima / tripack
  counterparts.
Plots the conditional coefficients ("marginal effects") of
    variables included in multiplicative interaction terms.
Hourly interpolation of daily minimum and maximum temperature
    series. Carries out interpolation on multiple series ad once. Requires some
    hourly series for calibration (alternatively can use default calibration
    table).
Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
Annotate and interpret deconvoluted mass spectra (mass*intensity pairs) from high resolution mass spectrometry devices.
Compute permutation- based performance measures and create partial
    dependence plots for (cross-validated) 'randomForest' and 'ada' models.
Generates three inter-related genomic datasets : methylation, gene expression and protein expression.
Provides an R version of the 'InterVA4' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Provides an R version of the 'InterVA5' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests.
Perform fast and memory efficient time-weighted averaging of values 
    measured over intervals into new arbitrary intervals. 
	This package is useful in the context of data measured or represented
	as constant values over intervals on a one-dimensional discrete axis 
	(e.g. time-integrated averages of a curve over defined periods). 
	This package was written specifically to deal with air 
	pollution data recorded or predicted as averages over sampling periods. 
	Data in this format often needs to be shifted to non-aligned periods 
	or averaged up to periods of longer duration (e.g. averaging data measured over 
	sequential non-overlapping periods to calendar years).
Tools for working with and comparing sets of points and intervals.
Manipulate integer-bounded intervals including finding overlaps, piling and merging.
Performs hypothesis testing
    using the interval estimates (e.g., confidence intervals). The
    non-overlapping interval estimates indicates the statistical
    significance. References to these procedures can be found at
    Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>,
    Bonett and Seier (2003) <doi:10.1198/0003130032323>, and
    Lemm (2006) <doi:10.1300/J082v51n02_05>.
This package allows a dynamic Bayesian network to be inferred from microarray timecourse data with interventions (inhibitors).
The 'inti' package is part of the 'inkaverse' project for developing 
    different procedures and tools used in plant science and experimental designs. 
    The mean aim of the package is to support researchers during the planning of
    experiments and data collection (tarpuy()), data analysis and graphics (yupana())
    , and technical writing (rtciles()). 
    Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
An interval-valued extension of ordinary and simple kriging.
    Optimization of the function is based on a generalized interval distance.
    This creates a non-differentiable cost function that requires a
    differentiable approximation to the absolute value function. This
    differentiable approximation is optimized using a Newton-Raphson algorithm
    with a penalty function to impose the constraints. Analyses in the package
    are driven by the 'intsp' and 'intgrd' 
    classes, 
    which are interval-valued extensions of
    'SpatialPointsDataFrame' and 'SpatialPixelsDataFrame' respectively. 
    The package includes several wrappers to functions in the 
    'gstat' and 'sp' packages.
This package calculates the integrated likelihood numerically. Given the Likelihood function and the prior function, this package integrates out the nuisance parameters by Metropolis-Hastings (MCMC) Algorithm.
Implementing the interventional effects for mediation analysis for up to 3 mediators.
    The methods used are based on VanderWeele, Vansteelandt and Robins (2014) <doi:10.1097/ede.0000000000000034>, 
    Vansteelandt and Daniel (2017) <doi:10.1097/ede.0000000000000596> and Chan and Leung (2020; unpublished manuscript, available on request from the author of this package).
    Linear regression, logistic regression and Poisson regression are used for continuous, binary 
    and count mediator/outcome variables respectively.
Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.
Performs Goodness of Fit for regression models 
  using Integrated Regression method. Works for several 
  different fitting techniques.
Estimate the proportions of the null and the reproducibility and
    non-reproducibility of the signal group for the input data set. The Bayes factor
    calculation and EM (Expectation Maximization) algorithm procedures are also
    included.
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.
introgress provides functions for analyzing introgression
        of genotypes between divergent, hybridizing lineages, including
        estimating genomic clines from multi-locus genotype data and
        testing for deviations from neutral expectations. Functions are
        also provided for maximum likelihood estimation of molecular
        hybrid index and graphical analysis.
Evaluating if values
  of vectors are within different open/closed intervals
  (`x %[]% c(a, b)`), or if two closed
  intervals overlap (`c(a1, b1) %[]o[]% c(a2, b2)`).
  Operators for negation and directional relations also implemented.
Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.
Contains implementations of
    integrative survival analysis routines, including
    regular Cox cure rate model proposed by
    Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531>
    via an EM algorithm proposed by
    Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>,
    regularized Cox cure rate model with elastic net penalty following
    Masud et al. (2018) <doi:10.1177/0962280216677748>, and
    Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and
    weighted concordance index for cure models proposed by
    Asano and Hirakawa (2017) <doi:10.1080/10543406.2017.1293082>.
Function to implement the horseshoe shrinkage prior in 
             integrated survival and binary regression as developed in Maity et. al. (2019) 
             <doi:10.1111/rssc.12377>.
Provides tools for importing, merging, and analysing data from 
  international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).
Interface to popular R functions with formulas and data,
  such as 'lm', so they can be included painlessly in data
  science pipelines implemented by 'magrittr'
  with the operator %>%.
Enum-type representation of vectors and representation
  of intervals, including a method of coercing variables in data frames.
Provides an up-to-date version of the 'InvaCost' database 
    (<doi:10.6084/m9.figshare.12668570>) in R, and
    several functions to analyse the costs of invasive alien species.
Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.
Vector operations between grapes: An infix-only package! The 'invctr' functions perform common and less common operations on vectors, data frames matrices and list objects:
    - Extracting a value (range), or, finding the indices of a value (range).
    - Trimming, or padding a vector with a value of your choice.
    - Simple polynomial regression.
    - Set and membership operations.
    - General check & replace function for NAs, Inf and other values.
Simulate inventory policies, facilitate inventory analysis calculations such as  stock levels and re-order points,pricing and promotions calculations. 
  The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations.
    The package includes revenue management techniques such as Multi-product optimization,logit model optimization.
  The functions are referenced from :
  1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. <isbn10: 135–136, 152>.
  2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. <isbn: 0-07- 2231265-3. Chapter 4>.
  3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. <isbn: 978-0471119470>. 
  4-Ballou, R.H. Business Logistics Management. <isbn: 978-0130661845>. Chapter 9.
  5-MIT Micromasters Program.
  6- Columbia University  course for supply and demand analysis.
  8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W
  For further details or correspondence :<www.linkedin.com/in/haythamomar>, <www.rescaleanalytics.com>.
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.
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 types, etc 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.
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 needed for data cleaning and
    formatting and forms data cleaning and wrangling backend for the
    following packages: 'ggstatsplot', 'pairwiseComparisons', and
    'statsExpressions'.
Flexibly implements Integral Projection Models using an expression 
  based framework. This will not abstract away the model selection process, 
  but it will make sure you get everything after that correct. It 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.
Provides an R interface to the 'iPTMnet' database REST API, which can be used to retrieve 
    Post Translational Modification (PTM) data in systems biology context. This package handles all the aspects
    of communicating with the API, which involve sending the request, checking the error codes and parsing the
    response in a format that is ready to integrate into existing workflows.
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.
A R interface to setup, run and read IRATE model runs to assess band recovery (conventional tagging) data (i.e. age-dependent or independent fishing and natural mortality rates).
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>.
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.
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.
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.
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.
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.
Characterisation and calibration of single or multiple Ion Selective Electrodes (ISEs); 
    activity estimation of experimental samples. Implements methods described in:
	Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) <doi:10.1002/elan.201100510> and 
	Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) <doi:10.1109/ICSENS.2017.8233898>.
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'.
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 <http://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.
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.
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.
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.
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.
Performs multivariate normal imputation through iterative sequential 
  regression.  Conditional dependency structure between imputed variables can be 
  specified a priori to accelerate imputation.
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.
Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales). The statistics reported in this package can be found in any measurement textbook such as Crocker and Algina (2006, ISBN:9780495395911).
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.
Individual tree model is an instrument to support the decision with
    regard to forest management. This package provides functions that let you work
    with data for this model. Also other support functions and extension related to
    this model are available.
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 linear programming. Support for
    three linear programming 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. 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. 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.
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.
An estimation method that can use computer simulations to
    approximate maximum-likelihood estimates even when the likelihood function can not
    be evaluated directly. It can be applied whenever it is feasible to conduct many
    simulations, but works best when the data is approximately Poisson distributed.
    It was originally designed for demographic inference in evolutionary
    biology. It has optional support for conducting coalescent simulation using
    the 'coala' package.
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.
	Systematic patterns may be captured by latent variables using principal component analysis (PCA), factor analysis (FA), and related methods. The jackstraw enables statistical testing for association between observed variables and latent variables, as captured by PCs 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.
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.
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.
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.
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. References - Bivariate change point detection (2019+), Michael Messer.
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.
Simplifies and extends data exchange between 'R' and 'Java'.
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>.
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 sample using various methods according to the International Standards on Auditing. Furthermore, the package implements Bayesian equivalents of these methods.
Support the analysis of financial and econometric time series, including recursive forecasts for machine learning.
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.
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>.
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.
Fit joint models of continuous or ordinal longitudinal data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang Li and Ning Li (2016, ISBN:9781439807828); Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> .
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 makes 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.
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 (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). 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>
 and Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>.
 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.
    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' 
    <http://mcmc-jags.sourceforge.net/> 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 the basis
    for services like OAuth 2.0 or LetsEncrypt and are natively supported by browsers
    via the JavaScript WebCryptoAPI.
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.
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.
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.
Provides a high-level integration for the 'Java' platform that makes 'Java' objects easy to use from within 'R'; provides a unified interface to integrate 'R' with several programming languages; and features extensive data exchange between 'R' and 'Java'. The 'jsr223'-supported programming languages include 'Groovy', 'JavaScript', 'JRuby' ('Ruby'), 'Jython' ('Python'), and 'Kotlin'. Any of these languages can use and extend 'Java' classes in natural syntax. Furthermore, solutions developed in any of the 'jsr223'-supported languages are also accessible to 'R' developers. The 'jsr223' package also features callbacks, script compiling, and string interpolation. In all, 'jsr223' significantly extends the computing capabilities of the 'R' software environment.
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.
Functions and helpers to import metadata, ngrams and full-texts 
    delivered by Data for Research by JSTOR.
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>.
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>.
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.
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.
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.
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.
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.
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>.
    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>.
    Fox et al. (2020) <https://CRAN.R-project.org/package=car>.
    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>.
    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.
Data cleaning, processing, visualisation and analysis for manual (Goldmann) and automated (Octopus 900) kinetic visual field data.
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.
Get pictures of cats and their meows from your R
    console.
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.
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.
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>.
K-means implementation is base on "Yingyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup". While it introduces some overhead and many conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd algorithm.  K-nearest neighbors employ the same triangle inequality idea and require precalculated centroids and cluster assignments, similar to the flattened ball tree.
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.
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>.
The k-means 'NUMA' Optimized Routine library or 'knor' is a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access ('NUMA') architectures. Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns (2017) <arXiv:1606.08905>.
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'.
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 products of (joint) cumulants of a given population distribution 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, 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.
Supports cubic Hermite splines (Fritsch & Carlson, 1980 <doi:10.1137/0717021>), along with their derivatives/integrals. Also, supports monotonically increasing/decreasing splines, and computes their roots and minimum/maximum points.
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
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>.
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.
Support labeled loop and escape from nested loop
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/>.
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'.
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.
This package includes 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.
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). The README describes the history of the package development process.
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.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
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.
Model selection for the (possibly high-dimensional) linear regression model 
    with Lasso-Zero, an L1-based methodology relying on the repeated use of noise 
    dictionaries, as described by Descloux, P. and Sardy, S. (2018) <arXiv:1805.05133>.
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'.
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' 
 <http://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.
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>.
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
    '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.
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.
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.
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).
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).
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:10.1101/2021.02.08.430270>.
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'.
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).
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 used in code 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>.
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/>).
Designed to query Longitudinal Employer-Household Dynamics (LEHD) 
    workplace/residential association and origin-destination flat files and 
    optionally aggregate Census block-level data to block group, tract, county, 
    or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES7/>.
Implements the 'Python leidenalg' module to be called in R.
    Enables clustering using the leiden algorithm for partition a graph into communities.
    See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg>
    Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: <https://github.com/vtraag/leidenalg>. The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) <doi:10.1038/s41598-019-41695-z>.
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.
A collection of lexical hash tables, dictionaries, and word lists.
Provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts.
    As of now, two Portuguese lexicons are available: 'SentiLex-PT02' and 'OpLexicon' (v2.1 and v3.0).
My PhD supervisor once told me that everyone doing newspaper
    analysis starts by writing code to read in files from the 'LexisNexis' newspaper
    archive (retrieved e.g., from <http://www.nexis.com/> or any of the partner
    sites). However, while this is a nice exercise I do recommend, not everyone has
    the time. This package takes files downloaded from the newspaper archive of
    'LexisNexis', reads them into R and offers functions for further processing.
Plots empty Lexis grids, adds lifelines and highlights certain areas of the grid, like cohorts and age groups.
An R implementation of the LexRank algorithm described by G. Erkan and D. R. Radev (2004) <DOI:10.1613/jair.1523>.
Provides an extension to factors called 'lfactor' that are similar
    to factors but allows users to refer to 'lfactor' levels by either the level or
    the label.
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)".
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 efficient Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.
Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.
Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.
Given an RNA-seq derived cell-by-gene matrix and an DNA-seq derived copy number segmentation, LIAYSON predicts the number of clones present in a tumor, their size, the copy number profile of each clone and the clone membership of each single cell (Andor, N. & Lau, B., et al. (2018) <doi:10.1101/445932>).
Provides functions for validating and normalizing bibliographic
   codes such as ISBN, ISSN, and LCCN. Also includes functions to communicate
   with the WorldCat API, translate Call numbers (Library of Congress and
   Dewey Decimal) to their subject classifications or subclassifications,
   and provides various loadable data files such call number / subject
   crosswalks and code tables.
Basic infrastructure for linear test statistics and permutation
  inference in the framework of Strasser and Weber (1999) <https://epub.wu.ac.at/102/>.
  This package must not be used by end-users. CRAN package 'coin' implements all
  user interfaces and is ready to be used by anyone.
Provides the Open Source Geometry Engine ('GEOS') as a
  C API that can be used to write high-performance C and C++
  geometry operations using R as an interface. Headers are provided
  to make linking to and using these functions from C++ code as
  easy and as safe as possible. This package contains an internal
  copy of the 'GEOS' library to guarantee the best possible
  consistency on multiple platforms.
A wrapper around the LIBLINEAR C/C++ library for machine
        learning (available at
        <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a
        simple library for solving large-scale regularized linear
        classification and regression. It currently supports
        L2-regularized classification (such as logistic regression,
        L2-loss linear SVM and L1-loss linear SVM) as well as
        L1-regularized classification (such as L2-loss linear SVM and
        logistic regression) and L2-regularized support vector
        regression (with L1- or L2-loss). The main features of
        LiblineaR include multi-class classification (one-vs-the rest,
        and Crammer & Singer method), cross validation for model
        selection, probability estimates (logistic regression only) or
        weights for unbalanced data. The estimation of the models is
        particularly fast as compared to other libraries.
Solving the linear SVM problem with coordinate descent
    is very efficient and is implemented in one of the most often used packages,
    'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
    It has been shown that the uniform selection of coordinates can be
    accelerated by using an online adaptation of coordinate frequencies (ACF).
    This package implements ACF and is based on 'LIBLINEAR' as well as
    the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>).
    It currently supports L2-regularized L1-loss as well as L2-loss linear SVM.
    Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and
    Crammer & Singer method) and cross validation for model selection is
    supported. The training of the models based on ACF is much faster than
    standard 'LIBLINEAR' on many problems.
The 'LibOPF' is a framework to develop pattern recognition techniques based on optimum-path forests (OPF), João P. Papa and Alexandre X. Falcão (2008) <doi:10.1007/978-3-540-89639-5_89>, with methods for supervised learning and data clustering.
Contains a set of functions to create data libraries,
    generate data dictionaries, and simulate a data step.
    The libname() function will load a directory of data into 
    a library in one line of code.  The dictionary() function
    will generate data dictionaries for individual
    data frames or an entire library.  And the datestep() function
    will perform row-by-row data processing.
Efficient procedures for fitting the regularization path
    for linear, binomial, multinomial, Ising and Potts models with lasso,
    group lasso or column lasso(only for multinomial) penalty.
    The package uses Linearized Bregman Algorithm to solve the
    regularization path through iterations. Bregman Inverse Scale Space Differential
    Inclusion solver is also provided for linear model with lasso penalty.
Automatically install, update, and load 'CRAN', 'GitHub', and 'Bioconductor' 
    packages in a single function call. By accepting bare unquoted names for packages, 
    it's easy to add or remove packages from the list.
Generate a local library copy with relevant packages. 
    All packages currently found within the search path - except base packages - 
    will be copied to the directory provided and can be used later on with the 
    .libPaths() function.
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.
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/.
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).
Tools to determine DNA profile Weight of Evidence. 
            For further information see the 'likeLTD' guide provided, 
            Balding, D.J. (2013) <DOI:10.1073/pnas.1219739110>,
	    or Steele, C.D. et al. (2016) <DOI:10.1515/sagmb-2016-0038>.
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>.
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.
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.
Provides various methods of linear ordering of data. Supports weights and positive/negative impacts.
    Currently included methods:
    * Sum of ranks
    * Standardized sums
    * Hellwig's (Hellwig, 1968, <https://unesdoc.unesco.org/ark:/48223/pf0000158559.locale=en>)
    * TOPSIS (Yoon & Hwang, 1981, ISBN:978-3-642-48318-9).
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().
Gaussian processes regression models with linear inequality constraints
    (Lopez-Lopera et al., 2018) <doi:10.1137/17M1153157>.
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 liquid chromatography mass spectrometry 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, and 
    data frames 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.
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.
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.  The method is from
  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>.
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>.
Test zero variance components in linear mixed models and 
  test additivity in nonparametric regression 
  using the restricted likelihood ratio test and the generalized F-test.
  Details can be found at Zhang et al (2016) <doi:10.1002/cjs.11295>.
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.
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.
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:10.2307/1267351>
  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>.
Provides tools and a consistent interface to support analyses using General, Generalized, and Multi-level Linear Models.
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, and Hoffman (<arXiv:2006.01366>), 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.
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 "Statistics: Unlocking the Power of Data" by Lock^5
    Datasets for the first and second editions of the book.  Older editions of revised data often have an extra 1e in the name.
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.
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>.
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.
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. 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 simulating the expected shares of a set of alternatives using 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 (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.
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 reliable 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.
It provides a bridge between the 'loon' and  'ggplot2' packages. Data analysts who value the grammar pipeline provided by 'ggplot2' can turn these static plots into interactive 'loon' plots. Conversely, data analysts who explore data interactively with 'loon' can turn any 'loon' plot into a 'ggplot2' plot structure. The function 'loon.ggplot()' is applied to one plot structure will return 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://github.com/xavi-rp/LPD/blob/master/ATBD/LPD_ATBD.pdf> 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).
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>.
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.
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.
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.
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. Reads object-oriented data in results files (.res) produced by LSD and creates appropriate multi-dimensional arrays in R. Supports multiple core parallelization 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 <http://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 <http://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 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.
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. The principal references are
 Dahlhaus (1997) <doi:10.1214/aos/1034276620>,
 Dahlhaus and Giraitis (1998) <doi:10.1111/1467-9892.00114>,
 Brockwell, et al. (2002) <isbn:978-3-319-29854-2>,
 Ljung and Box (1978) <doi:10.1093/biomet/65.2.297>,
 Palma (2007) <isbn:978-0-470-11402-5>,
 Palma, et al. (2011) <doi:10.1002/for.1259>,
 and Palma and Olea (2010) <doi:10.1214/10-AOS812>.
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.
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) <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.
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>.
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.
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.
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.
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.
Connects to <https://machi.na/> and allows the creation
    of time series, and running backtests on selected strategy if requested.
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.
Performs Quality Analysis on Machina algebraic indicators 'sma' (simple moving average), 'wavg' (weighted average),'xavg' (exponential moving average), 'hma' (Hull moving average), 'adma' (adaptive moving average), 'tsi' (true strength index), 'rsi' (relative strength index), 'gauss' (Gaussian elimination), 'momo' (momentum), 't3' (triple exponential moving average), 'macd' (moving average convergence divergence). Machina is a strategy creation and backtesting engine for quants and financial professionals (see <https://machi.na/> for more information).
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 <http://www.smith.edu/ceeds/macleish.php> 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.
Mutation Annotation Format (MAF) is a tabular data format used for storing genetic mutation data. 
    For example, The Cancer Genome Atlas (TCGA) project has made MAF files from each project publicly available.
    This package contains a set of tools to easily create an HTML dashboard to summarize and visualize data from MAF file.
    The resulting HTML file serves as a self-contained report that can be used to explore the result.
    Mayank Tandon & Ashish Jain (2020) <doi:10.5281/zenodo.4472978>.
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 from the null generalized linear mixed-effect model (GLMM) to perform variant set-based 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."
Provides a graphical user interface for analysing DNA microarray data. It performs functional enrichment on genes of interest, identifies gene symbols and also builds co-expression network.
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
Mail merge using markdown documents and gmail. 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.
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.
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.
Calculates the nonparametric adaptive MAMSE
        weights for univariate, right-censored or multivariate data.
        The MAMSE weights can be used in a weighted likelihood or to
        define a mixture of empirical distribution functions. The package
        includes functions for the MAMSE weighted Kaplan-Meier estimate
        and for MAMSE weighted ROC curves.
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. For more details, please 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.
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. (2018) <arXiv:1801.08002>.
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 'caret' functions 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.
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.
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.
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 (CCAA, 
    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.
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').
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.
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()).
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>.
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.
Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of softwares to investigate splicing dynamics at single-cell resolution. 'MARVEL' quantifies percent spliced-in (PSI) values for the five primary exon-level splicing events, namely skipped-exon (SE), mutually exclusive exons (MXE), retained intron (RI), alternative 5' and 3' splice sites (A5SS and A3SS, respectively). Additionally, 'MARVEL' performs differential splicing analysis to identify splicing events whose PSI distribution differ between groups of cells. Finally, 'MARVEL' models the PSI distribution for each event as a beta distribution and categorises each distribution into modalities (inspired by Song (2017) <doi:10.1016/j.molcel.2017.06.003>).
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>).
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).
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>.
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.
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://github.com/FarhadPishgar/MatchThem> 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 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.
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.
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>.
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.
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 install '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>.
Interactive shiny application for running a meta-analysis,
    provides support for both random effects and fixed effects models with the 'metafor' package.
    Additional support is included for calculating effect sizes plus
    support for single case designs, graphical output, and detecting publication bias.
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.
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,
    and cache management.
A suite of utility functions for discovering and managing metadata 
    associated with sets of spatially unique "known locations".
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, timezone, 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'.
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>).
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>.
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 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 at this 
	point MBESS 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 <http://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 balanced designs from a set of (contiguous) potential sampling locations in a study region for point-based and for transect-based surveys. Accommodates, without detrimental effects on spatial balance, sites that the researcher wishes to include in the survey for reasons other than the current randomisation (legacy sites).
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>.
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>.
Multivariate time series regression using dynamic linear models fit by MCMC. See Qiu, Jammalamadaka and Ning (2018) <http://www.jmlr.org/papers/volume19/18-009/18-009.pdf>.
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 and zero-inflated count data.
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>.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'mcbette' allows to do a Bayesian model comparison over some
    site and clock models, 
    using 'babette' (<https://github.com/ropensci/babette/>).
Monte Carol based tests for the Behrens Fisher Problem enhance the statistical power 
           and performs better than Welch's t-approximation, see Ullah et al. (2019).
Computes the prime implicants or a minimal disjunctive normal form for a
  logic expression presented by a truth table or a logic tree. Has been particularly 
  developed for logic expressions resulting from a logic regression analysis, i.e.
  logic expressions typically consisting of up to 16 literals, where the prime implicants 
  are typically composed of a maximum of 4 or 5 literals.
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.
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).
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.
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>.
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). '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).
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 collection of methods for agglomerative hierarchical clustering
  strategies on a matrix of distances, implemented using the variable-group
  approach introduced in Fernandez and Gomez (2008)
  <doi:10.1007/s00357-008-9004-x>. Descriptive measures to analyze the resulting
  hierarchical trees are also provided. In addition to the usual clustering
  methods, two parameterized methods are provided to explore an infinite family
  of hierarchical clustering strategies. When there are ties in proximity
  values, the hierarchical trees obtained are unique and independent of the
  order of the elements in the input matrix.
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) 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 that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, and time of last dose.
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.
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.
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. (2019) <arXiv:1908.07963>. 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.
Provides quick and easy access to market expectations data to the main macroeconomic indicators in the Focus report, made available by the Central Bank of Brazil through the Expectations System data API <https://dadosabertos.bcb.gov.br/>. This package offers an R interface to the API and other advantages, such as using a caching system and the option to use all the machine's cores (parallel computing).
Analyze data with measurement error when there is a validation subsample randomly selected from the full sample.  The method assumes surrogate variables measured with error are available for the full sample, and reference variables measured with little or no error are available for this randomly selected subsample of the full sample.  Measurement errors may be differential or non differential, in any or all predictors (simultaneously) as well as outcome.  The "augmented" estimates derived are based upon the multivariate correlation between regression model parameter estimates for the reference variables and for the surrogate variables in the validation subset.  Because the validation subsample is chosen at random whatever biases are imposed by measurement error, non-differential or differential, are reflected in this correlation and can be used to derive estimates for the reference variables using data from the whole sample.  The main functions in the package are meerva.fit which calculates estimates for a dataset, and meerva.sim.block which simulates multiple datasets as described by the user, and analyzes these datasets, storing the regression coefficient estimates for inspection. This work derives from Chen Y-H, Chen H. (2000) <doi:10.1111/1467-9868.00243>, Chen Y-H. (2002) <doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015) <doi:10.1016/j.jmva.2015.05.017> and Tong J, Huang J, Chubak J, et al. (2020) <doi:10.1093/jamia/ocz180>.
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.
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.
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 pre-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 et al., 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.
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.
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.
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 <http://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>).
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>.
Access to the 'Metabolights' REST API <https://www.ebi.ac.uk/metabolights/index>. Retrieve elements of publicly available 'Metabolights' studies.
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.
Simulating dynamic (longitudinal, time-resolved) metabolomics data based on an underlying biological network. The network is initiating with certain concentrations and evolves over a simulated time period. Optionally external influxes (concentration drivers) can be added.
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>.
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.
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.
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, 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.
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), <arXiv:2002.02627>,
    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 meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance, as described by Debray et al. (2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry, as described by Debray et al. (2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering.
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>.
The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new methods such as weighted Fisher’s method and ordmeta we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method and ordmeta are useful for detecting partially associated features. Thus, the users can choose the function based on their purpose.
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).
Compute variance-covariance matrix for multivariate meta-analysis. 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>.
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 titled
    "Effective plots to assess bias and precision in method comparison studies"
    published in Statistical Methods in Medical Research, P. Taffe (2016) <doi:10.1177/0962280216666667>.
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 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).
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.
Creates and manages a PostgreSQL database suitable for storing fisheries data
             and aggregating ready for use within a Gadget <https://hafro.github.io/gadget> 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> 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 uses an expectation conditional maximization algorithm to obtain maximum likelihood estimates for all model parameters and maximum a posteriori classifications of the input vectors. 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.
Ten tools for bioinformatical processing and analysis of major 
    histocompatibility complex (MHC) data. The functions are tailored for amplicon data 
    sets that have been filtered using the 'dada2' method (for more information on 
    'dada2', visit <https://benjjneb.github.io/dada2/> ), but even other types of data 
    sets can be analyzed.
    The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise 
    comparisons of all sequences in a data set, and mean distances of all pairwise 
    comparisons within each sample in a data set. The function additionally outputs five 
    tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for 
    each amino acid position in all sequences in the data set. These tables may be useful 
    for further downstream analyses, such as estimation of MHC supertypes.
    The HpltFind() function infers putative haplotypes from families in the data 
    set. 
    The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of 
    the haplotype inference.
    The PapaDiv() function compares parent pairs in the data set and calculate their 
    joint MHC diversity, taking into account sequence variants that occur in both 
    parents.
    The ReplMatch() function matches replicates in data sets in order to evaluate 
    genotyping success.
    The GetReplTable() and GetReplStats() functions perform such an evaluation.
    The CreateFas() function creates a fasta file with all the sequences in the data 
    set.
    The CreateSamplesFas() function creates individual fasta files for each sample in 
    the data set.
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.
Implements tests controlling the false discovery rate (FDR) / family-wise error rate (FWER) for some copula models.
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.
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'.
Provides imputation models and functions for binary or continuous Missing Not At Random (MNAR) outcomes through the use of the 'mice' package. The mice.impute.hecknorm() function provides imputation model for continuous outcome based on Heckman's model also named sample selection model as described in Galimard et al (2018) and Galimard et al (2016) <doi:10.1002/sim.6902>. The mice.impute.heckprob() function provides imputation model for binary outcome based on bivariate probit model as described in Galimard et al (2018).
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.
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.
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.
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>.
Indirect methods for estimating bilateral migration flows in the presence of partial or missing data, including the estimation of bilateral migration flows from changes in bilateral migrant stock tables (e.g. Abel (2013) <doi:10.4054/DemRes.2013.28.18>).
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.
Provides mechanisms for classifying border crossings using a rules-based methodology. The goal of performing this type of classification is to identify any potential long-term migrants.  A long-term migration is defined as a border crossing involving a change in residence status. A border crossing counts as a long-term migration to/from a country if it entails a change from non-residence to residence / residence to non-residence.  The rules-based classification that used to determine a long-term migration is defined by a threshold duration and a test duration, alternatively named window size. Under a 12/16 rule, for instance, the threshold duration is 12 months and the test duration (window size) is 16 months. With a 9/12 rule, the threshold duration is 9 months and the test duration (window size)  is 12 months. For more information about the methodology applied, please visit Stats NZ (2020) <https://www.stats.govt.nz/methods/defining-migrants-using-travel-histories-and-the-1216-month-rule>.
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.
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 <http://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>.
Importa datos de la API de <https://mindicador.cl> en formato de cuadro de datos o serie de tiempo.
    El objetivo es facilitar el uso de algunos datos economicos a periodistas y profesionales que requieren
    informacion desplegada de forma clara y concisa.
    (Imports data from <https://mindicador.cl> API in data frame or time series format. The goal
    is to ease using certain economic data with different R packages, having in mind journalists and professionals
    that require information displayed in a clear and concise way.)
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') files 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>) and 'XMind' (<http://www.xmind.net>).
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.
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.
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.
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 is a collection of fast tools for application in quantitative genetics. For instance, the SNP matrix can be stored in a minimum of memory  and the calculation of the genomic relationship matrix is based on a rapid algorithm. It also contains the window scanning approach by Kabluchko and Spodarev (2009), <doi:10.1239/aap/1240319575> to detect anomalous genomic areas <doi:10.1186/s12864-018-5009-y>. Furthermore, the package is used in the Modular Breeding Program Simulator (MoBPS, <https://github.com/tpook92/MoBPS>, <http://www.mobps.de/>). The tools are based on SIMD (Single Instruction Multiple Data, <https://en.wikipedia.org/wiki/SIMD>) and OMP (Open Multi-Processing, <https://de.wikipedia.org/wiki/OpenMP>).
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>.
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 and quantile regression (Fu et al. (2020+), in preparation). 
  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.
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.
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.
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' 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>.
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, coefficient alpha and omega, and various effect size measures), missing data (e.g., descriptive statistics for missing data, missing data pattern 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, Welch's test, z-test, and sample size determination).
Flexible multivariate function approximation using adapted
        Mixture of Student t Distributions. Mixture of t distribution
        is obtained using Importance Sampling weighted Expectation
        Maximization algorithm.
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.
Implements the Mittag-Leffler function, distribution,
  random variate generation, and estimation. Based on the Laplace-Inversion
  algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem.  Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>). The imputation models are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>), univariate Bayesian imputation (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian regression imputation.
Estimation/multiple imputation programs for mixed categorical
    and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for model-based clustering and classification.
 It supports various types of data: continuous, categorical and counting
 and can handle mixed data of these types. It can fit Gaussian (with diagonal
 covariance structure), gamma, categorical and Poisson models. The algorithms
 also support missing values. This package can be used as an independent
 alternative to the (not free) 'mixtcomp' software available at
 <https://massiccc.lille.inria.fr/>.
Model time series using mixture autoregressive (MAR)
             models.  Implemented are frequentist (EM) and Bayesian
             methods for estimation, prediction and model
             evaluation. See Wong and Li (2002)
             <doi:10.1111/1467-9868.00222>, Boshnakov (2009)
             <doi:10.1016/j.spl.2009.04.009>), and the extensive
             references in the documentation.
Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects.
Deconvolution of thermal decay curves allows you to quantify proportions 
    of biomass components in plant litter. Thermal decay curves derived from 
    thermogravimetric analysis (TGA) are imported, modified, and then modelled in a 
    three- or four- part  mixture model using the Fraser-Suzuki function. The output 
    is estimates for weights of pseudo-components corresponding to hemicellulose, 
    cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, 
    H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. 
    (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and 
    Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.
Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.
Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet 
    (Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
    to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
    infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
    paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
    applicable to large datasets. It is described and tested in the accompanying paper by 
    Ahlmann-Eltze and Yau (2018) <doi:10.1109/DSAA.2018.00068>.
Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.
Semi-parametric approach for sparse canonical correlation analysis 
    which can handle mixed data types: continuous, binary and truncated continuous.
    Bridge functions are provided to connect Kendall's tau to latent correlation
    under the Gaussian copula model. The methods are described in 
    Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and 
    Yoon, 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.
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. 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), <doi:10.1167/12.11.26>.
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'(Tortora et al. (2016) <arXiv:1403.2332v7>), mixture of  multiple scaled generalized hyperbolic distributions. The 'cMSGHD' (Tortora et al. (2016) <arXiv:1403.2332v7>) is a 'MSGHD' with convex contour plots. The 'MCGHD' (Tortora et al. (2016) <arXiv:1403.2332v7>), mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
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 Mixture Link model <arXiv:1612.03302> is a proposed extension to generalized linear models, where the outcome distribution is a finite mixture of J > 1 densities. This package supports Mixture Link computations for Poisson and Binomial outcomes. This includes the distribution functions, numerical maximum likelihood estimation, Bayesian analysis, and quantile residuals to assess model fit.
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) <arXiv:1907.09565>.
    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'.
Provides the function 'mixor' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
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.