Jointly model the accuracy of cognitive responses and item choices
    within a Bayesian hierarchical framework as described by Culpepper and
    Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package
    contains the datasets used within the analysis of the paper.
Specific dimension reduction methods for replicated graphs 
    (multiple undirected graphs repeatedly measured on a common set of 
    nodes). The package contains efficient procedures for estimating 
    a shared baseline propensity matrix and graph-specific low rank 
    matrices. The algorithm uses block coordinate descent algorithm to 
    solve the model, which alternatively performs L2-penalized logistic 
    regression and multiple partial eigenvalue decompositions, as described 
    in the paper Wang et al. (2017) <arXiv:1707.06360>.
A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.
Supports quantitative
    research in scientometrics and bibliometrics. Provides
    various tools for preprocessing bibliographic
    data retrieved, e.g., from Elsevier's SciVerse Scopus,
    computing bibliometric impact of individuals,
    or modeling many phenomena encountered in the social sciences.
A collection of functions to extract citation information from 'R' packages and to deal with files in 'citation file format' (<https://citation-file-format.github.io/>), extending the functionality already provided by the citation() function in the 'utils' package.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Breast Cancer Molecular Subtypes described in Guedj et al 2012.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Colon Cancer Molecular Subtypes described in Marisa et al 2013.
Client for the Open Citations Corpus (<http://opencitations.net/>).
    Includes a set of functions for getting one identifier type from another,
    as well as getting references and citations for a given identifier.
Functions to append confidence intervals, prediction intervals,
    and other quantities of interest to data frames. All appended quantities
    are for the response variable, after conditioning on the model and covariates.
    This package has a data frame first syntax that allows for easy piping.
    Currently supported models include (log-) linear, (log-) linear mixed,
    generalized linear models, generalized linear mixed models, and
    accelerated failure time models.
Input: a csv-file for each database table and a
        controlfile describing relations between tables. Output: An
        extended ER diagram
Retrieves data and estimates unmeasured flows of water through the 
  urban network. Any city may be modeled with preassembled data, but data for 
  US cities can be gathered via web services using this package and dependencies 
  'geoknife' and 'dataRetrieval'.
Implementation of the Contextual Importance and Utility 
    (CIU) concepts for Explainable AI (XAI). A recent description of CIU
    can be found in e.g. Främling (2020) <arXiv:2009.13996>.
Computes a confidence interval for a specified linear combination of the 
  regression parameters in a linear regression model with iid normal errors
  with known variance when there is uncertain prior information that a distinct
  specified linear combination of the regression parameters takes a given 
  value.  This confidence interval, found by numerical constrained 
  optimization, has the required minimum coverage and utilizes this uncertain 
  prior information through desirable expected length properties.
  This confidence interval has the following three practical applications. 
  Firstly, if the error variance has been accurately estimated from previous 
  data then it may be treated as being effectively known. Secondly, for 
  sufficiently large (dimension of the response vector) minus (dimension of 
  regression parameter vector), greater than or equal to 30 (say),
  if we replace the assumed known value of the error variance by its usual 
  estimator in the formula for the confidence interval then the resulting 
  interval has, to a very good approximation, the same coverage probability 
  and expected length properties as when the error variance is known. Thirdly,
  some more complicated models can be approximated by the linear regression 
  model with error variance known when certain unknown parameters are replaced 
  by estimates. This confidence interval is described in Kabaila, P. and 
  Mainzer, R. (2017) <arXiv:1708.09543>, and is a member of the family of 
  confidence intervals proposed by Kabaila, P. and Giri, K. (2009) 
  <doi:10.1016/j.jspi.2009.03.018>.
A convenient interface for making
  requests directly to the 'Civis Platform API' <https://www.civisanalytics.com/platform/>.
  Full documentation available 'here' <https://civisanalytics.github.io/civis-r/>.
We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2019, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.
An R implementation of the Average Marginal Component-specific
    Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto
    T. (2014) <DOI:10.1093/pan/mpt024> Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional
    Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Client for 'CKAN' API (<https://ckan.org/>). Includes interface
    to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
    resources. In addition, provides an interface to the 'datastore' API.
Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials.
Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).)
Fast, optimal, and reproducible weighted univariate
 clustering by dynamic programming. Four problem are solved, including
 univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015>
 (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median,
 k-segments, and multi-channel weighted k-means. Dynamic programming
 is used to minimize the sum of (weighted) within-cluster distances
 using respective metrics. Its advantage over heuristic clustering in
 efficiency and accuracy is pronounced at a large number of clusters.
 Weighted k-means can also process time series to perform peak
 calling. Multi-channel weighted k-means groups multiple univariate
 signals into k clusters. An auxiliary function generates histograms
 adaptive to patterns in data. This package provides a powerful set
 of tools for univariate data analysis with guaranteed optimality,
 efficiency, and reproducibility.
Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical
  mean-variance portfolio optimization, see Markowitz (1952) <doi:10.2307/2975974>.
  Care has been taken for correctness in light of previous buggy implementations.
Measures morphological diversity from discrete character data and
    estimates evolutionary tempo on phylogenetic trees. Imports morphological
    data from #NEXUS (Maddison et al. (1997) <doi:10.1093/sysbio/46.4.590>)
    format with read_nexus_matrix(), and writes to both #NEXUS and TNT format 
    (Goloboff et al. (2008) <doi:10.1111/j.1096-0031.2008.00217.x>). Main
    functions are test_rates(), which implements AIC and likelihood
    ratio tests for discrete character rates introduced across Lloyd et al.
    (2012) <doi:10.1111/j.1558-5646.2011.01460.x>, Brusatte et al. (2014)
    <doi:10.1016/j.cub.2014.08.034>, Close et al. (2015)
    <doi:10.1016/j.cub.2015.06.047>, and Lloyd (2016) <doi:10.1111/bij.12746>,
    and MatrixDistances(), which implements multiple discrete character
    distance metrics from Gower (1971) <doi:10.2307/2528823>, Wills (1998)
    <doi:10.1006/bijl.1998.0255>, Lloyd (2016) <doi:10.1111/bij.12746>, and
    Hopkins and St John (2018) <doi:10.1098/rspb.2018.1784>. This also includes
    the GED correction from Lehmann et al. (2019) <doi:10.1111/pala.12430>.
    Multiple functions implement morphospace plots:
    plot_chronophylomorphospace() implements Sakamoto and Ruta (2012)
    <doi:10.1371/journal.pone.0039752>, plot_morphospace() implements Wills et
    al. (1994) <doi:10.1017/S009483730001263X>, plot_changes_on_tree()
    implements Wang and Lloyd (2016) <doi:10.1098/rspb.2016.0214>, and
    plot_morphospace_stack() implements Foote (1993)
    <doi:10.1017/S0094837300015864>. Other functions include
    safe_taxonomic_reduction(), which implements Wilkinson (1995)
    <doi:10.1093/sysbio/44.4.501>, map_dollo_changes() implements
    the Dollo stochastic character mapping of Tarver et al. (2018)
    <doi:10.1093/gbe/evy096>, and estimate_ancestral_states() implements
    the ancestral state options of Lloyd (2018) <doi:10.1111/pala.12380>.
Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events.  Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state).  This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue.  C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations.  CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it!  Type: citation(package="cladoRcpp") to get the citation information.
Performs 'classical' age-depth modelling of dated sediment deposits - prior to applying more sophisticated techniques such as Bayesian age-depth modelling. Any radiocarbon dated depths are calibrated. Age-depth models are constructed by sampling repeatedly from the dated levels, each time drawing age-depth curves. Model types include linear interpolation, linear or polynomial regression, and a range of splines. See Blaauw (2010). <doi:10.1016/j.quageo.2010.01.002>.
Implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES) <doi:10.1109/TNB.2018.2845126> and Heterogeneous Idealization by Local testing and DEconvolution (HILDE) <arXiv:2008.02658>. Further details on how to use them are given in the accompanying vignette.
Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.
Get description of images from Clarifai API. For more information,
    see <http://clarifai.com>. Clarifai uses a large deep learning cloud to come
    up with descriptive labels of the things in an image. It also provides how
    confident it is about each of the labels.
Implementation of Clarke's distribution-free test of
  non-nested models.  Currently supported model functions are: 
  lm(), glm() ('binomial', 'poisson', 'negative binomial' links), 
  polr() ('MASS'), clm() ('ordinal'), and multinom() ('nnet').  
  For more information on the test, see Clarke (2007) 
  <doi:10.1093/pan/mpm004>.
Various functions for classification, including k-nearest
  neighbour, Learning Vector Quantization and Self-Organizing Maps.
Defines the classes used for "class comparison" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  comparison includes tests for differential expression; see Simon's
  book for details on typical problem types.
Defines the classes used for "class discovery" problems
  in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class
  discovery primarily consists of unsupervised clustering methods with
  attempts to assess their statistical significance.
Construct directed graphs of S4 class hierarchies and
  visualize them.  In general, these graphs typically are DAGs (directed
  acyclic graphs), often simple trees in practice.
Generates a visualization of binary classifier performance as a grid of
  diagnostic plots with just one function call. Includes ROC curves,
  prediction density, accuracy, precision, recall and calibration plots, all using
  ggplot2 for easy modification.
  Debug your binary classifiers faster and easier!
Given $p$-dimensional training data containing
    $d$ groups (the design space), a classification
    algorithm (classifier) predicts which group new data
    belongs to.  Generally the input to these algorithms is
    high dimensional, and the boundaries between groups
    will be high dimensional and perhaps curvilinear or
    multi-faceted. This package implements methods for
    understanding the division of space between the groups.
Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.
Access to the ClassyFire RESTful API <http://classyfire.wishartlab.com>. Retrieve existing entity classifications and submit new entities for classification.
Bindings to Google's C++ library Compact Language Detector 2
    (see <https://github.com/cld2owners/cld2#readme> for more information). Probabilistically
    detects over 80 languages in plain text or HTML. For mixed-language input it returns the
    top three detected languages and their approximate proportion of the total classified 
    text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3'
    package on CRAN which uses a neural network model instead.
Google's Compact Language Detector 3 is a neural network model for language 
    identification and the successor of 'cld2' (available from CRAN). The algorithm is still
    experimental and takes a novel approach to language detection with different properties
    and outcomes. It can be useful to combine this with the Bayesian classifier results 
    from 'cld2'. See <https://github.com/google/cld3#readme> for more information.
Contains a time series classification method that obtains a set of filters that maximize the between-class and minimize the within-class distances.
A wrapper around the new 'cleaner' package, that allows
  data cleaning functions for classes 'logical', 'factor', 'numeric', 
  'character', 'currency' and 'Date' to make data cleaning fast and
  easy. Relying on very few dependencies, it provides smart guessing,
  but with user options to override anything if needed.
Cleans biological sequences massively, quickly, without errors and 
 without internet connection.  Biological sequences cleaning is performed by 
 aligning the forward and / or revers primers or ends of cloning vectors with the sequences 
 to be cleaned. After the alignment, new subsequences are generated without biological fragment 
 not desired by the user.
 Pozzi et al (2016, ISSN:2314-1484).
Wrapper of .Call() that runs exit handlers to clean up
    C resources. Helps managing C (non-R) resources while using the
    R API.
Functions to work with data frames to prepare data for further analysis.
    The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process.
Data cleaning functions for classes logical,
  factor, numeric, character, currency and Date to make
  data cleaning fast and easy. Relying on very few dependencies, it 
  provides smart guessing, but with user options to override 
  anything if needed.
How to deal with missing data?Based on the concept of almost functional dependencies, a method is proposed to fill missing data, as well as help you see what data is missing.
    The user can specify a measure of error and how many combinations he wish to test the dependencies against, the closer to the length of the dataset, the more precise.
    But the higher the number, the more time it will take for the process to finish.
    If the program cannot predict with the accuracy determined by the user it shall not fill the data, the user then can choose to increase the error or deal with the data another way.
Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issues.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.
Provides a set of fast tools for converting a textual corpus into
  a set of normalized tables. Users may make use of the 'udpipe' back end with
  no external dependencies, or two Python back ends with 'spaCy'
  <https://spacy.io> or 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/>.
  Exposed annotation tasks include tokenization, part of speech tagging, named
  entity recognition, and dependency parsing.
Check your R code for some of the most common
    layout flaws.  Many tried to teach us how to write code less dreadful,
    be it implicitly as B. W. Kernighan and D. M. Ritchie (1988)
    <ISBN:0-13-110362-8> in 'The C Programming Language' did, be it
    explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in 'Clean Code:
    A Handbook of Agile Software Craftsmanship' did.  So we should check
    our code for files too long or wide, functions with too many lines,
    too wide lines, too many arguments or too many levels of nesting.
    Note: This is not a static code analyzer like pylint or the like.
    Checkout <https://cran.r-project.org/package=lintr> instead.
Implements an empirical Bayes approach for
    simultaneous variable clustering and regression. This version also
    (re)implements in C++ an R script proposed by Howard Bondell that fits
    the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see
    Sharma et al (2013) <DOI:10.1080/15533174.2012.707849>).
Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.
Tools for evaluating link prediction and clustering algorithms 
    with respect to ground truth. Includes efficient implementations of 
    common performance measures such as pairwise precision/recall, 
    cluster homogeneity/completeness, variation of information, 
    Rand index etc.
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728>).
A suite of tools to build attractive command line interfaces
    ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs,
    etc. Supports custom themes via a 'CSS'-like language. It also contains a
    number of lower level 'CLI' elements: rules, boxes, trees, and
    'Unicode' symbols with 'ASCII' alternatives. It integrates with the
    'crayon' package to support 'ANSI' terminal colors.
Create rich command line applications, with colors, headings,
    lists, alerts, progress bars, etc. It uses CSS for custom themes.
Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.
Provides an expectation maximization (EM) algorithm to fit a mixture of continuous time Markov models for use with clickstream or other sequence type data. Gallaugher, M.P.B and McNicholas, P.D. (2018) <arXiv:1802.04849>.
Tools for assessing data quality, performing exploratory analysis, 
    fixing data errors in numerical, factor and date variables and creating report 
    tables from models and summaries.
A set of tools to read, analyze and write lists of click sequences
    on websites (i.e., clickstream). A click can be represented by a number,
    character or string. Clickstreams can be modeled as zero- (only computes
    occurrence probabilities), first- or higher-order Markov chains.
A suite of routines for Clifford algebras, using the
   'Map' class of the Standard Template Library.  Canonical
   reference: Hestenes (1987, ISBN 90-277-1673-0, "Clifford algebra
   to geometric calculus").  Special cases including Lorentz transforms,
   quaternion multiplication, and Grassman algebra, are discussed.
   Conformal geometric algebra theory is implemented.
CliFlo is a web portal to the New Zealand National Climate
    Database and provides public access (via subscription) to around 6,500
    various climate stations (see <https://cliflo.niwa.co.nz/> for more
    information). Collating and manipulating data from CliFlo
    (hence clifro) and importing into R for further analysis, exploration and
    visualisation is now straightforward and coherent. The user is required to
    have an internet connection, and a current CliFlo subscription (free) if
    data from stations, other than the public Reefton electronic weather
    station, is sought.
Implementation of Hurst exponent estimators based on complex-valued lifting wavelet energy from Knight, M. I and Nunes, M. A. (2018) <doi:10.1007/s11222-018-9820-8>.
A profile likelihood based method of estimation and inference on the correlation coefficient of bivariate data with different types of censoring and missingness.
Tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API <https://opendata.aemet.es/> and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.).
Automatize downloading of meteorological and hydrological data from publicly available repositories:
    OGIMET (<http://ogimet.com/index.phtml.en>), 
    University of Wyoming - atmospheric vertical profiling data (<http://weather.uwyo.edu/upperair>),
    Polish Institute of Meterology and Water Management - National Research Institute (<https://dane.imgw.pl>),
    and National Oceanic & Atmospheric Administration (NOAA).
    This package also allows for adding geographical coordinates for each observation.
Climate stability measures are not formalized in the literature and
  tools for generating stability metrics from existing data are nascent.
  This package provides tools for calculating climate stability from raster data
  encapsulating climate change as a series of time slices. The methods follow
  Owens and Guralnick <doi:10.17161/bi.v14i0.9786> Biodiversity Informatics.
Functions for the quality control, homogenization and missing data infilling of climatological series and to obtain climatological summaries and grids from the results. Also functions to draw wind-roses and Walter&Lieth climate diagrams.
Supports analysis of trends in climate change, ecological and crop modelling.
Classification of climate according to Koeppen - Geiger, of aridity
    indices, of continentality indices, of water balance after Thornthwaite, of
    viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy,
    Bagnouls-Gaussen.
PCIC's implementation of Climdex routines for computation of
    extreme climate indices. Further details on the extreme climate indices
    can be found at <http://etccdi.pacificclimate.org/list_27_indices.shtml>
    and in the package manual.
A suite of routines for downscaling coarse scale global
    climate model (GCM) output to a fine spatial resolution. Includes
    Bias-Corrected Spatial Downscaling (BCDS), Constructed Analogues
    (CA), Climate Imprint (CI), and Bias Correction/Constructed
    Analogues with Quantile mapping reordering (BCCAQ). Developed by
    the the Pacific Climate Impacts Consortium (PCIC), Victoria,
    British Columbia, Canada.
A robust constrained L1 minimization method for estimating
        a large sparse inverse covariance matrix (aka precision
        matrix), and recovering its support for building graphical
        models.  The computation uses linear programming.
Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from 'extRemes', including for fitting the
    statistical models. Details are given in Paciorek, Stone, and Wehner (2018)
    <doi:10.1016/j.wace.2018.01.002>.
Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).
API client for 'ClimMob', an open source software for crowdsourcing 
    citizen science in agriculture under the 'tricot' method <https://climmob.net/>. 
    Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the 
    research paradigm on its head; instead of a few researchers designing complicated 
    trials to compare several technologies in search of the best solutions, 
    it enables many farmers to carry out reasonably simple experiments that 
    taken together can offer even more information. 'ClimMobTools' enables project 
    managers to deep explore and analyse their 'ClimMob' data in R.
Set of tools to compute metrics and indices for climate analysis.
    The package provides functions to compute extreme indices, evaluate the
    agreement between models and combine theses models into an ensemble. Multi-model
    time series of climate indices can be computed either after averaging the 2-D
    fields from different models provided they share a common grid or by combining
    time series computed on the model native grid. Indices can be assigned weights
    and/or combined to construct new indices.
Contains functions to detect and visualise periods of climate
    sensitivity (climate windows) for a given biological response. 
    Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> 
    and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details.
Bayesian and ML Emax model fitting, graphics and simulation for clinical dose
    response.  The summary data from the dose response meta-analyses in  
    Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and
    Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> 
    Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> 
    are included 
    in the package.  The prior distributions for the Bayesian analyses default to
    the posterior predictive distributions derived from these references.
Utilities to make your clinical collaborations easier if not
          fun. It contains functions for designing studies such as Simon
          2-stage and group sequential designs and for data analysis such
          as Jonckheere-Terpstra test and estimating survival quantiles.
Provides estimates of several summary measures for clinical trials including
    the average hazard ratio, the weighted average hazard ratio, the restricted 
    superiority probability ratio, the restricted mean survival difference and 
    the ratio of restricted mean times lost, based on the short-term and 
    long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) 
    which accommodates various non-proportional hazards scenarios. The inference
    procedures and the asymptotic results for the summary measures are discussed 
    in Yang (2018, <doi:10.1002/sim.7676>).
Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" <http://hdl.handle.net/1773/44068>.
Every research team have their own script for calculation of hemodynamic indexes. 
   This package makes it possible  to insert a long-format dataframe, and add both 
   periods of interest (trigger-periods), and delete artifacts with deleter-files.
Calculates equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.
Functions for calculating clinical significance.
This package provides the estimation of an index measuring
        the clinical utility of DNA testing in the context of
        gene-environment interactions on a disease. The corresponding
        gene-environment interaction effect on the additive scale can
        also be obtained. The estimation is based on case-control or
        cohort data. The method was developed by Nguyen et al. 2013.
A fast and general implementation of the Elston-Stewart algorithm
 that can calculate the likelihoods of large and complex pedigrees without loops. 
 References for the Elston-Stewart algorithm are
 Elston & Stewart (1971) <doi:10.1159/000152448>,
 Lange & Elston (1975) <doi:10.1159/000152714> and
 Cannings et al. (1978) <doi:10.2307/1426718>.
Simple utility functions to read from and write to
    the Windows, OS X, and X11 clipboards.
Clique percolation community detection for weighted and 
    unweighted networks as well as threshold and plotting functions.
    For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180>
    and Palla et al. (2005) <doi:10.1038/nature03607>.
A small subset of Unicode symbols, that are useful
    when building command line applications. They fall back to
    alternatives on terminals that do not support Unicode.
    Many symbols were taken from the 'figures' 'npm' package
    (see <https://github.com/sindresorhus/figures>).
Estimation and inference for linear models where some or all of the
    fixed-effects coefficients are subject to order restrictions. This package uses
    the robust residual bootstrap methodology for inference, and can handle some
    structure in the residual variance matrix.
A set of functions to fit a boosting conditional logit model.
Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples. Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015) <doi:10.1186/1471-2105-16-S6-S1>.
Defines the classes and functions used to simulate and
  to analyze data sets describing copy number variants and,
  optionally, sequencing mutations in order to detect clonal subsets.
  See Zucker et al. (2019) <doi:10.1093/bioinformatics/btz057>.
Analyze data from next-generation sequencing experiments on genomic samples. 'CLONETv2' offers a set of functions to compute allele specific copy number and clonality from segmented data and SNPs position pileup. The package has also calculated the clonality of single nucleotide variants given read counts at mutated positions. The package has been developed at the laboratory of Computational and Functional Oncology, Department of CIBIO, University of Trento (Italy), under the supervision of prof Francesca Demichelis. References: Prandi et al. (2014) <doi:10.1186/s13059-014-0439-6>; Carreira et al. (2014) <doi:10.1126/scitranslmed.3009448>.
Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.
Interface to the Google Cloud Machine Learning Platform
  <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine
  learning models.
Provides means of plots for comparing utilization data of compute systems.
R Interface to C API of COIN-OR Clp, depends on COIN-OR Clp Version >= 1.12.0.
A new methodology for linear regression with both curve response 
    and curve regressors, which is described in Cho, Goude, Brossat and Yao 
    (2013) <doi:10.1080/01621459.2012.722900> and (2015) 
    <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is 
    dimension reduction based on a singular value decomposition in a Hilbert 
    space, which reduces the curve regression problem to several scalar linear 
    regression problems.
This package provides and implementation of a one step
        smoothing newton method for the solution of second order cone
        programming problems, originally described by Xiaoni Chi and
        Sanyang Liu.
Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.
Provides several cluster-robust variance estimators (i.e.,
    sandwich estimators) for ordinary and weighted least squares linear regression
    models, including the bias-reduced linearization estimator introduced by Bell
    and McCaffrey (2002) 
    <https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and 
    developed further by Pustejovsky and Tipton (2017) 
    <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating
    the variance- covariance matrix and for testing single- and multiple-
    contrast hypotheses based on Wald test statistics. Tests of single regression
    coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-
    contrast hypotheses use an approximation to Hotelling's T-squared distribution.
    Methods are provided for a variety of fitted models, including lm() and mlm
    objects, glm(), ivreg() (from package 'AER'), plm() (from package 'plm'), gls()
    and lme() (from 'nlme'), lmer() (from `lme4`), robu() (from 'robumeta'), and 
    rma.uni() and rma.mv() (from 'metafor').
CLUster Evaluation (CLUE) is a computational method for identifying 
              optimal number of clusters in a given time-course dataset clustered by 
              cmeans or kmeans algorithms and subsequently identify key kinases or 
              pathways from each cluster. Its implementation in R is called ClueR. 
              See README on <https://github.com/PengyiYang/ClueR> for more details.
              P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>.
Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.
Clustering algorithms are designed to cluster objects into a number of clusters. Any clustering algorithm provides the 'best'
             grouping of objects according to some criterion.
             This does not guarantee a 'good' clustering solution in the sense that some allocations were not simply the result of chance.
             This package allows the user to apply any clustering algorithm to a data set. The cluster allocations are subjected to a 
             bootstrap analysis 
             to determine the extent to which the clustering structure is stable and fundamental to the data set. For more information 
             see <https://slubbe.wixsite.com/academic-cv/conference-presentations>.
Clustered covariate regression enables estimation and inference in 
 both linear and non-linear models with linear predictor functions even when the
 design matrix is column rank deficient. Routines in this package implement algorithms
 in Soale and Tsyawo (2019) <doi:10.13140/RG.2.2.32355.81441>.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.
Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).
	Simultaneously perform clustering analysis and signature 	gene selection on high-dimensional transcriptome data sets. 	To do so, 'ClussCluster' incorporates a Lasso-type 	regularization penalty term to the objective function of K-	means so that cell-type-specific signature genes can be 	identified while clustering the cells.
Tests, utilities, and case studies for analyzing significance in
  clustered binary matched-pair data. The central function clust.bin.pair uses
  one of several tests to calculate a Chi-square statistic. Implemented are the
  tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998)
  <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>,
  Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010)
  <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996>
  included for comparison. The utility functions nested.to.contingency and
  paired.to.contingency convert data between various useful formats. Thyroids
  and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989)
  <doi:10.1016/0165-1781(89)90196-0> respectively.
Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package.
A method for determining groups in multiple 
    curves with an automatic selection of their number based on k-means or 
    k-medians algorithms. The selection of the optimal number is provided by 
    bootstrap methods. The methodology can be applied both in regression and survival framework.
     Implemented methods are:
    Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Functions to identify the pattern of a dose-response
		curve. Then fit a set of appropriate models to it according to the identified pattern, 
		followed by model averaging to estimate the effective dose.
Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.
Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
A collection of data sets for teaching cluster analysis.
Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.
Test for cluster tendency (clusterability) of a data set.
    The methods implemented - reducing the data set to a single dimension using principal component analysis or computing
    pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - 
    are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms
    are appropriate for a data set.
Provides functionality for the analysis of clustered data using the cluster bootstrap.
Estimating causal effects from observational studies assuming 
    clustered (or partial) interference. These inverse probability-weighted
    estimators target new estimands arising from population-level treatment 
    policies. The estimands and estimators are introduced in Barkley et al. 
    (2017) <arXiv:1711.04834>.
Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.
We developed the clusterGeneration package to provide functions 
        for generating random clusters, generating random 
        covariance/correlation matrices,
        calculating a separation index (data and population version)
        for pairs of clusters or cluster distributions, and 1-D and 2-D
        projection plots to visualize clusters.  The package also
        contains a function to generate random clusters based on
        factorial designs with factors such as degree of separation,
        number of clusters, number of variables, number of noisy
        variables.
The Partitioning Algorithm based on Recursive Thresholding
        (PART) is used to recursively uncover clusters and subclusters
        in the data. Functionality is also available for visualization
        of the clustering.
One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
  clusterhap groups together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, clusterhap groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, clusterhap calculates such
  probability from relative frequencies.
The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided.
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used.
Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.
Evaluate arbitrary function calls using workers on HPC schedulers
    in single line of code. All processing is done on the network without
    accessing the file system. Remote schedulers are supported via SSH.
Integrative context-dependent clustering for heterogeneous
    biomedical datasets. Identifies local clustering structures in related
    datasets, and a global clusters that exist across the datasets.
The clustering 'NUMA' Optimized Routines package or 'clusternor' is a highly optimized package for performing clustering in parallel with accelerations specifically targeting multi-core Non-Uniform Memory Access ('NUMA') hardware architectures. Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns (2019) <arXiv:1902.09527>.
Calculate power for cluster randomized trials (CRTs) that compare two means, two proportions, or two counts using closed-form solutions. In addition, calculate power for cluster randomized  crossover trials using Monte Carlo methods. For more information, see Reich et al. (2012) <doi:10.1371/journal.pone.0035564>.
Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.
This is a function for validating microarray clusters via reproducibility,
 based on the paper referenced below.
Calculate p-values and confidence intervals using cluster-adjusted
    t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). 
 (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, 
 HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, 
 RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, 
 JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, 
 MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, 
 JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, 
 DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, 
 CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>,
 HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, 
 TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, 
 BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, 
 WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).
Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.
Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins' statistic is implemented.
An R package that provides a suite of tools to evaluate
        clustering algorithms, clusterings, and individual clusters.
Implements a Ward-like hierarchical clustering
    algorithm including spatial/geographical constraints.
This clustering algorithm deals with missing data via weights that are imposed on missings and successively increased. See the vignette for details.
Model-based clustering of mixed data (i.e. data which consist of
    continuous, binary, ordinal or nominal variables) using a parsimonious
    mixture of latent Gaussian variable models.
Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304, <DOI:10.1023/A:1009769707641>.
An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.
A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <DOI:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <DOI:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.
Deciding what resolution to use can be a difficult question when
    approaching a clustering analysis. One way to approach this problem is to
    look at how samples move as the number of clusters increases. This package
    allows you to produce clustering trees, a visualisation for interrogating
    clusterings as resolution increases.
Returns an edit-distance based clusterization of an input vector of strings.
    Each cluster will contain a set of strings w/ small mutual edit-distance
    (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by
    stringdist::stringdist(). The set of all mutual edit-distances is then used by
    graph algorithms (from package 'igraph') to single out subsets of high connectivity.
An interactive document on  the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.
A complimentary package for use with hclust; simprof tests
        to see which (if any) clusters are statistically different. The
        null hypothesis is that there is no a priori group structure.
        See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008.
        Testing of null hypothesis in exploratory community analyses:
        similarity profiles and biota-environment linkage. J. Exp. Mar.
        Biol. Ecol. 366, 56-69.
Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.
Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.
Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification.
Package contains most of the popular internal and external
        cluster validation methods ready to use for the most of the
        outputs produced by functions coming from package "cluster".
        Package contains also functions and examples of usage for
        cluster stability approach that might be applied to algorithms
        implemented in "cluster" package as well as user defined
        clustering algorithms.
Statistical and biological validation of clustering results. This package implements Dunn Index, Silhouette, Connectivity, Stability, BHI and BSI. Further information can be found in Brock, G et al. (2008) <doi: 10.18637/jss.v025.i04>.
Probabilistic latent customer attrition models (also known as "buy-'til-you-die models") are used to 
    predict future purchase behavior of customers. This package includes fast and accurate implementations of various 
    probabilistic latent customer attrition models for non-contractual settings (e.g., retail business) with and 
    without time-invariant and time-varying covariates. Currently, the package includes the Pareto/NBD model 
    (Pareto/Negative-Binomial-Distribution), the BG/NBD mode (Beta-Gamma/Negative-Binomial-Distribution) and the GGom/NBD 
    (Gamma-Gompertz/Negative-Binomial-Distribution) for the purchase and the attrition processes as well as the Gamma/Gamma model 
    for the spending process. For reference to the Pareto/NBD model, see Schmittlein DC, Morrison DG, Colombo R (1987) <doi:10.1287/mnsc.33.1.1>, 
    for the BG/NBD model, see Fader PS, Hardie BG, Lee K (2005) <doi:10.1287/mksc.1040.0098> and for the GGom/NBD model see Bemmaor AC, Glady N (2012) 
    <doi:10.1287/mnsc.1110.1461>. For reference to the Gamma/Gamma model, see Fader PS, Hardie BG, Lee K (2005) <doi:10.1509/jmkr.2005.42.4.415>.
Single objective optimization using a CMA-ES.
Pure R implementation of the Covariance Matrix Adaptation -
    Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).
Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>. 
  It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.
Provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) <doi:10.1002/sim.6880>. Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors.
Calculation and plot of the stepwise Cronbach-Mesbah Curve
Implements the Bayesian calibration model described
    in Pratola and Chkrebtii (2018) <DOI:10.5705/ss.202016.0403> for stochastic 
    and deterministic simulators.  Additive and multiplicative discrepancy models 
    are currently supported. See <http://www.matthewpratola.com/software> for 
    more information and examples.
An open-source implementation of the 'Congruent Matching Cells' 
    method for cartridge case identification as proposed by Song (2013) <https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=911193> as well 
    as an extension of the method proposed by Tong et al. (2015) <doi:10.6028/jres.120.008>.
    Provides a wide range of pre, inter, and post-processing options when
    working with cartridge case scan data and their associated comparisons. See
    the cmcR package website for more details and examples.
Writing interfaces to command line software is cumbersome. 
    'cmdfun' provides a framework for building function calls to seamlessly 
    interface with shell commands by allowing lazy evaluation of command line arguments. 
    'cmdfun' also provides methods for handling user-specific paths to tool installs or secrets like API keys. 
    Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside 
    R when they might usually leave to execute non-R software.
Provide helper functions for UNICEF child mortality estimation.
Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) <doi:10.1080/01621459.2018.1448828>). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs can quantify more interpretable interaction effects in many problems. The current implementation performs variable selection on only binary CMEs; we are working on an extension for the continuous setting.
Collective matrix factorization (CMF) finds joint low-rank
	representations for a collection of matrices with shared row or column
	entities. This code learns a variational Bayesian approximation for CMF,
	supporting multiple likelihood potentials and missing data, while
	identifying both factors shared by multiple matrices and factors private
	for each matrix. For further details on the method see
	Klami et al. (2014) <arXiv:1312.5921>.
	The package can also be used to learn Bayesian canonical correlation
	analysis (CCA) and group factor analysis (GFA) models, both of which are
	special cases of CMF. This is likely to be useful for people looking for
	CCA and GFA solutions supporting missing data and non-Gaussian likelihoods.
	See Klami et al. (2013) <http://www.jmlr.org/papers/v14/klami13a.html> and
	Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html>
	for details on Bayesian CCA and GFA, respectively.
Collective matrix factorization (a.k.a. multi-view or multi-way factorization,
	Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a matrix 'X' as the
	product of two low-dimensional matrices aided with secondary information matrices about rows
	and/or columns of 'X' which are also factorized using the same latent components.
	The intended usage is for recommender systems, dimensionality reduction, and missing value imputation.
	Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce
	different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky,
	(2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model,
	(Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>),
	or the enhanced model with 'implicit features' (Rendle, Zhang,
	Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based
	procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009)
	<doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver
	(Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative
	coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>),
	providing efficient methods for sparse and dense data, and mixtures thereof.
	Supports L1 and L2 regularization in the main models,
	offers alternative most-popular and content-based models, and implements functionality
	for cold-start recommendations and imputation of 2D data.
Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm.
Quite extensive package for maximum likelihood estimation and
    weighted least squares estimation of categorical marginal models (CMMs;
    e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?;
    Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.
CEU (CEU San Pablo University) Mass Mediator is an on-line tool for aiding researchers in 
  performing metabolite annotation. 'cmmr' (CEU Mass Mediator RESTful API) allows 
  for programmatic access in R: batch search, batch advanced search, MS/MS (tandem mass spectrometry) search, etc.
  For more information about the API Endpoint please go to <https://github.com/lzyacht/cmmr>.
Provides the source and examples for James P. Howard, II, 
             "Computational Methods for Numerical Analysis with R," 
             <https://jameshoward.us/cmna>, a book on numerical
             methods in R.
Perceptually uniform palettes for commonly used
	variables in oceanography as functions taking an integer
	and producing character vectors of colours.
	See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M.
	and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for
	the guidelines adhered to when creating the palettes.
Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out. Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull's eye style plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals. Additional functions include: highlight signals, a group of SNPs, chromosome visualization and candidate genes around SNPs.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks, as described in Gray
 (1988), A class of K-sample tests for comparing the cumulative
 incidence of a competing risk, Ann. Stat. 16:1141-1154
 <DOI:10.1214/aos/1176350951>, and Fine JP and
 Gray RJ (1999), A proportional hazards model for the subdistribution
 of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks using quantile regressions,
 as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.
A collection of useful helper routines developed by
  students of the Center for Mathematical Research, Stankin,
  Moscow.
Uses the 'CMS' application programming interface <https://dnav.cms.gov/api/healthdata>
    to provide users databases containing yearly
    Medicare reimbursement rates in the United States. Data can be acquired
    for the entire United States or only for specific localities. Currently,
    support is only provided for the Medicare Physician Fee Schedule,
    but support will be expanded for other 'CMS' databases in future versions.
The Satellite Application Facility on Climate Monitoring (CM SAF) 
    is a ground segment of the European Organization for the Exploitation of 
    Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
    Facilities. The CM SAF contributes to the sustainable monitoring of the climate 
    system by providing essential climate variables related to the energy and water 
    cycle of the atmosphere (<http://www.cmsaf.eu>). It is a joint cooperation of eight 
    National Meteorological and Hydrological Services.
    The 'cmsaf' R-package includes a 'shiny' based interface for an easy application of 
    the 'cmsafops' and 'cmsafvis' packages - the CM SAF R Toolbox. The Toolbox offers an 
    easy way to prepare, manipulate, analyse and visualize CM SAF NetCDF formatted data. 
    Other CF conform NetCDF data with time, longitude and latitude dimension should be 
    applicable, but there is no guarantee for an error-free application.
    CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu>).
    Detailed information and test data are provided on the CM SAF webpage 
    (<http://www.cmsaf.eu/R_toolbox>).
The Satellite Application Facility on Climate Monitoring (CM SAF) 
    is a ground segment of the European Organization for the Exploitation of 
    Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
    Facilities. The CM SAF contributes to the sustainable monitoring of the climate 
    system by providing essential climate variables related to the energy and water 
    cycle of the atmosphere (<http://www.cmsaf.eu>). It is a joint cooperation of eight 
    National Meteorological and Hydrological Services.
    The 'cmsafops' R-package provides a collection of R-operators for the analysis and 
    manipulation of CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, 
	longitude and latitude dimension should be applicable, but there is no guarantee for 
	an error-free application.
    CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu>).
    Detailed information and test data are provided on the CM SAF webpage 
    (<http://www.cmsaf.eu/R_toolbox>).
The Satellite Application Facility on Climate Monitoring (CM SAF) 
    is a ground segment of the European Organization for the Exploitation of 
    Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
    Facilities. The CM SAF contributes to the sustainable monitoring of the climate 
    system by providing essential climate variables related to the energy and water 
    cycle of the atmosphere (<http://www.cmsaf.eu>). It is a joint cooperation of eight 
    National Meteorological and Hydrological Services.
    The 'cmsafvis' R-package provides a collection of R-operators for the analysis and
    visualization of CM SAF NetCDF data.
	CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu>).
    Detailed information and test data are provided on the CM SAF webpage 
    (<http://www.cmsaf.eu/R_toolbox>).
An interactive document on  the topic of confusion matrix analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ConfusionMatrixShiny/>.
An implementation of the statistical methods commonly
  used for advanced composite materials in aerospace applications.
  This package focuses on calculating basis values (lower tolerance
  bounds) for material strength properties, as well as performing the
  associated diagnostic tests. This package provides functions for
  calculating basis values assuming several different distributions,
  as well as providing functions for non-parametric methods of computing
  basis values. Functions are also provided for testing the hypothesis
  that there is no difference between strength and modulus data from an
  alternate sample and that from a "qualification" or "baseline" sample.
  For a discussion of these statistical methods and their use, see the
  Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4).
  Additional details about this package are available in the paper by
  Kloppenborg (2020, <doi:10.21105/joss.02265>).
Various utilities for the complex multivariate Gaussian distribution.
Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures.
Implementation of the CNAIM standard in R. Contains a series of
    algorithms which determine the probability of failure, consequences of
    failure and monetary risk associated with electricity distribution
    companies' assets such as transformers and cables. Results are visualized
    in an easy-to-understand risk matrix.
This is an add-on to the 'cna' package <https://CRAN.R-project.org/package=cna> comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see <https://people.uib.no/mba110/docs/ConCovOpt.pdf>.
Provided R functions for working with the Conditional Negative Binomial distribution.
A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.
Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.
Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.
Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.
Conventional methods for producing standard scores in psychometrics or biometrics 
    are often plagued with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low 
    confidence for assessing extreme scores. The continuous norming method introduced by A. 
    Lenhard et al. (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>) and generates continuous test norm 
    scores on the basis of the raw data from standardization samples, without requiring 
    assumptions about the distribution of the raw data: Norm scores are directly established 
    from raw data by modeling the latter ones as a function of both percentile scores and an 
    explanatory variable (e.g., age). The method minimizes bias arising from sampling and 
    measurement error, while handling marked deviations from normality, addressing bottom 
    or ceiling effects and capturing almost all of the variance in the original norm data 
    sample. An online demonstration is available via <https://cnorm.shinyapps.io/cNORM/>.
This package evaluates DNA copy number data, using both their initial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm.
Efficient computations for null models that require shuffling columns on big matrix data.
             This package provides functions for faster computation of diversity measure statistics
             when independent random shuffling is applied to the columns of a given matrix. 
             Given a diversity measure f and a matrix M, the provided functions can generate random samples 
             (shuffled matrix rows of M), the mean and variance of f, and the p-values of this measure 
             for two different null models that involve independent random shuffling of the columns of M.
             The package supports computations of alpha and beta diversity measures.
Chinese numerals processing in R, such as conversion between 
    Chinese numerals and Arabic numerals as well as detection and extraction of
    Chinese numerals in character objects and string. This package supports 
    the casual scale naming system and the respective SI prefix systems used 
    in mainland China and Taiwan: 
    "China Statutory Measurement Units"
    State Administration for Market Regulation (2019) <http://gkml.samr.gov.cn/nsjg/jls/201902/t20190225_291134.html>
    "Names, Definitions and Symbols of the Legal Units of Measurement and the Decimal Multiples and Submultiples" 
    Ministry of Economic Affairs (2019) <https://gazette.nat.gov.tw/egFront/detail.do?metaid=108965>.
Implements Dirichlet multinomial modelling of relative abundance data using functionality provided by the 'Stan' software. The purpose of this package is to provide a user friendly way to interface with 'Stan' that is suitable for those new to modelling. For more regarding the modelling mathematics and computational techniques we use see our publication in Molecular Ecology Resources titled "Dirichlet multinomial modelling outperforms alternatives for analysis of ecological count data" (Harrison et al. 2020 <doi:10.1111/1755-0998.13128>).
Provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps.
Coalescent simulators can rapidly simulate biological sequences
    evolving according to a given model of evolution.
    You can use this package to specify such models, to conduct the simulations
    and to calculate additional statistics from the results.
    It relies on existing simulators for doing the simulation, and currently
    supports the programs 'ms', 'msms' and 'scrm'. It also supports finite-sites
    mutation models by combining the simulators with the program 'seq-gen'.
Flexible framework for coalescent analyses in R. It includes a main function running the  MCMC algorithm, auxiliary functions for tree rearrangement, and some functions to compute population genetic parameters.
An implementation of a Bayesian framework for the opinion poll
    based estimation of event probabilities in multi-party electoral systems
    (Bender and Bauer (2018) <doi:10.21105/joss.00606>).
Functions to analyze coarse data.
    Specifically, it contains functions to (1) fit parametric accelerated
    failure time models to interval-censored survival time data, and (2)
    estimate the case-fatality ratio in scenarios with under-reporting.
    This package's development was motivated by applications to infectious
    disease: in particular, problems with estimating the incubation period and
    the case fatality ratio of a given disease.  Sample data files are included
    in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>, 
    Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and 
    Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.
Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with longitudinal treatments.
Implementation of a probabilistic method for biclustering
    adapted to overdispersed count data. It is a Gamma-Poisson Latent Block Model.
    It also implements two selection criteria in order to select the number of
    biclusters.
This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. If the user has no valuable knowledge on the data, four defaults machines wrappers are implemented so as to cover a minimal spectrum of prediction methods. If necessary, the computations may be parallelized. The method is described in Biau, Fischer, Guedj and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".
Qualitatively Constrained (Regression) Smoothing Splines via
        Linear Programming and Sparse Matrices.
Contains the R functions needed to perform Cluster-Of-Clusters Analysis (COCA)  and Consensus Clustering (CC). For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.
A 'shiny' app to discover cocktails. The
    app allows one to search for cocktails by ingredient,
    filter on rating, and number of ingredients. The
    package also contains data with the ingredients of
    nearly 26 thousand cocktails scraped from the web.
A copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2016). "Clustering dependent observations with copula functions". Statistical Papers, p.1-17. <doi:10.1007/s00362-016-0822-3>.
Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data.
Statistical tests for the comparison between two correlations
    based on either independent or dependent groups. Dependent correlations can
    either be overlapping or nonoverlapping. A web interface is available on the
    website http://comparingcorrelations.org. A plugin for the R GUI and IDE RKWard
    is included. Please install RKWard from https://rkward.kde.org to use this
    feature. The respective R package 'rkward' cannot be installed directly from a
    repository, as it is a part of RKWard.
Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.
Statistical tests for the comparison between two or more alpha
    coefficients based on either dependent or independent groups of individuals.
    A web interface is available at http://comparingcronbachalphas.org. A plugin
    for the R GUI and IDE RKWard is included. Please install RKWard from https://
    rkward.kde.org to use this feature. The respective R package 'rkward' cannot be
    installed directly from a repository, as it is a part of RKWard.
Provides functions for summarizing and plotting the
	output from Markov Chain Monte Carlo (MCMC) simulations, as
	well as diagnostic tests of convergence to the equilibrium
	distribution of the Markov chain.
A minimum set of functions to perform compositional data analysis
    using the log-ratio approach introduced by John Aitchison (1982) <http://www.jstor.org/stable/2345821>. Main functions
    have been implemented in c++ for better performance.
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2020) <arXiv:2004.07881>
    for transformation-free linear regression for compositional outcomes and predictors.
Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.
Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis.
Easily automate the following tasks to describe data frames:
		Summarise the distributions, and labelled missings of variables graphically
		and using descriptive statistics.
		For surveys, compute and summarise reliabilities (internal consistencies, 
		retest, multilevel) for psychological scales.
		Combine this information with metadata (such as item labels and labelled 
		values) that is derived from R attributes.
		To do so, the package relies on 'rmarkdown' partials, so you can generate 
		HTML, PDF, and Word documents. 
		Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so
		that search engines can find your data and index the metadata.
		The metadata are also available at your fingertips via RStudio Addins.
Logic game in the style of the early 1980s home computers 
    that can be played in the R console. This game is inspired by 
    Mastermind, a game that became popular in the 1970s.
    Can you break the code?
Tools for analyzing R expressions
  or blocks of code and determining the dependencies between them.
  It focuses on R scripts, but can be used on the bodies of functions.
  There are many facilities including the ability to summarize  or get a high-level
  view of code, determining dependencies between variables,  code improvement
  suggestions.
The 'Codemeta' Project defines a 'JSON-LD' format
    for describing software metadata, as detailed at
    <https://codemeta.github.io>. This package provides utilities to
    generate, parse, and modify 'codemeta.json' files automatically for R
    packages, as well as tools and examples for working with
    'codemeta.json' 'JSON-LD' more generally.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
Fast categorization of items based on external code data identified by 
  regular expressions. A typical use case considers patient with medically coded 
  data, such as codes from the International Classification of Diseases ('ICD') or 
  the Anatomic Therapeutic Chemical ('ATC') classification system. 
  Functions of the package relies on a triad of objects: (1) case data with unit 
  id:s and possible dates of interest; (2) external code data for corresponding 
  units in (1) and with optional dates of interest and; (3) a classification 
  scheme ('classcodes' object) with regular expressions to identify and 
  categorize relevant codes from (2). 
  It is easy to introduce new classification schemes ('classcodes' objects) or  
  to use default schemes included in the package. Use cases includes patient 
  categorization based on 'comorbidity indices' such as 'Charlson', 'Elixhauser', 
  'RxRisk V', or the 'comorbidity-polypharmacy' score (CPS), as well as adverse 
  events after hip and knee replacement surgery.
Predicts 3 to 12 months prognosis in Chronic Obstructive Pulmonary Disease (COPD) patients hospitalized for severe exacerbations, as described in Almagro et al. (2014) <doi:10.1378/chest.13-1328>.
Augment clinical data with metadata to create
    output used in conventional publications and reports.
Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.
A collection of coding functions as alternatives to the standard
    functions in the stats package, which have names starting with 'contr.'.  Their
    main advantage is that they provide a consistent method for defining marginal
    effects in factorial models.  In a simple one-way ANOVA model the
    intercept term is always the simple average of the class means.
Univariate and multivariate temporal and spatial diversity indices, 
    rank abundance curves, and community stability measures. The functions 
    implement measures that are either explicitly temporal and include the 
    option to calculate them over multiple replicates, or spatial and include 
    the option to calculate them over multiple time points. Functions fall into 
    five categories: static diversity indices, temporal diversity indices, 
    spatial diversity indices, rank abundance curves, and community stability 
    measures. The diversity indices are temporal and spatial analogs to 
    traditional diversity indices. Specifically, the package includes functions 
    to calculate community richness, evenness and diversity at a given point in 
    space and time. In addition, it contains functions to calculate species 
    turnover, mean rank shifts, and lags in community similarity between two 
    time points. Details of the methods are available in
    Hallett et al. (2016) <doi:10.1111/2041-210X.12569> and Avolio 
    et al. (2019) <doi:10.1002/ecs2.2881>.
Cronbach's alpha and McDonald's omega are widely used reliability or internal consistency measures in social, behavioral and education sciences. Alpha is reported in nearly every study that involves measuring a construct through multiple test items. The package 'coefficientalpha' calculates coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Since Version 0.5, the bootstrap confidence intervals were added.
Plots the coefficients from model objects. This very quickly shows the user the point estimates and confidence intervals for fitted models.
Simulate species occurrence and abundances (counts) along
    gradients.
Simulates the composition of samples of vegetation
        according to gradient-based vegetation theory.  Features a
        flexible algorithm incorporating competition and complex
        multi-gradient interaction.
species coexistence modeling under asymmetric dispersal
        and fluctuating source-sink dynamics;testing the proportion of
        coexistence scenarios driven by neutral and niche processes
Contrast analysis for factorial designs is an alternative to the classical ANOVA approach with the advantage of testing focused hypothesis. The method is mainly based on Rosenthal, Rosnow and Rubin (2000, ISBN:978-0521659802) and Sedlmeier and Renkewitz (2018, ISBN:978-3868943214).
Generate cofeature (feature by sample) matrices. The package 
  utilizes ggplot2::geom_tile() to generate the matrix allowing for easy
  additions from the base matrix.
Calculates complete functional regulation analysis and visualize
    the results in a single heatmap. The provided example data is for biological
    data but the methodology can be used for large data sets to compare quantitative
    entities that can be grouped. For example, a store might divide entities into
    cloth, food, car products etc and want to see how sales changes in the groups
    after some event. The theoretical background for the calculations are provided
    in New insights into functional regulation in MS-based drug profiling, Ana Sofia
    Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports <doi:10.1038/srep18826>.
Evaluation for density and distribution function of convolution of gamma
    distributions in R. Two related exact methods and one approximate method are
    implemented with efficient algorithm and C++ code. A quick guide for choosing
    correct method and usage of this package is given in package vignette. For the
    detail of methods used in this package, we refer the user to
    Mathai(1982)<doi:10.1007/BF02481056>,
    Moschopoulos(1984)<doi:10.1007/BF02481123>,
    Hu et al.(2019)<doi:10.1007/s00180-019-00924-9>.
Functions for building cognitive maps based on qualitative data. Inputs are textual sources (articles, transcription of qualitative interviews of agents,...). These sources have been coded using relations and are linked to (i) a table describing the variables (or concepts) used for the coding and (ii) a table describing the sources (typology of agents, ...). Main outputs are Individual Cognitive Maps (ICM), Social Cognitive Maps (all sources or group of sources) and a list of quotes linked to relations. This package is linked to the work done during the PhD of Frederic M. Vanwindekens (CRA-W / UCL) hold the 13 of May 2014 at University of Louvain in collaboration with the Walloon Agricultural Research Centre (project MIMOSA, MOERMAN fund).
Provides authentication for Shiny applications using 'Amazon Cognito' ( <https://aws.amazon.com/es/cognito/>).
Functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes 'Epi' package 'Lexis' data.
Designed for the cleaning, contextualization and assessment of cytochrome c 
    oxidase I DNA barcode data (COI-5P, or the five prime portion of COI). It contains 
    functions for placing COI-5P barcode sequences into a common reading frame, 
    translating DNA sequences to amino acids and for assessing the likelihood that a 
    given barcode sequence includes an insertion or deletion error. The error assessment 
    relies on the comparison of input sequences against nucleotide and amino acid profile
    hidden Markov models (PHMMs) (for details see Durbin et al. 1998, ISBN: 9780521629713) 
    trained on a taxonomically diverse set of reference sequences. The functions are 
    provided as a complete pipeline and are also available individually for efficient and
    targeted analysis of barcode data.
Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications.
Conditional inference procedures for the general independence
  problem including two-sample, K-sample (non-parametric ANOVA), correlation,
  censored, ordered and multivariate problems described in
  \doi{10.18637/jss.v028.i08}.
Extract and monitor price and market cap of 'Cryptocurrencies' from 'Coin Market Cap' <https://coinmarketcap.com/api/>.
Methods for obtaining simultaneous confidence interval for
        multinomial proportion have been proposed by many authors and
        the present study include a variety of widely applicable
        procedures. Seven classical methods (Wilson, Quesenberry and
        Hurst, Goodman, Wald with and without continuity correction,
        Fitzpatrick and Scott, Sison and Glaz) and Bayesian Dirichlet
        models are included in the package. The advantage of MCMC pack
        has been exploited to derive the Dirichlet posterior directly
        and this also helps in handling the Dirichlet prior parameters.
        This package is prepared to have equal and unequal values for
        the Dirichlet prior distribution that will provide better scope
        for data analysis and associated sensitivity analysis.
Builds the 
  coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016) 
  <doi:10.1016/j.spl.2015.11.008>.
  This methodology studies the relationship between a couple of
  time series based on the the set of turning points of each
  time series. The coincident profile establishes if two time
  series are coincident, or one of them leads the second.
We propose a consistent monitoring procedure to detect a
    structural change from a cointegrating relationship to a spurious
    relationship. The procedure is based on residuals from modified least
    squares estimation, using either Fully Modified, Dynamic or Integrated
    Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in
    that it is based on parameter estimation on a pre-break "calibration" period
    only, rather than being based on sequential estimation over the full sample.
    See the discussion paper <DOI:10.2139/ssrn.2624657> for further information.
    This package provides the monitoring procedures for both the cointegration
    and the stationarity case (while the latter is just a special case of the
    former one) as well as printing and plotting methods for a clear
    presentation of the results.
Cointegration methods are widely used in empirical macroeconomics
    and empirical finance. It is well known that in a cointegrating
    regression the ordinary least squares (OLS) estimator of the
    parameters is super-consistent, i.e. converges at rate equal to the
    sample size T. When the regressors are endogenous, the limiting
    distribution of the OLS estimator is contaminated by so-called second
    order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>.
    The presence of these bias terms renders inference difficult. Consequently,
    several modifications to OLS that lead to zero mean Gaussian mixture
    limiting distributions have been proposed, which in turn make
    standard asymptotic inference feasible. These methods include
    the fully modified OLS (FM-OLS) approach of Phillips and Hansen
    (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips
    and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991)
    <DOI:10.1017/S0266466600004217> and Stock and Watson (1993)
    <DOI:10.2307/2951763> and the new estimation approach called integrated
    modified OLS (IM-OLS) of Vogelsang and Wagner (2014)
    <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented
    partial sum (integration) transformation of the regression model. IM-OLS is
    similar in spirit to the FM- and D-OLS approaches, with the key difference
    that it does not require estimation of long run variance matrices and avoids
    the need to choose tuning parameters (kernels, bandwidths, lags). However,
    inference does require that a long run variance be scaled out.
    This package provides functions for the parameter estimation and inference
    with all three modified OLS approaches. That includes the automatic
    bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and
    of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of
    the long run variance.
Performs regression analysis for longitudinal count data,  
   allowing for serial dependence among observations from a given 
   individual and two dimensional random effects on the linear predictor. 
   Estimation is via maximization of the exact likelihood of a suitably 
   defined model. Missing values and unbalanced data are allowed; 
   M. Helena Goncalves et al.(2007) <doi:10.1016/j.csda.2007.03.002>.
Performs least squares constrained optimization on a linear objective function. It contains
    a number of algorithms to choose from and offers a formula syntax similar to lm().
Contains functions to help in selecting and exploring features ( or variables ) in binary classification problems.
             Provides functions to compute and display information value and weight of evidence (WoE) of the variables , and to convert numeric variables to categorical variables by binning.
             Functions are also provided  to determine which levels ( or categories ) of a categorical variable can be collapsed (or combined ) based on their response rates.
             The functions provided only work for binary classification problems.
Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it.
    Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame.
    See 'collapsibleTree' website for more information and examples.
We often want to take all (or most) of the objects in one environment (such as the parameter values of a function) and pass them to another. This might be calling a second function, or iterating over a list, calling the same function. These functions wrap often repeated code.
    Current stable version (committed on October 14, 2017).
Provides high performance container data types such
    as queues, stacks, deques, dicts and ordered dicts. Benchmarks
    <https://randy3k.github.io/collections/articles/benchmark.html> have
    shown that these containers are asymptotically more efficient than
    those offered by other packages.
An open source process for collecting quantified data inputs from 
  subject matter experts. Intended for feeding into an OpenFAIR analysis 
  <https://www2.opengroup.org/ogsys/catalog/C13K> using 
  a tool such as 'evaluator' <https://evaluator.tidyrisk.org>.
Check for namespace collisions between a string input (your function or package name) 
    and half a million packages and functions on CRAN.
These functions implement collocation-inference
    for continuous-time and discrete-time stochastic processes.
    They provide model-based smoothing, gradient-matching,
    generalized profiling and forwards prediction error methods.
Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.
Provides some low level functions for processing PLINK input and output files.
Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21.
Performs the colocalisation tests described in
    Plagnol et al (2009) <doi:10.1093/biostatistics/kxn039>,
    Wallace et al (2013) <doi:10.1002/gepi.21765> and
    Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>.
Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearson’s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.
Also abbreviates to "CCSeq". Finds clusters of colocalized sequences in .bed annotation files
    up to a specified cut-off distance. Two sequences are colocalized if they
    are within the cut-off distance of each other, and clusters are sets of
    sequences where each sequence is colocalized to at least one other sequence
    in the cluster. For a set of .bed annotation tables provided in a list 
    along with a cut-off distance, the program will output a file containing
    the locations of each cluster. Annotated .bed files are from 
    the 'pwmscan' application at <https://ccg.epfl.ch/pwmtools/pwmscan.php>. 
    Personal machines might crash or take excessively long depending 
    on the number of annotated sequences in each file and whether chromsearch() 
    or gensearch() is used.
Automate the co-localization analysis of fluorescence microscopy 
  images. Selecting regions of interest, extract pixel intensities from 
  the image channels and calculate different co-localization statistics. The
  methods implemented in this package are based on Dunn et al. (2011) 
  <doi:10.1152/ajpcell.00462.2010>.
Provide the safe color set for color blindness, 
         the simulator of protanopia, deuteranopia.
         The color sets are collected from:
         Wong, B. (2011) <doi:10.1038/nmeth.1618>,
         <http://mkweb.bcgsc.ca/biovis2012/>, and
         <http://geog.uoregon.edu/datagraphics/color_scales.htm>.
         The simulations of the appearance of the colors to color-deficient
         viewers were based on algorithms in
         Vienot, F., Brettel, H. and Mollon, J.D. (1999) 
         <doi:10.1002/(SICI)1520-6378(199908)24:4%3C243::AID-COL5%3E3.0.CO;2-3>.
         The cvdPlot() function to generate 'ggplot' grobs of simulations 
         were modified from <https://github.com/clauswilke/colorblindr>.
Colorful Data Frames in the terminal. The new class does
             change the behaviour of any of the objects, but adds a style
             definition and a print method. Using ANSI escape codes, it
             colors the terminal output of data frames. Some column types
             (such as p-values and identifiers) are automatically
             recognized.
Loads and displays images, selectively masks specified background
    colors, bins pixels by color using either data-dependent or
    automatically generated color bins, quantitatively measures color
    similarity among images using one of several distance metrics for
    comparing pixel color clusters, and clusters images by object color
    similarity. Uses CIELAB, RGB, or HSV color spaces. Originally written
    for use with organism coloration (reef fish color diversity, butterfly
    mimicry, etc), but easily applicable for any image set.
It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively.
Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one.
    This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation).  The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.
Extracts colors from various image types, returns customized reports and plots treemaps 
    and 3D scatterplots of image compositions. Color palettes can also be created.
Given 2-,3- or 4-dimensional data, plots a Venn diagram, i.e. 'crossing circles'. The user can specify values, labels for each circle-group and unique colors for each plotted part. Here is what it would look like for a 3-dimensional plot: http://elliotnoma.files.wordpress.com/2011/02/venndiagram.png. To see what the 4-dimensional plot looks like, go to http://elliotnoma.files.wordpress.com/2013/03/4dplot.png.
Build dendrograms with sample groups highlighted by different colors. Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters.
Color and visualize wildlife distributions in
    space-time using raster data. In addition to enabling display of
    sequential change in distributions through the use of small multiples,
    'colorist' provides functions for extracting several features of
    interest from a sequence of distributions and for visualizing those
    features using HCL (hue-chroma-luminance) color palettes. Resulting
    maps allow for "fair" visual comparison of intensity values (e.g.,
    occurrence, abundance, or density) across space and time and can be
    used to address questions about where, when, and how consistently a
    species, group, or individual is likely to be found.
Call the 'DeOldify' <https://github.com/jantic/DeOldify> image
    colorization API on 'DeepAI'<https://deepai.org/machine-learning-model/colorizer>
    to colorize black and white images.
Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'.
Different methods to generate a color palette based on a specified base color and a number of colors that should be created.
Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values 
    within a single diagram. This is especially useful for rendering gene expression data as well as
    other types of differential experiments. In addition to different rendering methods (ggplot extensions)
    functionality for perceptually optimizing color palettes are provided.
    Furthermore the package provides extension methods of the colorspace color-class in order to
    simplify the work with palettes (a.o. length, as.list, and append are supported).
Color palettes for EPL, MLB, NBA, NHL, and NFL teams.
Methods and data for color science - color conversions by observer,
        illuminant, and gamma. Color matching functions and chromaticity diagrams.
        Color indices, color differences, and spectral data conversion/analysis.
Carries out mapping between assorted color spaces including RGB, HSV, HLS,
             CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB.
	     Qualitative, sequential, and diverging color palettes based on HCL colors
	     are provided along with corresponding ggplot2 color scales.
	     Color palette choice is aided by an interactive app (with either a Tcl/Tk
	     or a shiny graphical user interface) and shiny apps with an HCL color picker and a
	     color vision deficiency emulator. Plotting functions for displaying
	     and assessing palettes include color swatches, visualizations of the
	     HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation
	     functions include: desaturation, lightening/darkening, mixing, and
	     simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly).
	     Details can be found on the project web page at <http://colorspace.R-Forge.R-project.org/>
	     and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical
	     Software, <doi:10.18637/jss.v096.i01>).
Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners.
    Build complex systems from simpler parts using a spectral product algebra. For light sources,
    compute CCT, CRI, and SSI.  For object colors, compute optimal colors and Logvinenko coordinates.
    Work with the standard CIE illuminants and color matching functions, and read spectra from 
    text files, including CGATS files.  Estimate a spectrum from its response. A user guide and 9 vignettes are included.
R package with handy functions to help users select and play with
    color schemes in an HSV color model
Provides access to the COLOURlovers <https://www.colourlovers.com/>
    API, which offers color inspiration and color palettes.
A colour picker that can be used as an input in 'Shiny' apps
    or Rmarkdown documents. The colour picker supports alpha opacity, custom
    colour palettes, and many more options. A Plot Colour Helper tool is
    available as an 'RStudio' Addin, which helps you pick colours to use in your
    plots. A more generic Colour Picker 'RStudio' Addin is also provided to let 
    you select colours to use in your R code.
Maps one of the viridis colour palettes, or a user-specified palette to values. 
    Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, 
    and were set as the default palette for the 'Python' 'Matplotlib' library <https://matplotlib.org/>. 
    Other palettes available in this library have been derived from 
    'RColorBrewer' <https://CRAN.R-project.org/package=RColorBrewer> and 
    'colorspace' <https://CRAN.R-project.org/package=colorspace> packages.
Colour vision models, colour spaces and colour thresholds. Provides flexibility to build user-defined colour vision models for n number of photoreceptor types. Also includes Vorobyev & Osorio (1998) Receptor Noise Limited models, Chittka (1992) colour hexagon, and Endler & Mielke (2005) model. Models have been extended to accept any number of photoreceptor types.
Powerful functions to select and rename columns in dataframes, lists and numeric types 
  by 'Perl' regular expression. Regular expression ('regex') are a very powerful grammar to match 
  strings, such as column names.
A collection of command-line color styles based on the 'crayon'
  package. 'Colt' styles are defined in themes that can easily be switched, to
  ensure command line output looks nice on dark as well as light consoles.
Builds co-occurrence matrices based on spatial raster data.
    It includes creation of weighted co-occurrence matrices (wecoma) and 
    integrated co-occurrence matrices 
    (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).
Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps).
Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal.  Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>.
Includes functions to calculate scores and marks for track and
    field combined events competitions. The functions are based on the scoring
    tables for combined events set forth by the International Association of 
    Athletics Federation (2001)
    <https://www.worldathletics.org/about-iaaf/documents/technical-information>.
Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested.
We offer two statistical tests to combine p-values: selfcontained.test vs competitive.test. The goal is to test whether a vector of pvalues are jointly significant when we combine them together.
Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design.
Provides iterators for combinations, permutations, subsets, and
    Cartesian product, which allow one to go through all elements without creating a
    huge set of all possible values.
Functions for computing optimal convex combinations of
        model selection criteria based on ranks, along with utility
        functions for constructing model lists, MSCs, and priors on
        model lists.
Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.
An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.
A convenient 'R' wrapper to the 'Comet' API, which is a cloud
    platform allowing you to track, compare, explain and optimize machine
    learning experiments and models. Experiments can be viewed on the 'Comet'
    online dashboard at <https://www.comet.ml>.
Functions to calculate various common and less common thermal comfort indices, convert physical variables, and evaluate the performance of thermal comfort indices.
A set of common functions to be used for displaying messages, checking variables, 
  finding absolute paths, starting applications, etc. More functions will be added later.
Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.
Posterior inference under the convex mixture regression (CoMiRe) models introduced by Canale, Durante, and Dunson (2018) <doi.org/10.1111/biom.12917>.
An S4 representation of the Command design pattern. The
  Operation class is a simple implementation using closures and supports
  forward and reverse (undo) evaluation. The more complicated Protocol
  framework represents each type of command (or analytical protocol) by
  a formal S4 class. Commands may be grouped and consecutively executed
  using the Pipeline class. Example use cases include logging, do/undo,
  analysis pipelines, GUI actions, parallel processing, etc.
Autosimilarity curves, standardization of spatial extent, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. This should be a complement to available packages, particularly 'vegan'.
Functions to
    produce nicely formatted comments to use in R-scripts (or
    Latex/HTML/markdown etc). A comment with formatting is printed to the
    console and can then be copied to a script.
Useful libraries for building a Java based GUI under R are provided.
The CommonMark specification defines a rationalized version of markdown
    syntax. This package uses the 'cmark' reference implementation for converting
    markdown text into various formats including html, latex and groff man. In
    addition it exposes the markdown parse tree in xml format. Also includes opt-in
    support for GFM extensions including tables, autolinks, and strikethrough text.
Estimate bivariate common mean vector under copula models with known correlation. In the current version, available copulas include the Clayton, Farlie-Gumbel-Morgenstern (FGM), and Gaussian copulas. See Shih et al. (2019) <doi:10.1080/02331888.2019.1581782> for details under the FGM copula.
Java JAR files for the Apache Commons Mathematics Library for use by users and other packages.
Provides functions to measure the difference between constrained and unconstrained gene tree distributions using various tree distance metrics. Constraints are enforced prior to this analysis via the estimation of a tree under the community tree model.
The CommunityCorrelogram package is designed for the geostatistical analysis of ecological community datasets with either a spatial or temporal distance component.
Computing comorbidity scores such as the weighted Charlson score
  (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser
  comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>)
  using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>).
Comparison of two ROC curves through the methodology proposed by Ana C. Braga.
Regression methodologies with compositional covariates, including (1) sparse log-contrast regression with compositional covariates proposed by Lin et al. (2014) <doi:10.1093/biomet/asu031>, and (2) sparse log-contrast regression with functional compositional predictors proposed by Sun et al. (2020) <arXiv:1808.02403>.
Creates empty plots with compact axis notation to which users can
    add whatever they like (points, lines, text, etc.) The notation is more
    compact in the sense that the axis-labels and tick-labels are closer to the
    axis and the tick-marks are shorter. Also, if the plot appears as part of a
    matrix, the x-axis notation is suppressed unless the plot appears along the
    bottom row and the y-axis notation is suppress unless the plot appears
    along the left column.
Implements functions for comparing strings, sequences and 
    numeric vectors for clustering and record linkage applications. 
    Supported comparison functions include: generalized edit distances 
    for comparing sequences/strings, Monge-Elkan similarity for fuzzy 
    comparison of token sets, and L-p distances for comparing numeric 
    vectors. Where possible, comparison functions are implemented in 
    C/C++ to ensure good performance.
Functions to compare a model object to a comparison object.
  If the objects are not identical, the functions can be instructed to
  explore various modifications of the objects (e.g., sorting rows,
  dropping names) to see if the modified versions are identical.
Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.
Unified interface for the estimation of causal networks, including
    the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate
    additive noise model), 'bivariateCAM' (bivariate causal additive model),
    'CAM' (causal additive model) (from package 'CAM'; the package is 
    temporarily unavailable on the CRAN repository; formerly available versions 
    can be obtained from the archive), 'hiddenICP' (invariant
    causal prediction with hidden variables), 'ICP' (invariant causal prediction)
    (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence
    search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC
    Algorithm), 'FCI' (fast causal inference), 
    'RFCI' (really fast causal inference) (all from package 'pcalg') and
    regression.
It has been designed to calculate the required sample size in randomized clinical trials with composite endpoints. This package also includes functions to calculate the probability of observing the composite endpoint and the expected effect on the composite endpoint, among others. The methods implemented can be found in  Bofill & Gómez (2019) <doi:10.1002/sim.8092> and Gómez & Lagakos (2013) <doi:10.1002/sim.5547>.
Compares two dataframes which have the same column
    structure to show the rows that have changed. Also gives a git style diff format
    to quickly see what has changed in addition to summary statistics.
Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
Input: 2 ODM files (ODM version 1.3) Output: list of
        identical, matching, similar and differing data items
Quickly run experiments to compare the run time and output of
    code blocks. The function mbc() can make fast comparisons of code,
    and will calculate statistics comparing the resulting outputs.
    It can be used to compare model fits to the same data or
    see which function runs faster.
    The function ffexp() runs a function using all possible combinations
    of selected inputs. This is useful for comparing the effect of
    different parameter values. It can also run in parallel and
    automatically save intermediate results, which is very useful
    for long computations.
A standard test is observed on all specimens.  We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens.  Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control.  We treat the total sample as stratified two-phase sampling and use inverse probability weighting.  We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)).  See: Katki HA, Li Y, Edelstein DW, Castle PE.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations. 
        The package includes the two-level functions to calculate the LR assuming multivariate normality, and another with drops 
        this assumption and uses a multivariate kernel density estimate. The package also contains code to perform empirical 
        cross entropy (ECE) calibration of likelihood ratios. The LR functions are based primarily on 
        Aitken, C.G.G. and Lucy, D. (2004) <doi:10.1046/j.0035-9254.2003.05271.x>, "Evaluation of trace evidence in the form of multivariate data," 
        Journal of the Royal Statistical Society: Series C (Applied Statistics), 53: 109-122. The ECE functions are based primarily 
        on  D. Ramos and J. Gonzalez-Rodrigues, (2008) "Cross-entropy analysis of the information in forensic speaker recognition," 
        in Proc. IEEE Odyssey, Speaker Lang. Recognit. Workshop.
Statistical methods for competing risks data in comparing cumulative incidence function curves between two groups, including overall hypothesis tests and arbitrary tests in Lyu et al. (2020) <doi:10.1002/pst.2028>, and the fixed-point tests in Chen et al. (2018) <doi:10.1080/03610918.2018.1476697>.
Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) <doi:10.1371/journal.pone.0116774>, fixed-point tests in Klein et al. (2007) <doi:10.1002/sim.2864>, short-term tests, and long-term tests in Logan et al. (2008) <doi:10.1111/j.1541-0420.2007.00975.x>. Some commonly used descriptive statistics and plots are also included.
Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.
C++ implementation of component-wise boosting implementation of component-wise 
  boosting written in C++ to obtain high runtime performance and full memory control. The main 
  idea is to provide a modular class system which can be extended without editing the source code. 
  Therefore, it is possible to use R functions as well as C++ functions for custom base-learners, 
  losses, logging mechanisms or stopping criteria.
Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user.  Also fits the user specified distribution to a given data set.  More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017)  CompDist: Multisection composite distributions.
Package for the systematic retrieval and storage of
    functional genomics data via a MySQL database.
Compute ranking and rating based on competition
    results. Methods of different nature are implemented: with fixed
    Head-to-Head structure, with variable Head-to-Head structure and with
    iterative nature. All algorithms are taken from the book 'Who’s #1?:
    The science of rating and ranking' by Amy N. Langville and Carl D.
    Meyer (2012, ISBN:978-0-691-15422-0).
Tools for storing and managing competition results.
    Competition is understood as a set of games in which players gain some
    abstract scores.  There are two ways for storing results: in long (one
    row per game-player) and wide (one row per game with fixed amount of
    players) formats. This package provides functions for creation and
    conversion between them. Also there are functions for computing their
    summary and Head-to-Head values for players. They leverage grammar of
    data manipulation from 'dplyr'.
A graphical user interface for simulating the effects of mergers, tariffs, and quotas under an
             assortment of different economic models. The interface is powered by the 'Shiny' web application framework from 
             'RStudio'.
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
Contains functions to enhance approaches to the Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps.
Retail shopping transactions for 2,469 households over one year.
    Originates from the 84.51° Complete Journey 2.0 source files 
    <https://www.8451.com/area51> which also includes useful metadata on 
    products, coupons, campaigns, and promotions.
Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.
Extension of several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.
UpSet plots are an improvement over Venn Diagram for set overlap visualizations.
    Striving to bring the best of the 'UpSetR' and 'ggplot2', this package offers a way to create
    complex overlap visualisations, using simple and familiar tools, i.e. geoms of 'ggplot2'.
    For introduction to UpSet concept, see Lex et al. (2014) <doi:10.1109/TVCG.2014.2346248>.
Robust regression methods for compositional data.
    The distribution of the estimates can be approximated with various bootstrap
    methods. These bootstrap methods are available for the compositional as well
    as for standard robust regression estimates. This allows for direct
    comparison between them.
Computes the probability density function, cumulative distribution function, quantile function, random numbers of any composite model based on the lognormal distribution.
A suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package.
Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models
    to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The
    package provides functions for model estimation, dispersion testing, and
    diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016)
    <doi:10.1016/j.csda.2016.01.007> is also supported.
Provides functions for the consistent analysis of compositional 
  data (e.g. portions of substances) and positive numbers (e.g. concentrations) 
  in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
An ensemble of time series outlier detection methods that can be used for 
    compositional, multivariate and univariate data. It uses the four R packages 
    'forecast', 'tsoutliers', 'otsad' and 'anomalize' to detect time series outliers.
Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions).
 Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>,
 statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>,
 Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al. (2019)<DOI:10.1016/j.cmpb.2018.10.020>.
 Algorithms for generating correlated gene expressions are also available.
Tools for extracting occurrences, assessing potential driving factors, predicting occurrences, and quantifying impacts of compound events in hydrology and climatology. Please see Hao Zengchao et al. (2019) <doi:10.1088/1748-9326/ab4df5>.
Computing Continuous Distributions Obtained by Compounding
        a Continuous and a Discrete Distribution
Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.
Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
Provides 'Python'-style list comprehensions.
    List comprehension expressions use usual loops (for(), while() and repeat()) and
    usual if() as list producers. In many cases it gives more concise notation than
    standard "*apply + filter" strategy.
Provides a set of tools for creating yearly data sets of St. Louis 
    Metropolitan Police Department (SLMPD) crime data, which are available from
    January 2008 onward as monthly CSV releases on their website 
    (<http:www.slmpd.org/Crimereports.shtml>). Once data are validated and created 
    (monthly data releases have varying numbers of columns 
    as well as different column names and formats), 'compstatr' also provides 
    functions for categorizing and mapping crimes in St. Louis. The categorization 
    tools that are provided will also work with any police department that uses 5 
    and 6 digit numeric codes to identify specific crimes. These data provide researchers
    and policy makers detailed data for St. Louis, which in the last several years 
    has had some of the highest or the highest violent crime rates in the United States.
Several functions are available for calculating the most
    widely used effect sizes (ES), along with their variances, confidence
    intervals and p-values.  The output includes ES's of d (mean difference), g
    (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and
    OR (odds ratio and log odds ratio). In addition, NNT (number needed to
    treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are
    computed. This package uses recommended formulas as described in The
    Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, &
    Valentine, 2009).
Flexible framework for trait-based simulation of community assembly, where components could be replaced by user-defined function and that
             allows variation of traits within species.
Interface with and extract data from the United Nations Comtrade 
  API <https://comtrade.un.org/data/>. Comtrade provides country level shipping 
  data for a variety of commodities, these functions allow for easy API query 
  and data returned as a tidy data frame.
To calculate the AQI (Air Quality Index) from pollutant concentration data.
    O3, PM2.5, PM10, CO, SO2, and NO2 are available currently.
    The method can be referenced at Environmental Protection Agency, United States as follows:
    EPA (2016) <https://www3.epa.gov/airnow/aqi-technical-assistance-document-may2016.pdf>.
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.
There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
The four functions svdcp (cp for column partitioned),
        svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for
        a simultaneous optimization of one set of r solutions),
        correspond to a "SVD by blocks" notion, by supposing each block
        depending on relative subspaces, rather than on two whole
        spaces as usual SVD does. The other functions, based on this
        notion, are relative to two column partitioned data matrices x
        and y defining two sets of subsets xi and yj of variables and
        amount to estimate a link between xi and yj for the pair (xi,
        yj) relatively to the links associated to all the other pairs.
A set of utilities for matching products in different
	     classification codes used in international trade
	     research. It supports concordance between the Harmonized
	     System (HS0, HS1, HS2, HS3, HS4, HS5, HS combined), the Standard 
	     International Trade Classification (SITC1, SITC2, SITC3, SITC4), 
	     the North American Industry Classification System (NAICS combined),
	     as well as the Broad Economic Categories (BEC), the International 
	     Standard of Industrial Classification (ISIC), and the Standard Industrial 
	     Classification (SIC). It also provides code nomenclature/descriptions 
	     look-up, Rauch classification look-up (via concordance to SITC2), and
	     trade elasticity look-up (via concordance to HS0 or SITC3
	     codes).
The Concordance Test is a non-parametric method for testing whether two o more samples originate from the same distribution. It extends the Kendall Tau correlation coefficient when there are only two groups. For details, see Monge (2019) <arXiv:1912.12880v1>.
Contains the CONCOR (CONvergence of iterated CORrelations) 
    algorithm and a series of supplemental functions for easy running, 
    plotting, and blockmodeling. The CONCOR algorithm is used on social network
    data to identify network positions based off a definition of structural 
    equivalence; see Breiger, Boorman, and Arabie (1975) 
    <doi:10.1016/0022-2496(75)90028-0> and Wasserman and Faust's book Social 
    Network Analysis: Methods and Applications (1994). This version allows 
    multiple relationships for the same set of nodes and uses both incoming and
    outgoing ties to find positions.
Implements concordance regression which can be used to estimate generalized odds of concordance.
	Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and 
    for continuous outcome data. The method was introduced by Dunkler, Schemper and Heinze (2010) <doi:10.1093/bioinformatics/btq035>.
Implements a kernel-based association test for copy number 
  variation (CNV) aggregate analysis
  in a certain genomic region (e.g., gene set, chromosome, or genome) that is
  robust to the within-locus and across-locus etiological heterogeneity, and 
  bypass the need to define a "locus" unit for CNVs. 
  Brucker, A., et al. (2020) <doi:10.1101/666875>.
Computes compatibility (confidence) distributions along with their corresponding P-values,
    S-values, and likelihoods. The intervals can be plotted to form the distributions themselves.  
    Functions can be compared to one another to see how much they overlap. Results can be
    exported to Microsoft Word, Powerpoint, and TeX documents. The package currently supports 
    resampling methods, computing differences, generalized linear models, mixed-effects models, 
    survival analysis, and meta-analysis. These methods are discussed 
    by Schweder T, Hjort NL. (2016, ISBN:9781316445051) and 
    Rafi Z, Greenland S. (2020) <doi:10.1186/s12874-020-01105-9>.
Higher order likelihood-based inference for logistic and 
  loglinear models.
Apply and visualize conditional formatting to data frames in R.
    It renders a data frame with cells formatted according to
    criteria defined by rules, using a tidy evaluation syntax. The table is
    printed either opening a web browser or within the 'RStudio' viewer if
    available. The conditional formatting rules allow to highlight cells
    matching a condition or add a gradient background to a given column. This
    package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and
    exporting to an 'xlsx' file.
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.
Code for a variety of nonlinear conditional independence tests: 
  Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>),
  Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>),
  Invariant environment prediction,
  Invariant target prediction,
  Invariant residual distribution test,
  Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).
Set of functions for the easy analyses of conditioning data.
Provides ability to control how many times in function
    calls conditions are thrown (shown to the user). Includes control of
    warnings and messages.
Neural network conditional mixtures are mixture models whose parameters are predicted by a neural network. The mixture model can thus change its parameters in response to changes in predictive covariates. Mixtures included are gaussian, log-normal and hybrid Pareto mixtures. The latter relies on the generalized Pareto distribution to account for the presence of large extreme events. The unconditional mixtures are also available.
Computes conditional multivariate normal densities, probabilities, and random deviates.
An implementation of the computational strategy for the
    comprehensive analysis of condition-dependent operon maps in prokaryotes
    proposed by Fortino et al. (2014) <DOI:10.1186/1471-2105-15-145>. 
    It uses RNA-seq transcriptome profiles to improve prokaryotic operon map inference.
Method to implement some newly developed methods for the
    estimation of the conditional survival function.
Computes the density and probability for the conditional truncated multivariate normal (Horrace (2005) p. 4, <doi:10.1016/j.jmva.2004.10.007>). Also draws random samples from this distribution.
Runs a function iteratively over each row of either a dataframe 
    or the results of a query.  Use the 'BigQuery' and 'DBI' wrappers to 
    iteratively pass each row of query results to a function.  If a field 
    contains a 'JSON' string, it will be converted to an object.  This is 
    helpful for queries that return 'JSON' strings that represent objects.  
    These fields can then be treated as objects by the pipeline.
Exploring fitted models by interactively taking 2-D and 3-D
  sections in data space.
Constructs a shiny app function with interactive displays for conditional visualization of models, 
     data and density functions. An extended version of package 'condvis'. 
     Mark O'Connell, Catherine B. Hurley, Katarina Domijan (2017) <doi:10.18637/jss.v081.i05>.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.
Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis
  of confidence region simulations and (3) calculating confidence intervals and the associated 
  actual coverage for binomial proportions. Each is given in greater detail next. 
  (1) Plots the two-dimensional confidence region for probability distribution parameters 
  (supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif, 
  weibull) corresponding to a user-given complete or right-censored dataset and level of 
  significance.  The crplot() algorithm plots more points in areas of greater curvature to 
  ensure a smooth appearance throughout the confidence region boundary.  An alternative 
  heuristic plots a specified number of points at roughly uniform intervals along its boundary. 
  Both heuristics build upon the radial profile log-likelihood ratio technique for plotting 
  confidence regions given by Jaeger (2016) <doi:10.1080/00031305.2016.1182946>, and
  are detailed in a publication by Weld (2019) <doi:10.1080/00031305.2018.1564696>. 
  (2) Performs confidence region coverage simulations for a random sample drawn from a user-
  specified parametric population distribution, or for a user-specified dataset and point of 
  interest with coversim(). (3) Calculates confidence interval bounds for a binomial proportion 
  with binomTest(), calculates the actual coverage with binomTestCoverage(), and plots the 
  actual coverage with binomTestCoveragePlot(). Calculates confidence interval bounds for the
  binomial proportion using an ensemble of constituent confidence intervals with 
  binomTestEnsemble(). Calculates confidence interval bounds for the binomial proportion using 
  a complete enumeration of all possible transitions from one actual coverage acceptance curve 
  to another which minimizes the root mean square error for n <= 15 and follows the transitions 
  for well-known confidence intervals for n > 15 using binomTestMSE().
This small library contains a series of simple tools for
        constructing and manipulating confounded and fractional
        factorial designs.
Functions for estimating and reporting multi-year averages and
    corresponding confidence intervals and distributions. A potential use case
    is reporting the chemical and ecological status of surface waters according
    to the European Water Framework Directive.
Manage configuration values across multiple environments (e.g.
  development, test, production). Read values using a function that determines
  the current environment and returns the appropriate value.
Enhances the 'ini' package by adding the ability to interpolate variables.
	     The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module)
	     and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.
Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.
R functions for criterion profile analysis, Davison and Davenport (2002) <doi:10.1037/1082-989X.7.4.468> and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2020) <doi:10.1037/met0000305>. Sensitivity analyses to aid in interpreting criterion profile analysis results are also included.
Produces descriptive interpretations of confidence intervals.
    Includes (extensible) support for various test types, specified as sets
    of interpretations dependent on where the lower and upper confidence limits
    sit. Provides plotting functions for graphical display of interpretations.
Calculates classic and/or bootstrap confidence
    intervals for many parameters such as the population mean, variance,
    interquartile range (IQR), median absolute deviation (MAD), skewness,
    kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median),
    proportions, different types of correlation measures, difference in
    means, quantiles and medians. Many of the classic confidence intervals
    are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap
    confidence intervals are calculated with the R package 'boot'. Both
    one- and two-sided intervals are supported.
Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.
R's default conflict management system gives the most recently
    loaded package precedence. This can make it hard to detect conflicts, 
    particularly when they arise because a package update creates ambiguity
    that did not previously exist. 'conflicted' takes a different approach, 
    making every conflict an error and forcing you to choose which function 
    to use.
Provides utilities for working with various 'Confluence' API 
    <https://docs.atlassian.com/ConfluenceServer/rest/latest/>, including a
    functionality to convert an R Markdown document to 'Confluence' format and
    upload it to 'Confluence' automatically.
Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.
Conducts sensitivity analyses for unmeasured confounding in
    random-effects meta-analysis per Mathur & VanderWeele (in preparation).
    Given output from a random-effects meta-analysis with a relative risk
    outcome, computes point estimates and inference for: (1) the proportion
    of studies with true causal effect sizes more extreme than a specified threshold
    of scientific significance; and (2) the minimum bias factor and confounding
    strength required to reduce to less than a specified threshold the proportion
    of studies with true effect sizes of scientifically significant size.
    Creates plots and tables for visualizing these metrics across a range of bias values.
    Provides tools to easily scrape study-level data from a published forest plot or 
    summary table to obtain the needed estimates when these are not reported.
Implements three covariate-balance diagnostics for time-varying confounding and selection-bias in complex longitudinal data, as described in Jackson (2016) <doi:10.1097/EDE.0000000000000547> and Jackson (2019) <doi:10.1093/aje/kwz136>. Diagnostic 1 assesses measured confounding/selection-bias, diagnostic 2 assesses exposure-covariate feedback, and diagnostic 3 assesses residual confounding/selection-bias after inverse probability weighting or propensity score stratification. All diagnostics appropriately account for exposure history, can be adapted to assess a particular depth of covariate history, and can be implemented in right-censored data. Balance assessments can be obtained for all times, selected-times, or averaged across person-time. The balance measures are reported as tables or plots. These diagnostics can be applied to the study of multivariate exposures including time-varying exposures, direct effects, interaction, and censoring.
Offers several functions for Configural Frequencies
    Analysis (CFA), which is a useful statistical tool for the analysis of
    multiway contingency tables. CFA was introduced by G. A. Lienert as
    'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). 
    Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. 
    Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
For multiple testing.
    Computes estimates and confidence bounds for the
    False Discovery Proportion (FDP), the fraction of false positives among
    all rejected hypotheses.
    The methods in the package use permutations of the data. Doing so, they
    take into account the dependence structure in the data.
Includes the 100 datasets simulated by Congreve and Lamsdell (2016)
  <doi:10.1111/pala.12236>, and analyses of the partition and quartet distance of
  reconstructed trees from the generative tree, as analysed by Smith (2019)
  <doi:10.1098/rsbl.2018.0632>.
Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.
plot conics (ellipses, hyperbolas, parabolas)
Provides a set of tools that can be used across 'data.frame' and
    'imputationList' objects.
This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method.
Implementation of a procedure (Domingue, 2012; see also
        Karabatsos, 2001 and Kyngdon, 2011) to test the single and
        double cancellation axioms of conjoint measure in data that is
        dichotomously coded and measured with error.
Builds synthetic data applicable across multiple domains. This package also provides flexibility to control data distribution to make it relevant to many industry examples.
Enables 'DBI' compliant packages to integrate with the 'RStudio' connections 
  pane, and the 'pins' package. It automates the display of schemata, tables, views, as well 
  as the preview of the table's top 1000 records.
Collects several different methods for analyzing and
    working with connectivity data in R.  Though primarily oriented towards
    marine larval dispersal, many of the methods are general and useful for
    terrestrial systems as well.
The congeneric normal-ogive model is a popular model for 
   psychometric data (McDonald, R. P. (1997) <doi:10.1007/978-1-4757-2691-6_15>).
   This model estimates the model, calculates theoretical and concrete 
   reliability coefficients, and predicts the latent variable of the model. 
   This is the companion package to Moss (2020) <doi:10.31234/osf.io/nvg5d>.
Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.
Fast and accurate convolution-type smoothed quantile regression. Implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Construct confidence intervals for regression coefficients using multiplier bootstrap.
Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning).
    Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command 'put').
    Requires 'ACER ConQuest' version 5.12.3 or later. A demonstration version can be downloaded from <https://shop.acer.edu.au/acer-conquest-5.html>.
Multi-species estimation of geographical range parameters
	for preliminary assessment of conservation status following Criterion B of the 
	International Union for Conservation of Nature (IUCN, 
	see <http://www.iucnredlist.org>).
Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline.  Function outputs fits and various types of inference.
Compute the median ranking according to the Kemeny's axiomatic approach. 
    Rankings can or cannot contain ties, rankings can be both complete or incomplete. 
    The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. 
    The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. 
    The package also provide some useful utilities for deal with preference rankings.
    This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions.
    Please type '?ConsRank-deprecated'
    Essential references:
    Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>; 
    D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>; 
    Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>; 
    D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>.
Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. 
       The package makes it easy to incorporate constraints into the model's coefficients. 
          The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), 
          a vector of bound constraints 
          for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions
          among coefficients (i.e beta1 > beta2).
          The references of this packages are the same as 'stats' package for glm() and arima() functions.
          See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528).
          For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages.
CODATA internationally recommended values of the fundamental
    physical constants, provided as symbols for direct use within the R language.
    Optionally, the values with uncertainties and/or units are also provided if
    the 'errors', 'units' and/or 'quantities' packages are installed.
    The Committee on Data for Science and Technology (CODATA) is an
    interdisciplinary committee of the International Council for Science which
    periodically provides the internationally accepted set of values of the
    fundamental physical constants. This package contains the "2018 CODATA"
    version, published on May 2019:
    Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2020)
    <http://physics.nist.gov/constants>.
Examine any number of time series data frames to identify 
    instances in which various criteria are met within specified time
    frames. In clinical medicine, these types of events are often
    called "constellations of signs and symptoms", because a single 
    condition depends on a series of events occurring within a certain 
    amount of time of each other. This package was written to work with
    any number of time series data frames and is optimized for speed 
    to work well with data frames with millions of rows.
Provides functions for
        efficient computations of nonlinear spatial predictions with
        local change of support. This package supplies functions for
        tow-dimensional spatial interpolation by constrained,
        covariance-matching constrained and universal (external drift)
        kriging for points or block of any shape for data with a
        nonstationary mean function and an isotropic weakly stationary
        variogram. The linear spatial interpolation methods,
        constrained and covariance-matching constrained kriging,
        provide approximately unbiased prediction for nonlinear target
        values under change of support. This package
        extends the range of geostatistical tools available in R and
        provides a veritable alternative to conditional simulation for
        nonlinear spatial prediction problems with local change of
        support.
A method for modeling genetic data as a combination of discrete
    layers, within each of which relatedness may decay continuously with geographic
    distance. This package contains code for running analyses (which are implemented
    in the modeling language 'rstan') and visualizing and interpreting output. See the
    paper for more details on the model and its utility.
Process spatially- and temporally-discrete data into contact and 
   social networks, and facilitate network analysis by randomizing 
   individuals' movement paths and/or related categorical variables. To use 
   this package, users need only have a dataset containing spatial data 
   (i.e., latitude/longitude, or planar x & y coordinates), individual IDs 
   relating spatial data to specific individuals, and date/time information 
   relating spatial locations to temporal locations. The functionality of this 
   package ranges from data "cleaning" via multiple filtration functions, to 
   spatial and temporal data interpolation, and network creation and analysis. 
   Functions within this package are not limited to describing interpersonal 
   contacts. Package functions can also identify and quantify "contacts" 
   between individuals and fixed areas (e.g., home ranges, water bodies, 
   buildings, etc.). As such, this package is an incredibly useful resource 
   for facilitating epidemiological, ecological, ethological and sociological 
   research.
Data package for the supplementary data in Prem et al. (2017)
    <doi:10.1371/journal.pcbi.1005697>.
    Provides easy access to contact data for 152 countries, for use in
    epidemiological, demographic or social sciences research.
Common container data structures deque, set and dict (resembling
    'Python's dict type) with typical member functions to insert, delete and
    access container elements. Provides iterators and reference semantics.
Fits mixtures of multivariate contaminated normal distributions
        (with eigen-decomposed scale matrices) via the expectation conditional-
	maximization algorithm under a clustering or classification paradigm
	Methods are described in Antonio Punzo, Angelo Mazza, and Paul D McNicholas (2018) <doi:10.18637/jss.v085.i10>.
An interface for creating, registering, and resolving content-based
             identifiers for data management. Content-based identifiers rely on
             the 'cryptographic' hashes to refer to the files they identify, thus,
             anyone possessing the file can compute the identifier using a 
             well-known standard algorithm, such as 'SHA256'.  By registering
             a URL at which the content is accessible to a public archive, such
             as <https://hash-archive.org>, the content identifier can serve 
             many functions typically associated with A Digital Object Identifier
             ('DOI').  Unlike location-based identifiers like 'DOIs', content-based
             identifiers permit the same content to be registered in many locations.
Facilitates the simulation and evaluation of context-free
    and contextual multi-Armed Bandit policies or algorithms to ease the
    implementation, evaluation, and dissemination of both existing and
    new bandit algorithms and policies.
Various utilities for evaluating continued fractions.
Bayesian analysis of complete and incomplete contingency tables.
Create contour lines for a non regular series of points, potentially from a non-regular canvas.
Provides functions for making contour plots.
  The contour plot can be created from grid data, a function,
  or a data set. If non-grid data is given, then a Gaussian
  process is fit to the data and used to create the contour plot.
Plots a set of x,y,z co-ordinates in a contour map. Designed to be similar to plots in base R so additional elements can be added using lines(), points() etc. This package is intended to be better suited, than existing packages, to displaying circular shaped plots such as those often seen in the semi-conductor industry.
One degree of freedom contrasts for 'lm', 'glm', 'gls', and 'geese' objects.
Contribution table for credit assignment based on 'ggplot2'.
    This can improve the author contribution information in academic journals and personal CV.
Solves control systems problems relating to time/frequency response, LTI systems design and analysis, transfer function manipulations, and system conversion.
Nonparametric two-sample procedure for comparing survival quantiles.
Functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).
This is a pedagogical package, designed to help students understanding convergence of
             random variables. It provides a way to investigate interactively various modes of
	     convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d.
	     random variables. Visualisation of simulated sample paths is possible through interactive
	     plots. The approach is illustrated by examples and exercises through the function
	     'investigate', as described in
	     Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>.
	     The user can study his/her own sequences of random variables.
Indicators and measures by country and time describe
    what happens at economic and social levels. This package provides
    functions to calculate several measures of convergence after imputing
    missing values. The automated downloading of Eurostat data,
    followed by the production of country fiches and indicator fiches,
    makes possible to produce automated reports.
    The Eurofound report (<doi:10.2806/68012>)
    "Upward convergence in the EU: Concepts, measurements and indicators", 
    2018, is a detailed  presentation of  convergence.
Converts graphical file formats (SVG,
    PNG, JPEG, BMP, GIF, PDF, etc) to one another. The exceptions are the
    SVG file format that can only be converted to other formats and in contrast,
    PDF format, which can only be created from others graphical formats.
    The main purpose of the package was to provide a solution for converting SVG
    file format to PNG which is often needed for exporting graphical files
    produced by R widgets.
Provides conversion functionality between a broad range of
    scientific, historical, and industrial unit types.
Quantifies and assesses the significance of convergent evolution using two different methods (and 5 different measures) as described in Stayton (2015) <DOI:  10.1111/evo.12729>.  Also displays results in a phylomorphospace framework.
Provides a simple high-level wrapper for
    'Julia' package 'Convex.jl' (see <https://github.com/JuliaOpt/Convex.jl> for
    more information),
    which makes it easy to describe and solve convex optimization problems in R.
    The problems can be dealt with include:
    linear programs,
    second-order cone programs,
    semidefinite programs,
    exponential cone programs.
Variance estimation on indicators of income concentration and
    poverty using complex sample survey designs. Wrapper around the
    'survey' package.
Fits convolution-based nonstationary
    Gaussian process models to point-referenced spatial data. The nonstationary
    covariance function allows the user to specify the underlying correlation
    structure and which spatial dependence parameters should be allowed to
    vary over space: the anisotropy, nugget variance, and process variance.
    The parameters are estimated via maximum likelihood, using a local
    likelihood approach. Also provided are functions to fit stationary spatial
    models for comparison, calculate the Kriging predictor and standard errors,
    and create various plots to visualize nonstationarity.
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
Read and preprocess fasta format data, and construct the co-occurrence network for downstream analyses. This R package is to construct the co-occurrence network with the algorithm developed by Du (2008) <DOI:10.1101/gr.6969007>. It could be used to transform the data with high-dimension, such as DNA or protein sequence, into co-occurrence networks. Co-occurrence network could not only capture the co-variation pattern between variables, such as the positions in DNA or protein sequences, but also reflect the relationship between samples. Although it is originally used in DNA and protein sequences, it could be also used to other kinds of data, such as RNA, SNP, etc.
Fast implementations of the co-operations: covariance,
    correlation, and cosine similarity.  The implementations are
    fast and memory-efficient and their use is resolved
    automatically based on the input data, handled by R's S3
    methods.  Full descriptions of the algorithms and benchmarks
    are available in the package vignettes.
The theory of cooperative games with transferable utility offers 
   useful insights into the way parties can share gains from cooperation and 
   secure sustainable agreements, see e.g. one of the books by Chakravarty, 
   Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, 
   ISBN:978-9027727299) for more details. A comprehensive set of tools for 
   cooperative game theory with transferable utility is provided. Users can 
   create special families of cooperative games, like e.g. bankruptcy games, 
   cost sharing games and weighted voting games. There are functions to check 
   various game properties and to compute five different set-valued solution 
   concepts for cooperative games. A large number of point-valued solution 
   concepts is available reflecting the diverse application areas of 
   cooperative game theory. Some of these point-valued solution concepts can 
   be used to analyze weighted voting games and measure the influence of 
   individual voters within a voting body. There are routines for visualizing 
   both set-valued and point-valued solutions in the case of three or four 
   players.
Computes cooperative games and allocation rules associated with linear production programming problems.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) <doi:10.1111/2041-210X.13152>.
Extensive functions for bivariate copula (bicopula) computations and related operations 
 for bicopula theory. The lower, upper, product, and select other bicopula are implemented along 
 with operations including the diagonal, survival copula, dual of a copula, co-copula, and
 numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical 
 derivatives and inverses of a bicopula are provided through which simulation is implemented. 
 Bicopula composition, convex combination, and products also are provided. Support 
 extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau,
 Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer-
 Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and 
 positive/negative quadrant dependency, left (right) increasing (decreasing) are available. 
 Other features include Kullback-Leibler divergence, Vuong procedure, spectral measure, and 
 Lcomoments for inference, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit.
Provides tools for fitting the copCAR (Hughes, 2015)
    <DOI:10.1080/10618600.2014.948178> regression model for discrete
	areal data. Three types of estimation are supported (continuous
	extension, composite marginal likelihood, and distributional transform),
	for three types of outcomes (Bernoulli, negative binomial, and Poisson).
Semiparametric estimation for censored time series
    with lower detection limit. The latent response is a sequence of
    stationary process with Markov property
    of order one.
    Estimation of copula parameter(COPC) and Conditional quantile estimation
    are included for five available copula functions.
    Copula selection methods based on L2 distance from empirical copula function
    are also included.
Provides functions to  compute and plot Coverage
    Probability Excursion (CoPE) sets
    for real valued functions on a 2-dimensional domain. CoPE sets are obtained
    from repeated noisy observations of the function on the entire domain.
    They are designed to bound the excursion
    set of the target function at a given level from above and below with
    a predefined probability. The target
    function can be a parameter in spatially-indexed linear regression.
    Support by NIH grant R01 CA157528 is gratefully acknowledged.
The nonparametric method for estimating copula entropy is implemented. The method composes of two simple steps: estimating empirical copula by rank statistic and estimating copula entropy with k-Nearest-Neighbour method. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and proved to be equivalent to mutual information. Estimating copula entropy can be applied to many cases, including but not limited to variable selection and causal discovery (by estimating transfer entropy). Please refer to Ma and Sun (2011) <doi: 10.1016/S1007-0214(11)70008-6> for more information.
The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. 
    The model was based on the evaluation of local alternatives, representing 
    regional potentialities, so as to fulfill demands of economic projects. 
    After defining demand profiles in terms of their technological coefficients, 
    the degree of importance of factors is defined so as to represent  
    the productive activity. The method can detect a surplus of supply without 
    the restriction of the distance of classical algebra, defining a hierarchy 
    of location alternatives. In COPPE-Cosenza Model, the distance between 
    factors is measured in terms of the difference between grades of memberships
    of the same factors belonging to two or more  sets under comparison. The 
    required factors are classified under the following linguistic variables: 
    Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant 
    (I). And the alternatives can assume the following linguistic variables: 
    Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and 
    Inexistent (In). The model also provides flexibility, allowing different 
    aggregation rules to be performed and defined by the Decision Maker. Such 
    feature is considered in this package, allowing the user to define other 
    aggregation matrices, since it considers the same linguistic variables 
    mentioned.
Computes the required number of patients for two time-to-event end-points as primary endpoint in phase III clinical trial.
Cluster optimized proximity scaling (COPS) refers to multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration. They achieve this by transforming proximities/distances with power functions and augment the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly for given parameters (COPS-C) for ratio, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://escholarship.org/uc/item/4ps3b5mj>), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745>, r-stress (de Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>), power-stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>) and power elastic scaling, power Sammon mapping and approximated power stress (Rusch, Mair & Hornik, 2015, <https://bach-s59.wu.ac.at/4888/>). All of these models can also solely be fit as MDS with power transformations. The package further contains a function for pattern search optimization, the "Adaptive Luus-Jakola Algorithm" (Rusch, Mair & Hornik, 2015, <https://bach-s59.wu.ac.at/4888/>).
Classes (S4) of commonly used elliptical, Archimedean,
 extreme-value and other copula families, as well as their rotations,
 mixtures and asymmetrizations. Nested Archimedean copulas, related
 tools and special functions. Methods for density, distribution, random
 number generation, bivariate dependence measures, Rosenblatt transform,
 Kendall distribution function, perspective and contour plots. Fitting of
 copula models with potentially partly fixed parameters, including
 standard errors. Serial independence tests, copula specification tests
 (independence, exchangeability, radial symmetry, extreme-value
 dependence, goodness-of-fit) and model selection based on
 cross-validation. Empirical copula, smoothed versions, and
 non-parametric estimators of the Pickands dependence function.
Estimation and statistical process control are performed under
 copula-based time-series models.
 Available are statistical methods in Long and Emura (2014 JCSA),
 Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>,
 Huang and Emura (2019 Commun Stat-Simul) <DOI:10.1080/03610918.2019.1602647>,
 Lin et al. (2019 Comm Stat-Simul) <DOI:10.1080/03610918.2019.1652318>,
 Sun et al. (2020 to appear in JSS Series in Statistics, Springer), and Huang, Chen and Emura (2019-, in revision).
Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted).
Perform association analysis of bivariate survival data based on copula models.
 Two different ways to estimate the association parameter in copula models are implemented.
 A goodness-of-fit test for a given copula model is implemented.
 See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.
 A more general reference for the copula-based analysis of bivariate survival data is
 Emura, Matsui, and Rondeau (2019) <doi:10.1007/978-981-13-3516-7>.
Copula-based regression models for bivariate censored data, including 
 bivariate right-censored data and bivariate interval-censored data. Currently 
 supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For 
 marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and
 semiparametric (Cox and transformation) models. Includes methods for convenient
 prediction and plotting. Also provides a bivariate time-to-event simulation function. 
 Method details can be found in Sun et.al. (2018) < doi:10.1007/s10985-018-09459-5> 
 and Sun et.al (2019) <arXiv:1901.01918>.
Data sets used for copula modeling in addition to those in
 the package 'copula'. These include a random subsample from the US National
 Education Longitudinal Study (NELS) of 1988 and nursing home data from
 Wisconsin.
Modelling of sensitivity and specificity on their natural scale
    using copula based bivariate beta-binomial distribution to yield marginal
    mean sensitivity and specificity. The intrinsic negative correlation between
    sensitivity and specificity is modelled using a copula function. A forest plot
    can be obtained for categorical covariates or for the model with intercept only.
    Nyaga VN, Arbyn M, Aerts M (2017) <doi:10.18637/jss.v082.c01>.
Provides a platform where EDAs (estimation of
    distribution algorithms) based on copulas can be implemented and
    studied. The package offers complete implementations of various
    EDAs based on copulas and vines, a group of well-known
    optimization problems, and utility functions to study the
    performance of the algorithms. Newly developed EDAs can be easily
    integrated into the package by extending an S4 class with generic
    functions for their main components.
The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) <doi:10.1002/sim.6595>. The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) <doi:10.1177/0962280215596769> and also accounting for non-evaluable subjects in Nikoloulopoulos (2018) <arXiv:1812.03685>. The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) <doi:10.1177/0962280216682376>. The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2018) <doi:10.1177/0962280218796685>. The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2018) <arXiv:1812.05915>.
Contains several IRT and non-IRT based response similarity indices proposed in the literature for multiple-choice examinations such as the Omega index, Wollack (1997) <doi:10.1177/01466216970214002>; Generalized Binomial Test, van der Linden & Sotaridona (2006) <doi:10.3102/10769986031003283>; K index, K1 and K2 indices, Sotaridona & Meijer (2002) <doi:10.1111/j.1745-3984.2002.tb01138.x>;  and S1 and S2 indices, Sotaridona & Meijer (2003) <doi:10.1111/j.1745-3984.2003.tb01096.x>.
Duplicated publication data (pre-processed and formatted) for entity resolution. This data set contains a total of 1879 records. The following variables are included in the data set: id, title, book title, authors, address, date, year, editor, journal, volume, pages, publisher, institution, type, tech, note. The data set has a respective gold data set that provides information on which records match based on id.
Calculates the co-ranking matrix to assess the
    quality of a dimensionality reduction.
Allows the user to apply nice color gradients to 'shiny' elements.
    The gradients are extracted from the 'colorffy' website. See <https://www.colorffy.com/gradients/catalog>.
Provides a bundle of basic and fundamental bioinformatics tools,
    such as network querying and alignment, subnetwork extraction and search,
    network biomarker identification.
We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S.,  Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with specified correlation structures." The American Statistician. See <doi:10.1080/00031305.2020.1816213> for a detailed presentation of the method.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering.  This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions.  Read http://arxiv.org/abs/1508.01939 for more details.
We describe four different summary statistics, to ensure power and flexibility under various settings, including a new connectivity statistic that is sensitive to changes in overall covariance magnitude.
Functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2017) <doi:10.1080/10618600.2017.1349664>. There is an R native version and a version that uses 'ELKI', with methods for printing, summarizing, and plotting the result. There also is an interface to the reference implementation of OPTICS in 'ELKI'.
given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.
Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.
Core Hunter is a tool to sample diverse, representative subsets from large germplasm
    collections, with minimum redundancy. Such so-called core collections have applications in plant
    breeding and genetic resource management in general. Core Hunter can construct cores based on
    genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many
    provided evaluation measures depending on the precise purpose of the core (e.g. high diversity,
    representativeness, or allelic richness). In addition, multiple measures can be simultaneously
    optimized as part of a weighted index to bring the different perspectives closer together.
    The Core Hunter library is implemented in Java 8 as an open source project (see
    <http://www.corehunter.org>).
A suite of machine learning algorithms written in C++ with the R 
 interface contains several learning techniques for classification and regression.
 Predictive models include e.g., classification and regression trees with
 optional constructive induction and models in the leaves, random forests, kNN, 
 naive Bayes, and locally weighted regression. All predictions obtained with these
 models can be explained and visualized with the 'ExplainPrediction' package.  
 This package is especially strong in feature evaluation where it contains several variants of
 Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, 
 information gain, MDL, and DKM. These methods can be used for feature selection 
 or discretization of numeric attributes.
 The OrdEval algorithm and its visualization is used for evaluation
 of data sets with ordinal features and class, enabling analysis according to the 
 Kano model of customer satisfaction. 
 Several algorithms support parallel multithreaded execution via OpenMP.  
 The top-level documentation is reachable through ?CORElearn.
The 'Certifiably Optimal RulE ListS (Corels)' learner by
 Angelino et al described in <arXiv:1704.01701> provides interpretable decision
 rules with an optimality guarantee, and is made available to R with this package.
 See the file 'AUTHORS' for a list of copyright holders and contributors.
Provides a minimal interface for applying
    annotators from the 'Stanford CoreNLP' java library. Methods
    are provided for tasks such as tokenisation, part of speech
    tagging, lemmatisation, named entity recognition, coreference
    detection and sentiment analysis.
Core functions for simulating quantities of interest
    from generalised linear models (GLM). This package will form the backbone of
    a series of other packages that improve the interpretation of GLM estimates.
Use to analysis case-parent trio sequencing studies. Test the compound heterozygous and recessive disease models
Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features 
	using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables 
	using data transformations.
A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.
We proposed a new model-based clustering method, called the clustering of 
             regression models method(CORM), which groups genes that share a similar 
             relationship to the covariate(s). This method provides a unified approach 
             for a family of clustering procedures and can be applied to data collected 
             with various experimental designs. This package includes the implementation 
             for two such clustering procedures: (1) the Clustering of Linear Models 
             (CLM) method, and (2) the Clustering of Linear Mixed Models (CLMM) method.
Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'.

         The desktop application 'Cornerstone' (<https://www.camline.com/en/products/cornerstone/cornerstone-core.html>)
    is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster.
    The engineers incorporate intensified hands-on statistics into their projects.
    They benefit from an intuitive and uniquely designed graphical Workmap concept: you design
    experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon,
    immediately, interactively, and without any programming.

         While 'Cornerstone's' interface to the statistical programming language 'R' has been available
    since version 6.0, the latest interface with 'R' is even much more efficient.
    'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the
    standard 'Cornerstone' GUI.
    Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R'
    routines, immediately and without the need to deal with programming code.
    Additionally, your 'R' programming team develops corresponding 'R' packages detached from
    'Cornerstone' in their favorite 'R' environment.

         Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel 
    (<https://www.youtube.com/watch?v=HEQHwq_laXU>) (available in German).
Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2019). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.
Provides 'coroutines' for R, a family of functions that
    can be suspended and resumed later on. This includes 'async'
    functions (which await) and generators (which yield). 'Async'
    functions are based on the concurrency framework of the 'promises'
    package. Generators are based on a dependency free iteration
    protocol defined in 'coro' and are compatible with iterators from
    the 'reticulate' package.
Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>.
Manipulate and view coronavirus data and other societally relevant data at a basic level.
Provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.
Implements a James-Stein-type shrinkage estimator for 
  the covariance matrix, with separate shrinkage for variances and correlations.  
  The details of the method are explained in Schafer and Strimmer (2005) 
  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) 
  <DOI:10.2202/1544-6115.1252>.  The approach is both computationally as well
  as statistically very efficient, it is applicable to "small n, large p" data, 
  and always returns a positive definite and well-conditioned covariance matrix.  
  In addition to inferring the covariance matrix the package also provides 
  shrinkage estimators for partial correlations and partial variances.  
  The inverse of the covariance and correlation matrix 
  can be efficiently computed, as well as any arbitrary power of the 
  shrinkage correlation matrix.  Furthermore, functions are available for fast 
  singular value decomposition, for computing the pseudoinverse, and for 
  checking the rank and positive definiteness of a matrix.
Utility functions for the statistical analysis of corpus frequency data.
        This package is a companion to the open-source course "Statistical Inference: 
        A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL').
A set of functions used to compare co-occurrence between two corpora.
Facilitates dynamic exploration of text collections through an
    intuitive graphical user interface and the power of regular expressions.
    The package contains 1) a helper function to convert a data frame to a
    'corporaexplorerobject', 2) a 'Shiny' app for fast and flexible exploration
    of a 'corporaexplorerobject', and 3) a 'Shiny' app for simple
    retrieval/extraction of documents from a 'corporaexplorerobject' in a
    reading-friendly format. The package also includes demo apps with which
    one can explore Jane Austen's novels and the State of the Union Addresses
    (data from the 'janeaustenr' and 'sotu' packages respectively).
Text corpus data analysis, with full support for international text (Unicode).  Functions for reading data from newline-delimited 'JSON' files, for normalizing and tokenizing text, for searching for term occurrences, and for computing term occurrence frequencies, including n-grams.
Provides text analysis in R, focusing on the use of a tokenized text format. In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations).
    Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences),
    similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages,
    and the possibility to reconstruct original text from tokens to facilitate interpretation.
Implementation of two-dimensional (2D) correlation analysis based
    on the Fourier-transformation approach described by Isao Noda (I. Noda
    (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot
    functions for the resulting correlation matrix: The first one creates
    colored 2D plots, while the second one generates 3D plots.
The goal of 'corrarray' is to create a multi-sample
  correlation array by combining the correlation matrices of a data set
  stratified by a grouping variable. 
  For two specified levels of the variable, 'corrarray' displays one level's 
  correlation matrix in the lower triangular matrix and the other level's
  correlation matrix in the upper triangular matrix.
  Such an output can enable visualization of correlations from two samples
  in a single correlation matrix or corrgram.
Implements non-parametric analyses for clustered
    binary and multinomial data. The elements of the cluster are assumed
    exchangeable, and identical joint distribution (also known as marginal
    compatibility, or reproducibility) is assumed for clusters of different
    sizes. A trend test based on stochastic ordering is implemented.
Using a computationally efficient method, the package can
    be used to find the corrected coverage estimate of a credible set 
    of putative causal variants from Bayesian genetic fine-mapping. 
    The package can also be used to obtain a corrected credible set
    if required; that is, the smallest set of variants required such 
    that the corrected coverage estimate of the resultant credible set is  
    within some user defined accuracy of the desired coverage.
    Maller et al. (2012) <doi:10.1038/ng.2435>,
    Wakefield (2009) <doi:10.1002/gepi.20359>,
    Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.
Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.
Correcting area under ROC (AUC) for measurement error based on probit-shift model.
There are many estimators of false discovery rate. In this package we compute the Nonlocal False
   Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery
   Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. 
   Bickel, D. R. (2016) <http://hdl.handle.net/10393/34277>.
Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.
A collection of tools for correspondence regression, i.e. the
    correspondence analysis of the crosstabulation of a categorical variable Y
    in function of another one X, where X can in turn be made up of the
    combination of various categorical variables. Consequently, correspondence
    regression can be used to analyze the effects for a polytomous or
    multinomial outcome variable.
Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Relies on the easystats ecosystem (Lüdecke, Waggoner & Makowski (2019) <doi:10.21105/joss.01412>).
Speeds up exploratory data analysis (EDA)
    by providing a succinct workflow and interactive visualization tools for understanding
    which features have relationships to target (response). Uses binary correlation analysis
    to determine relationship. Default correlation method is the Pearson method. 
    Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.
Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.
Calculates correlation of variables and displays the results
    graphically. Included panel functions can display points, shading, ellipses, and
    correlation values with confidence intervals. See Friendly (2002) <doi:10.1198/000313002533>.
When exploring data or models we often examine variables one by one. 
  This analysis is incomplete if the relationship between these variables is 
  not taken into account. The 'corrgrapher' package facilitates simultaneous 
  exploration of the Partial Dependence Profiles and the correlation between 
  variables in the model.
  The package 'corrgrapher' is a part of the 'DrWhy.AI' universe.
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
A graphical display of a correlation matrix or general matrix.
    It also contains some algorithms to do matrix reordering. In addition,
    corrplot is good at details, including choosing color, text labels,
    color labels, layout, etc.
A tool for exploring correlations.
    It makes it possible to easily perform routine tasks when
    exploring correlation matrices such as ignoring the diagonal,
    focusing on the correlations of certain variables against others,
    or rearranging and visualizing the matrix in terms of the
    strength of the correlations.
Statistical summary of Structure output.
Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.
Set of methods to constrain numerical series and time series within
             arbitrary boundaries.
Provides S4 classes and methods to fit several copula models: The classic empirical checkerboard copula and the empirical checkerboard copula with known margins, see Cuberos, Masiello and Maume-Deschamps (2019) <doi:10.1080/03610926.2019.1586936> are proposed. These two models allow to fit copulas in high dimension with a small number of observations, and they are always proper copulas. Some flexibility is added via a possibility to differentiate the checkerboard parameter by dimension. The last model consist of the implementation of the Copula Recursive Tree algorithm proposed by Laverny, Maume-Deschamps, Masiello and Rullière (2020) <arXiv:2005.02912>, including the localised dimension reduction, which fits a copula by recursive splitting of the copula domain. We also provide an efficient way of mixing copulas, allowing to bag the algorithm into a forest, and a generic way of measuring d-dimensional boxes with a copula.
There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in  6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation.  ST5() and ST6() used two different ways to combine ST3() and ST4().  We highly recommend ST5() according to the article titled ''New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs'' published in Scientific Reports.  Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.
We present 'corto' (Correlation Tool), a simple package to infer
 gene regulatory networks and visualize master regulators from gene expression
 data using DPI (Data Processing Inequality) and bootstrapping to recover edges.
 An initial step is performed to calculate all significant
 edges between a list of source nodes (centroids) and target genes.
 Then all triplets containing two centroids and one target are tested
 in a DPI step which removes edges. A bootstrapping process then calculates
 the robustness of the network, eventually re-adding edges previously removed by DPI.
 The algorithm has been optimized to run outside a computing cluster, using a fast correlation
 implementation. The package finally provides functions to calculate network enrichment
 analysis from RNA-Seq and ATAC-Seq signatures as described in the article by
 Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.
Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes.
Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'.
Implements bound constrained optimal sample allocation (BCOSA) framework described in Bulus & Dong (2019) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Separate tools for statistical power and minimum detectable effect size computations are provided.
To identify the globally most discriminative subnetwork from gene 
             expression profiles using an optimization model and genetic algorithm
cosinor is a set of simple functions that transforms longitudinal
    data to estimate the cosinor linear model as described in Tong (1976).
    Methods are given to summarize the mean, amplitude and acrophase, to
    predict the mean annual outcome value, and to test the coefficients.
Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). <doi:10.1186/1742-4682-11-16>.
Package encapsulates standard expressions for distances,
        times, luminosities, and other quantities useful in
        observational cosmology, including molecular line observations.
        Currently coded for a flat universe only.
A single framework, unifying, extending, and improving a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific 'parent' Gaussian process Papalexiou (2018) <doi:10.1016/j.advwatres.2018.02.013>.
COSSO is a new regularization method that automatically
        estimates and selects important function components by a
        soft-thresholding penalty in the context of smoothing spline
        ANOVA models. Implemented models include mean regression,
        quantile regression, logistic regression and the Cox regression
        models.
Parameter estimation, one-step ahead forecast and new location
    prediction methods for spatio-temporal data.
Contains functions that can determine whether a time series
	is second-order stationary or not (and hence evidence for
	locally stationarity). Given two non-stationary series (i.e.
	locally stationary series) this package can then discover
	time-varying linear combinations that are second-order stationary.
Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation.
	Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest.
	Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers
	that don't accept observation weights.
Density, distribution function, quantile function, random
    generation and survival function for the Cosine Weibull Distribution as defined
    by SOUZA, L. New Trigonometric Class of Probabilistic Distributions. 219 p.
    Thesis (Doctorate in Biometry and Applied Statistics) - Department of Statistics
    and Information, Federal Rural University of Pernambuco, Recife, Pernambuco,
    2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Count transformation models featuring 
        parameters interpretable as discrete hazard ratios, odds ratios,
        reverse-time discrete hazard ratios, or transformed expectations. 
        An appropriate data transformation for a count outcome and
        regression coefficients are simultaneously estimated by maximising
        the exact discrete log-likelihood using the computational framework
        provided in package 'mlt', technical details are given in
        Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>.
Implements cointegration/co-trending rank selection
        algorithm in Guo and Shintani (2013) "Consistent
        co-trending rank selection when both stochastic and nonlinear
        deterministic trends are present". 
        The Econometrics Journal 16: 473-483 
        <doi:10.1111/j.1368-423X.2012.00392.x>.
        Numbered examples correspond to Feb 2011 preprint 
        <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.
Interface to the couchDB document database <http://couchdb.apache.org>.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Counts colors within color range(s) in images, and
    provides a masked version of the image with targeted pixels
    changed to a different color. Output includes the locations
    of the pixels in the images, and the proportion of the image
    within the target color range with optional background masking.
    Users can specify multiple color ranges for masking.
Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.
A large number of measurements generate count data. This is a statistical data type 
    that only assumes non-negative integer values and is generated by counting. Typically, counting 
    data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. 
    The number of DNA double-strand breaks can be counted in individual cells using various 
    bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of 
    the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. 
    Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software 
    offers functions for a comprehensive automated evaluation of distribution models of count 
    data. In addition to programmatic interaction, a graphical user interface (web server) 
    is included, which enables fast and interactive data-scientific analyses. The user is supported 
    in selecting the most suitable counting distribution for his own data set.
Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.
Flexible univariate count models based on renewal
    processes. The models may include covariates and can be specified
    with familiar formula syntax as in glm() and package 'flexsurv'.
    The methodology is described in a forthcoming paper in the Journal
    of Statistical Software <10.18637/jss.v090.i13> (included as
    vignette 'Countr_guide' in the package).
Standardize country names, convert them into one of
    40 different coding schemes, convert between coding schemes, and
    assign region descriptors.
Modeling under- and over-dispersed count data using extended Poisson process models (EPPM) as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .
Implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.
Provide data transformation functions to transform counts in RNA-seq data analysis. Please see the reference: Zhang Z, Yu D, Seo M, Hersh CP, Weiss ST, Qiu W. (2019) <doi.org/10.1038/s41598-019-41315-w>.
Quantifies United States flood impacts at the county level using
    United States Geological Service (USGS) River Discharge data for the USGS
    API. This package builds on R packages from the USGS, with the goal of
    creating county-level time series of flood status that can be more easily
    joined with county-level impact measurements, including health outcomes.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631), the Colorado Water Center,
    and the National Science Foundation, Integrative Graduate Education and
    Research Traineeship (IGERT) Grant No. DGE-0966346 "I-WATER: Integrated
    Water, Atmosphere, Ecosystems Education and Research Program" at
    Colorado State University.
Inputs date-times in Coordinated Universal Time (UTC)
    and converts to a local date-time and local date for US counties, based on
    each county's Federal Information Processing Standard (FIPS) code.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631) and the National Science
    Foundation (1331399).
Interacts with NOAA data sources (including the NCDC API at
  <http://www.ncdc.noaa.gov/cdo-web/webservices/v2> and ISD data) using
  functions from the 'rnoaa' package to obtain and compile weather time
  series for U.S. counties. This work was supported in part by grants from the
  National Institute of Environmental Health Sciences (R00ES022631) and the
  Colorado State University Water Center.
Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
Tests on properties of space-time covariance functions.
    Tests on symmetry, separability and for assessing 
    different forms of non-separability are available. Moreover tests on 
    some classes of covariance functions, such that the classes of 
    product-sum models, Gneiting models and integrated product models have 
    been provided.  It is the companion R package to the papers of  
    Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability 
    and some classes of space-time covariance function models <doi:10.1007/s00477-017-1472-2>
    and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for
    selecting a class of space-time covariance functions <doi:10.18637/jss.v094.i01>.
Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.
Combine partial covariance matrices using a Wishart-EM algorithm. 
    Methods are described in the November 2019 article by Akdemir et al. 
    <https://www.biorxiv.org/content/10.1101/857425v1>.
    It can be used to combine partially overlapping covariance matrices 
    from independent trials, partially overlapping multi-view relationship
    data from genomic experiments, partially overlapping Gaussian graphs
    described by their covariance structures. 
    High dimensional covariance estimation, 
    multi-view data integration.
    high dimensional covariance graph estimation.
Produce forest plots to visualize covariate effects using either
    the command line or an interactive 'Shiny' application.
Computes p-values using the largest root test using 
    an approximation to the null distribution by Johnstone (2008) <DOI:10.1214/08-AOS605>.
Converts any word, sentence or speech into Trump's infamous
  "covfefe" format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>.
  Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>.
Direct sparse covariance matrix estimation via the covariance graphical lasso by Bien, Tibshirani (2011) <doi:10.1093/biomet/asr054> using the fast coordinate descent algorithm of Wang (2014) <doi:10.1007/s11222-013-9385-5>.
Download COVID-19 data across governmental sources at national, regional, and city level, as described in Guidotti and Ardia (2020) <doi:10.21105/joss.02376>. Includes the time series of vaccines, tests, cases, deaths, recovered, hospitalizations, intensive therapy, and policy measures by 'Oxford COVID-19 Government Response Tracker' <https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker>. Provides a seamless integration with 'World Bank Open Data' <https://data.worldbank.org/>, 'Google Mobility Reports' <https://www.google.com/covid19/mobility/>, 'Apple Mobility Reports' <https://covid19.apple.com/mobility>.
Load and analyze updated time series worldwide data of reported cases for the Novel CoronaVirus Disease (CoViD-19) from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository <https://github.com/CSSEGISandData/COVID-19>. The datasets are available in two main modalities, as a time series sequences and aggregated for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread.
Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at <https://covid.saude.gov.br/>, is available at country, region, state, and city-levels. The package also downloads the world-level COVID-19 data from the John Hopkins University's repository.
Provides different datasets parsed from 'Drugbank' 
    <https://www.drugbank.ca/covid-19> database using 'dbparser' package. 
    It is a smaller version from 'dbdataset' package. It contains only information
    about COVID-19 possible treatment.
Imports and cleans 'opencovid19-fr'
    <https://github.com/opencovid19-fr/data> data on COVID-19 in France.
Provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile <http://www.protezionecivile.it/>.
Ready to use Japanese covid-19 datasets.
Scrapes trends in mobility after the Covid-19
    outbreak from different sources. Currently, the package scrapes data
    from Google <https://www.google.com/covid19/mobility/>, Apple
    <https://www.apple.com/covid19/mobility>, and will add others. The
    data returned uses the tidy Covid19R project data standard
    <https://covid19r.github.io/documentation/> as well as the controlled
    vocabularies for measurement types.
Accesses the NY Times Covid-19 county-level data
    for the US, described in
    <https://www.nytimes.com/article/coronavirus-county-data-us.html> and
    available at <https://github.com/nytimes/covid-19-data>. It then returns
    the data in a tidy data format according to the Covid19R Project data
    specification. If you plan to use the data or publicly display the data
    or results, please make sure cite the original NY Times source. Please read and
    follow the terms laid out in the data license at 
    <https://github.com/nytimes/covid-19-data/blob/master/LICENSE>.
Provides a verity of summary tables of the Covid19 cases in San Francisco. Data source: San Francisco, Department of Public Health - Population Health Division <https://datasf.org/opendata/>.
Provides a daily summary of the Coronavirus (COVID-19) cases in Switzerland cantons and Principality of Liechtenstein. Data source: Specialist Unit for Open Government Data Canton of Zurich <https://www.zh.ch/de/politik-staat/opendata.html>.
A wrapper around the 'COVID Tracking Project API'
    <https://covidtracking.com/api/> providing data on cases of COVID-19
    in the US.
Provides tools for downloading, reading, and analysing the PNAD COVID19
	household survey from Brazilian Institute of Geography and Statistics - IBGE.
	The data must be downloaded from the official website <https://www.ibge.gov.br/>. 
	Further analyses must be made using package 'survey'.
A feasible framework for mutation analysis and reverse transcription 
  polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including 
  mutation profile visualization, statistics and mutation ratio of each assay. 
  The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in 
  large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020) 
  <doi:10.20944/preprints202004.0529.v1>.
Estimation of latent class models with covariate effects
        on underlying and measured variables. The measured variables
        are dichotomous or polytomous, all with the same number of
        categories.
Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' <https://codecov.io> or
    'Coveralls' <https://coveralls.io>. Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.
This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates.
The cov.nnve() function implements robust covariance estimation
        by the nearest neighbor variance estimation (NNVE) method of
        Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.
Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) <DOI:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <DOI:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.
Functions for testing if the covariance structure of 2-dimensional data
    (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) =
    C_1 x C_2.
    A complete descriptions of the implemented tests can be found in the paper
    Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in
    nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017),
    no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.
User specifies population covariance matrix. Marginal information may be fully
        specified, for which the package implements the VITA (VIne-To-Anything) algorithm. 
        Groenneberg and Foldnes (2017) <doi:10.1007/s11336-017-9569-6>.
        Alternatively, marginal skewness and kurtosis may be specified, for which the package
        implements the IG (independent generator) algorithm. Foldnes and Olsson (2016) <doi:10.1080/00273171.2015.1133274>.
Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance
  matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of 
  covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix
  tests use C++ to speed performance and allow larger data sets.
Covariance is of universal prevalence across various disciplines within statistics.
    We provide a rich collection of geometric and inferential tools for convenient analysis of
    covariance structures, topics including distance measures, mean covariance estimator,
    covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
    For an introduction to covariance in multivariate statistical analysis,
    see Schervish (1987) <doi:10.1214/ss/1177013111>.
Implements a specific form of segmented linear regression
    with two independent variables. The visualization of that function looks 
    like a quarter segment of a cowbell giving the package its name. 
    The package has been specifically constructed for the case where minimum 
    and maximum value of the dependent and two independent variables 
    are known a prior, which is usually the case
    when those values are derived from Likert scales.
Provides various features that help with creating publication-quality figures
    with 'ggplot2', such as a set of themes, functions to align plots and arrange
    them into complex compound figures, and functions that make it easy to annotate
    plots and or mix plots with images. The package was originally written for
    internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package).
    It has also been used extensively in the book Fundamentals of Data
    Visualization.
Allows printing of character strings as messages/warnings/etc.
    with ASCII animals, including cats, cows, frogs, chickens, ghosts,
    and more.
Functions for generating, simulating, and visualizing expected durations and marginal changes in duration from the Cox proportional hazards model as described in Kropko and Harden (2017) <doi:10.1017/S000712341700045X> and Harden and Kropko (2018) <doi:10.1017/psrm.2018.19>.
Fits Cox-type models based on interval-censored data from a survival
             or illness-death process.
Fit Cox proportional hazards models containing both 
 fixed and random effects.  The random effects can have a general form, of which
 familial interactions (a "kinship" matrix) is a particular special case. 
 Note that the simplest case of a mixed effects Cox model, i.e. a single random 
 per-group intercept, is also called a "frailty" model.  The approach is based
 on Ripatti and Palmgren, Biometrics 2002.
Fast algorithms for fitting a Cox mixed-effects model for e.g., genome-wide association studies. See Liang He and Alexander Kulminski (2019) <doi:10.1101/729285>.
Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
  which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see 
  Heinze and Schemper (2001) and Heinze and Dunkler (2008).
  The program fits profile penalized likelihood confidence intervals which were proved to outperform
  Wald confidence intervals.
Performs analysis of right-censored length-biased data using Cox model. It contains model fitting and checking, and the stationarity assumption test. The model fitting and checking methods are described in Qin and Shen (2010) <doi:10.1111/j.1541-0420.2009.01287.x> and Lee, Ning, and Shen (2018) <doi:10.1007/s10985-018-9422-y>.
Sparse estimation for Cox PH models is done via
    Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, 
    Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. MIC mimics the best 
    subset selection using a penalized likelihood approach yet with no need 
    of a tuning parameter. The problem is further reformulated with a 
    re-parameterization step so that it reduces to one unconstrained non-convex
    yet smooth programming problem, which can be solved efficiently. Furthermore,
    the re-parameterization tactic yields an additional advantage in terms of
    circumventing post-selection inference.
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
Implements weighted estimation in Cox regression as proposed by
             Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, 
             <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and 
             Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). 
             Weighted Cox regression provides unbiased average hazard ratio 
             estimates also in case of non-proportional hazards. 
             Approximated generalized concordance probability an effect size measure for clear-cut
             decisions can be obtained.
             The package provides options to estimate time-dependent effects conveniently by
             including interactions of covariates with arbitrary functions of time, with or without
             making use of the weighting option.
A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables.
Calculate the R-squared, aka explained randomness, based on the partial likelihood ratio statistic under the Cox Proportional Hazard model [J O'Quigley, R Xu, J Stare (2005) <doi:10.1002/sim.1946>].
Fit robustly proportional hazards regression model
Fits Cox regression based on retrospectively ascertained times-to-event. The method uses Inverse-Probability-Weighting estimating equations.
Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Functions for calculating the conditional power
             for different models in survival time analysis
             within randomized clinical trials
             with two different treatments to be compared
             and survival as an endpoint.
Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.
The package includes functions to test and compare causal models.
Implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.
A Bayesian meta-analysis method for studying cross-phenotype
    genetic associations. It uses summary-level data across multiple phenotypes to
    simultaneously measure the evidence of aggregate-level pleiotropic association
    and estimate an optimal subset of traits associated with the risk locus. CPBayes
    is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) <doi:10.1371/journal.pgen.1007139>.
This package contains methods to perform Common Principal
    Component Analysis (CPCA). The stepwise method by Trendafilov is published
    in the current version. Please see Trendafilov (2010). Stepwise estimation
    of common principal components. Computational Statistics & Data Analysis,
    54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.
Probability mass function, distribution function, quantile function and random generation for the Complex Triparametric Pearson (CTP) and Complex Biparametric Pearson (CBP) distributions developed by Rodriguez-Avi et al (2003) <doi:10.1007/s00362-002-0134-7>, Rodriguez-Avi et al (2004) <doi:10.1007/BF02778271> and Olmo-Jimenez et al (2018) <doi:10.1080/00949655.2018.1482897>. The package also contains maximum-likelihood fitting functions for these models.
Functions to calculate concordance probability estimates in survival analysis.
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug's maximum concentration, (13) dlar.fn for calculating loading dose based on drug's accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
This is the R Interface to the C API of IBM ILOG CPLEX. It necessarily depends on IBM ILOG CPLEX (>= 12.1).
Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <https://link.springer.com/article/10.1007/s11222-012-9343-7>.
Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. A full description of this package is available in Ross, G.J (2015) - "Parametric and nonparametric sequential change detection in R" available at <https://www.jstatsoft.org/article/view/v066i03>.
We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.
Provides a header only, C++11 interface to R's C
    interface.  Compared to other approaches 'cpp11' strives to be safe
    against long jumps from the C API as well as C++ exceptions, conform
    to normal R function semantics and supports interaction with 'ALTREP'
    vectors.
Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm.
    Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>),
    bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://pdfs.semanticscholar.org/0761/18dfbe1d5a220f6ac59b4de4ad07b50283ac.pdf>),
    A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>),
    new bidirectional A* (Pijls & Post (2009) <http://repub.eur.nl/pub/16100/ei2009-10.pdf>),
    Contraction hierarchies (R. Geisberger, P. Sanders, D. Schultes and D. Delling (2008) <doi:10.1007/978-3-540-68552-4_24>),
    PHAST (D. Delling, A.Goldberg, A. Nowatzyk, R. Werneck (2011) <doi:10.1016/j.jpdc.2012.02.007>).
Implementation of the Control Polygon Reduction and Control Net
    Reduction methods for finding parsimonious B-spline regression models.
Permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function). See <doi:10.1111/j.1467-9876.2010.00729.x>.
Implements the three-step workflow for robust analysis of change in 
    two repeated measurements of continuous outcomes, described in Ning et al.
    (in press), "Robust estimation of the effect of an exposure on the change in
    a continuous outcome", BMC Medical Research Methodology.
Calculate date of birth, age, and gender, and generate anonymous
    sequence numbers from CPR numbers.
    <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>.
Implements multiple change searching algorithms for a variety of 
    frequently considered parametric change-point models. In particular, it 
    integrates a criterion proposed by Zou, Wang and Li (2020) 
    <doi:10.1214/19-AOS1814> to select the number of change-points in a 
    data-driven fashion. Moreover, it also provides interfaces for 
    users-customized change-point models with their own cost function and 
    estimation routines.
Nonparametric change point estimation for survival data based on p-values of exact binomial tests.
Simulates time-to-event data
    with type I right censoring using two methods: the inverse CDF
    method and our proposed memoryless method. The latter method
    takes advantage of the memoryless property of survival and
    simulates a separate distribution between change-points. We
    include two parametric distributions: exponential and Weibull.
    Inverse CDF method draws on the work of Rainer Walke (2010), 
    <https://www.demogr.mpg.de/papers/technicalreports/tr-2010-003.pdf>.
Provides automated methods for downloading, recoding, and merging 
    selected years of the Current Population Survey's Voting and Registration 
    Supplement, a large N national survey about registration, voting, and 
    non-voting in United States federal elections. Provides documentation for 
    appropriate use of sample weights to generate statistical estimates, 
    drawing from Hur & Achen (2013) <doi:10.1093/poq/nft042> and McDonald (2018) 
    <http://www.electproject.org/home/voter-turnout/voter-turnout-data>.
Non-parametric test for equality of multivariate distributions.  Trains a classifier to classify (multivariate) observations as coming from one of several distributions.  If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.
Incorporates colour gradients from the 'cpt-city' web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>.
Allows to retrieve data from the
    'CPTEC/INPE' weather forecast API. 'CPTEC' stands for 'Centro de Previsão
    de Tempo e Estudos Climáticos' and 'INPE' for 'Instituto Nacional de
    Pesquisas Espaciais'. 'CPTEC' is the most advanced numerical weather and
    climate forecasting center in Latin America, with high-precision short
    and medium-term weather forecasting since the beginning of 1995. See
    <http://www.cptec.inpe.br/> for more information.
Access data from the 'Care Quality Commission', the health 
    and adult social care regulator for England. The 'Care Quality Commission' 
    operates an API 
    <https://www.cqc.org.uk/about-us/transparency/using-cqc-data#api>, with data
    available under the Open Government License. Data includes information on 
    service providers, locations such as hospitals, care homes and 
    medical clinics, and ratings and inspection reports.
Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).
Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. For large time dimensions of the panel, the computation of the proposed models involves a recursive function adapted from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)).
This package contains R-functions to perform power
        calculation in a group sequential clinical trial with censored
        survival data and possibly unequal patient allocation between
        treatment and control groups. The fuctions can also be used to
        determine the study duration in a clinical trial with censored
        survival data as the sum of the accrual duration, which
        determines the sample size in a traditional sense, and the
        follow-up duration, which more or less controls the number of
        events to be observed. This package also contains R functions
        and methods to display the computed results.
R functions for cosmological research.
  The main functions are similar to the python library, cosmolopy.
Implements Cragg-Donald (1993) <doi:10.1017/S0266466600007519> and Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006> tests for weak instruments in R.
Provides R routine for the so called two-sample
        Cramer-Test.  This nonparametric two-sample-test on equality
        of the underlying distributions can be applied to 
        multivariate data as well as univariate data. It offers two 
        possibilities to approximate the critical value both of which 
        are included in this package.
The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution <arXiv:2008.03073>, of which functions are provided.
Functions for completing and recalculating rankings and sorting.
A set of functions to manage 'CRAN'-like repositories
    efficiently.
'API' to the database of 'CRAN' package downloads from the
    'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>,
    see <https://github.com/r-hub/cranlogs.app> for the raw 'API'.
Core visualizations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organizing the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, producing package dependence trees, and for computing useful summaries and producing interactive visualizations from the resulting networks and summaries. The resulting networks can be coerced to 'igraph' <https://CRAN.R-project.org/package=igraph> objects for further analyses and modelling.
One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.
Various R tools for http://crantastic.org/
Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.
Fit continuous-time correlated random walk models with time indexed
    covariates to animal telemetry data. The model is fit using the Kalman-filter on
    a state space version of the continuous-time stochastic movement process.
Colored terminal output on terminals that support 'ANSI'
    color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI'
    color support is automatically detected. Colors and highlighting can
    be combined and nested. New styles can also be created easily.
    This package was inspired by the 'chalk' 'JavaScript' project.
Different approaches to censored or truncated regression with 
  conditional heteroscedasticity are provided. First, continuous 
  distributions can be used for the (right and/or left censored or truncated)
  response with separate linear predictors for the mean and variance. 
  Second, cumulative link models for ordinal data
  (obtained by interval-censoring continuous data) can be employed for
  heteroscedastic extended logistic regression (HXLR). In the latter type of
  models, the intercepts depend on the thresholds that define the intervals.
Provides a new method for identification of clusters of genomic
 regions within chromosomes. Primarily, it is used for calling clusters of 
 cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions
 in the tissue or cell type of interest, such as those generated from chromatin-based 
 assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements 
 within chromosomes of a given sample to identify COREs in the following steps:
 1) It identifies window size or the maximum allowed distance between the elements 
 within each CORE, 2) It identifies number of elements which should be clustered 
 as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which 
 does not pass the threshold considered in the approach.
Setup and retrieve HTTPS and SSH credentials for use with 'git' and 
    other services. For HTTPS remotes the package interfaces the 'git-credential' 
    utility which 'git' uses to store HTTP usernames and passwords. For SSH 
    remotes we provide convenient functions to find or generate appropriate SSH 
    keys. The package both helps the user to setup a local git installation, and
    also provides a back-end for git/ssh client libraries to authenticate with 
    existing user credentials.
A set of functions for computing the CreditMetrics risk model
Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization. Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster.
Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013): "Counterparty Credit Risk, Collateral and Funding. With Pricing Cases for All Asset Classes".
Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method. Package documentation includes the vignette included in this package, and the paper by Schnell, Fiecas, and Carlin (2020, <doi:10.18637/jss.v094.i07>).
It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.
Simple tidying, analysis, and visualization of conjoint (factorial) experiments, including estimation and visualization of average marginal component effects ('AMCEs') and marginal means ('MMs') for weighted and un-weighted survey data, along with useful reference category diagnostics and statistical tests. Estimation of 'AMCEs' is based upon methods described by Hainmueller, Hopkins, and Yamamoto (2014) <doi:10.1093/pan/mpt024>.
Builds a 'SQLite' database file of pre-calculated transcription 
    factor/microRNA-gene correlations (co-expression) in cancer from the 
    Cistrome Cancer Liu et al. (2011) <doi:10.1186/gb-2011-12-8-r83> and 
    'miRCancerdb' databases (in press). Provides custom classes and functions 
    to query, tidy and plot the correlation data.
Implements modeling and computational tools for conditional
    random fields (CRF) model as well as other probabilistic undirected
    graphical models of discrete data with pairwise and unary potentials.
Wraps the 'CRFsuite' library <https://github.com/chokkan/crfsuite> allowing users 
    to fit a Conditional Random Field model and to apply it on existing data.
    The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models 
    for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application
    is included in the package to allow you to easily construct training data.
Tools for analyzing performances of cricketers based on stats in
    ESPN Cricinfo Statsguru. The toolset can  be used for analysis of Tests,ODIs 
    and Twenty20 matches of both batsmen and bowlers. The package can also be used to
    analyze team performances.
A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories.
Gives convenient access to publicly available police-recorded open
    crime data from large cities in the United States that are included in the
    Crime Open Database <https://osf.io/zyaqn/>.
Statistical Methods for Crime Series Linkage. This package provides 
      code for criminal case linkage, crime series identification, crime series 
      clustering, and suspect identification.
A collection of functions that make it easier to understand crime (or other)
    data, and assist others in understanding it. The package helps you read data 
    from various sources, clean it, fix column names, and graph the data.
Implements convex regression with interpretable sharp partitions
    (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
Designs guide sequences for CRISPR/Cas9 genome editing and 
    provides information on sequence features pertinent to guide 
    efficiency. Sequence features include annotated off-target 
    predictions in a user-selected genome and a predicted efficiency 
    score based on the model described in Doench et al. (2016) 
    <doi:10.1038/nbt.3437>. Users are able to import additional genomes 
    and genome annotation files to use when searching and annotating 
    off-target hits. All guide sequences and off-target data can be 
    generated through the 'R' console with sgRNA_Design() or through 
    'crispRdesignR's' user interface with crispRdesignRUI(). CRISPR 
    (Clustered Regularly Interspaced Short Palindromic Repeats) and the 
    associated protein Cas9 refer to a technique used in genome editing.
Functions for phase I clinical trials using the continual reassessment method.
Text mining client for 'Crossref' (<https://crossref.org>). Includes
    functions for getting getting links to full text of articles, fetching full
    text articles from those links or Digital Object Identifiers ('DOIs'),
    and text extraction from 'PDFs'.
Implements the Cross-contribution Compensating Multiple
    standard Normalization (CCMN) method described in Redestig et
    al. (2009) Analytical Chemistry <doi:10.1021/ac901143w>
    and other normalization algorithms.
Implements a wide range of model-based dose
    escalation designs, ranging from classical and modern continual
    reassessment methods (CRMs) based on dose-limiting toxicity endpoints to
    dual-endpoint designs taking into account a biomarker/efficacy outcome. The
    focus is on Bayesian inference, making it very easy to setup a new design
    with its own JAGS code. However, it is also possible to implement 3+3
    designs for comparison or models with non-Bayesian estimation. The whole
    package is written in a modular form in the S4 class system, making it very
    flexible for adaptation to new models, escalation or stopping rules.
Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) <DOI:10.1016/j.csda.2020.106944>) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) <DOI:10.1007/s11222-018-9831-5>).
The crn package provides the core functions required to
        download and format data from the Climate Reference Network.
        Both daily and hourly data are downloaded from the ftp, a
        consolidated file of all stations is created, station metadata
        is extracted. In addition functions for selecting individual
        variables and creating R friendly datasets for them is
        provided.
Functions to help implement the extraction / subsetting / indexing
    function '[' and replacement function '[<-' of custom matrix-like types
    (based on S3, S4, etc.), modeled as closely to the base matrix class as
    possible (with tests to prove it).
A dashboard supports the usage of 'cromwell'. 
 'Cromwell' is a scientific workflow engine for command line users.
 This package utilizes 'cromwell' REST APIs and provides these convenient 
 functions: timing diagrams for running workflows, 'cromwell' engine status, 
 a tabular workflow list. For more information about 'cromwell', 
 visit <http://cromwell.readthedocs.io>.
Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece.
Functions to carry out the most important crystallographic calculations for crystal 
             structures made of 1d Gaussian-shaped atoms, especially useful for methods development.
             Main reference: E. Smith, G. Evans, J. Foadi (2017) <doi:10.1088/1361-6404/aa8188>.
Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers
    to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts.
A device closing function which is able to crop graphics (e.g.,
  PDF, PNG files) on Unix-like operating systems with the required underlying
  command-line tools installed.
Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes
             6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially,  in excel files which has been transformed
             and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell.
             The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.
A helpful tool for the identification of crop rows. Methods of this package include: Excess Green color scale <https://www.researchgate.net/publication/270613992_Color_Indices_for_Weed_Identification_Under_Various_Soil_Residue_and_Lighting_Conditions>, Otsu Thresholding <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4310076>, and Morphology <https://en.wikipedia.org/wiki/Mathematical_morphology>.
Interface to easily access Cropland Data Layer (CDL) data for any area of interest via the 'CropScape' <https://nassgeodata.gmu.edu/CropScape/> web service.
Contains functions for the construction of carryover
        balanced crossover designs. In addition contains functions to
        check given designs for balance.
Provides an extension to the 'purrr' family of
    mapping functions to apply a function to each combination of elements
    in a list of inputs.  Also includes functions for automatically
    detecting output type in mapping functions, finding every combination
    of elements of lists or rows of data frames, and applying multiple
    models to multiple subsets of a dataset.
This package performs a test for comparing two
        multivariate distributions by using the distance between
        observations.  The input is a distance matrix and the labels of
        the two groups to be compared, the output is the number of
        cross-matches and a p-value.
Package Crossover provides different crossover designs from
    combinatorial or search algorithms as well as from literature and a GUI to
    access them.
This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using 
 the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
Joint distribution of number of crossings and the 
  longest run in a series of independent Bernoulli trials. The
  computations uses an iterative procedure where computations 
  are based on results from shorter series. The procedure 
  conditions on the start value and partitions by further 
  conditioning on the position of the first crossing (or none).
Provides building blocks for allowing HTML widgets to communicate
    with each other, with Shiny or without (i.e. static .html files). Currently
    supports linked brushing and filtering.
Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (versions 1.4.1 & 1.5.1)
  or the WHO 2014 questionnaire for automated coding of Cause of Death using the InSilicoVA (data.type = "WHO2016") and
  InterVA5 algorithms. Previous versions of this package supported user-supplied mappings (via the map_records function), but
  this functionality has been removed.  This package is made available by WHO and the Bloomberg Data for Health Initiative.
Contains generic functions for performing 
  cross validation and for computing diagnostic errors.
Defines classes and methods to cross-validate various
  binary classification algorithms used for "class prediction"
  problems.
A pair of functions for renaming and encoding data frames
	     using external crosswalk files. It is especially useful when
	     constructing master data sets from multiple smaller data
	     sets that do not name or encode variables consistently
	     across files. Based on similar commands in 'Stata'.
Generate crosswords from a list of words.
Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2019) <doi:10.1007/s11222-018-9842-2>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then the monotonicity direction classification procedure establishes it. Two monotonicity tests are also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.
Chinese restaurant process [Pitman (1995) <doi:10.1007/BF01213386>] is used in order to compose Dirichlet process [Ferguson (1973) <doi:10.1214/aos/1176342360>]. The clustering which uses Chinese restaurant process does not need to decide the number of clusters in advance. This algorithm automatically adjusts it. And this package calculates the ambiguity of clusters as entropy [Yngvason (1999) <doi:10.1016/S0370-1573(98)00082-9>].
In order to facilitate analysis of datasets hosted on the Crunch
    data platform <https://crunch.io/>, the 'crplyr' package implements 'dplyr'
    methods on top of the Crunch backend. The usual methods 'select', 'filter',
    'group_by', 'summarize', and 'collect' are implemented in such a way as to
    perform as much computation on the server and pull as little data locally
    as possible.
Auto, Cross and Multi-dimensional recurrence quantification analysis. 
    Different methods for computing recurrence, cross vs. multidimensional
    or profile iti.e., only looking at the diagonal recurrent points, 
    as well as functions for optimization and plotting are proposed.
    in-depth measures of the whole cross-recurrence plot,
    Please refer to by Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510> 
    and Wallot (2018) <doi:10.1080/00273171.2018.1512846>
    for further details about the method.
In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions.
Extension of cmprsk to Stratified and Clustered data.
        Goodness of fit test for Fine-Gray model.
Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <http://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <http://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>).
Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <doi:10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss.
Obtain coordinate system metadata from various data formats. There 
 are functions to extract a 'CRS' (coordinate reference system, 
 <https://en.wikipedia.org/wiki/Spatial_reference_system>) in 'EPSG' (European 
 Petroleum Survey Group, <http://www.epsg.org/>), 'PROJ4' <https://proj.org/>, 
 or 'WKT2' (Well-Known Text 2, 
 <http://docs.opengeospatial.org/is/12-063r5/12-063r5.html>) forms. This is 
 purely for getting simple metadata from in-memory formats, please use other 
 tools for out of memory data sources.
Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
  Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.
An algorithm for identifying candidate driver combinations in cancer. CRSO 
  is based on a theoretical model of cancer in which a cancer rule 
  is defined to be a collection of two or more events (i.e., alterations) that are minimally 
  sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively 
  are assumed to account for all of ways to cause cancer in the population. In CRSO every 
  event is designated explicitly as a passenger or driver within each patient. 
  Each event is associated with a patient-specific, event-specific passenger penalty, 
  reflecting how unlikely the event would have happened by chance, i.e., as a passenger.
  CRSO evaluates each rule set by assigning all samples to a rule in the rule set,
  or to the null rule, and then calculating the total statistical penalty from all
  unassigned event. CRSO uses a three phase procedure find the best rule set of 
  fixed size K for a range of Ks. A core rule set is then identified from among
  the best rule sets of size K as the rule set that best balances rule set size and 
  statistical penalty. 
  Users should consult the 'crso' vignette for an example walk through of a full CRSO run.
  The full description, of the CRSO algorithm is presented in: 
  Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." 
  BioRxiv 674234 [Preprint]. June 19, 2019. <doi:10.1101/674234>.
  Please cite this article if you use 'crso'.
Tidies and performs preliminary analysis of 'Coursera' research
    export data. These export data can be downloaded by anyone who has classes
    on Coursera and wants to analyze the data. Coursera is one of the leading 
    providers of MOOCs and was launched in January 2012. With over 25 million 
    learners, Coursera is the most popular provider in the world being followed 
    by EdX, the MOOC provider that was a result of a collaboration between 
    Harvard University and MIT, with over 10 million users. Coursera has over 
    150 university partners from 29 countries and offers a total of 2000+ 
    courses from computer science to philosophy. Besides, Coursera offers 180+ 
    specialization, Coursera's credential system, and four fully online Masters 
    degrees. For more information about Coursera check Coursera's
    About page on <https://blog.coursera.org/about/>.
Provides wrapper functions for running classification and
    regression tests using different machine learning techniques, such as Random
    Forests and decision trees. The package provides standardized methods for
    preparing data to suit the algorithm's needs, training a model, making
    predictions, and evaluating results. Also, some functions are provided to run
    multiple instances of a test.
Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).
Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
A simple HTTP client, with tools for making HTTP requests,
    and mocking HTTP requests. The package is built on R6, and takes
    inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>).
    The package name is a play on curl, the widely used command line tool
    for HTTP, and this package is built on top of the R package 'curl', an
    interface to 'libcurl' (<https://curl.haxx.se/libcurl>).
The Crunch.io service <https://crunch.io/> provides a cloud-based
    data store and analytic engine, as well as an intuitive web interface.
    Using this package, analysts can interact with and manipulate Crunch
    datasets from within R. Importantly, this allows technical researchers to
    collaborate naturally with team members, managers, and clients who prefer a
    point-and-click interface.
To facilitate building custom dashboards on the Crunch data
    platform <https://crunch.io/>, the 'crunchy' package provides tools for
    working with 'shiny'. These tools include utilities to manage authentication
    and authorization automatically and custom stylesheets to help match the
    look and feel of the Crunch web application. The package also includes
    several gadgets for use in 'RStudio'.
Functions for reading in and manipulating CRU TS3.21: Climatic
    Research Unit (CRU) Time-Series (TS) Version 3.21 data.
A utility program oriented to create maps, plot data, and do basic data summaries
    of 'DAS' data <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> 
    produced by 'WinCruz' from the Southwest Fisheries Science Center.
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
To re-calculate the coefficients and the standard deviation when changing the reference group.
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. 'csa' package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.
The joint distribution of potential outcomes is not typically identified under standard identifying assumptions such as selection on observables or even when individuals are randomly assigned to being treated. This package contains methods for obtaining tight bounds on distributional treatment effect parameters when panel data is available and under a Copula Stability Assumption as in Callaway (2017) <https://ssrn.com/abstract=3028251>.
Monte Carlo conditional inference for the parameters of a
  linear nonnormal regression model.
Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.
Calculates pointwise confidence intervals for the cumulative distribution function of the event time for current status data, data where each individual is assessed at one time to see if they had the event or not by the assessment time.
Germline and somatic locus data which contain the total read depth and B allele 
    read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster 
    model can deal with the SNVs mutation and the CNAs mutation.
Estimate, assess, test, and study linear, nonlinear, hierarchical 
  and multigroup structural equation models using composite-based approaches 
  and procedures, including estimation techniques such as partial least squares 
  path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), 
  generalized structured component analysis (GSCA), generalized structured 
  component analysis with uniqueness terms (GSCAm), generalized canonical 
  correlation analysis (GCCA), principal component analysis (PCA), 
  factor score regression (FSR) using sum score, regression or 
  bartlett scores (including bias correction using Croon’s approach), 
  as well as several tests and typical postestimation procedures 
  (e.g., verify admissibility of the estimates, assess the model fit, 
  test the model fit etc.).
Mines contiguous sequential patterns in text.
Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) <doi:10.1128/JCM.00696-14> that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping.
Applies factor analysis methodology to microarray data in order
    to derive connectivity scores between compounds. The package also contains an
    implementation of the connectivity score algorithm by Zhang and Gant (2008) <doi:10.1186/1471-2105-9-258>.
Package for CShapes, a GIS dataset of country boundaries (1946-today). Includes functions for data extraction and the computation of distance matrices and -lists.
Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.
Provides functions for computing the density
    and the log-likelihood function of closed-skew normal variates,
    and for generating random vectors sampled from this distribution.
    See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004).
    The closed skew normal distribution, 
    Skew-elliptical distributions and their applications: a journey beyond normality,
    Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Imports 'PxStat' data in JSON-stat format and (optionally) reshapes it into wide
	format. The Central Statistics Office (CSO) is the national statistical institute of Ireland
	and 'PxStat' is the CSOs online database of Official Statistics. This database contains current
	and historical data series compiled from CSO statistical releases and is accessed at
	<http://data.cso.ie>.
	The CSO 'PxStat' Application Programming Interface (API), which is accessed in this package, provides
	access to 'PxStat' data in JSON-stat format at <http://data.cso.ie>.
	This dissemination tool allows developers machine to machine access to CSO 'PxStat' data.
Provides the Correlates of State Policy data set for easy use in R.
Calculate the predictive discrete Fourier transform, complete discrete Fourier transform, complete periodogram, and tapered complete periodogram. This algorithm is based on the preprint "Spectral methods for small sample time series: A complete periodogram approach" (2020) by Sourav Das, Suhasini Subba Rao, and Junho Yang.
A tool that imports, subsets, visualizes, and exports the Correlates of State Policy Project dataset assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy>. The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, create map visualizations, export the citations associated with their searches, and more.
Contains the Correlates of State Policy Project dataset (+ codebook) assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy> used by the 'cspp' package. The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States.
Includes two functions to evaluate the hypothesis of complete spatial randomness (csr) in point processes.  The function 'mwin' calculates quadrat counts to estimate the intensity of a spatial point process through the moving window approach proposed by Bailey and Gatrell (1995).  Event counts are computed within a window of a set size over a fine lattice of points within the region of observation.  The function 'pielou' uses the nearest neighbor test statistic and asymptotic distribution proposed by Pielou (1959) to compare the observed point process to one generated under csr.  The value can be compared to that given by the more widely used test proposed by Clark and Evans (1954).
Cell-type specific differential expression of a microarray
        experiment of heterogeneous tissue samples, using SAM.
A collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.
Selection of the number of clusters in cluster analysis using
    stability methods.
Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014).
Exploits dynamical seasonal forecasts in order to provide
    information relevant to stakeholders at the seasonal timescale. The package
    contains process-based methods for forecast calibration, bias correction,
    statistical and stochastic downscaling, optimal forecast combination and
    multivariate verification, as well as basic and advanced tools to obtain
    tailored products. This package was developed in the context of the
    ERA4CS project MEDSCOPE and the H2020 S2S4E project.
    Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>.
    Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>.
    Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>.
    Straus et al. (2007) <doi:10.1175/JCLI4070.1>.
    Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>.
    Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>.
    D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>.
    Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>.
    Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>.
Implementation of CSUV from C. Yuen and P. Fryzlewicz (2020) <arXiv:2003.02791> "Exploiting disagreement between high-dimensional variable selectors for uncertainty visualization". CSUV aims to perform variable selection and illustrate variable selection uncertainties by combining variable selection results from various methods.
Reads and writes CSV with selected conventions.
 Uses the same generic function for reading and writing to promote consistent formats.
Functions for loading large (10M+ lines) CSV
    and other delimited files, similar to read.csv, but typically faster and
    using less memory than the standard R loader. While not entirely general,
    it covers many common use cases when the types of columns in the CSV file
    are known in advance. In addition, the package provides a class 'int64',
    which represents 64-bit integers exactly when reading from a file. The
    latter is useful when working with 64-bit integer identifiers exported from
    databases. The CSV file loader supports common column types including
    'integer', 'double', 'string', and 'int64', leaving further type
    transformations  to the user.
Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.
An interactive application for working with contingency Tables. The application has a template for solving contingency table problems like chisquare test of independence,association plot between two categorical variables. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/CategoricalDataAnalysis/>.
A method for pattern discovery in weighted graphs. Two use cases are achieved: 1) Given a weighted graph and a subset of its nodes, do the nodes show significant connectedness? 2) Given a weighted graph and two subsets of its nodes, are the subsets close neighbors or distant?
It is assumed that psychological distances between the categories are equal for the measurement instruments consisted of polytomously scored items. According to Muraki, this assumption must be tested. In the examination process of this assumption, the fit indexes are obtained and evaluated. This package provides that this assumption is removed. By with this package, the converted scale values of all items in a measurement instrument can be calculated by estimating a category parameter set for each item. Thus, the calculations can be made without any need to usage of the common category parameter set. Through this package, the psychological distances of the items are scaled. The scaling of a category parameter set for each item cause differentiation of score of the categories will be got from items. Also, the total measurement instrument score of an individual can be calculated according to the scaling of item score categories by with this package.This package provides that the place of individuals related to the structure to be measured with a measurement instrument consisted of polytomously scored items can be reveal more accurately. In this way, it is thought that the results obtained about individuals can be made more sensitive, and the differences between individuals can be revealed more accurately. On the other hand, it can be argued that more accurate evidences can be obtained regarding the psychometric properties of the measurement instruments.
A shortcut procedure is proposed to implement closed testing for large-scale multiple testings, especially with the global test. This shortcut is asymptotically equivalent to closed testing and post hoc. Users could detect any possible sets of features or pathways with family-wise error rate controlled. The global test is powerful to detect associations between a group of features and an outcome of interest.
Estimation and inference methods for the continuous threshold expectile regression.
    It can fit the continuous threshold expectile regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."
Identification and network inference of genetic loci associated
  with correlation changes in quantitative traits (called correlated trait loci, CTLs).
  Arends et al. (2016) <doi:10.21105/joss.00087>.
The CTM package is designed to solve problems of text mining and is specific for Chinese document.
Functions for estimating Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal adjustment, weighted adjustment and quasi-optimization of matrix logarithm based candidate solutions, an expectation-maximization algorithm as well as a Gibbs sampler.
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) <doi:10.1177/0962280217729845>) and the glmnet-C-TMLE algorithm (Ju et al. (2017) <arXiv:1706.10029>).
This is a package for constructing hypothesis trees based on the closure principle and analysing
  Closed Testing Procedures (CTP) using adjusted p-values. For reference, see
  Marcus, R., Peritz, E, and Gabriel, K.R. (1976) <doi:10.2307/2335748> and
  Bauer, P (1991) <doi:10.1002/sim.4780100609>.
Estimation of quantile regression models for survival data. The package implements the two-step estimator described in Frumento and Bottai (2017),  <doi: 10.1016/j.csda.2016.08.015>.
Provides functions for querying, retrieving and analyzing
        protocol- and results-related information on clinical trials from
        two public registers, the 'European Union Clinical Trials Register'
        ('EUCTR', <https://www.clinicaltrialsregister.eu/>) and
        'ClinicalTrials.gov' ('CTGOV', <https://clinicaltrials.gov/>). The
        trial information is transformed and stored in a database ('SQLite' 
        or 'MongoDB', via 'nodbi'). Functions are provided to identify de-
        duplicated records, to easily find and extract variables (fields)
        of interest even from complex nesting as used by the registers, 
        and to update previous queries that users retrieved in a database. 
        The package can be used for meta analysis and trend-analysis of
        the design and conduct as well as results of clinical trials.
A simple way to assess the stability of candidate housekeeping genes is implemented in this package.
Hierarchical continuous time state space modelling, for linear
    and nonlinear systems measured by  continuous variables, with limited support for 
    binary data. The subject specific dynamic system is modelled as a stochastic 
    differential equation (SDE), measurement models are typically multivariate normal factor models. 
    Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default.
    Nonlinearities,  (state dependent parameters) and random effects on all parameters
    are possible, using either max likelihood / max a posteriori optimization 
    (with optional importance sampling) or
    Stan's Hamiltonian Monte Carlo sampling. See  <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf>
    for details. Priors may be used. For the conceptual
    overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>.
    Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> .
    Stan based functions are not available on 32 bit Windows systems at present. 
    <https://cdriver.netlify.app/> contains some tutorial blog posts.
Original 'ctsem' (continuous time structural equation modelling)
    functionality, based on the 'OpenMx' software, as described in 
    Driver, Oud, Voelkle (2017) <doi:10.18637/jss.v077.i05>, with updated details in vignette. 
    Combines stochastic differential equations representing latent processes with 
    structural equation measurement models. These functions were split off from
    the main package of 'ctsem', as the main package uses the 'rstan' package as a backend now --
    offering estimation options from max likelihood to Bayesian.
    There are nevertheless use cases for the wide format SEM style approach as offered here, 
    particularly when there are no individual differences in observation timing and the
    number of individuals is large. For the main 'ctsem' package, see <https://cran.r-project.org/package=ctsem>.
An interactive document on  the topic of classification tree analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/CTShiny/>.
A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs.
A Shiny interface developed in close coordination with the CTT package, providing a GUI that guides the user through CTT analyses.
Interactive shiny application for running classical test theory (item analysis).
Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated
             packages (client-side tools), generating HTML pages and storing task view information in the repository
	     (server-side tools).
For ordinal rating data, estimate and test models within the family of
    CUB models and their extensions (where CUB stands for Combination of a 
	discrete Uniform and a shifted Binomial distributions). Simulation routines, plotting facilities 
	and fitting measures are also provided.
R wrappers around the cubature C library of Steven
    G. Johnson for adaptive multivariate integration over hypercubes
    and the Cuba C library of Thomas Hahn for deterministic and
    Monte Carlo integration. Scalar and vector interfaces for 
    cubature and Cuba routines are provided; the vector interfaces
    are highly recommended as demonstrated in the package
    vignette.
An implementation of a data cube extracted out of
    'dplyr' for backward compatibility.
Creates a 3D data cube view of a RasterStack/Brick, typically a 
    collection/array of RasterLayers (along z-axis) with the same geographical 
    extent (x and y dimensions) and resolution, provided by package 'raster'. 
    Slices through each dimension (x/y/z), freely adjustable in location, 
    are mapped to the visible sides of the cube. The cube can be freely rotated. 
    Zooming and panning can be used to focus on different areas of the cube.
Estimating mutation and selection coefficients on synonymous
       codon bias usage based on models of ribosome overhead cost (ROC).
       Multinomial logistic regression and Markov Chain Monte Carlo are used to
       estimate and predict protein production rates with/without the presence
       of expressions and measurement errors. Work flows with examples for
       simulation, estimation and prediction processes are also provided
       with parallelization speedup. The whole framework is tested with
       yeast genome and gene expression data of Yassour, et al. (2009)
       <doi:10.1073/pnas.0812841106>.
Functions for visualizing, animating, solving and 
        analyzing the Rubik's cube. Includes data structures for
		solvable and unsolvable cubes, random moves and random 
		state scrambles and cubes, 3D displays and animations 
		using 'OpenGL', patterned cube generation, and lightweight 
		solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the
		Kociemba solver.
Regression modeling using rules with added instance-based corrections.
Utility functions that provides wrapper to descriptive base functions
  like cor, mean and table.  It makes use of the formula interface to pass
  variables to functions.  It also provides operators to concatenate (%+%), to
  repeat (%n%) and manage character vectors for nice display.
Provides tools for measuring the compositionality of signalling systems (in particular the information-theoretic measure due to Spike (2016) <http://hdl.handle.net/1842/25930> and the Mantel test for distance matrix correlation (after Dietz 1983) <doi:10.1093/sysbio/32.1.21>), functions for computing string and meaning distance matrices as well as an implementation of the Page test for monotonicity of ranks (Page 1963) <doi:10.1080/01621459.1963.10500843> with exact p-values up to k = 22.
Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.
Extends ddply to allow calculation of cumulative
        quantities.
Estimation of number and location of change points in
        mean-shift (piecewise constant) models. Particularly useful (but not confined) to
        model genomic sequences of continuous measurements.
Cumulative descriptive statistics for (arithmetic, geometric, harmonic) mean, median, mode, variance, skewness and kurtosis.
Access the 'Cumulocity' API and retrieve data on devices, measurements, and events. Documentation for the API can be found at <https://www.cumulocity.com/guides/reference/rest-implementation/>.
Contains functions for estimating generalized parametric mixture and non-mixture cure models, loss of lifetime, mean residual lifetime, and crude event probabilities.
Expectation-Maximization (EM) algorithm for point estimation and variance estimation to
    the nonparametric maximum likelihood estimator (NPMLE) for 
    logistic-Cox cure-rate model with left truncation and right-
    censoring. See Hou, Chambers and Xu (2017) <doi:10.1007/s10985-017-9415-2>.
The curl() and curl_download() functions provide highly
    configurable drop-in replacements for base url() and download.file() with
    better performance, support for encryption (https, ftps), gzip compression,
    authentication, and other 'libcurl' goodies. The core of the package implements a
    framework for performing fully customized requests where data can be processed
    either in memory, on disk, or streaming via the callback or connection
    interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly
    web client see the 'httr' package which builds on this package with http
    specific tools and logic.
The currentSurvival package contains functions for the
        estimation of the current cumulative incidence (CCI) and the
        current leukaemia-free survival (CLFS). The CCI is the
        probability that a patient is alive and in any disease
        remission (e.g. complete cytogenetic remission in chronic
        myeloid leukaemia) after initiating his or her therapy (e.g.
        tyrosine kinase therapy for chronic myeloid leukaemia). The
        CLFS is the probability that a patient is alive and in any
        disease remission after achieving the first disease remission.
Partial application is the process of reducing the arity of a
    function by fixing one or more arguments, thus creating a new function
    lacking the fixed arguments. The curry package provides three different ways
    of performing partial function application by fixing arguments from either
    end of the argument list (currying and tail currying) or by fixing multiple
    named arguments (partial application). This package provides this
    functionality through the %<%, %-<%, and %><% operators which allows for
    a programming style comparable to modern functional languages. Compared
    to other implementations such a purrr::partial() the operators in curry
    composes functions with named arguments, aiding in autocomplete etc.
A toolbox for developing applications, games, simulations, or
    agent-based models in the R terminal. Included functions allow users to move the 
    cursor around the terminal screen, change text colors and attributes, clear the
    screen, hide and show the cursor, map key presses to functions, draw shapes and curves,
    among others. Most functionalities require users to be in a terminal (not the R GUI).
Computes the maximum likelihood estimator, the smoothed maximum likelihood
    estimator and pointwise bootstrap confidence intervals for the distribution 
    function under current status data. 
    Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>.
Performs multiple comparison procedures on curve observations among different treatment groups. The methods are applicable in a variety of situations (such as independent groups with equal or unequal sample sizes, or repeated measures) by using parametric bootstrap. References to these procedures can be found at Konietschke, Gel, and Brunner (2014) <doi:10.1090/conm/622/12431> and Westfall (2011) <doi:10.1080/10543406.2011.607751>.
Data recorded as paths or trajectories may be suitably described 
  by curves, which are independent of their parametrization. For the space of 
  such curves, the package provides functionalities for reading curves, sampling 
  points on curves, calculating distance between curves and for computing Tukey
  curve depth of a curve w.r.t. to a bundle of curves. For details see 
  Lafaye De Micheaux, Mozharovskyi, and Vimond (2019) <arXiv:1901.00180>.
Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.
Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, & Wagenmakers, 2009, JSS, 32:8;
        Cobb, L, 1981, Behavioral Science, 26:1, 75--78).
        Includes a cusp() function for model fitting, and several
        utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.
Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.
Customized training is a simple technique for transductive
    learning, when the test covariates are known at the time of training. The
    method identifies a subset of the training set to serve as the training set
    for each of a few identified subsets in the training set. This package
    implements customized training for the glmnet() and cv.glmnet() functions.
Create complicated drawing areas for multiple elements by combining much simpler layouts. It is an extended version of layout function from the 'graphics' package, but it also works with 'grid' graphics. It also supports arranging elements inside 'PowerPoint' slides created using the 'officer' package.
Customizable higher-order recipe step functions for 
    the 'recipes' package. These step functions take 'prep' and 'bake' helper 
    functions as inputs and create specifications of customized recipe steps 
    as output.
Calculate the distribution of costs for the installation of an elevator based on the different distribution rules.
Provides functions for constructing and evaluating
    CUSUM charts and RA-CUSUM charts with focus on false signal probability.
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.
Seek the significant cutoff value for a continuous variable, which will 
    be transformed into a classification, for linear regression, 
    logistic regression, logrank analysis and cox regression. First of all, 
    all combinations will be gotten by combn() function. Then n.per argument, 
    abbreviated of total number percentage, will be used to remove the combination 
    of smaller data group. In logistic, Cox regression and logrank analysis, 
    we will also use p.per argument, patient percentage, to filter the lower 
    proportion of patients in each group. Finally, p value in regression 
    results will be used to get the significant combinations and output 
    relevant parameters. In this package, there is no limit to the number of 
    cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, 
    typical Bonferroni and Duglas G (1994) <doi: 10.1093/jnci/86.11.829>, to 
    adjust the p value, Missing values will be deleted by na.omit() function 
    before analysis.
This package provides a set of tools for spatio-temporal imputation 
          in R. It includes the implementation for then CUTOFF imputation method, 
          a useful cross-validation function that can be used not only by the 
          CUOTFF method but also by some other imputation  functions to help 
          choosing an optimal value for relevant parameters, such as the number 
          of k-nearest neighbors for the KNN imputation method, or the number of
          components for the SVD imputation method. It also contains tools for 
          simulating data with missing values with respect to some specific 
          missing pattern, for example, block missing. Some useful visualisation
          functions for imputation purposes are also provided in the package.
Estimate cutpoints that optimize a specified metric in binary classification tasks
    and validate performance using bootstrapping. Some methods for more robust cutpoint
    estimation are supported, e.g. a parametric method assuming normal distributions,
    bootstrapped cutpoints, and smoothing of the metric values per cutpoint using
    Generalized Additive Models. Various plotting functions are included. For an overview
    of the package see 'Thiele and Hirschfeld (2020)' <arXiv:2002.09209>.
Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.
This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model
Compute expected shortfall (ES) and Value at Risk (VaR) from a
    quantile function, distribution function, random number generator or
    probability density function.  ES is also known as Conditional Value at
    Risk (CVaR). Virtually any continuous distribution can be specified.
    The functions are vectorized over the arguments. The computations are
    done directly from the definitions, see e.g. Acerbi and Tasche (2002)
    <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided,
    as well.
This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
Statistical inferences for estimating the calibration equation with error-in observations
Provides some easy-to-use functions and classes to calculate 
    variability measures such as coefficient of variation with confidence 
    intervals provided with all available methods. References are 
    Panichkitkosolkul (2013) <doi:10.1155/2013/324940> ,
    Altunkaynak & Gamgam (2018) <doi:10.1080/03610918.2018.1435800> ,
    Albatineh, Kibria, Wilcox & Zogheib (2014) <doi:10.1080/02664763.2013.847405> .
Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> 
            is suitable for cluster randomized trials (CRTs) with a
            small number of clusters (e.g., 20 or fewer). The procedure of
            constrained randomization is based on the baseline values of some
            cluster-level covariates specified. The intervention effect on
            the individual outcome can then be analyzed through
            clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. 
            Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline
            values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.
Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.
Implementation of Cross-Validated Kernel Ensemble (CVEK), 
  a flexible modeling framework for robust nonlinear regression and 
  hypothesis testing based on ensemble learning with kernel-ridge estimators 
  (Jeremiah et al. (2017) <arXiv:1710.01406> and 
  Wenying et al. (2018) <arXiv:1811.11025>). It allows user to conduct 
  nonlinear regression with minimal assumption on the function form by 
  aggregating nonlinear models generated from a diverse collection of kernel 
  families. It also provides utilities to test for the estimated nonlinear 
  effect under this ensemble estimator, using either the asymptotic or 
  the bootstrap version of a generalized score test.
Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.
Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>.
Estimates a lasso penalized precision matrix via the blockwise coordinate descent (BCD). This package is a simple wrapper around the popular 'glasso' package that extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations.
              See Friedman et al (2008) <doi:10.1093/biostatistics/kxm045> for details regarding the estimation method.
Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828> (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) <doi:10.2307/3315735> and Lockhart, Spinelli and Stephens (2007) <doi:10.1002/cjs.5550350111> (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) <doi:10.2307/3316040>. Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values.
It is devoted to Cramer-von Mises goodness-of-fit tests.
    It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
Cross-validate one or multiple regression and classification models
    and get relevant evaluation metrics in a tidy format. Validate the
    best model on a test set and compare it to a baseline evaluation.
    Alternatively, evaluate predictions from an external model. Currently
    supports regression and classification (binary and multiclass).
    Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., 
    & Wambugu M. (2019, ISBN: 9781838550134).
The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient. This value can be calculated with an external data set or by cross validation.
Perform canonical variate regression (CVR) for two sets of covariates and a univariate
            response, with regularization and weight parameters tuned by cross validation.
Calculate various cardiovascular disease risk scores from the
    Framingham Heart Study (FHS), the American College of Cardiology (ACC),
    and the American Heart Association (AHA) as described in D’agostino, et al
    (2008) <doi:10.1161/circulationaha.107.699579>, Goff, et al (2013)
    <doi:10.1161/01.cir.0000437741.48606.98>, and Mclelland, et al (2015)
    <doi:10.1016/j.jacc.2015.08.035>.
The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
This package carries out level-dependent cross-validation
        method for the selection of thresholding value in wavelet
        shrinkage. This procedure is implemented by coupling a
        conventional cross validation with an imputation method due to
        a limitation of data length, a power of 2. It can be easily
        applied to classical leave-one-out and k-fold cross validation.
        Since the procedure is computationally fast, a level-dependent
        cross validation can be performed for wavelet shrinkage of
        various data such as a data with correlated errors.
Tools that allow developers to write functions for
        cross-validation with minimal programming effort and assist
        users with model selection.
This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.
An iterative algorithm for solving a convex
    formulation of the biclustering problem.
An object-oriented modeling language for disciplined
    convex programming (DCP) as described in Fu, Narasimhan, and Boyd
    (2020, <doi:10.18637/jss.v094.i14>). It allows the user to
    formulate convex optimization problems in a natural way following
    mathematical convention and DCP rules. The system analyzes the
    problem, verifies its convexity, converts it into a canonical
    form, and hands it off to an appropriate solver to obtain the
    solution. Interfaces to solvers on CRAN and elsewhere are
    provided, both commercial and open source.
The 'Corpus Workbench' ('CWB', <http://cwb.sourceforge.net/>) offers a classic and mature
 approach for working with large, linguistically and structurally annotated corpora. The 'CWB'
 is memory efficient and its design makes running queries fast (Evert and Hardie 2011,
 <http://www.stefan-evert.de/PUB/EvertHardie2011.pdf>). The 'cwbtools' package offers
 pure R tools to create indexed corpus files as well as high-level wrappers for the original C
 implementation of CWB as exposed by the 'RcppCWB' package
 <https://CRAN.R-project.org/package=RcppCWB>. Additional functionality to add and
 modify annotations of corpora from within R makes working with CWB indexed corpora
 much more flexible and convenient. The 'cwbtools' package in combination with the R packages
 'RcppCWB' (<https://CRAN.R-project.org/package=RcppCWB>) and 'polmineR'
 (<https://CRAN.R-project.org/package=polmineR>) offers a lightweight infrastructure
 to support the combination of quantitative and qualitative approaches for working
 with textual data.
Miscellaneous useful or interesting functions.
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
Computes the convex hull in arbitrary dimension, based on the Qhull library (<http://www.qhull.org>). The package provides a complete description of the convex hull: edges, ridges, facets, adjacencies. Triangulation is optional.
extend cxxfunction by saving the dynamic shared objects
        for reusing across R sessions
A suite of functions for analysing cycling data.
An interface to the cycle routing/data services provided by 'CycleStreets',
  a not-for-profit social enterprise and advocacy organisation.
  The application programming interfaces (APIs) provided by 'CycleStreets' are documented at (<https://www.cyclestreets.net/api/>).
  The focus of this package is the journey planning API, which aims to emulate the routes taken by a knowledgeable cyclist.
  An innovative feature of the routing service of its provision of fastest, quietest and balanced profiles.
  These represent routes taken to minimise time, avoid traffic and compromise between the two, respectively.
Cyclomatic complexity is a software metric (measurement),
    used to indicate the complexity of a program. It is a quantitative
    measure of the number of linearly independent paths through a program's
    source code. It was developed by Thomas J. McCabe, Sr. in 1976.
Tools for calculating coordinate representations of
    hypocycloids, epicyloids, hypotrochoids, and epitrochoids
    (altogether called 'cycloids' here) with different scaling
    and positioning options. The cycloids can be visualised with
    any appropriate graphics function in R.
Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period.  The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) <doi:10.1111/2041-210X.13305>). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations, such that the respective periods of higher mortality can be identified as "mortality seasons".
This model fitting tool incorporates cyclic coordinate descent and
    majorization-minimization approaches to fit a variety of regression models
    found in large-scale observational healthcare data.  Implementations focus
    on computational optimization and fine-scale parallelization to yield
    efficient inference in massive datasets.  Please see:
    Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.
A toolkit for querying 'Team Cymru' <http://team-cymru.org> 'IP'
    address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon
    and 'Malware' Hash Data Services.
This library contains a primary function that divides
        chewing sequences in cycles and cycles into phases.  See
        get.all.breaks for an example.
Encryption wrappers, using low-level support from
    'sodium' and 'openssl'.  'cyphr' tries to smooth over some pain
    points when using encryption within applications and data analysis
    by wrapping around differences in function names and arguments in
    different encryption providing packages.  It also provides
    high-level wrappers for input/output functions for seamlessly
    adding encryption to existing analyses.
The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.
Tools to interface with Cytobank's API via R, organized by various
    endpoints that represent various areas of Cytobank functionality. Learn more
    about Cytobank at <https://www.cytobank.org>.
Tools to process cytometry data from Cytobank into easily usable form for analysis of populations, markers, and signaling using the 'CytobankAPI' package. Learn more about Cytobank at <https://www.cytobank.org>. For more information about types of cytometry data that can be analyzed, please see: Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E. D., Krutzik, P. O., Finck, R.,... Nolan, G. P. (2011) <doi:10.1126/science.1198704> and Adan, A., Alizada, G., Kiraz, Y., Baran, Y., Nalbant, A. (2017). <doi:10.3109/07388551.2015.1128876>.
A collection of tools that leverage the CytobankAPI R package <https://cran.r-project.org/web/packages/CytobankAPI/vignettes/cytobank-quickstart.html> to complete more complex workflows, and add/extend various Cytobank features.
Calculates ecological diversity indices for a microbial
        community
An implementation of Fan plots for cytometry data in 'ggplot2'. 
    For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart 
    <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).
Given the hypothesis of a bi-modal distribution of cells for
    each marker, the algorithm constructs a binary tree, the nodes of which are
    subpopulations of cells. At each node, observed cells and markers are modeled
    by both a family of normal distributions and a family of bi-modal normal mixture
    distributions. Splitting is done according to a normalized difference of AIC
    between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, 
    Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.
Typical morphological profiling datasets have millions of cells
    and hundreds of features per cell. When working with this data, you must
    clean the data, normalize the features to make them comparable across
    experiments, transform the features, select features based on their
    quality, and aggregate the single-cell data, if needed. 'cytominer' makes
    these steps fast and easy. Methods used in practice in the field are
    discussed in Caicedo (2017) <doi:10.1038/nmeth.4397>. An overview of the
    field is presented in Caicedo (2016) <doi:10.1016/j.copbio.2016.04.003>.
Interface to interest rates as published by the Czech National Bank. Currently supported are the PRIBOR rates (PRague InterBank Offered Rate - the CZK member of the IBOR family of rates) and two-week repo rate - a key policy rate of CNB.
Get programmatic access to the open data provided by the
    Czech Statistical Office (CZSO, <https://czso.cz>).
Creates interactive genome browser with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
This packages is intended to make it easy to create D3 JavaScript
    network, tree, dendrogram, and Sankey graphs from R using data frames.
    !!! NOTE: Active development has moved to the networkD3 package. !!!
Builds interactive 'd3.js' hierarchical visualisation easily. D3partitionR makes it easy to build and customize sunburst, circle treemap, treemap, partition chart, ...
Provides functions that offer seamless 'D3Plus' integration. The examples provided here are taken from the official 'D3Plus' website <http://d3plus.org>.
Provides a suite of functions to help ease the use of 'd3.js' in R.
              These helpers include 'htmltools::htmlDependency' functions, hierarchy
              builders, and conversion tools for 'partykit', 'igraph,' 'table',
              and 'data.frame' R objects into the 'JSON' that 'd3.js' expects.
Create and customize interactive collapsible 'D3' trees using the 'D3'
    JavaScript library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
    When in Shiny the tree layout is observed by the server and can be used as a reactive filter
    of structured data.
Various data sets used in examples and exercises in the
        book Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
        Analysis and Graphics Using R".
Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.
Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.
Data Analysis using Bootstrap-Coupled ESTimation.
    Estimation statistics is a simple framework that avoids the pitfalls of
    significance testing. It uses familiar statistical concepts: means,
    mean differences, and error bars. More importantly, it focuses on the
    effect size of one's experiment/intervention, as opposed to a false
    dichotomy engendered by P values.
    An estimation plot has two key features:
    1. It presents all datapoints as a swarmplot, which orders each point to
    display the underlying distribution.
    2. It presents the effect size as a bootstrap 95% confidence interval on a
    separate but aligned axes.
    Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105.
    <doi:10.1038/s41592-019-0470-3>.
    The free-to-view PDF is located at <https://rdcu.be/bHhJ4>.
Provides functions to manage databases: select, update, insert,
    and delete records, list tables, backup tables as CSV files, and import
    CSV files as tables.
An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (in prep).
The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
Displays a terrible joke, the kind only dads crack.
The content falls into the following groupings: (i) Data, (ii)
    Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v)
    Matrix functions, (vi) Projector and canonical efficiency functions, and (vii)
    Miscellaneous functions. There is a vignette describing how to use the 
    design functions for randomizing and assessing designs available as a 
    vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of 
    information when the 'Error' function has been used in the call to 'aov'. 
    The package 'dae' can also be installed from 
  <http://chris.brien.name/rpackages/>.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Diff, patch and merge for data frames. Document changes in data
    sets and use them to apply patches. Changes to data can be made visible by using
    render_diff. The V8 package is used to wrap the 'daff.js' JavaScript library
    which is included in the package.
Data and miscellanea to support the book "Introduction to
        Data analysis with R for Forensic Scientists", Curran, J.M.
        2010 CRC Press ISBN: 978-1-4200-8826-7
A port of the web-based software 'DAGitty', available at 
    <http://dagitty.net>, for analyzing structural causal models 
    (also known as directed acyclic graphs or DAGs).
    This package computes covariate adjustment sets for estimating causal
    effects, enumerates instrumental variables, derives testable
    implications (d-separation and vanishing tetrads), generates equivalent
    models, and includes a simple facility for data simulation.
Functions to draw, manipulate, evaluate directed
        acyclic graphs and simulate corresponding data.
Reverse and model the effects of changing deposition rates on geological data and rates. Based on Hohmann (2018) <doi:10.13140/RG.2.2.23372.51841> .
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See e.g. Valente et al. 2015. Ecology Letters 18: 844-852, <DOI:10.1111/ele.12461>.
Functions and an example dataset for the psychometric theory of
  knowledge spaces.  This package implements data analysis methods and
  procedures for simulating data and quasi orders and transforming different
  formulations in knowledge space theory.  See package?DAKS for an overview.
Unverified black box model is the path to the failure. Opaqueness leads to distrust. 
  Distrust leads to ignoration. Ignoration leads to rejection. 
  DALEX package xrays any model and helps to explore and explain its behaviour.
  Machine Learning (ML) models are widely used and have various applications in classification 
  or regression. Models created with boosting, bagging, stacking or similar techniques are often
  used due to their high performance. But such black-box models usually lack of direct interpretability.
  DALEX package contains various methods that help to understand the link between input variables 
  and model output. Implemented methods help to explore model on the level of a single instance 
  as well as a level of the whole dataset.
  All model explainers are model agnostic and can be compared across different models.
  DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration.
  Find more details in (Biecek 2018) <arXiv:1806.08915>.
Provides wrapper of various machine learning models. 
  In applied machine learning, there 
  is a strong belief that we need to strike a balance 
  between interpretability and accuracy. 
  However, in field of the interpretable machine learning, 
  there are more and more new ideas for explaining black-box models, 
  that are implemented in 'R'. 
  'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models
  including those created using 'python' 'scikit-learn' and 'keras' libraries, and 'java' 'h2o' library. 
  Important part of the package is Champion-Challenger analysis and innovative approach
  to model performance across subsets of test data presented in Funnel Plot. 
  Third branch of 'DALEXtra' package is aspect importance analysis
  that provides instance-level explanations for the groups of explanatory variables.
The DALY Calculator is a free, open-source Graphical User
             Interface (GUI) for stochastic disability-adjusted life year
             (DALY) calculation.
A collection of functions which aim to assist common computational workflow for analysis of matabolomic data..
Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.
Miscellaneous set of functions I use in my teaching either at the University of Western Ontario or the Inter-university Consortium for Political and Social Research (ICPSR) Summer Program in Quantitative Methods.  Broadly, the functions help with presentation and interpretation of LMs and GLMs, but also implement some new tools like Alternating Least Squares Optimal Scaling for dependent variables, a Bayesian analog to the ALSOS algorithm.  There are also tools to help understand interactions in both LMs and binary GLMs.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
Loads behavioural data from the widely used Drosophila Activity Monitor System (DAMS, TriKinetics <https://trikinetics.com/>) into the rethomics framework.
The single largest source of dams in the United States is the
    National Inventory of Dams (NID) <http://nid.usace.army.mil> from the US
    Army Corps of Engineers. Entire data from the NID cannot be obtained all at
    once and NID's website limits extraction of more than a couple of thousand
    records at a time. Moreover, selected data from the NID's user interface
    cannot not be saved to a file. In order to make the analysis of this data
    easier, all the data from NID was extracted manually. Subsequently, the raw
    data was checked for potential errors and cleaned. This package provides
    sample cleaned data from the NID and provides functionality to access the
    entire cleaned NID data.
Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.
Provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size 
 calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to 
 design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.
Discriminant Adaptive Nearest Neighbor Classification is a 
    variation of k nearest neighbors where the shape of the neighborhood is 
    data driven. This package implements dann and sub_dann from
    Hastie (1995) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
The purpose of the package is to enable an R function interface into the Statistics Denmark Databank API mainly for research purposes. 
    The Statistics Denmark Databank API has four endpoints, see here for more information and testing the API in their console: <https://www.dst.dk/en/Statistik/statistikbanken/api>.
    This package mimics the structure of the API and provides four main functions to match the functionality of the API endpoints.
An implementation of Discriminant Analysis via Projections (DAP) method for high-dimensional binary classification in the case of unequal covariance matrices. See Irina Gaynanova and Tianying Wang (2018) <arXiv:1711.04817v2>.
An easy-to-use, dependency-free set of functions for iterating over
    elements of various input objects. Functions are wrappers around base
    apply()/lapply()/vapply() functions but designed to have similar
    functionality to the mapping functions in the 'purrr' package
    <https://purrr.tidyverse.org/>. Specifically, function names more explicitly
    communicate the expected class of the output and functions also allow for
    the convenient shortcut of '~ .x' instead of the more verbose
    'function(.x) .x'.
Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...).
The recovery of visual sensitivity in a dark environment is known
    as dark adaptation. In a clinical or research setting the recovery is typically
    measured after a dazzling flash of light and can be described by the Mahroo,
    Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take
    dark adaptation data and use nonlinear regression to find the parameters of the
    model that 'best' describe the data. They do this by firstly, generating rapid
    initial objective estimates of data adaptation parameters, then a multi-start
    algorithm is used to reduce the possibility of a local minimum. There is also a
    bootstrap method to calculate parameter confidence intervals. The functions rely
    upon a 'dark' list or object. This object is created as the first step in the
    workflow and parts of the object are updated as it is processed.
Estimation of dark diversity and site-specific species pools using species co-occurrences. 
    It includes implementations of probabilistic dark diversity based on the 
    Hypergeometric distribution, as well as estimations based on the Beals index,
    which can be transformed to binary predictions using different thresholds, 
    or transformed into a favorability index. All methods include the possibility of
    using a calibration dataset that is used to estimate the indication matrix 
    between pairs of species, or to estimate dark diversity directly on a single 
    dataset. See De Caceres and Legendre (2008) <doi:10.1007/s00442-008-1017-y>, 
    Lewis et al. (2016) <doi:10.1111/2041-210X.12443>, 
    Partel et al. (2011) <doi:10.1016/j.tree.2010.12.004>, Real et al. (2017) <doi:10.1093/sysbio/syw072>
    for further information.
Provides programmatic access to the 'Dark Sky' 'API' 
    <https://darksky.net/dev/docs>, which provides current or historical global 
    weather conditions.
Functions are provided that facilitate the import and analysis of
    SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. The main focus is on data generated
    by DarT (Diversity Arrays Technology). However, once SNP or related fragment
    presence/absence data from any source is imported into a genlight object many
    of the functions can be used. Functions are available for input and output of
    SNP and silicodart data, for reporting on and filtering on various criteria
    (e.g. CallRate, Heterozygosity, Reproducibility, maximum allele frequency).
    Advanced filtering is based on Linkage Disequilibrium and HWE (Hardy-Weinberg equilibrium). Other functions
    are available for visualization after PCoA (Principle Coordinate Analysis), or to facilitate transfer of data
    between genlight/genind objects and newhybrids, related, phylip, structure, faststructure packages.
Are you aiming at the right spot in darts? Maybe not! Use
        this package to find your optimal aiming location. For a better
        explanation, go to
        http://www-stat.stanford.edu/~ryantibs/darts/ or see the paper
        "A Statistician Plays Darts".
A framework for building analytical web applications, 'dash' offers a pleasant and productive development experience. No JavaScript required.
The dashboard package allows users to create web pages which display 
	interactive data visualizations working in a standard modern browser. It displays them locally 
	using the Rook server. Nor knowledge about web technologies nor Internet connection are 
	required. D3.js is a JavaScript library for manipulating documents based on data. 
	D3 helps the dashboard package bring data to life using HTML, SVG and CSS.
Allows manual creation of themes and logos to be used in
    applications created using the 'shinydashboard' package. Removes the need to
    change the underlying css code by wrapping it into a set of convenient R
    functions.
'Dash' ships with supercharged components for interactive user interfaces. A core set of components, written and maintained by the 'Dash' team, is available in the 'dashCoreComponents' package. The source for this package is on GitHub: plotly/dash-core-components.
'Dash' is a web application framework that provides pure Python and R abstraction around HTML, CSS, and JavaScript. Instead of writing HTML or using an HTML templating engine, you compose your layout using R functions within the 'dashHtmlComponents' package. The source for this package is on GitHub: plotly/dash-html-components.
Pivot tables are useful for interactive presentation of  summary statistics computed for data contained in another table. The 'dashPivottable' package wraps 'react-pivottable', making it easy to add drag-and-drop tables into your Dash for R applications.
An interactive table component designed for editing and exploring large datasets, 'dashDataTable' is rendered with standard, semantic HTML <table/> markup, which makes it accessible, responsive, and easy to style. This component was written from scratch in 'React.js' specifically for the 'dash' community. Its API was designed to be ergonomic and its behaviour is completely customizable through its  properties.
Provides methods for reading, displaying,
    processing and writing files originally arranged for
    the 'DSSAT-CSM' fixed width format. The 'DSSAT-CSM' 
    cropping system model is described at J.W. Jones,
    G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor,
    L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, 
    J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.
An implementation of common higher order functions with syntactic
    sugar for anonymous function. Provides also a link to 'dplyr' and
    'data.table' for common transformations on data frames to work around non
    standard evaluation by default.
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and traverse the
    tree in various orders. Aggregate, cumulate, print, plot, convert to and from
    data.frame and more. Useful for decision trees, machine learning, finance,
    conversion from and to JSON, and many other applications.
Provides methods for using differential equations as modelling  
 objects as described in J. Ramsay G. and Hooker (2017,ISBN 978-1-4939-7188-6) 
 Dynamic Data Analysis, New York: Springer. Data sets and script files for 
 analyzing many of the examples in this book are included. This version 
 corrects bugs and adds two new demos, CruiseControl and monotoneSmoothDemo. 
 This version incorporates a new system for indexing coefficient list objects.
 The step of setting up a set of coefficient list objects has been 
 eliminated and the functions make.Coef() and coefCheck have been dropped.
 'Matlab' versions of the code and sample analyses are available by ftp from 
 the website.  There you find a set of .zip files containing the functions 
 and sample analyses, as well as two .txt files giving instructions for 
 installation and some additional information.
Makes it easy to engage with the Application Program Interface (API)
    of the 'TCdata360' and 'Govdata360' platforms at <https://tcdata360.worldbank.org/>
    and <https://govdata360.worldbank.org/>, respectively.
    These application program interfaces provide access to over 5000 trade, competitiveness, and governance
    indicator data, metadata, and related information from sources
    both inside and outside the World Bank Group.
    Package functions include easier download of data sets, metadata, and
    related information, as well as searching based on user-inputted query.
An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 
    'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', and 'SQLite'). Also includes support for
    fetching data as 'Andromeda' objects. Uses 'Java Database Connectivity' ('JDBC') to connect to databases (except SQLite).
Provides external JAR dependencies for the 'DatabaseConnector' package.
Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>. A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.
Includes functions that researchers or practitioners may use to clean
    raw data, transferring html, xlsx, txt data file into other formats. And it
    also can be used to manipulate text variables, extract numeric variables from
    text variables and other variable cleaning processes. It is originated from a
    author's project which focuses on creative performance in online education
    environment. The resulting paper of that study will be published soon.
Flexible and efficient cleaning of data with interactivity.
  'datacleanr' facilitates best practices in data analyses and reproducibility with built-in features and by translating interactive/manual operations to code. 
  The package is designed for interoperability, and so seamlessly fits into reproducible analyses pipelines in 'R'.
Tools for combining and cleaning data sets, particularly
    with grouped and time series data.
Easy comparison of two tabular data
    objects in R. Specifically designed to show differences between two sets of
    data in a useful way that should make it easier to understand the differences,
    and if necessary, help you work out how to remedy them. Aims
    to offer a more useful output than all.equal() when your two data sets do not
    match, but isn't intended to replace all.equal() as a way to test for equality.
An R interface for Rho's 'web-codebook' 'JavaScript' charting library, which provides a simple interactive framework for exploring data. 'datadigest' includes two key functions (codebook() and explorer()) that deliver a concise summary of every variable in a data frame, along with interactive features such as real-time filters, grouping, and highlighting. Each function has an associated 'RStudio' addin/'Shiny' app for quick and convenient exploration of one or more data frames.
Query for metrics from 'Datadog' (<https://www.datadoghq.com/>) via its API.
An interactive editor built on 'rhandsontable' to allow the 
  interactive viewing, entering, filtering and editing of data in R 
  <https://dillonhammill.github.io/DataEditR/>.
This is a GUI application for defining
  attributes and setting valid values of variables, and then,
  entering questionnaire data in a data.frame.
Automated data exploration process for analytic tasks and predictive modeling, so
    that users could focus on understanding data and extracting insights. The package scans and
    analyzes each variable, and visualizes them with typical graphical techniques. Common
    data processing methods are also available to treat and format data.
Real life data is muddy, fuzzy and unpredictable. This makes data manipulations tedious and bringing the data to right shape alone is a major chunk of work. Functions in this package help us get an understanding of dataframes to dramatically reduces data coding time.
Writes data frames to xls files. It supports 
        multiple sheets and basic formatting.
Automatic generation of finite state machine models of dynamic 
    decision-making that both have strong predictive power and are 
    interpretable in human terms. We use an efficient model representation and 
    a genetic algorithm-based estimation process to generate simple 
    deterministic approximations that explain most of the structure of complex 
    stochastic processes. We have applied the software to empirical data, and 
    demonstrated it's ability to recover known data-generating processes by 
    simulating data with agent-based models and correctly deriving the 
    underlying decision models for multiple agent models and degrees of
    stochasticity.
Functions to save either '.dtable' or '.dtbin' files that can be read by 'DataGraph', a graphing and analysis application for mac OS. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://community.visualdatatools.com/datagraph/knowledge-base/r-package/>.
Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.
Data screening is an important first step of any statistical
    analysis. dataMaid auto generates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset.
datamap utilizes variable bindings and objects of class
        "UserDefinedDatabase" to provide a simple mapping system to
        foreign objects. Maps can be used as environments or attached
        to the search path, and changes to either are persistent.
        Mapped foreign objects are fetched in real-time and are never
        cached by the mapping system.
Provides an S4 infrastructure for unified handling
    of internal datasets and web based data sources. The package is
    currently in beta; things may break, change or go away without
    warning.
Designed to create a basic data dictionary and append to the original dataset's attributes list. The package makes use of a tidy dataset and creates a data frame that will serve as a linker that will aid in building the dictionary. The dictionary is then appended to the list of the original dataset's attributes. The user will have the option of entering variable and item descriptions by writing code or use alternate functions that will prompt the user to add these.
Creating, refining, and clustering data nuggets. 
    Data nuggets reduce a large dataset into a small collection of nuggets of 
    data, each containing a center (location), weight (importance), and scale 
    (variability) parameter. Data nugget centers are created by choosing 
    observations in the dataset which are as equally spaced apart as possible. 
    Data nugget weights are created by counting the number observations 
    closest to a given data nugget’s center. We then say the data nugget 
    'contains' these observations and the data nugget center is recalculated 
    as the mean of these observations. Data nugget scales are created by 
    calculating the trace of the covariance matrix of the observations 
    contained within a data nugget divided by the dimension of the dataset. 
    Data nuggets are refined by 'splitting' data nuggets which have scales or 
    shapes (defined as the ratio of the two largest eigenvalues of the 
    covariance matrix of the observations contained within the data nugget) 
    deemed too large. Data nuggets are clustered by using a weighted form of 
    k-means clustering which uses both the centers and weights of data nuggets 
    to optimize the clustering assignments.
Post Global Financial Crisis derivatives reforms have lifted the 
  veil off over-the-counter (OTC) derivative markets. Swap Execution Facilities
  (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are 
  traded on or reported to those facilities (respectively). This package provides
  you the ability to get this data from supported sources.
Provides read and write access to data and metadata from
    the DataONE network <https://www.dataone.org> of data repositories.  
    Each DataONE repository implements a consistent repository application 
    programming interface. Users call methods in R to access these remote 
    repository functions, such as methods to query the metadata catalog, get 
    access to metadata for particular data packages, and read the data objects 
    from the data repository. Users can also insert and update data objects on 
    repositories that support these methods.
Provides a flexible container to transport and manipulate complex
    sets of data. These data may consist of multiple data files and associated
    meta data and ancillary files. Individual data objects have associated system
    level meta data, and data files are linked together using the OAI-ORE standard
    resource map which describes the relationships between the files. The OAI-
    ORE standard is described at <https://www.openarchives.org/ore/>. Data packages
    can be serialized and transported as structured files that have been created
    following the BagIt specification. The BagIt specification is described at
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.
Work with 'Frictionless Data Packages' (<https://specs.frictionlessdata.io//data-package/>). Allows to load and validate any descriptor for a data package profile, create and modify descriptors and provides expose methods for reading and streaming data in the package. When a descriptor is a 'Tabular Data Package', it uses the 'Table Schema' package (<https://CRAN.R-project.org/package=tableschema.r>) and exposes its functionality, for each resource object in the resources field.
RStudio addins and R functions that make copy-pasting vectors and tables to text painless.
Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools.
    These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation of moving averages.
    Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data.
    They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods.
    Auto-identification of consecutive NA via moving averages is used in observation deletion, outlier removal, and NA interpolation;
    thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed.
    Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation.
    These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.
Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of 'data.table' efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.
The package performs variable level data quality checks including
    missing values, unique values, frequency tables, and generates summary
    statistics
Data screening is an important first step of any statistical
    analysis. 'dataReporter' auto generates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset. See Petersen AH, Ekstrøm CT (2019). "dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R." _Journal of Statistical Software_, *90*(6), 1-38 <doi:10.18637/jss.v090.i06> for more information.
Quality control and formatting tools developed for the Copernicus Data Rescue Service. The package includes functions to handle the Station Exchange Format (SEF), various statistical tests for climate data at daily and sub-daily resolution, as well as functions to plot the data. For more information and documentation see <https://datarescue.climate.copernicus.eu/st_data-quality-control>.
Collection of functions to help retrieve U.S. Geological Survey
    (USGS) and U.S. Environmental Protection Agency (EPA) water quality and
    hydrology data from web services. USGS web services are discovered from 
    National Water Information System (NWIS) <https://waterservices.usgs.gov/> and <https://waterdata.usgs.gov/nwis>. 
    Both EPA and USGS water quality data are obtained from the Water Quality Portal <https://www.waterqualitydata.us/>.
Contains data organized by topics: categorical data, regression model, 
            means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.
For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.
A row by row data processing tool. You can write data processing code in 'DataSailr' script which is specially intended for data manipulation. The package uses 'libsailr' (C/C++ library) for its 'DataSailr' code parsing and its execution.
The Datasaurus Dozen is a set of datasets with the same summary statistics. They 
             retain the same summary statistics despite having radically different distributions.
             The datasets represent a larger and quirkier object lesson that is typically taught
             via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet
             contains four very different distributions with the same summary statistics and as 
             such highlights the value of visualisation in understanding data, over and above
             summary statistics. As well as being an engaging variant on the Quartet, the data
             is generated in a novel way. The simulated annealing process used to derive datasets 
             from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating 
             Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" 
             <doi:10.1145/3025453.3025912>.
Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.
Visual interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces.
Companion to the book "An Introduction to Clustering with R" by P. Giordani, M.B. Ferraro and F. Martella (Springer, Singapore, 2020). The datasets are used in some case studies throughout the text.
Provides a convenient API interface to access immunological data
    within 'the CAVD DataSpace'(<https://dataspace.cavd.org>), a data sharing 
    and discovery tool that facilitates exploration of HIV immunological data 
    from pre-clinical and clinical HIV vaccine studies.
The goal of 'dataspice' is to make it easier for researchers to
  create basic, lightweight, and concise metadata files for their datasets.
  These basic files can then be used to make useful information available during
  analysis, create a helpful dataset "README" webpage, and produce more complex
  metadata formats to aid dataset discovery. Metadata fields are based on
  the 'Schema.org' and 'Ecological Metadata Language' standards.
For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) <doi:10.20944/preprints201710.0098.v1>.
Based on a SAS data step. This allows for row-wise dynamic building
    of data, iteratively importing slices of existing dataframes, conducting
    analyses, and exporting to a results frame. This is particularly useful for
    differential or time-series analyses, which are often not well suited to vector-
    based operations.
Provides a set of functions and a class to connect, extract and
    upload information from the 'Refinitiv Datastream' database. This
    package uses the 'DSWS' API and server used by the 'Datastream DFO addin'.  
    Details of this API on the 'DSWS' page of <https://developers.refinitiv.com/en>.
    Please report issues at <https://github.com/CharlesCara/DatastreamDSWS2R/issues>.
Implementation of advanced data structures such as hashmaps,
    heaps, or queues. Advanced data structures are essential
    in many computer science and statistics problems, for example graph
    algorithms or string analysis. The package uses 'Boost' and 'STL' data
    types and extends these to R with 'Rcpp' modules.
Provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5),
    enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0,
    use the archived 'dvn' package <https://cran.r-project.org/package=dvn>.
Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.
Gives access to data visualisation methods that are relevant from the statistician's point of view. Using 'D3''s existing data visualisation tools to empower R language and environment. The throw chart method is a line chart used to illustrate paired data sets (such as before-after, male-female).
A Shiny Input for date-ranges, which pops up two calendars for selecting dates, times, or predefined ranges like "Last 30 Days". It wraps the JavaScript library 'daterangepicker' which is available at <https://www.daterangepicker.com>.
Provides methods for working with nominal dates, times, and 
 durations. Base R has sophisticated facilities for handling time, but these 
 can give unexpected results if, for example, timezone is not handled properly. 
 This package provides a more casual approach to support cases which 
 do not require rigorous treatment.  It systematically deconstructs the 
 concepts origin and timezone, and de-emphasizes the display of seconds. It 
 also converts among nominal durations such as seconds, hours, days, and weeks.
 See '?datetime' and '?duration' for examples. Adapted from 'metrumrg' 
 <http://r-forge.r-project.org/R/?group_id=1215>.
Utilities for handling dates and times, such
   as selecting particular days of the week or month,
   formatting timestamps as required by RSS feeds, or
   converting timestamp representations of other software
   (such as 'MATLAB' and 'Excel') to R. The package is
   lightweight (no dependencies, pure R implementations) and
   relies only on R's standard classes to represent dates
   and times ('Date' and 'POSIXt'); it aims to provide
   efficient implementations, through vectorisation and the
   use of R's native numeric representations of timestamps
   where possible.
A thin wrapper around the 'Datorama' API.
    Ideal for analyzing marketing data from <https://datorama.com>.
Provee una versión traducida de los siguientes
    conjuntos de datos: 'airlines', 'airports', 'AwardsManagers',
    'babynames', 'Batting', 'diamonds', 'faithful', 'fueleconomy',
    'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers',
    'mpg', 'mtcars', 'atmos', 'People, 'Pitching', 'planes',
    'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b',
    'table5', 'vehicles', 'weather', 'who'.  English: It provides a
    Spanish translated version of the datasets listed above.
Interface with the 'Dat' p2p network protocol <https://datproject.org>. Clone archives from the network, share your own files, and install packages from the network.
Profiles datasets (collecting statistics and informative summaries 
    about that data) on data frames and 'ODBC' tables: maximum, minimum, mean, standard deviation, nulls,
    distinct values, data patterns, data/format frequencies.
A collection of functions accompanying the book "Data Analysis in Vegetation Ecology". 3rd ed. CABI, Oxfordshire, Boston.
Various utilities for the Davies distribution.
In applications it is usual that some additional information is available. This package dawai (an acronym for Discriminant Analysis With Additional Information) performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means. It also computes several estimations of the true error rate.
Programmatic interface to the 'Daymet' web services
    (<http://daymet.ornl.gov>). Allows for easy downloads of
    'Daymet' climate data directly to your R workspace or your computer.
    Routines for both single pixel data downloads and
    gridded (netCDF) data are provided.
Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'.
Goodness-of-fit and two sample comparison tests using sample entropy
A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.
Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) <doi:10.1037/1082-989X.5.1.87>. 
              The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small.
Reads and writes files to SQLite databases <https://www.sqlite.org/index.html> as flobs 
    (a flob is a blob that preserves the file extension).
This package provides methods and examples to support a
        method of Gene Set Analysis (GSA).  DBGSA is a novel
        distance-based gene set enrichment analysis method. We consider
        that, the distance between 2 groups with different phenotype by
        focusing on the gene expression should be larger, if a certain
        gene functional set was significantly associated with a
        particular phenotype.
A database interface definition for communication
    between R and relational database management systems.  All classes in
    this package are virtual and need to be extended by the various R/DBMS
    implementations.
A helper that tests 'DBI' back ends for conformity
    to the interface.
Use EM algorithm to compute the NPMLE of CDF and also the
        two censoring distributions. For doubly censored data (as
        described in Chang and Yang (1987) Ann. Stat. 1536-47). You can
        also specify a constraint, it will return the constrained NPMLE
        and the -2 log empirical likelihood ratio. This can be used to
        test the hypothesis about the constraint and find confidence
        intervals for probability or quantile via empirical likelihood
        ratio theorem. Influence function of hat F may also be
        calculated (but may be slow).
It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode  and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).
Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single 
  logistic regression model, but not a sequence of logistic regression models. The fitted model
  object returned from the model training consists of two tables. One table is used to give the
  boundaries of bins for each continuous variable as well as the corresponding coefficients,
  and the other one is used for discrete variables. This package can also be used for binning
  continuous variables for other statistical analysis.
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>.
             Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). 
             Relies on 'spatstat' for some core calculation.
It allows to learn the structure of univariate time series, learning parameters and forecasting. 
             Implements a model of Dynamic Bayesian Networks with temporal windows, 
             with collections of linear regressors for Gaussian nodes, 
             based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and 
             Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.
Learning and inference over dynamic Bayesian networks of arbitrary 
    Markovian order. Extends some of the functionality offered by the 'bnlearn' 
    package to learn the networks from data and perform exact inference. 
    It offers two structure learning algorithms for dynamic Bayesian networks
    and the possibility to perform forecasts of arbitrary length. A tool for 
    visualizing the structure of the net is also provided via the 'visNetwork' 
    package.
This tool is for parsing the 'DrugBank' XML database <https://www.drugbank.ca/>. The parsed 
    data are then returned in a proper 'R' dataframe with the ability to save 
    them in a given database.
Leverages 'dplyr' to process the calculations of a plot inside a database. 
    This package provides helper functions that abstract the work at three levels:
    outputs a 'ggplot', outputs the calculations, outputs the formula
    needed to calculate bins.
A 'dplyr' back end for databases that allows you to
    work with remote database tables as if they are in-memory data frames.
    Basic features works with any database that has a 'DBI' back end; more
    advanced features require 'SQL' translation to be provided by the
    package author.
A fast reimplementation of several density-based algorithms of
    the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial
    clustering of applications with noise) and OPTICS (ordering points to identify
    the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier
    factor) algorithm. The implementations use the kd-tree data structure (from
    library ANN) for faster k-nearest neighbor search. An R interface to fast kNN
    and fixed-radius NN search is also provided. 
    See Hahsler M, Piekenbrock M and Doran D (2019) <doi:10.18637/jss.v091.i01>.
Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.
Provides select, insert, update, upsert, and delete database operations. Supports 'PostgreSQL', 'MySQL', 'SQLite', and more, and plays nicely with the 'DBI' package.
Performs differential network analysis to infer disease specific gene networks.
Finding dominant latent signals that regulate dynamic correlation between many pairs of variables.
A collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA). 
    This package implements the results presented in Prass, T.S. and Pumi, G. (2019). "On the behavior of the DFA and DCCA in trend-stationary processes" <arXiv:1910.10589>.
Functions for analyzing dichotomous choice contingent 
   valuation (CV) data. It provides functions for estimating parametric and 
   nonparametric models for single-, one-and-one-half-, and double-bounded 
   CV data. For details, see Aizaki et al. (2014) <doi:10.1201/b17292>.
Performs checks for common metadata quality issues. Used by the
    data coordinating centers for the 'AMP-AD' consortium
    (<https://adknowledgeportal.synapse.org>), 'PsychENCODE' consortium
    (<http://www.psychencode.org>), and others to validate metadata prior to
    data releases.
A differential community detection (DCD) based approach to effectively locate differential sub-networks in paired scale-free biological networks, e.g. case vs control - Raghvendra Mall et al (2017) <doi:10.1145/3107411.3107418>.
Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering 
    big data (gaussian mixture models for both multivariate and univariate datasets). This version 
    implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure. 
    The implementation supports both random and K-means++ based initialization. Reference: Hasan Kurban, Mark Jenne, 
    Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by 
    NCI Grant 1R01CA213466-01.
Data cloud geometry (DCG) applies random walks in finding community structures for social networks. 
    Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).
Functions for 1) gene filtration; 2) link filtration; 3) differential co-expression analysis: DCG (Differential Coexpressed
        Gene) identification and DCL (Differentially Coexpressed Link)
        identification; and 4) differential regulation analysis: DRG (Differential Regulated
        Gene) identification, DRL (Differential Regulated Link)
        identification, DRL visualization and regulator ranking.
There lacks a package for analysing domain-centric ontologies and annotations, particularly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a comprehensive domain-centric database for annotating protein domains using a panel of ontologies including Gene Ontology. With the package, users are expected to analyse and visualise domain-centric ontologies and annotations. Supported analyses include but are not limited to: easy access to a wide range of ontologies and their domain-centric annotations; able to build customised ontologies and annotations; domain-based enrichment analysis and visualisation; construction of a domain (semantic similarity) network according to ontology annotations; significance analysis for estimating a contact (statistical significance) network via Random Walk with Restart; and high-performance parallel computing. The new functionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input protein sequences (precisely domain content in the form of architectures) plus to assess the predictions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony.
Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing 
    attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic 
    dynamic systems which can behave like an erratic and apparently random motion. A relevant field
    inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour 
    from empirical time series data. One of the main features of chaos is the well known initial value 
    sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify 
    the initial value sensitive property estimating the Lyapunov exponents. The DChaos package 
    provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos 
    based on the Lyapunov exponent in order to know if the data generating process behind time series 
    behave chaotically or not.
Functions for reading DCP and CDF.bin files generated by the dChip software.
Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
Low level functions for implementing
    maximum likelihood estimating procedures for
    complex models using data cloning and Bayesian
    Markov chain Monte Carlo methods
    as described in Solymos 2010 (R Journal 2(2):29--37).
    Sequential and parallel MCMC support
    for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.
Contains a single function dclust() for divisive hierarchical clustering based on 
    recursive k-means partitioning (k = 2). Useful for clustering large datasets
    where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records).
    For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.
A set of functions for the detection of spatial clusters
        of disease using count data. Bootstrap is used to estimate
        sampling distributions of statistics.
Model-based methods for the detection of disease clusters
  using GLMs, GLMMs and zero-inflated models. These methods are described
  in 'V. Gómez-Rubio et al.' (2019) <doi:10.18637/jss.v090.i14> and
  'V. Gómez-Rubio et al.' (2018) <doi:10.1007/978-3-030-01584-8_1>.
A set of asymptotic methods that can be used to directly estimate the expected (Fisher) information matrix by Liu, Tian, and Xin (2016) <doi:10.3102/1076998615621293> in diagnostic classification models or cognitive diagnostic models are provided when marginal maximum likelihood estimation is used. For these methods, both the item and structural model parameters are considered simultaneously. Specifically, the observed information matrix, the empirical cross-product information matrix and the sandwich-type co-variance matrix that can be used to estimate the asymptotic co-variance matrix (or the model parameter standard errors) within the context of diagnostic classification models are provided.
S4 classes around infrastructure provided by the
    'coda' and 'dclone' packages to make package development easy as a breeze
    with data cloning for hierarchical models.
Data cleaning scripts typically contain a lot of 'if this change that'
    type of statements. Such statements are typically condensed expert knowledge.
    With this package, such 'data modifying rules' are taken out of the code and
    become in stead parameters to the work flow. This allows one to maintain, document,
    and reason about data modification rules as separate entities.
Traversal graph algorithm for listing linear n-peptide constraints for overlapping protein regions. (Lebre and Gascuel, The combinatorics of overlapping genes, freely available from arXiv at : http://arxiv.org/abs/1602.04971).
Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) <doi:10.1214/009053607000000505>; Székely and Rizzo (2013) <doi:10.1016/j.jmva.2013.02.012>; Székely and Rizzo (2014) <doi:10.1214/14-AOS1255>; Huo and Székely (2016) <doi:10.1080/00401706.2015.1054435>.
Computing and plotting the distance covariance and correlation
             function of a univariate or a multivariate time series. Both versions
             of biased and unbiased estimators of distance covariance and correlation are provided.
             Test statistics for testing pairwise independence are also implemented. 
             Some data sets are also included.
Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data.  'DCPO' uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with 'Stan'.  References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.
Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) <doi:10.1073/pnas.0803205106>) varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension.
    The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics. 
    The goal of this package is to show an alternative to variable selection (columns) or individuals (rows). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation ||A-CUR||. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest.
A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by
  easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density,
  Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)
  <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of
  the loglikelihood and a random generator for Davidian curves.
This package performs several conventional
        Cross-validation statistical methods for climate-growth model
        in the climate reconstruction from tree rings, including Sign
        Test statistic, Reduction of Error statistic, Product Mean
        Test, Durbin-Watson statistic etc. This package is at its
        primary stage, the functions have not been tested exhaustively
        and more functions would be added in the comming days.
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 <doi:10.18637/jss.v091.i05>).
Implements maximum likelihood and bootstrap methods based on
 the diversity-dependent birth-death process to test whether
 speciation or extinction are diversity-dependent, under various
 models including various types of key innovations.
 See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
 <DOI:10.1098/rspb.2011.1439>,
 Etienne & Haegeman 2012, Am. Nat. 180: E75-E89,
 <DOI:10.1086/667574> and
 Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099,
 <DOI:10.1111/2041-210X.12565>.
 Also contains functions to simulate the diversity-dependent
 process.
Solves ordinary and delay differential equations, where
    the objective function is written in either R or C.  Suitable only
    for non-stiff equations, the solver uses a 'Dormand-Prince' method
    that allows interpolation of the solution at any point.  This
    approach is as described by Hairer, Norsett and Wanner (1993)
    <ISBN:3540604529>.  Support is also included for iterating
    difference equations.
Contains the normalizing and variance stabilizing
	Data-Driven Haar-Fisz algorithm. Also contains related algorithms
	for simulating from certain microarray gene intensity models and
	evaluation of certain transformations. Contains cDNA and shipping
	credit flow data.
Implements Meng's data defect index (ddi), which represents
    the degree of sample bias relative to an iid sample. The data defect 
    correlation (ddc) represents the correlation between the outcome of interest
    and the selection into the sample; when the sample selection is independent
    across the population, the ddc is zero. Details are in Meng (2018) 
    <doi:10.1214/18-AOAS1161SF>, "Statistical Paradises and Paradoxes in Big Data (I): 
    Law of Large Populations, Big Data Paradox, and the 2016 US Presidential 
    Election." Survey estimates from the Cooperative Congressional Election Study 
    (CCES) is included to replicate the article's results.
The Data Driven I-V Feature Extraction is used to extract 
    Current-Voltage (I-V) features from I-V curves. I-V curves indicate the 
    relationship between current and voltage for a solar cell or Photovoltaic (PV) 
    modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), 
    series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), 
    fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) 
    contain important information of the performance for PV modules. The traditional 
    method uses the single diode model to model I-V curves and extract I-V features. 
    This package does not use the diode model, but uses data-driven a method which 
    select different linear parts of the I-V curves to extract I-V features. 
    This method also uses a sampling method to calculate uncertainties when extracting 
    I-V features. Also, because of the partially shaded array, "steps" occurs in 
    I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves.
    This material is based upon work supported by the U.S. Department of Energy’s 
    Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy 
    Technologies Office (SETO) Agreement Number DE-EE0007140.
Useful functions for various DDI (Data Documentation Initiative) related inputs and outputs.
A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.
Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) <doi:10.1145/782010.782021>, Angiulli, F., & Pizzuti, C. (2002) <doi:10.1007/3-540-45681-3_2>, Hautamaki, V., & Ismo, K. (2004) <doi:10.1109/ICPR.2004.1334558> and Zhang, K., Hutter, M. & Jin, H. (2009) <doi:10.1007/978-3-642-01307-2_84>. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) <doi:10.1007/3-540-47887-6_53>, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) <doi:10.1007/11731139_68>, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) <doi:10.1137/1.9781611973440.63>, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) <doi:10.1007/978-3-540-73499-4_6>, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) <doi:10.1109/ICDE.2003.1260802>, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) <doi:10.1145/342009.335388>, Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) <doi:10.1145/1645953.1646195>, Zhu, Q., Feng, Ji. & Huang, J. (2016) <doi:10.1016/j.patrec.2016.05.007>, Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) <doi:10.1016/j.knosys.2015.10.014>, Tang, B. & Haibo, He. (2017) <doi:10.1016/j.neucom.2017.02.039> and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) <doi:10.1007/978-3-642-20847-8_23>.
The desirable Dietary Pattern (DDP)/ PPH score
  measures the variety of food consumption. The (weighted) score is calculated 
  based on the type of food. This package is intended to calculate
  the DDP/ PPH score that is faster than traditional method via 
  a manual calculation by BKP (2017) <http://bkp.pertanian.go.id/storage/app/uploads/public/5bf/ca9/06b/5bfca906bc654274163456.pdf> and is simpler than the nutrition survey 
  <http://www.nutrisurvey.de>. The database to create weights and baseline values
  is the Indonesia national survey in 2017.
Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>)  by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.
An interface to explore, analyze, and visualize droplet digital PCR
    (ddPCR) data in R. This is the first non-proprietary software for analyzing
    two-channel ddPCR data. An interactive tool was also created and is available
    online to facilitate this analysis for anyone who is not comfortable with
    using R.
Functions designed to connect disease-related differential proteins and 
  co-expression network. It provides the basic statics analysis included t test, ANOVA analysis. 
  The network construction is not offered by the package, you can used 'WGCNA' package which you 
  can learn in Peter et al. (2008) <doi:10.1186/1471-2105-9-559>. It also provides module analysis 
  included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and 
  hub analysis.
Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.
Allows to build Multi-Data-Driven Sparse Partial Least Squares models. Multi-blocks with 
    high-dimensional settings are particularly sensible to this. It comes with visualization
    functions and uses 'Rcpp' functions for fast computations and 'doParallel' to parallelize cross-validation.
    This is based on H Lorenzo, J Saracco, R Thiebaut (2019) <arXiv:1901.04380>.
    Many applications have been successfully realized.
    See <https://hadrienlorenzo.netlify.com/> for more information, documentation and examples.
Smooth testing of goodness of fit. These tests are data
    driven (alternative hypothesis is dynamically selected based on data). In this
    package you will find various tests for exponent, Gaussian, Gumbel and uniform
    distribution.
Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.
Bayesian networks with continuous and/or discrete
        variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
Dealing GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) RAW.tar file using the robust multi-array average expression measure and returning expression profile.
deamer provides deconvolution algorithms for the
        non-parametric estimation of the density f of an error-prone
        variable x with additive noise e. The model is y = x + e where
        the noisy variable y is observed, while x is unobserved.
        Estimation may be performed for i) a known density of the error
        ii) with an auxiliary sample of pure noise and iii) with an
        auxiliary sample of replicate (repeated) measurements.
        Estimation is performed using adaptive model selection and
        penalized contrasts.
Set of functions for Data Envelopment Analysis. It runs both classic and fuzzy DEA models.See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). <doi:10.1287/mnsc.30.9.1078>, Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). <doi:10.1016/0377-2217(78)90138-8> and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). <doi:10.1287/mnsc.27.6.668>.
The 'debar' sequence processing pipeline is designed for denoising high throughput 
    sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package 
    is designed to detect and correct insertion and deletion errors within sequencer outputs. 
    This is accomplished through comparison of input sequences against a profile hidden Markov 
    model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, 
    ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted 
    for through the introduction of a placeholder character. Since the PHMM is a probabilistic 
    representation of the COI barcode, corrections are not always perfect. For this reason 
    'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder 
    characters (default is 7 base pairs in either direction, this feature can be disabled).
    Testing has shown that this censorship results in the correct sequence length being restored, 
    and erroneous base pairs being masked the vast majority of the time (>95%).
A Bayesian framework for parameter inference in differential equations.
    This approach offers a rigorous methodology for parameter inference as well as
    modeling the link between unobservable model states and parameters, and
    observable quantities. Provides templates for the DE model, the
    observation model and data likelihood, and the model parameters and their prior
    distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
    to estimate the posterior distributions of the parameters and any derived
    quantities, including the model trajectories. Further functionality is provided
    to facilitate MCMC diagnostics and the visualisation of the posterior distributions
    of model parameters and trajectories.
Specify debug messages as special string constants, and
    control debugging of packages via environment variables.
Tool to print out the value of R objects/expressions while running
  an R script. Outputs can be made dependent on user-defined conditions/criteria. 
  Debug messages only appear when a global option for debugging is set. 
  This way, 'debugr' code can even remain in the debugged code for later use 
  without any negative effects during normal runtime.
Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2020) <arXiv:2005.01379>.
Calculates various estimates for measures of educational
        differentials, the relative importance of primary and secondary
        effects in the creation of such differentials and compares the
        estimates obtained from two datasets.
Provides constrained triangulation of polygons. Ear cutting (or 
 ear clipping) applies constrained triangulation by successively 'cutting'
 triangles from a polygon defined by path/s. Holes are supported by introducing
 a bridge segment between polygon paths. This package wraps the 'header-only' 
 library 'earcut.hpp' <https://github.com/mapbox/earcut.hpp.git> which includes
 a reference to the method used by Held, M. (2001) <doi:10.1007/s00453-001-0028-4>.
Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.
Aides in the multi objective decision analysis process by simplifying 
  the creation of value hierarchy tree plots, calculating and plotting single and 
  multi attribute value function scores, and conducting sensitivity analysis. Linear, 
  exponential, and categorical single attribute value functions are supported. For 
  details see Parnell (2013, ISBN:978-1-118-17313-8) Kirkwood (1997, ISBN:0-534-51692-0).
Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.
Makes 'deck.gl' <https://deck.gl/>, a WebGL-powered open-source JavaScript framework
  for visual exploratory data analysis of large datasets, available within R via the 'htmlwidgets' package.
  Furthermore, it supports basemaps from 'mapbox' <https://www.mapbox.com/> via
  'mapbox-gl-js' <https://github.com/mapbox/mapbox-gl-js>.
Researchers can characterize and learn about the properties of
    research designs before implementation using `DeclareDesign`. Ex ante
    declaration and diagnosis of designs can help researchers clarify the 
    strengths and limitations of their designs and to improve their 
    properties, and can help readers evaluate a research strategy prior
    to implementation and without access to results. It can also make it
    easier for designs to be shared, replicated, and critiqued.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Main function "decode" is used to decode coded key values to plain
    text. Function "code" can be used to code plain text to code if there is a
    1:1 relation between the two. The concept relies on 'keyvalue' objects used
    for translation. There are several 'keyvalue' objects included in the areas
    of geographical regional codes, administrative health care unit codes,
    diagnosis codes and more. It is also easy to extend the use by arbitrary 
    code sets.
Predict future values with hybrid combinations of Pattern Sequence based
        Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode
        Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based
        hybrid methods.
Tools to apply Ensemble Empirical Mode 
    Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new 
    algorithm, extricate, that performs EEMD in seconds, a linear interpolation 
    algorithm  using the greatest rational common divisor of depth or time, 
    different algorithms to compute instantaneous amplitude, frequency and  
    ratios of frequencies, and functions to verify and visualise the outputs.
    The functions were developed during the CRASH project (Checking the 
    Reproducibility of Astrochronology in the Hauterivian). When using for 
    publication please cite Wouters, S., Da Silva, A.C. Crucifix, M., Sinnesael,
    M., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2019, "Litholog 
    generation with the 'StratigrapheR' package and signal decomposition for 
    cyclostratigraphic purposes". Geophysical Research Abstracts Vol. 21, 
    EGU2019-5520, 2019, EGU General Assembly 2019. 
    <http://hdl.handle.net/2268/234402>.
Two global-value-chain decompositions are implemented. Firstly, the
    Wang-Wei-Zhu (Wang, Wei, and Zhu, 2013) algorithm splits bilateral gross exports
    into 16 value-added components. Secondly, the Leontief decomposition (default)
    derives the value added origin of exports by country and industry, which is also
    based on Wang, Wei, and Zhu (Wang, Z., S.-J. Wei, and K. Zhu. 2013. "Quantifying
    International Production Sharing at the Bilateral and Sector Levels.").
This package contains a collection of functions to deal
        with nonparametric measurement error problems using
        deconvolution kernel methods. We focus two measurement error
        models in the package: (1) an additive measurement error model,
        where the goal is to estimate the density or distribution
        function from contaminated data; (2) nonparametric regression
        model with errors-in-variables. The R functions allow the
        measurement errors to be either homoscedastic or
        heteroscedastic. To make the deconvolution estimators
        computationally more efficient in R, we adapt the "Fast Fourier
        Transform" (FFT) algorithm for density estimation with
        error-free data to the deconvolution kernel estimation. Several
        methods for the selection of the data-driven smoothing
        parameter are also provided in the package. See details in:
        Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
        measurement error models: The R package decon. Journal of
        Statistical Software, 39(10), 1-24.
Takes sample information in the form of the fraction of mutations
    in each of 96 trinucleotide contexts and identifies the weighted combination
    of published signatures that, when summed, most closely reconstructs the
    mutational profile.
Empirical Bayes methods for learning prior distributions from data.
    An unknown prior distribution (g) has yielded (unobservable) parameters, each of
    which produces a data point from a parametric exponential family (f). The goal
    is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
    Bayes methods. Details and examples are in the paper by Narasimhan and Efron
    (2020, <doi:10.18637/jss.v094.i11>).
Retrieves code comment decorations for C++
    languages of the form '\\ [[xyz]]', which are used for automated
    wrapping of C++ functions.
DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
    of Artificial Training Examples) builds an ensemble of J48 trees by recursively
    adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) <DOI:10.1016/j.inffus.2004.04.001>").
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
A example GUI plug-in package to serve as a template.
A Deducer plug-in for factor analysis, reliability
        analysis and discriminant analysis, using psych, GPArotation
        and mvnormtest packages.
A Deducer plug-in for spatial data analysis. Includes The
        ability to plot and explore open street map and Bing satellite
        images.
Adds Kaplan-Meier Survival Analysis to the Deducer GUI
A collection of methods for automated data cleaning where all actions are logged.
Attempt to repair inconsistencies and missing values in data 
    records by using information from valid values and validation rules 
    restricting the data.
Explore neural networks in a layer oriented way, the framework
    is intended to give the user total control of the
    internals of a net without much effort. Use classes like PerceptronLayer
    to create a layer of Percetron neurons, and specify how many you want. The
    package does all the tricky stuff internally leaving you focused in what
    you want. I wrote this package during a neural networks course to help me
    with the problem set.
Provides deep boosting models training, evaluation, predicting and
 hyper parameter optimising using grid search and cross validation.
 Based on Google's Deep Boosting algorithm, and Google's C++ implementation.
 Cortes, C., Mohri, M., & Syed, U. (2014) <http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.
Provides tools for exploration of R package dependencies. 
    The main deepdep() function allows to acquire deep dependencies of any package and plot them in an elegant way.
    It also adds some popularity measures for the packages e.g. in the form of download count through the 'cranlogs' package. 
    Uses the CRAN metadata database <http://crandb.r-pkg.org> and Bioconductor metadata <http://bioconductor.org>.
    Other data acquire functions are: get_dependencies(), get_downloads() and get_description(). 
    The deepdep_shiny() function runs shiny application that helps to produce a nice 'deepdep' plot.
Aims to provide simple intuitive functions to create quick prototypes of artificial neural network or deep learning models. In addition novel ensemble models like 'deeptree' and 'deepforest' has been included which combines decision trees and neural network.
Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) 
    <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures 
    to multiple layers. Each layer contains a set of latent variables that follow a mixture of 
    Gaussian distributions. To avoid overparameterized solutions, dimension reduction is 
    applied at each layer by way of factor models.
Performs model fitting and sequential design for deep Gaussian
     processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.  Models 
     extend up to three layers deep; a one layer model is equivalent to typical Gaussian 
     process regression.  Sequential design criteria include integrated mean-squared 
     error (IMSE), active learning Cohn (ALC), and expected 
     improvement (EI).  Covariance structure is based on inverse exponentiated
     squared euclidean distance.  Applicable to noisy and deterministic functions.  
     Incorporates SNOW parallelization and utilizes C under the hood.
Implement some deep learning architectures and neural network
    algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
A tool that contains trained deep learning models
    for predicting effector proteins. 'deepredeff' has been trained to
    identify effector proteins using a set of known experimentally
    validated effectors from either bacteria, fungi, or oomycetes.
    Documentation is available via several vignettes, and the paper by
    Kristianingsih and MacLean (2020) <doi:10.1101/2020.07.08.193250>.
Provides mean squared error (MSE) and plot the kernel densities related to extreme value distributions with their estimated values.
    By using Gumbel and Weibull Kernel. See Salha et al. (2014) <doi:10.4236/ojs.2014.48061> and Khan and Akbar (2020).
A simple syntax to change the default values for function
  arguments, whether they are in packages or defined locally.
Creates a directory of archived files with a descriptive 'PDF'
 document at the root level (i.e. 'define.pdf') containing tables
 of definitions of data items and relative-path hyperlinks to 
 the documented files. Converts file extensions to 'txt' per FDA
 expectations and converts 'CSV' files to 'SAS' Transport format.
 Relies on data item descriptors stored as per R package 'spec'.
 See 'package?define'. See also '?define'. 
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
Simple functions to deflate nominal Brazilian Reais using several popular price indexes downloaded from the Brazilian Institute for Applied Economic Research.
Numerical quadrature of functions of one variable over a finite or infinite interval
	with double exponential formulas.
Efficient Bayesian parameter inference for systems of ordinary 
    differential equations. The inference is based on adaptive gradient matching 
    (AGM, Dondelinger et al. 2013 <http://proceedings.mlr.press/v31/dondelinger13a.pdf>, 
    Macdonald 2017 <http://theses.gla.ac.uk/7987/1/2017macdonaldphd.pdf>), 
    which offers orders-of-magnitude improvements in computational 
    efficiency over standard methods that require solving the differential 
    equation system. Features of the package include flexible specification 
    of custom ODE systems as R functions, support for missing variables,
    Bayesian inference via population MCMC.
Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.
Provides a low-level interface for a deisotoper container 
  implemented in the 'Java' programming language and means of S3 helper 
  functions for plotting and debugging isotopes of mass spectrometric data. 
  The deisotoper algorithm detects and aggregates peaks which belong to the 
  same isotopic cluster of a given mass spectrum.
Performs reference based multiple imputation of recurrent event data
    based on a negative binomial regression model, as described
    by Keene et al (2014) <doi:10.1002/pst.1624>.
Provides probability mass, distribution, quantile, random-variate
    generation, and method-of-moments parameter-estimation functions for the
    Delaporte distribution with parameterization based on Vose (2008)
    <isbn:9780470512845>. The Delaporte is a discrete probability distribution
    which can be considered the convolution of a negative binomial distribution
    with a Poisson distribution. Alternatively, it can be considered a counting
    distribution with both Poisson and negative binomial components. It has been
    studied in actuarial science as a frequency distribution which has more
    variability than the Poisson, but less than the negative binomial.
Mechanisms to parallelize dependent tasks in a manner that
    optimizes the compute resources available. It provides access to "delayed"
    computations, which may be parallelized using futures. It is, to an extent,
    a facsimile of the 'Dask' library (<https://dask.org/>), for the 'Python'
    language.
Provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is homogeneous across the individual subject. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.
Calculates the Delaunay triangulation and the Dirichlet
	or Voronoi tessellation (with respect to the entire plane) of
	a planar point set. Plots triangulations and tessellations in
	various ways.  Clips tessellations to sub-windows. Calculates
	perimeters of tessellations.  Summarises information about the
	tiles of the tessellation.
Implements the DeLorean model (Reid & Wernisch (2016)
    <doi:10.1093/bioinformatics/btw372>) to estimate pseudotimes for
    single cell expression data. The DeLorean model uses a Gaussian process
    latent variable model to model uncertainty in the capture time of
    cross-sectional data.
We implement methods for estimating multivariate densities.
  We include a discretized kernel estimator,
  an adaptive histogram (a greedy histogram and a CART-histogram), 
  stagewise minimization, and bootstrap aggregation.
Measure of agreement delta was originally by Martín & Femia (2004) <DOI:10.1348/000711004849268>. 
    Since then has been considered as agreement measure for different 
    fields, since their behavior is usually better than the usual kappa index
    by Cohen (1960) <DOI:10.1177/001316446002000104>. The main issue with delta 
    is that can not be computed by hand contrary to kappa. The current algorithm
    is based on the Version 5 of the delta windows program that can be found on
    <https://www.ugr.es/~bioest/software/delta/cmd.php?seccion=downloads>.
The deltaPlotR package implements Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported (Magis and Facon (2014) <doi:10.18637/jss.v059.c01>).
Computes the regional correction factor of 14C age offset in marine-derived samples (Delta R). This is possible with the recently established "marine13" calibration curve and the 'BchronCalibrate' function from the 'Bchron' package. The algorithm of Delta R computation includes four steps: measure radiocarbon age of a marine sample; identify its true age; compute its modeled radiocarbon age corresponding to the true age; and finally calculate the difference between its measured and modeled radiocarbon ages. This package has functions that compute Delta R with three methods: by using marine samples with known collection dates (before 1950s), usually molluscan shells from museum collections, (function dr_shell()); by measuring other radioactive isotopes, mainly uranium-thorium (230Th/234U), ratio (dr_pair() function and the "pair" method in the dr_df() function); and finally by using a pair of coeval samples, one being marine and the other terrestrial (dr_pair() function and the "pair" method for the dr_df() function as well). Usually such samples originate from archaeological sites where the context, in which paired samples were found, is taken as a guarantee of their synchronicity.  
 References: 
 Oeschger H, Siegenthaler U, Schotterer U, Gugelmann A (1975) <doi:10.3402/tellusa.v27i2.9900>
 Reimer PJ, Bard E, Bayliss A, Beck JW, Blackwell PG, Bronk Ramsey C, Buck CE, Cheng H, Edwards RL, Friedrich M, Grootes PM, Guilderson TP, Haflidason H, Hajdas I, Hatté C, Heaton TJ, Hoffmann DL, Hogg AG, Hughen KA, Kaiser KF, Kromer B, Manning SW, Niu M, Reimer RW, Richards DA, Scott EM, Southon JR, Staff RA, Turney CSM, van der Plicht J (2013) <doi:10.2458/azu_js_rc.55.16947>
 Soulet G (2015) <doi:10.1016/j.quageo.2015.05.023>
 Stuiver M, Braziunas TF (1993) <doi:10.1017/S0033822200013874> .
A collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. 
    Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull.
    For details see Chen (2000a, b), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.
Functions to calculate pairwise relatedness on diploid genetic datasets. Different estimators for relatedness can be combined with information on geographical distances. Information on heterozygosity, allele- and genotype diversity as well as genetic F-statistics are provided for each population.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Two general demographic decomposition methods are offered: Pseudo-continuous decomposition proposed by Horiuchi, Wilmoth, and Pletcher (2008) <doi:10.1353/dem.0.0033> and stepwise replacement decomposition proposed by Andreev, Shkolnikov and Begun (2002) <doi:10.4054/DemRes.2002.7.14>.
Construction and analysis of matrix population models in R.
Connects to the <https://genderize.io/>, <https://agify.io/> and <https://nationalize.io/> APIs to estimate gender, age and nationality of a first name.
The Demographic Table in R combines contingency table for categorical variables, mean and standard deviation for continuous variables. t-test, chi-square test and Fisher's exact test calculated the p-value of two groups. The standardized mean difference were performed with 95 % confident interval, and writing table into document file.
Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.
Demonstration code showing how (univariate) kernel
	     density estimates are computed, at least conceptually,
	     and allowing users to experiment with different kernels,
	     should they so wish.  NOTE: the density function in the
	     stats package should be used for computational efficiency.
Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).
Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.
Implements the Determinantal point process (DPP) based optimal design
    emulator described in Pratola, Lin and Craigmile (2018) <arXiv:1804.02089> for
    Gaussian process regression models.  See <http://www.matthewpratola.com/software>
    for more information and examples.
Offers a set of functions for extending
    'dendrogram' objects in R, letting you visualize and compare trees of
    'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters
    - the color, size, type, etc of its branches, nodes and labels. (2)
    Visually and statistically compare different 'dendrograms' to one another.
There are various functions for managing and cleaning data before the application of different approaches. This includes identifying and erasing sudden jumps in dendrometer data not related to environmental change, identifying the time gaps of recordings, and changing the temporal resolution of data to different frequencies. Furthermore, the package calculates daily statistics of dendrometer data, including the daily amplitude of tree growth. Various approaches can be applied to separate radial growth from daily cyclic shrinkage and expansion due to uptake and loss of stem water. In addition, it identifies periods of consecutive days with user-defined climatic conditions in daily meteorological data, then check what trees are doing during that period.
Provides extra functions to manipulate dendrograms
    that build on the base functions provided by the 'stats' package. The main
    functionality it is designed to add is the ability to colour all the edges
    in an object of class 'dendrogram' according to cluster membership i.e. each
    subtree is coloured, not just the terminal leaves. In addition it provides
    some utility functions to cut 'dendrogram' and 'hclust' objects and to 
    set/get labels.
Various functions to import, verify, process and plot high-resolution dendrometer using daily and stem-cycle approaches.
Provides functions for the calculation and plotting of synchrony in 
      tree growth from tree-ring width chronologies (TRW index). It combines
      variance-covariance (VCOV) mixed modelling with functions that quantify 
      the degree to which the TRW chronologies contain a common temporal 
      signal. It also implements temporal trends in spatial synchrony using a 
      moving window. These methods can also be used with other kind of ecological
      variables that have temporal autocorrelation corrected.
Provides novel dendroclimatological methods, primarily used by the
    Tree-ring research community. There are four core functions. The first one is 
    daily_response(), which finds the optimal sequence of days that are related 
    to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), 
    which implements partial correlations in the analysis of daily response functions.
    For the enthusiast of monthly data, there is monthly_response() function.
    The last core function is compare_methods(), which effectively compares several 
    linear and nonlinear regression algorithms on the task of climate reconstruction.
Re-arranges a dendrogram to optimize visualisation-based cost functions
An implementation of functions to optimize ordering of nodes in
    a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can
    be sorted based on the average distance of subtrees, or based on the smallest
    distance value. These sorting methods improve readability and interpretability
    of tree structure, especially for tasks such as comparison of different distance
    measures or linkage types and identification of tight clusters and outliers. As
    a result, it also introduces more meaningful reordering for a coupled heatmap
    visualization.
Provides functions for parameter estimation and real-time predictions  of a single dengue 
 outbreak taking into account model uncertainty using  model averaging.
Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of  a bottom-up model for PCR amplification. This model is  developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
Estimate a low rank matrix from noisy data using singular values
    thresholding and shrinking functions. Impute missing values with matrix completion. The method is described in <arXiv:1602.01206>.
An integrated toolset for the analysis of de novo (sporadic)
    genetic sequence variants. denovolyzeR implements a mutational model that
    estimates the probability of a de novo genetic variant arising in each human
    gene, from which one can infer the expected number of de novo variants in a
    given population size. Observed variant frequencies can then be compared against
    expectation in a Poisson framework. denovolyzeR provides a suite of functions
    to implement these analyses for the interpretation of de novo variation in human
    disease.
We provide tools to 
  (1) visualize multivariate density functions and density estimates 
      with level set trees,
  (2) visualize level sets with shape trees,
  (3) visualize multivariate data with tail trees, 
  (4) visualize scales of multivariate density estimates with 
      mode graphs and branching maps, and
  (5) visualize anisotropic spread with 2D volume functions and
      2D probability content functions.
  Level set trees visualize mode structure,
  shape trees visualize shapes of level sets of unimodal densities,
  and tail trees visualize connected data sets.
  The kernel estimator is implemented
  but the package may  also be applied for visualizing other density estimates.
Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.
Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020) <arXiv:2009.06182>.
Provides various tools for analysing density profiles
    obtained by resistance drilling. It can load individual or
    multiple files and trim the starting and ending part of each
    density profile. Tools are also provided to trim profiles
    manually, to remove the trend from measurements using several
    methods, to plot the profiles and to detect tree rings
    automatically. Written with a focus on forestry use of resistance
    drilling in standing trees.
Implementation in the density framework of the non-combinatorial algorithm and its greedy version, introduced by Magalhães and Rozenholc (2014), for T-estimation Hold-Out proposed in Birgé (2006, Section 9). The package provide an implementation which uses several families of estimators (regular and irregular histograms, kernel estimators) which may be used alone or combined. As a complement, provides also a comparison with other Held-Out derived from least-squares and maximum-likelihood. This package implements also the T-estimation Hold-Out derived from the test introduced in Baraud (2011).
An improved implementation (based on k-nearest neighbors) of the 
    density peak clustering algorithm, originally described by Alex Rodriguez 
    and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets
    (> 100, 000 samples) very efficiently. It was initially implemented by 
    Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by 
    Xiaojie Qiu to handle large datasets with kNNs.
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.
Density ratio estimation.
    The estimated density ratio function can be used in many applications such as
    anomaly detection, change-point detection, covariate shift adaptation.
    The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>),
    RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>),
    and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds"
    in <doi:10.1098/rsif.2019.0234>.  Using the functions in the package,
    that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data
    using other functions in this package (as in the manuscript) or from some other source.
    The package provides a vignette which walks through the analysis in the publication, as well as a function
    to generate a project skeleton for such an analysis.
Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
Differential Evolution (DE) stochastic algorithms for global
  optimization of problems with and without constraints.
  The aim is to curate a collection of its state-of-the-art variants that
  (1) do not sacrifice simplicity of design,
  (2) are essentially tuning-free, and
  (3) can be efficiently implemented directly in the R language.
  Currently, it only provides an implementation of the 'jDE' algorithm by
  Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.
Functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) <doi:10.2478/demo-2014-0004>; Liebscher (2017) <doi:10.1515/demo-2017-0012>; Liebscher (2019, submitted).
Estimation and testing methods for dependently truncated data.
 Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>,
 and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
 Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>.
 A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>.
 Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
Pigna [_pìn'n'a_] is the Italian word for pine
    cone.  In jargon, it is used to identify a task which is boring,
    banal, annoying, painful, frustrating and maybe even with a not so
    beautiful or rewarding result, just like the obstinate act of trying
    to challenge yourself in extracting pine nuts from a pine cone,
    provided that, in the end, you will find at least one inside it. Here
    you can find a backpack of functions to be used to solve small
    everyday problems of coding or analyzing (clinical) data, which would
    be normally solved using quick-and-dirty patches. You will be able to
    convert 'Hmisc' and 'rms' summary()es into data.frames ready to be
    rendered by 'pander' and 'knitr'. You can access easy-to-use wrappers
    to activate essential but useful progress bars (from 'progress') into
    your loops or functionals. Easy setup and control Telegram's bots
    (from 'telegram.bot') to send messages or to divert error messages to
    a Telegram's chat. You also have some utilities helping you in the
    development of packages, like the activation of the same user
    interface of 'usethis' into your package, or call polite functions to
    ask a user to install other packages. Finally, you find a set of
    thematic sets of packages you may use to set up new environments
    quickly, installing them in a single call.
Plots dependency logos from a set of input sequences.
Traditional phasing programs are limited to diploid organisms.
 Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo
 (MCMC) approaches, and builds a generic framework that allows haplotype searches
 in a multiple infection setting. This package is primarily developed as part of
 the Pf3k project, which is a global collaboration using the latest
 sequencing technologies to provide a high-resolution view of natural variation
 in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from
 patient blood sample, which often contains more than one parasite strain, with
 unknown proportions. This package is used for deconvoluting mixed haplotypes,
 and reporting the mixture proportions from each sample.
Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The 'Rdonlp2' package can optionally be used for optimization of the log-likelihood and is available from R-forge. See Visser et al. (2009, <DOI:10.1007/978-0-387-95922-1_13>) for examples and applications.
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).
Methods for analyzing population dynamics and movement tracks simulated using  the DEPONS model <https://www.depons.eu>, and for manipulating input raster files.
Tools for depth functions methodology applied 
        to multivariate analysis. Besides allowing calculation 
        of depth values and depth-based location estimators, the package
        includes functions or drawing contour plots and perspective plots
        of depth functions. Euclidian and spherical depths are supported.
Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
Data depth concept offers a variety of powerful and user friendly
    tools for robust exploration and inference for multivariate data. The offered
    techniques may be successfully used in cases of lack of our knowledge on
    parametric models generating data due to their nature. The
    package consist of among others implementations of several data depth techniques
    involving multivariate quantile-quantile plots, multivariate scatter estimators,
    multivariate Wilcoxon tests and robust regressions.
Implementation of different statistical tools for the 
        description and analysis of gene expression data based on the 
        concept of data depth, namely, the scale curves for 
        visualizing the dispersion of one or various groups of samples 
        (e.g. types of tumors), a rank test to decide whether two groups 
        of samples come from a single distribution and two methods of
        supervised classification techniques, the DS and TAD methods. 
        All these techniques are based on the Modified Band Depth, which
        is a recent notion of depth with a low computational cost, what 
        renders it very appropriate for high dimensional data such as 
        gene expression data.
Queues, stacks, and 'deques' are list-like, abstract data types. 
    These are meant to be very cheap to "grow", or insert new objects into.
    A typical use case involves storing data in a list in a streaming fashion,
    when you do not necessarily know how may elements need to be stored.
    Unlike R's lists, the new data structures provided here are not
    necessarily stored contiguously, making insertions and deletions at the
    front/end of the structure much faster.  The underlying implementation
    is new and uses a head/tail doubly linked list; thus, we do not rely on R's
    environments or hashing.  To avoid unnecessary data copying, most operations
    on these data structures are performed via side-effects.
Modeling the zero coupon yield curve using the dynamic De Rezende and
             Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable
             or fixed decaying parameters. For explanatory purposes, the package also
             includes various short datasets of interest rates for the BRICS countries.
R-based solution for symbolic differentiation. It admits
    user-defined function as well as function substitution
    in arguments of functions to be differentiated. Some symbolic
    simplification is part of the work.
A set of pricing and expository functions that should
    be useful in teaching a course on financial derivatives.
Discrete event simulation (DES) involves modeling of systems
   having discrete, i.e. abrupt, state changes. For instance, when a job
   arrives to a queue, the queue length abruptly increases by 1.  This
   package is an R implementation of the event-oriented approach to DES;
   see the tutorial in Matloff (2008) 
   <http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf>.
Tools to read, write, create, and manipulate DESCRIPTION files.
    It is intended for packages that create or manipulate other packages.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
Weighted frequency and contingency tables of categorical
       variables and of the comparison of the mean value of a numerical
       variable by the levels of a factor, and methods to produce xtable
       objects of the tables and to plot them. There are also functions to
       facilitate the character encoding conversion of objects, to quickly
       convert fixed width files into csv ones, and to export a data.frame to
       a text file with the necessary R and SPSS codes to reread the data.
Helper functions for descriptive tasks such as making print-friendly bivariate 
  tables, sample size flow counts, and visualizing sample distributions. Also 
  contains 'R' approximations of some common 'SAS' and 'Stata' functions such 
  as 'PROC MEANS' from 'SAS' and 'ladder', 'gladder', and 'pwcorr' from 'Stata'.
Produce publication quality graphics from output of 'GGobi'
    describe display plugin.
Allows users to quickly and easily describe data using
    common descriptive statistics.
Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface.
Generate descriptive statistics such as measures of location,
    dispersion, frequency tables, cross tables, group summaries and multiple
    one/two way tables.
Provides functions to create descriptive statistics tables for continuous and categorical variables.
    By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous
    variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to
    choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group
    differences of appropriated summary measures are automatically produces for two-group comparison.
    Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents.
    'DescrTab2' also allows printing tables to console and saving table objects for later use.
A toolbox for descriptive statistics, based on the computation of frequency, bins and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.
It generates summary statistics on the input dataset using different descriptive univariate 
    statistical measures on entire data or at a group level. Though there are other packages which does 
    similar job but each of these are deficient in one form or other, in the measures generated, in
    treating numeric, character and date variables alike, no functionality to view these measures on a
    group level or the way the output is represented. Given the foremost role of the descriptive statistics
    in any of the exploratory data analysis or solution development, there is a need for a more constructive, 
    structured and refined version over these packages. This is the idea behind the package and it brings 
    together all the required descriptive measures to give an initial understanding of the data quality, 
    distribution in a faster,easier and elaborative way.The function brings an additional capability to be 
    able to generate these statistical measures on the entire dataset or at a group level. It calculates measures 
    of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, 
    standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on 
    the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures 
    are generated based on the data types as required by them,rather than applying numerical measures on character and 
    data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful 
    when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a 
    summary report for the data.
Easily create descriptive and comparative tables.
    It makes use and integrates directly with the tidyverse family of packages, and pipes.
    Tables are produced as data frames/lists of data frames for easy manipulation after creation,
    and ready to be saved as csv, or piped to DT::datatable() or pander::pander() to integrate into reports.
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.
'RStudio' as of recently offers the option to define addins and assign shortcuts to them. This package contains addins for a few most frequently used functions in a data scientist's (at least mine) daily work (like str(), example(), plot(), head(), view(), Desc()). Most of these functions will use the current selection in the editor window and send the specific command to the console while instantly executing it. Assigning shortcuts to these addins will save you quite a few keystrokes.
Deseasonalize daily or monthly time series.
Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.
The package provides R scripts for designing genetical
        genomics experiments.
A simple interface to build designs using the package 'DeclareDesign'. 
    In one line of code, users can specify the parameters of individual 
    designs and diagnose their properties. The designers can also be used 
    to compare performance of a given design across a range of combinations 
    of parameters, such as effect size, sample size, and assignment probabilities.
Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'GLPK' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at <http://user.gurobi.com/download/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>.  We have also included directions in the gurobi_installation file in the inst folder.
Generate balanced factorial designs with crossed and nested random and fixed effects <https://github.com/mmrabe/designr>.
Functions for (1) ranking, selecting, and prioritising  genes,
    proteins, and metabolites from high dimensional biology experiments, (2)
    multivariate hit calling in high content screens, and (3) combining data
    from diverse sources.
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
Harrington and Derringer-Suich type desirability functions
This package implements a statistical data mining method to
    compare whole genome gene expression profiles, with respect to the presence
    of a recurrent genetic disturbance event, to identify the affected target
    genes.
Functions that solve initial value problems of a system
        of first-order ordinary differential equations ('ODE'), of
        partial differential equations ('PDE'), of differential
        algebraic equations ('DAE'), and of delay differential
        equations.  The functions provide an interface to the FORTRAN
        functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK'
        collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk'
        and a C-implementation of solvers of the 'Runge-Kutta' family with
        fixed or variable time steps.  The package contains routines
        designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D
        partial differential equations ('PDE') that have been converted
        to 'ODEs' by numerical differencing.
A function for plotting maps of agricultural field experiments that
    are laid out in grids.
Builds both ROC (Receiver Operating Characteristic) and DET (Detection Error Tradeoff) curves from a set of predictors, which are the
    results of a binary classification system. The curves give a general vision of the performance of the classifier,
    and are useful for comparing performance of different systems.
Create a details HTML tag around R objects to place
    in a Markdown, 'Rmarkdown' and 'roxygen2' documentation.
Models for analyzing site occupancy and count data models
  with detection error, including single-visit based models,
  conditional distance sampling and time-removal models.
  Package development was supported by the
  Alberta Biodiversity Monitoring Institute (<https://www.abmi.ca>)
  and the Boreal Avian Modelling Project (<https://borealbirds.ualberta.ca>).
Allows users to quickly and easily detect data containing
    Personally Identifiable Information (PII) through convenience functions.
Detection of runs of homozygosity and of heterozygosity
    in diploid genomes using two methods: sliding windows (Purcell et al (2007)
    <doi:10.1086/519795>) and consecutive runs (Marras et al (2015)
    <doi:10.1111/age.12259>).
Provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, <https://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a>) using 'ROI' <https://cran.r-project.org/package=ROI> or 'lpSolveAPI' <https://cran.r-project.org/package=lpSolveAPI>. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' <https://cran.r-project.org/package=brglm2> package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, <https://www.jstor.org/stable/2345845>), divergence of those ratios indicates data separation.
Solvers and test set for stiff and non-stiff differential equations, and 
  differential algebraic equations. 
  See Mazzia, F., Cash, J.R., and K. Soetaert (2012) <DOI:10.1016/j.cam.2012.03.014>.
Methods for valuation of life insurance premiums and reserves (including variable-benefit and fractional coverage) based on  "Actuarial Mathematics" by Bowers, H.U. Gerber, J.C. Hickman, D.A. Jones and C.J. Nesbitt (1997, ISBN: 978-0938959465), "Actuarial Mathematics for Life Contingent Risks" by Dickson, David C. M., Hardy, Mary R. and Waters, Howard R  (2009) <doi:10.1017/CBO9780511800146>  and "Life Contingencies" by Jordan, C. W (1952) <doi:10.1017/S002026810005410X>. It also contains functions for  equivalent interest and discount rate calculation, present and future values of annuities, and loan amortization schedule.
Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.
Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.
DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust
    Linear Regression.
Simple GUI to perform some standard tree-ring analyses.
Detrend fluorescence microscopy image series for
    fluorescence fluctuation and correlation spectroscopy ('FCS' and
    'FFS') analysis. This package contains functionality published in a
    2016 paper <doi:10.1093/bioinformatics/btx434> but it has been
    extended since then with the Robin Hood algorithm and thus contains
    unpublished work.
Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
In the new era of population genomics, surveys of genetic polymorphism ("genome scans") offer the opportunity to distinguish locus-specific from genome wide effects at many loci. Identifying presumably neutral regions of the genome that are assumed to be influenced by genome-wide effects only, and excluding presumably selected regions, is therefore critical to infer population demography and phylogenetic history reliably. Conversely, detecting locus-specific effects may help identify those genes that have been, or still are, targeted by natural selection. The software package DetSel has been developed to identify markers that show deviation from neutral expectation in pairwise comparisons of diverging populations.
Compare detrital zircon suites by uploading univariate,
      U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based
      user-interface. Outputs publication quality figures using 'ggplot2', and
      tables of statistics currently in use in the detrital zircon geochronology
      community.
A concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code.
Provides a non-drawing graphic device for benchmarking purpose.
    In order to properly benchmark graphic drawing code it is necessary
    to factor out the device implementation itself so that results are not 
    related to the specific graphics device used during benchmarking. The 
    'devoid' package implements a graphic device that accepts all the required
    calls from R's graphic engine but performs no action. Apart from 
    benchmarking it is unlikely that this device has any practical use.
Data sets and sample analyses from Jay L. Devore (2008),
        "Probability and Statistics for Engineering and the Sciences
        (7th ed)", Thomson.
A set of functions to quantify the relationship between development
    rate and temperature and to build phenological models. The package comprises 
    a set of models and estimated parameters borrowed from a literature review 
    in ectotherms. The methods and literature review are described in Rebaudo 
    et al. (2018) <doi:10.1111/2041-210X.12935> and Rebaudo and Rabhi (2018) 
    <doi:10.1111/eea.12693>. An example can be found in Rebaudo et al. (2017) 
    <doi:10.1007/s13355-017-0480-5>.
Collection of package development tools.
Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.
A system for the management, assessment, and psychometric analysis of data from educational and psychological tests.
Classical Test and Item analysis, 
  Item Response analysis and data management for educational and psychological tests.
Conditional Maximum Likelihood Calibration and data management of multistage tests. 
  Supports polytomous items and incomplete designs with linear as well as multistage tests.
  Extended Nominal Response and Interaction models, DIF and profile analysis.
  See Robert J. Zwitser and Gunter Maris (2015)<doi:10.1007/s11336-013-9369-6>.
Contains the Detrended Fluctuation Analysis (DFA), Detrended Cross-Correlation Analysis (DCCA), Detrended Cross-Correlation Coefficient (rhoDCCA), Delta Amplitude Detrended Cross-Correlation Coefficient (DeltarhoDCCA), log amplitude Detrended Fluctuation Analysis (DeltalogDFA), two DFA automatic methods for identification of crossover points and a Deltalog automatic method for identification of reference channels.
Produces SPSS- and SAS-like output for linear discriminant 
  function analysis and canonical correlation analysis. The methods are described in
  Manly & Alberto (2017, ISBN:9781498728966),
  Tabachnik & Fidell (2013, ISBN-10:0-205-89081-4), and
  Venables & Ripley (2002, ISBN:0-387-95457-0).
Computes small-sample degrees of freedom adjustment for
    heteroskedasticity robust standard errors, and for clustered standard errors
    in linear regression. See Imbens and Kolesár (2016)
    <doi:10.1162/REST_a_00552> for a discussion of these adjustments.
Phase I/II adaptive dose-finding design for combination
   studies where toxicity rates are supposed to increase with both agents.
Compares two dataframes with a common key
            and returns the delta records. The package will return
            three dataframes that contain the added, changed,
            and deleted records.
An implementation of multivariate linear process bootstrap (MLPB) method and sliding window technique to assess the dynamic functional connectivity (dFC) estimate by providing its confidence bands, based on Maria Kudela (2017) <doi: 10.1016/j.neuroimage.2017.01.056>.
  It also integrates features to visualize non-zero coverage for selected a-priori regions of interest estimated by the dynamic functional connectivity model (dFCM) and dynamic functional connectivity (dFC) curves for reward-related a-priori regions of interest where the activation-based analysis reported.
Provides functions to run the CRM and
        TITE-CRM in phase I trials and calibration tools for trial
        planning purposes.
Quickly and graphically show missing values and classes of each variable and each observation of a data.frame. The aim is to show pattern of missing values and if there is wrong class attribution.
Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.
A set of functions to perform Raju, van der Linden and Fleer's
    (1995, doi:10.1177/014662169501900405) Differential Functioning of Items
    and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item
    Parameter Replication approach (Oshima, Raju, & Nanda, 2006, doi:10.1111/j.
    1745-3984.2006.00001.x) for obtaining the associated statistical significance
    tests cut-off points. They may also be used for a priori and post-hoc power
    calculations (Cervantes, 2017, doi:10.18637/jss.v076.i05).
Meta-analysis approaches for Phase I dose finding early phases clinical trials in order to better suit requirements in terms of maximum tolerated dose (MTD) and maximal dose regimen (MDR). This package has currently three different approaches: (a) an approach proposed by Zohar et al, 2011, <doi:10.1002/sim.4121> (denoted as ZKO), (b) the Variance Weighted pooling analysis (called VarWT) and (c) the Random Effects Model Based (REMB) algorithm, where user can input his/her own model based approach or use the existing random effect logistic regression model (named as glimem) through the 'dfmeta' package.
Phase I/II adaptive dose-finding design for single-agent
   Molecularly Targeted Agent (MTA), according to the paper "Phase
   I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
   Determination using Adaptive Randomization", Riviere Marie-Karelle et
   al. (2016) <doi:10.1177/0962280216631763>.
Tools to identify, quantify, analyze, and visualize growth
    suppression events in tree rings that are often produced by insect
    defoliation. Described in Guiterman et al. (2020) <doi:10.1016/j.dendro.2020.125750>.
Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.
Provides R-implementation of Decision forest algorithm, which combines the predictions of
    multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel 
    pattern-recognition method which can be used to analyze: (1) DNA microarray data; 
    (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry  (SELDI-TOF-MS) data; and 
    (3) Structure-Activity Relation (SAR) data.
    In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV.  
    run Dforest() to see more instructions.
    Weida Tong (2003) <doi:10.1021/ci020058s>.
A tool for manipulating data using the generic formula. A single formula allows to easily add, replace and remove variables before running the analysis.
A unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults, is proposed in the 'dfped' package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through 'dfped' package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the 'dfped' package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial. These methods are proposed by Petit et al, (2016) <doi:10.1177/0962280216671348>.
Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed  without making any parametric assumptions on the stable (in-control) distribution.
Statistical methods involving PK measures are provided, in the dose allocation process during a Phase I clinical trials. These methods, proposed by Ursino et al, (2017) <doi:10.1002/bimj.201600084>, enter pharmacokinetics (PK) in the dose finding designs in different ways, including covariates models, dependent variable or hierarchical models. This package provides functions to generate data from several scenarios and functions to run simulations which their objective is to determine the maximum tolerated dose (MTD).
Implementing Function-on-Scalar Regression model in which the response function is dichotomized and observed sparsely. This package provides smooth estimations of functional regression coefficients and principal components for the dichotomized functional response regression (dfrr) model.
Decomposing value added growth into explanatory factors.
    A cost constrained value added function is defined to specify the 
    production frontier. Industry estimates can also be aggregated
    using a weighted average approach.
    Details about the methodology and data can be found in Diewert and Fox (2018)
    <doi:10.1093/oxfordhb/9780190226718.013.19>
    and Zeng, Parsons, Diewert and Fox (2018)
    <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.
Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
Feature selection from high dimensional data using a diploid
    genetic algorithm with Incomplete Dominance for genotype to phenotype mapping
    and Random Assortment of chromosomes approach to recombination.
Performs differential correlation analysis on input
    matrices, with multiple conditions specified by a design matrix. Contains
    functions to filter, process, save, visualize, and interpret differential
    correlations of identifier-pairs across the entire identifier space, or with
    respect to a particular set of identifiers (e.g., one). Also contains several
    functions to perform differential correlation analysis on clusters (i.e., modules)
    or genes. Finally, it contains functions to generate empirical p-values for the
    hypothesis tests and adjust them for multiple comparisons. Although the package
    was built with gene expression data in mind, it is applicable to other types of
    genomics data as well, in addition to being potentially applicable to data from
    other fields entirely. It is described more fully in the manuscript
    introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
Provides a flexible container to manage and annotate Differential Gene 
    Expression (DGE) analysis results (Smythe et. al (2015) <doi:10.1093/nar/gkv007>).  
    The DGEobj has data slots for row (gene), col (samples), assays (matrix n-rows 
    by m-samples dimensions) and metadata (not keyed to row, col, or assays).  
    A set of accessory functions to deposit, query and retrieve subsets of a data 
    workflow has been provided.  Attributes are used to capture metadata such as 
    species and gene model, including reproducibility information such that a 3rd 
    party can access a DGEobj history to see how each data object was created or 
    modified.  Since the DGEobj is customizable and extensible it is not limited 
    to RNA-seq analysis types of workflows -- it can accommodate nearly any data 
    analysis workflow that starts from a matrix of assays (rows) by samples (columns).
Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.
Model fitting and evaluation tools for double generalized linear
    models (DGLMs). This class of models uses one generalized linear model (GLM)
    to fit the specified response and a second GLM to fit the deviance of the first
    model.
Model estimation, dispersion testing and diagnosis of hyper-Poisson
    Saez-Castillo, A.J. and Conde-Sanchez, A. (2013) 
    <doi:10.1016/j.csda.2012.12.009> and Conway-Maxwell-Poisson Huang, A. (2017)
    <doi:10.1177/1471082X17697749> regression models.
A set of functions have been implemented to generate random data to perform Monte Carlo simulations on structural models with formative constructs and interaction and nonlinear effects (Two-Step PLS Mode B structural models). The setup of the true model considers a simple structure with three formative exogenous constructs related to one formative endogenous construct. The routines take into account the interaction and nonlinear effects of the exogenous constructs on the endogenous construct.
Provides data used by package 'dgo' in examples and vignettes.
This package contains a proposed revision to the
        stats::ks.test() function and the associated ks.test.Rd help
        page.  With one minor exception, it does not change the
        existing behavior of ks.test(), and it adds features necessary
        for doing one-sample tests with hypothesized discrete
        distributions.  The package also contains cvm.test(), for doing
        one-sample Cramer-von Mises goodness-of-fit tests.
Gumbel distribution functions (De Haan L. (2007)
    <doi:10.1007/0-387-34471-3>) implemented with the techniques of automatic
    differentiation (Griewank A. (2008) <isbn:978-0-89871-659-7>).
    With this tool, a user should be able to quickly model extreme
    events for which the Gumbel distribution is the domain of attraction.
    The package makes available the density function, the distribution
    function the quantile function and a random generating function. In
    addition, it supports gradient functions. The package combines 'Adept'
    (C++ templated automatic differentiation) (Hogan R. (2017)
    <doi:10.5281/zenodo.1004730>) and 'Eigen' (templated matrix-vector
    library) for fast computations of both objective functions and exact
    gradients. It relies on 'RcppEigen' for easy access to 'Eigen' and
    bindings to R.
This is a visualization tool for vegetation structure/succession
    in space and/or time mainly for forest gap models. However, it could also be used to
    visualize observed forest stands. If used for models, they should contain either
    individual trees or cohorts (e.g. LPJ-GUESS by Smith et al. (2014) <doi:10.5194/bg-11-2027-2014>).
    For a list of required and additional data fields see the vignette.
The 'DHARMa' package uses a simulation-based approach to create
    readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
    models. Currently supported are linear and generalized linear (mixed) models from 'lme4'
    (classes 'lmerMod', 'glmerMod'), 'glmmTMB' and 'spaMM', generalized additive models ('gam' from
    'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
    classes. Moreover, externally created simulations, e.g. posterior predictive simulations
    from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well.
    The resulting residuals are standardized to values between 0 and 1 and can be interpreted
    as intuitively as residuals from a linear regression. The package also provides a number of
    plot and test functions for typical model misspecification problems, such as
    over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.
Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.
Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.
Implements double hierarchical generalized linear models in which the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
The density, cumulative distribution, quantiles, 
    and i.i.d random variables of a heavy-headed distribution.
    For more information, please see the vignette.
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regression.
Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), 
  and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, 
  childhood mortality probabilities and rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), 
  Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-five Mortality Rate (U5MR), and adult mortality indicators 
  such as the Age Specific Mortality Rate (ASMR), Age Adjusted Mortality Rate (AAMR), Age Specific Maternal Mortality Rate (ASMMR),
  Age Adjusted Maternal Mortality Rate (AAMMR), Age Specific Pregnancy Related Mortality Rate (ASPRMR), 
  Age Adjusted Pregnancy Related Mortality Rate (AAPRMR), Maternal Mortality Ratio (MMR) and Pregnancy Related Mortality Ratio (PRMR).  
  In addition to the indicators, the 'DHS.rates' package estimates sampling errors indicators such as Standard Error (SE), 
  Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). 
  The package is developed according to the DHS methodology of calculating the fertility indicators and 
  the childhood mortality rates outlined in the 
  "Guide to DHS Statistics" (Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. 2018, <https://dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm>) 
  and the DHS methodology of estimating the sampling errors indicators outlined in 
  the "DHS Sampling and Household Listing Manual" (ICF International 2012, <https://dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf>).
Contains an implementation of the
	     d-variable Hilbert Schmidt independence criterion
	     and several hypothesis tests based on it, as described
	     in Pfister et al. (2017) <doi:10.1111/rssb.12235>.
A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. 
             The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood 
             and represents a proxy measure of aging and also can be served as
             a sensitive predictor of survival. For more information, see 
             (i)"Accumulation of Deficits as a Proxy Measure of Aging" 
             by Arnold B. Mitnitski et al. (2001), 
             The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>;
             (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" 
             by Arnold B Mitnitski et al. (2001), 
             BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.
Fast functions for effective sample size, weighted multivariate mean and variance computation, 
    and weight diagnostic plot for generic importance sampling type results.
Provides methods by Steinhauser et al. (2016) <DOI:10.1186/s12874-016-0196-1> for meta-analysis of diagnostic accuracy studies with several cutpoints.
Several tools for handling block-matrix diagonals and similar
    constructs are implemented. Block-diagonal matrices can be extracted or removed
    using two small functions implemented here. In addition, non-square matrices
    are supported. Block diagonal matrices occur when two dimensions of a data set
    are combined along one edge of a matrix. For example, trade-flow data in the
    'decompr' and 'gvc' packages have each country-industry combination occur along
    both edges of the matrix.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, 
     visualising webs, electrical networks, etc.
     Support for the book "A practical guide to ecological modelling -
     using R as a simulation platform"
     by Karline Soetaert and Peter M.J. Herman (2009), Springer.
     and the book "Solving Differential Equations in R"
     by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer.
     Includes demo(flowchart), demo(plotmat), demo(plotweb).
Build graph/network structures using functions for stepwise addition and
    deletion of nodes and edges. Work with data available in tables for bulk
    addition of nodes, edges, and associated metadata. Use graph selections
    and traversals to apply changes to specific nodes or edges. A wide
    selection of graph algorithms allow for the analysis of graphs. Visualize
    the graphs and take advantage of any aesthetic properties assigned to
    nodes and edges.
Allows for export of DiagrammeR Graphviz objects to SVG.
Parse, format, and validate international phone
    numbers using Google's 'libphonenumber' java library,
    <https://github.com/google/libphonenumber>.
Collects 'libphonenumber' jars required for the
    'dialr' package.
Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.
Visualize one-factor data frame. 
  Beads plot consists of diamonds of each factor of each data series. 
  A diamond indicates average and range. 
  Look over a data frame with many numeric columns and a factor column.
Calculates ecological information and biotic indices for diatoms in a sample. 
    It includes number/shape of chloroplasts diversity indices, size classes, ecological guilds, and multiple biotic indices.
    It outputs both a dataframe with all the results and plots of all the obtained data in a defined output folder.
    Sample data was taken from Nicolosi Gelis, Cochero & Gómez (2020, <doi:10.1016/j.ecolind.2019.105951>).
    The package uses the 'Diat.Barcode' database to calculate morphological and ecological information by Rimet & Couchez (2012, <doi:10.1051/kmae/2012018>),
    and calculates the DES index by Descy (1979, <http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=PASCAL8060205402>), 
    the EPID index by Dell'Uomo (1996, ISBN: 3950009002), 
    the IDAP index by Prygiel & Coste (1993, <doi:10.1007/BF00028033>),
    the ID-CH index by Hürlimann & Niederhauser (2007, <https://www.bafu.admin.ch/bafu/fr/home/themes/eaux/publications/publications-eaux/methodes-analyse-appreciation-cours-eau-diatomees.html>),
    the IDP index by Gómez & Licursi (2001, <doi:10.1023/A:1011415209445>), 
    the ILM index by Leclercq & Maquet (1987, <http://www.vliz.be/imisdocs/publications/286641.pdf>),
    the IPS index by Coste (1982, <https://www.oieau.org/eaudoc/notice/ETUDE-DES-METHODES-BIOLOGIQUES-DAPPRECIATION-QUANTITATIVE-DE-LA-QUALITE-DES-EAUX>), 
    the LOBO index by Lobo, Callegaro, & Bender (2002, ISBN:9788585869908), 
    the SLA by Sládeček (1986, <doi:10.1002/aheh.19860140519>), 
    the TDI index by Kelly, & Whitton (1995, <doi:10.1007/BF00003802>), 
    and the SPEAR(herbicide) index by Wood, Mitrovic, Lim, Warne, Dunlop, & Kefford (2019, <doi:10.1016/j.ecolind.2018.12.035>).
This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.
Space-Filling Designs and Uniformity Criteria.
Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
Performs cluster analysis using an ensemble
    clustering framework, Chiu & Talhouk (2018)
    <doi:10.1186/s12859-017-1996-y>.  Results from a diverse set of
    algorithms are pooled together using methods such as majority voting,
    K-Modes, LinkCluE, and CSPA. There are options to compare cluster
    assignments across algorithms using internal and external indices,
    visualizations such as heatmaps, and significance testing for the
    existence of clusters.
View 2D/3D sections, contour plots, mesh of excursion sets for computer experiments designs, surrogates or test functions.
Collapse red-green or green-blue distinctions to simulate
        the effects of different types of color-blindness.
This package contains the dictionaries for reading microdata
        from IBGE (Brazilian Institute of Geography and Statistics)
        surveys PNAD, PME and POF.
This function provides an interface between 'Matlab' and 'R' in facilitating fast processing for reading and saving DICOM images.
Given  an initial set of points, this package minimizes the number of elements to discard from this set such that there exists at least one monotonic and convex mapping within pre-specified upper and lower bounds.
A key-value dictionary data structure based on R6 class which is designed to be similar usages with other languages dictionary (e.g. 'Python') with reference semantics and extendabilities by R6.
The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group.  Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time.  This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2020) <https://www.ssrn.com/abstract=3148250>.  The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time.  These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these.  There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.
A basic, clear implementation of tree-based gradient boosting
    designed to illustrate the core operation of boosting models. Tuning
    parameters (such as stochastic subsampling, modified learning rate, or
    regularization) are not implemented. The only adjustable parameter is the
    number of training rounds. If you are looking for a high performance boosting
    implementation with tuning parameters, consider the 'xgboost' package.
Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.
Estimate the effect of a treatment on an outcome in sharp Difference-in-Difference designs with multiple groups and periods. 
    It computes the DIDM estimator introduced in Section 4 of "Two-Way Fixed Effects Estimators with Heterogeneous
    Treatment Effects" (Chaisemartin, D'Haultfoeuille (2020)  <doi:10.1257/aer.20181169>), which
    generalizes the standard DID estimator with two groups, two periods and a binary treatment to situations
    with many groups,many periods and a potentially non-binary treatment. For each pair of consecutive time
    periods t-1 and t and for each value of the treatment d, the package computes a DID estimator comparing
    the outcome evolution among the switchers, the groups whose treatment changes from d to some other value
    between t-1 and t, to the same evolution among control groups whose treatment is equal to d both in t-1 and t.
    Then the DIDM estimator is equal to the average of those DIDs across all pairs of consecutive time periods and
    across all values of the treatment. Under a parallel trends assumption, DIDM is an unbiased and consistent estimator
    of the average treatment effect among switchers, at the time period when they switch.
    The package can also compute placebo estimators that can be used to test the parallel trends assumption.
    Finally, in staggered adoption designs where each group's treatment is weakly increasing over time,
    it can compute estimators of switchers' dynamic treatment effects, one time period or more after they have
    started receiving the treatment.
This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
             customer transaction data. The function consumes a data frame with Transaction Number,
             Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
             The function returns a data frame with RFM data for the sales information.
An implementation of the metrics dief@t and dief@k to measure the diefficiency (or continuous efficiency) of incremental approaches, see Acosta, M., Vidal, M. E., & Sure-Vetter, Y. (2017) <doi:10.1007/978-3-319-68204-4_1>. The metrics dief@t and dief@k allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. dief@t and dief@k rely on the computation of the area under the curve of answer traces, and thus capturing the answer rate concentration over a time interval.
Physical constants. Gold, silver and glass permittivities,
    together with spline interpolation functions.
Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Froese & Pauly (2000, ISBN: 9718709991).
A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.
Performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) <doi:10.1111/bmsp.12060>.
Estimation of DIFferential COexpressed NETworks using diverse and user metrics.
			 This package is basically used for three functions related to the estimation
			 of differential coexpression. 
			 First, to estimate differential coexpression where
			 the coexpression is estimated, by default, by Spearman correlation. For this,
			 a metric to compare two correlation distributions is needed. The package includes
			 6 metrics. Some of them needs a threshold. A new metric can also be specified as
			 a user function with specific parameters (see difconet.run). The significance is
			 be estimated by permutations.
			 Second, to generate datasets with controlled differential correlation data. This 
			 is done by either adding noise, or adding specific correlation structure.
			 Third, to show the results of differential correlation analyses. Please see
			 <http://bioinformatica.mty.itesm.mx/difconet> for further information.
Computations of Fisher's z-tests concerning differences between correlations. diffcor.one() could be used to test for differences regarding an expected value, e.g., in construct validation. diffcor.two() may be useful in replication studies, to test if the original study and the replication study differed in terms of effects. diffcor.dep() can be applied to check if the correlation between one construct with another one (r12) is significantly different/higher/smaller than the correlation of one of the constructs with a third construct (r13), given the correlation of the constructs that are compared (r23). The outputs for all the three functions provide the test statistic in z-units, p-values, and alpha levels that were corrected in terms of multiple testing according to Bonferroni. To help interpret the output, the procedure prompts if a single p value is smaller than the corrected alpha. For diffcor.one() and diffcor.two(), the effect size Cohens q is additionally provided. It is a descriptive index to evaluate differences of independent correlations. According to Cohen (1988), q = |.10|, |.30| and |.50| are considered small, moderate, and large differences, respectively.
A method for identifying pattern changes between 2 experimental
         conditions in correlation networks (e.g., gene co-expression networks),
         which builds on a commonly used association measure, such as Pearson's
         correlation coefficient. This package includes functions to calculate
         correlation matrices for high-dimensional dataset and to test
         differential correlation, which means the changes in the correlation
         relationship among variables (e.g., genes and metabolites) between 2
         experimental conditions.
The package includes functions to calculate confidence
        intervals for the difference of dependent proportions. There
        are two functions implemented to edit the data (dichotomising
        with the help of cutpoints, counting accordance and discordance
        of two tests or situations). For the calculation of the
        confidence intervals entries of the fourfold table are needed.
Functions for comparing two data.frames against 
    each other. The core functionality is to provide a detailed breakdown of any differences 
    between two data.frames as well as providing utility functions to help narrow down the 
    source of problems and differences.
This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) <doi: 10.1371/journal.pone.0198139>. Given a list of gene symbols, 'diffEnrich'  will
  perform differential enrichment analysis using the Kyoto Encyclopedia of Genes
  and Genomes (KEGG) REST API. This package provides a number of functions that are 
  intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will
  download and clean species specific ENTREZ gene IDs and map them to their respective
  KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly
  enriched pathways from two gene sets, and finally, the user will identify 
  pathways that are differentially enriched between the two gene sets. In addition to 
  the analysis pipeline, this package also provides a plotting function.
An interface to 'DifferentialEquations.jl' <https://diffeq.sciml.ai/dev/> from the R programming language.
  It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE),
  delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality,
  including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods.
  'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users.
Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.
Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit using marginal maximum 
	likelihood. Parameter restrictions (fixed value and equality 
	constraints) are possible. In addition, factor scores (person drift 
	rate and person boundary separation) can be estimated. Model fit 
	assessment tools are also available. The traditional diffusion model 
	can be estimated as well.
An algorithm based on graph theory tools to detect differentiation
    problems. A differentiation problem occurs when aggregated 
    data are disseminated according to two different nomenclatures. By making the
    difference for an additive variable X between an aggregate composed of categories
    of the first nomenclature and an other aggregate, included in that first 
    aggregate, composed of categories of the second nomenclature, 
    it is sometimes possible to derive X on a small aggregate of records 
    which could then lead to a break of confidentiality.
    The purpose of this package is to detect the set of aggregates composed of 
    categories of the first nomenclature which lead to a differentiation problem, 
    when given a confidentiality threshold. Reference: Vianney Costemalle (2019) <doi: 10.3233/SJI-190564>.
This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution (i.e., the conceptually highest fidelity).
Provides an implementation of statistically significant 
    differential sub-network analysis for paired biological networks.
Generate a colorized diff of two R objects for an intuitive
    visualization of their differences.
An implementation of major general-purpose mechanisms for privatizing
    statistics, models, and machine learners, within the framework of differential
    privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms
    include the Laplace mechanism for releasing numeric aggregates, and the 
    exponential mechanism for releasing set elements. A sensitivity sampler 
    (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target 
    non-private function sensitivity; combined with the generic mechanisms, it 
    permits turn-key privatization of arbitrary programs.
An R interface to the 'codediff' JavaScript library (a copy of which is included in the package,
  see <https://github.com/danvk/codediff.js> for information).
  Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.
Provides data structures and methods for measuring, coding, 
    and analysing text within text corpora. The package allows for manual as 
    well computer aided coding on character, token and text pair level.
Interactive visualizations and tabulations for diffrprojects. 
    All presentations are based on the htmlwidgets framework allowing for 
    interactivity via HTML and Javascript, Rstudio viewer integration, 
    RMarkdown integration, as well as Shiny compatibility.
Various diffusion models to forecast new product growth. Currently
    the package contains Bass, Gompertz and Gamma/Shifted Gompertz curves. See
    Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.
Implements diffusion map method of data
    parametrization, including creation and visualization of
    diffusion map, clustering with diffusion K-means and
	  regression using adaptive regression model.
	  Richards (2009) <doi:10.1088/0004-637X/691/1/32>.
Tools for performing inference and analysis on a class of quadratic diffusion processes for both scalar and bivariate diffusion systems. For scalar diffusions, a module is provided for solving first passage time problems for both time-homogeneous and time-inhomogeneous GQDs.
Tools for performing inference and analysis using a data-imputation scheme and the method of lines.
Tools for performing inference and analysis on a class of quadratic jump diffusion processes.
Implementation of network diffusion algorithms such as
  heat diffusion or Markov random walks. Network diffusion algorithms generally
  spread information in the form of node weights along the edges of a graph to other nodes.
  These weights can for example be interpreted as temperature, an initial amount
  of water, the activation of neurons in the brain, or the location of a random
  surfer in the internet. The information (node weights) is iteratively propagated
  to other nodes until a equilibrium state or stop criterion occurs.
A HTML widget that shows differences between files
    (text, images, and data frames).
Statistical hypothesis testing of pattern heterogeneity
    via differences in underlying distributions across multiple
    contingency tables. Five tests are included: the comparative
    chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086>
    (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song
    test, the heterogeneity test, the marginal-change test (Sharma et
    al. 2020), and the strength test (Sharma et al. 2020). Under the
    null hypothesis that row and column variables are statistically
    independent and joint distributions are equal, their test
    statistics all follow an asymptotically chi-squared distribution.
    A comprehensive type analysis categorizes the relation among the
    contingency tables into type null, 0, 1, and 2 (Sharma et al.
    2020). They can identify heterogeneous patterns that differ in
    either the first order (marginal) or the second order (joint
    distribution deviation from product of marginals). Second-order
    differences may reveal more fundamental changes than first-order
    differences across heterogeneous patterns.
Performs DIFlasso as proposed by Tutz and Schauberger (2015) <doi:10.1007/s11336-013-9377-6>, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.
Detection of differential item functioning (DIF) among dichotomously scored items and differential distractor functioning (DDF) among unscored items with non-linear regression procedures based on generalized logistic regression models (Drabinova & Martinkova, 2017, <doi:10.1111/jedm.12158>).
Clustered or multilevel data structures are common in the assessment of differential item functioning (DIF), particularly in the context of large-scale assessment programs. This package allows users to implement extensions of the Mantel-Haenszel DIF detection procedures in the presence of multilevel data based on the work of Begg (1999) <doi:10.1111/j.0006-341X.1999.00302.x>, Begg & Paykin (2001) <doi:10.1080/00949650108812115>, 
	and French & Finch (2013) <doi:10.1177/0013164412472341>.
Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).
Differential Item Functioning (DIF) Analysis with shiny application interfaces. You can run the functions in this package without any arguments and perform your DIF analysis using user-friendly interfaces.
Item focussed recursive partitioning for simultaneous selection of items and variables that induce Differential Item Functioning (DIF) in dichotomous or polytomous items.
Implementation of a function 'digest()' for the creation of hash
 digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32',
 'xxhash', 'murmurhash', 'spookyhash' and 'blake3' algorithms) permitting easy
 comparison of R language objects, as well as functions such as'hmac()' to
 create hash-based message authentication code. Please note that this package
 is not meant to be deployed for cryptographic purposes for which more
 comprehensive (and widely tested) libraries such as 'OpenSSL' should be
 used.
The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
Import data from a digital image; it requires user input for
    calibration and to locate the data points. The end result is similar to
    'DataThief' and other other programs that 'digitize' published plots or
    graphs.
Combine multiple-relationship networks into a single weighted
    network.  The approach is similar to factor analysis in the that
    contribution from each constituent network varies so as to maximize the
    information gleaned from the multiple-relationship networks.
    This implementation uses Principal Component Analysis calculated using
    'prcomp' with bootstrap subsampling.  Missing links are imputed using
    the method of Chen et al.
    (2012).
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
A set of tools to extract bibliographic content from 'Digital Science Dimensions' using 'DSL' API <https://www.dimensions.ai/dimensions-apis/>.
The 'DImodels' package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) <doi:10.1890/08-1684.1>. Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data_prepare() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function).
The implemented methods are: Bass Standard model, Bass Generalized model (with rectangular shock, exponential shock, mixed shock and armonic shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function.
A collection of dimensionality reduction
    techniques from R packages and a common
    interface for calling the methods.
Estimate the Deterministic Input, Noisy "And" Gate (DINA)
    cognitive diagnostic model parameters using the Gibbs sampler described
    by Culpepper (2015) <doi:10.3102/1076998615595403>.
This function implements the DiNAMIC procedure for
        assessing the statistical significance of recurrent DNA copy
        number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
Decomposition of (income) inequality by population sub groups. 
    For a decomposition on a single variable the mean log deviation can be used
      (see Mookherjee Shorrocks (1982) <DOI:10.2307/2232673>).
    For a decomposition on multiple variables a regression based technique can be 
      used (see Fields (2003) <DOI:10.1016/s0147-9121(03)22001-x>).
    Recentered influence function regression for marginal effects of the (income 
      or wealth) distribution  (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>).
    Some extensions to inequality functions to handle weights and/or missings.
S3 classes and methods to create and work
    with year-quarter, year-month and year-isoweek vectors. Basic
    arithmetic operations (such as adding and subtracting) are supported,
    as well as formatting and converting to and from standard R date
    types.
Individual gene expression patterns are encoded into a series of eigenvector patterns ('WGCNA' package). Using the framework of linear model-based differential expression comparisons ('limma' package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see: Greenham K, Sartor RC, Zorich S, Lou P, Mockler TC and McClung CR. eLife. 2020 Sep 30;9(4). <doi:10.7554/eLife.58993>.
Implements the algorithm described in Jun Li and Alicia T. Lamere, 
    "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes"  (Unpublished). 
    Detects not only genes that show different 
    average expressions ("differential expression", DE), but also genes that show different 
    diversities of expressions in different groups ("differentially dispersed", DD). DD genes 
    can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the 
    quasi-likelihood function, and thus has superior robustness against outliers and other noise.
    Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi.
    (2) Added a function for estimating the proportion of outliers in the data.
    (3) Modified the input parameter names for clarity, and modified the output format for the main function.
Conducts a direction-projection-permutation test and displays several key plots to facilitate the visual assessment of the test. See Wei et. al (2016) <doi:10.1080/10618600.2015.1027773> and Lam et. al (2018) <doi:10.1080/10618600.2017.1366915> for more details.
Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization.
    Yu, R., and Rosenbaum, P. R. (2019). <doi:10.1111/biom.13098>.
Works as an "add-on" to packages like 'shiny', 'future', as well as 
    'rlang', and provides utility functions. Just like dipping sauce adding 
    flavors to potato chips or pita bread, 'dipsaus' for data analysis and 
    visualizations adds handy functions and enhancements to popular packages. 
    The goal is to provide simple solutions that are frequently asked for 
    online, such as how to synchronize 'shiny' inputs without freezing the app,
    or how to get memory size on 'Linux' or 'MacOS' system. The enhancements 
    roughly fall into these four categories: 1. 'shiny' input widgets; 2. 
    high-performance computing using 'RcppParallel' and 'future' package; 3. 
    modify R calls and convert among numbers, strings, and other objects. 4. 
    utility functions to get system information such like CPU chip-set, memory 
    limit, etc.
Compute Hartigan's dip test statistic for unimodality /
 multimodality and provide a test with simulation based p-values,  where
 the original public code has been corrected.
Estimation of the average treatment effect when controlling for
    high-dimensional confounders using debiased inverse propensity score
    weighting (DIPW). DIPW relies on the propensity score following a sparse
    logistic regression model, but the regression curves are not required to be
    estimable. Despite this, our package also allows the users to estimate 
    the regression curves and take the estimated curves as input to our
    methods. Details of the methodology can be found in Yuhao Wang and
    Rajen D. Shah (2020) "Debiased Inverse Propensity Score Weighting for
    Estimation of Average Treatment Effects with High-Dimensional Confounders"
    <arXiv:2011.08661>. The package relies on the optimisation
    software 'MOSEK' <https://www.mosek.com/> which must be installed separately;
    see the documentation for 'Rmosek'.
A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data.  It estimates the number of clusters directly from the data using a Dirichlet-process prior.  See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.
Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. 
    It allows to compute clustering coefficients that are not present in 'igraph' package. 
    A description of clustering coefficients can be found in "Directed clustering in weighted networks: a new perspective", Clemente, G.P., Grassi, R. (2017),  	
    <doi:10.1016/j.chaos.2017.12.007>.
A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216>.
A collection of functions for directional data (including massive, with millions of observations, data) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include a) Phillip J. Paine, Simon P. Preston Michail Tsagris and Andrew T. A. Wood (2018). An elliptically symmetric angular Gaussian distribution. Statistics and Computing 28(3): 689-697. <doi:10.1007/s11222-017-9756-4>. b) Tsagris M. and Alenazi A. (2019). Comparison of discriminant analysis methods on the sphere. Communications in Statistics: Case Studies, Data Analysis and Applications 5(4):467--491. <doi:10.1080/23737484.2019.1684854>. c) P. J. Paine, S. P. Preston, M. Tsagris and Andrew T. A. Wood (2020). Spherical regression models with general covariates and anisotropic errors. Statistics and Computing 30(1): 153--165. <doi:10.1007/s11222-019-09872-2>.
An extensible framework
 for automatically placing direct labels onto multicolor 'lattice' or
 'ggplot2' plots.
 Label positions are described using Positioning Methods
 which can be re-used across several different plots.
 There are heuristics for examining "trellis" and "ggplot" objects
 and inferring an appropriate Positioning Method.
Represents the content of a directory as an interactive collapsible tree. Offers
    the possibility to assign a text (e.g., a 'Readme.txt') to each folder
    (represented as a clickable node), so that when the user hovers the pointer
    over a node, the corresponding text is displayed as a tooltip.
Direction analysis is a set of tools designed to identify
    combinatorial effects of multiple treatments/conditions on pathways
    and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics
    data. See Yang P et al (2014) <doi:10.1093/bioinformatics/btt616>; and Yang P et al. (2016) <doi:10.1002/pmic.201600068>.
Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.
Perform nonparametric Bayesian analysis using Dirichlet 
    processes without the need to program the inference algorithms. 
    Utilise included pre-built models or specify custom 
    models and allow the 'dirichletprocess' package to handle the 
    Markov chain Monte Carlo sampling. 
    Our Dirichlet process objects can act as building blocks for a variety 
    of statistical models including and not limited to: density estimation, 
    clustering and prior distributions in hierarchical models.
    See Teh, Y. W. (2011) 
    <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, 
    among many other sources.
Implements Dirichlet regression models in R.
Implementation of Directional Metropolis Hastings Algorithm for
    MCMC.
Estimate parameters in Dirichlet-Multinomial and compute
        profile log-likelihoods.
Nonparametric kernel density estimation, bandwidth selection,
    and other utilities for analyzing directional data. Implements the estimator
    in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the
    cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987)
    <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in
    García-Portugués (2013) <doi:10.1214/13-ejs821>.
The twoStepsBenchmark() function and its wrappers allow you to disaggregate a low frequency time serie with time series of higher frequency, using the French National Accounts methodology.
	The aggregated sum of the resulting time-serie is strictly equal to the low-frequency serie within the benchmarking window.
	Typically, the low frequency serie is an annual one, unknown for the last year, and the high frequency is either quarterly or mensual.
	See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8).
Fits disaggregation regression models using 'TMB' ('Template Model
    Builder'). When the response data are aggregated to polygon level but
    the predictor variables are at a higher resolution, these models can be
    useful. Regression models with spatial random fields. A useful reference for disaggregation modelling is 
    Lucas et al. (2019) <doi:10.1101/548719>.
Access and manage the application programming interface (API) of the United Nations Office for the Coordination of Humanitarian Affairs' (OCHA) ReliefWeb disaster events at <https://reliefweb.int/disasters/>. The package requires a minimal number of dependencies. It offers functionality to retrieve a user-defined sample of disaster events from ReliefWeb, providing an easy alternative to scraping the ReliefWeb website. It enables a seamless integration of regular data updates into the research work flow.
An open, multi-algorithmic pipeline for easy, fast and efficient
	analysis of cellular sub-populations and the molecular signatures that
	characterize them. The pipeline consists of four successive steps: data
	pre-processing, cellular clustering with pseudo-temporal ordering, defining
	differential expressed genes and biomarker identification. This package
	implements extensions of the work published by Ghannoum et. al. (2019)
	<doi:10.1101/700989>.
Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) <doi:10.1093/biostatistics/kxy071>. There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function.
Client for the Discourse API. Discourse is a open source
    discussion forum platform (<https://www.discourse.org/>). It comes with 'RESTful'
    API access to an installation. This client requires that you are authorized
    to access a Discourse installation, either yours or another.
Computes discrete fast Fourier transform of river discharge data and the derived metrics.
  The methods are described in J. L. Sabo, D. M. Post (2008) <doi:10.1890/06-1340.1> and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) <doi:10.1126/science.aao1053>.
Discrete Laplace exponential family for models such as a generalized linear model
Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
Tests whether multivariate ordinal data may stem from discretizing a multivariate normal distribution.
             The test is described by Foldnes and Grønneberg (2019) <doi:10.1080/10705511.2019.1673168>.
Concordance and discordance of homologous gene regulation allows comparing
  reaction to stimuli in different organisms, 
  for example human patients and animal models of a disease. The package
  contains functions to calculate discordance and concordance score
  for homologous gene pairs, identify concordantly or
  discordantly regulated transcriptional modules and visualize the results.
  It is intended for analysis of transcriptional data.
Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Currently, the package contains data restructuring functions and functions for generating biometrically informed data for kin pairs.
Analyzes group patterns using discourse analysis data with graph theory mathematics. Takes the order of which individuals talk and converts it to a network edge and weight list. Returns the density, centrality, centralization, and subgroup information for each group. Based on the analytical framework laid out in Chai et al. (2019) <doi:10.1187/cbe.18-11-0222>.
Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Structure learning of Bayesian network using coordinate-descent
    algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
    and we use a multi-logit model to do the regression.
    The algorithm is described in Gu, Fu and Zhou (2016) <arXiv:1403.2310>.
Multiple testing procedures described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input a set of observed p-values and their discrete support under the null. A function to compute such p-values and supports for Fisher's exact tests is also provided, along with a wrapper allowing to apply discrete procedures directly from contingency tables.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.
Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.
Multiple testing procedures for discrete test statistics,
        that use the known discrete null distribution of the p-values
        for simultaneous inference.
We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), <doi:10.1073/pnas.1530509100>. The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values.
Create, manipulate, transform, and simulate from discrete random
    variables. The syntax is modeled after that which is used in mathematical
    statistics and probability courses, but with powerful support for more
    advanced probability calculations. This includes the creation of joint
    random variables, and the derivation and manipulation of their conditional
    and marginal distributions.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.
This package is a collection of supervised discretization
        algorithms. It can also be grouped in terms of top-down or
        bottom-up, implementing the discretization algorithms.
Bindings for additional classification models for use with the 
    'parsnip' package. Models include flavors of discriminant analysis, such as 
    linear (Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized 
    (Friedman (1989) <doi:10.1080/01621459.1989.10478752>), and flexible 
    (Hastie, Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), 
    as well as naive Bayes classifiers (Hand and Yu (2007) 
    <doi:10.1111/j.1751-5823.2001.tb00465.x>).
Functions for Discriminant Analysis and Classification purposes
    covering various methods such as descriptive, geometric, linear, quadratic,
    PLS, as well as qualitative discriminant analyses
Provides data transformations, estimation utilities,
    predictive evaluation measures and simulation functions for discrete time
    survival analysis.
Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.
Formatting of population and case data, calculation of Standardized
    Incidence Ratios, and fitting the BYM model using INLA.
Provides estimation methods for markets in equilibrium and
    disequilibrium. Specifically, it supports the estimation of an equilibrium and
    four disequilibrium models with both correlated and independent shocks.
    It also provides post-estimation analysis tools, such as aggregation and
    marginal effects calculations. The estimation methods are based on full
    information maximum likelihood techniques given in Maddala and Nelson (1974)
    <doi:10.2307/1914215>. They are implemented using the analytic derivative
    expressions calculated in Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>.
    The equilibrium estimation constitutes a special case of
    a system of simultaneous equations. The disequilibrium models, instead, replace
    the market clearing condition with a short side rule and
    allow for different specifications of price dynamics.
Estimate, summarize, and perform predictions with the market in 
  disequilibrium model, as found in Gourieroux, C. (2000) <doi:10.1017/CBO9780511805608> and 
  Maddala, G. (1983) <doi:10.1017/CBO9780511810176>. The parameters are estimated with maximum likelihood.
Model cell type heterogeneity of bulk renal cell carcinoma. The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions.
Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.
Implements methods for calculating disproportionate impact: the percentage point gap, proportionality index, and the 80% index.
 California Community Colleges Chancellor's Office (2017).  Percentage Point Gap Method. <https://www.cccco.edu/-/media/CCCCO-Website/About-Us/Divisions/Digital-Innovation-and-Infrastructure/Research/Files/PercentagePointGapMethod2017.ashx>.
 California Community Colleges Chancellor's Office (2014).  Guidelines for Measuring Disproportionate Impact in Equity Plans. <https://www.cccco.edu/-/media/CCCCO-Website/Files/DII/guidelines-for-measuring-disproportionate-impact-in-equity-plans-tfa-ada.pdf>.
A disk-based data manipulation tool for working with 
  large-than-RAM datasets. Aims to lower the barrier-to-entry for 
  manipulating large datasets by adhering closely to popular and 
  familiar data manipulation paradigms like 'dplyr' verbs and 
  'data.table' syntax.
A pipeline to analyze photographs of disk diffusion plates. This removes the need to analyze the plates themselves, and thus analysis can be done separate from the assay. Furthermore, diskImageR removes potential researcher bias, by quantitative assessment of drug resistance as the zone diameter at multiple cutoff values of growth inhibition. This method also extends the disk diffusion assay by measuring drug tolerance (in addition to drug resistance) as the fraction of the subpopulation that is able to grow above the resistance point ("FoG"), and drug sensitivity as the rate of change from no growth to full growth ("slope").
Methods for species distribution modeling, that is, predicting the environmental similarity of any site to that of the locations of known occurrences of a species.
The disparity filter algorithm is a network reduction technique to
    identify the 'backbone' structure of a weighted network without destroying
    its multi-scale nature. The algorithm is documented in M. Angeles Serrano,
    Marian Boguna and Alessandro Vespignani in "Extracting the multiscale
    backbone of complex weighted networks", Proceedings of the National Academy
    of Sciences 106 (16), 2009. This implementation of the algorithm supports
    both directed and undirected networks.
A package containing R functions for displaying data and
        results from high-throughput screening experiments.
Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 <doi:10.2307/2347792>), overdispersed binomial logit models (Williams, 1987 <doi:10.2307/2347977>), and overdispersed Poisson log-linear models (Breslow, 1984 <doi:10.2307/2347661>), using a quasi-likelihood approach.
Create disposable R packages for testing.
    You can create, install and load multiple R packages with a single
    function call, and then unload, uninstall and destroy them with another
    function call. This is handy when testing how some R code or an R package
    behaves with respect to other packages.
A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics (Guillerme (2018) <doi:10.1111/2041-210X.13022>). The package also provides functions for looking at disparity in a serial way (e.g. disparity through time - Guillerme and Cooper (2018) <doi:10.1111/pala.12364>) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.
Spatial downscaling of coarse grid mapping to fine grid
    mapping using predictive covariates and a model fitted using the 'caret'
    package. The original dissever algorithm was published by Malone et al. 
    (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al.
    (2017) <doi:10.1016/j.compag.2017.08.021>.
Tools to fit sample selection models in case of discrete response 
  variables, through a parametric formulation which represents a natural 
  extension of the well-known Heckman selection model are provided in the 
  package. The response variable can be of Bernoulli, Poisson or Negative 
  Binomial type. The sample selection mechanism allows to choose among a 
  Normal, Logistic or Gumbel distribution.
This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.
A simple way of fitting detection functions to distance sampling
    data for both line and point transects. Adjustment term selection, left and
    right truncation as well as monotonicity constraints and binning are
    supported. Abundance and density estimates can also be calculated (via a
    Horvitz-Thompson-like estimator) if survey area information is provided. See
    Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on
    methods and <https://examples.distancesampling.org/> for example analyses.
Calculates the study size (either number of detections, or
    proportion of region that should be covered) to achieve a target precision for
    the estimated abundance. The calculation allows for the penalty due to unknown
    detection function, and for overdispersion. The user must specify a guess at the
    true detection function.
Provides tools for constructing, manipulating and using distance metrics.
Provides tools to assess the dissimilarity between multivariate time-series. It is based on the psi measure described  by Birks and Gordon (1985 <doi:10.1002/jqs.3390020110>), which computes dissimilarity between irregular time-series constrained by sample order. However, in this package the original idea has been extended to work with any kind of multivariate time-series, no matter whether they are regular, irregular, aligned or unaligned. Furthermore, the package allows to assess the significance of dissimilarity values by applying a restricted permutation test, allows to measure the contribution of individual variables to dissimilarity, and offers tools to transfer attributes (generally time or age, but other are possible) between sequences based on the similarity of their samples.
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations.
Implementing algorithms and fitting models when sites (possibly remote) share
  computation summaries rather than actual data over HTTP with a master R process (using
  'opencpu', for example). A stratified Cox model and a singular value decomposition are
  provided. The former makes direct use of code from the R 'survival' package. (That is,
  the underlying Cox model code is derived from that in the R 'survival' package.)
  Sites may provide data via several means: CSV files, Redcap API, etc. An extensible
  design allows for new methods to be added in the future and includes facilities
  for local prototyping and testing. Web applications are provided (via 'shiny') for
  the implemented methods to help in designing and deploying the computations.
Creates discretised versions of continuous 
      distribution functions by mapping continuous values 
      to an underlying discrete grid, based on a (uniform) 
      frequency of discretisation, a valid discretisation 
      point, and an integration range. For a review of 
      discretisation methods, see 
      Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>.
Contains a range of functions covering the present
  development of the distributional method for the dichotomisation of continuous outcomes. 
  The method provides estimates with standard error of a comparison of proportions 
  (difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means. 
  See the URL below or <arXiv:1809.03279> for more information.
Download data from the FlorKart database of the floristic field mapping in Germany in a convenient way. The database incorporates distribution data for plants in Germany on the basis of quadrants on a topographical map with a resolution of 1 : 25000 (TK 25). The data is owned and provided by the German Federal Agency for Nature Conservation (BfN) and the Network Phytodiversity in Germany (NetPhyD). For further information please visit <http://www.floraweb.de/pflanzenarten/hintergrundtexte_florkart_organisation.html>. The author of this package is in no way associated with the BfN or NetPhyD.
Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.
Scientific and technical article format for the web. 'Distill' articles 
    feature attractive, reader-friendly typography, flexible layout options
    for visualizations, and full support for footnotes and citations.
Some very simple method functions for confidence interval calculation, bootstrap resampling aimed at atmospheric science applications, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.
Provides a high level API to interface over sources storing distance, dissimilarity, similarity matrices with matrix style extraction, replacement and other utilities. Currently, in-memory dist object backend is supported.
Geodesic distance between phylogenetic trees and
  associated functions. The theoretical background of 'distory'
  is published in Billera et al. (2001) "Geometry of the space of
  phylogenetic trees." <doi:10.1006/aama.2001.0759>.
S4-classes and methods for distributions.
An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) <doi:10.1007/978-0-387-98144-4> for sampling the Multivariate Normal distribution and Michael et al. (1976) <doi:10.2307/2683801> for sampling the Wald distribution.
Provides documentation in form of a common vignette to packages 'distr',
        'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.
Functions for visualizing distributional regression models fitted using the 'gamlss', 'bamlss' or 'betareg' R package. The core of the package consists of a 'shiny' application, where the model results can be interactively explored and visualized.
Distribution (S4-)classes for elliptically contoured distributions (based on
            package 'distr').
Extends package 'distr' by functionals, distances, and conditional distributions.
A different way for calculating pdf/pmf, cdf, quantile and random data such that the user is able to consider the name of related distribution as an argument and so easily can changed by a changing argument by user. It must be mentioned that the core and computation base of package 'DISTRIB' is package 'stats'. Although similar functions are introduced previously in package 'stats', but the package 'DISTRIB' has some special applications in some special computational programs.
Calculates expected values, variance, different moments (kth 
    moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR)
    and Tail Value-at-Risk (TVaR) as well as some density and cumulative 
    (survival) functions of continuous, discrete and compound distributions. 
    This package also includes a visual 'Shiny' component to enable students 
    to visualize distributions and understand the impact of their parameters.
    This package is intended to expand the 'stats' package so as
    to enable students to develop an intuition for probability.
Vectorised distribution objects with tools for manipulating, 
    visualising, and using probability distributions. Designed to allow model
    prediction outputs to return distributions rather than their parameters, 
    allowing users to directly interact with predictive distributions in a
    data-oriented workflow. In addition to providing generic replacements for
    p/d/q/r functions, other useful statistics can be computed including means,
    variances, intervals, and highest density regions.
Given an univariate dataset, returns the best fitting parameter families, as defined in Shao (2003) <doi:10.1007/B97553>, including their parameter estimates via maximum likelihood.
Fits Gaussian Mixtures by applying evolution. As fitness function a mixture of the chi square test for distributions and a novel measure for approximating the common area under curves between multiple Gaussians is used. The package presents an alternative to the commonly used Likelihood Maximization as is used in Expectation Maximization. The algorithm and applications of this package are published under: Lerch, F., Ultsch, A., Lotsch, J. (2020) <doi:10.1038/s41598-020-57432-w>. The evolution is based on the 'GA' package: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04> while the Gaussian Mixture Logic stems from 'AdaptGauss': Ultsch, A, et al. (2015) <doi:10.3390/ijms161025897>.
Tools to create and manipulate probability
    distributions using S3.  Generics random(), pdf(), cdf() and
    quantile() provide replacements for base R's r/d/p/q style functions.
    Functions and arguments have been named carefully to minimize
    confusion for students in intro stats courses. The documentation for
    each distribution contains detailed mathematical notes.
A library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.
Provides new types of omnibus tests which are generally much more powerful than traditional tests (including the Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling tests),see Zhang (2002) <doi:10.1111/1467-9868.00337>.
Utilities are provided which are of use in the
    packages I have developed for dealing with
    distributions. Currently these packages are GeneralizedHyperbolic,
    VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these
    packages requires DistributionUtils. Functionality includes sample
    skewness and kurtosis, log-histogram, tail plots, moments by
    integration, changing the point about which a moment is
    calculated, functions for testing distributions using inversion
    tests and the Massart inequality. Also includes an implementation
    of the incomplete Bessel K function.
Implements S4 classes for probability models based on packages 'distr' and
            'distrEx'.
In order to provide unified access to Linux distribution details
    in R, this package wraps the various files and commands that may exist
    on a system. It is similar in spirit to the 'lsb_release' command and the
    'Python' package of the same name.
Fast distributed/parallel estimation for multinomial logistic regression via Poisson factorization and the 'gamlr' package.  For details see: Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>.
Tools to estimate and manage empirical distributions,
    which should work with survey data. One of the main features is the 
    possibility to create data cubes of estimated statistics, that include
    all the combinations of the variables of interest (see for example functions
    dcc5() and dcc6()).
S4-distribution classes based on package distr for distributions from packages
        'fBasics' and 'fGarch'.
S4-classes for setting up a coherent framework for simulation within the distr
        family of packages.
Provides flexible examples of LLN and CLT for teaching purposes in secondary
        school.
Evaluation (S4-)classes based on package distr for evaluating procedures
        (estimators/tests) at data/simulation in a unified way.
A full definition for Weibull tails and Full-Tails Gamma and tools for fitting these distributions to empirical tails. This package build upon the paper by del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012) <doi:10.1017/asb.2017.9>.
Provides convenient methods for accessing the data in 'dist' objects with minimal memory and computational overhead. 'disttools' can be used to extract the distance between any pair or combination of points encoded by a 'dist' object using only the indices of those points. This is an improvement over existing functionality, which requires either coercing a 'dist' object into a matrix or calculating the one dimensional index corresponding to a pair of observations. Coercion to a matrix is undesirable because doing so doubles the amount of memory required for storage. In contrast, there is no inherent downside to the latter solution. However, in part due to several edge cases, correctly and efficiently implementing such a solution can be challenging. 'disttools' abstracts away these challenges and provides a simple interface to access the data in a 'dist' object using the latter approach.
Testing and documenting code that communicates with remote
  databases can be painful. Although the interaction with R is usually relatively 
  simple (e.g. data(frames) passed to and from a database), because they rely on 
  a separate service and the data there, testing them can be difficult to set up,
  unsustainable in a continuous integration environment, or impossible without 
  replicating an entire production cluster. This package addresses that by 
  allowing you to make recordings from your database interactions and then play 
  them back while testing (or in other contexts) all without needing to spin up 
  or have access to the database your code would typically connect to.
Provides tools for quality checks of georeferenced plant
        species accessions.
Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.
Contains functions for the 'DivE' estimator <doi:10.1371/journal.pcbi.1003646>. The 'DivE' estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.
Utilities to represent, visualize, filter, analyse, and summarize
	     time-depth recorder (TDR) data.  Miscellaneous functions for
	     handling location data are also provided.
Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework.
Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.
Contains a number of comparative 'phylogenetic' methods,
  mostly focusing on analysing diversification and character
  evolution.  Contains implementations of 'BiSSE' (Binary State
  'Speciation' and Extinction) and its unresolved tree extensions,
  'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE',
  'GeoSSE', and 'BiSSE-ness' Other included methods include Markov
  models of discrete and continuous trait evolution and constant rate
  'speciation' and extinction.
Allows the calculation of both genetic diversity partition 
  statistics, genetic differentiation statistics, and locus informativeness 
  for ancestry assignment. 
  It also provides users with various option to calculate 
  bootstrapped 95\% confidence intervals both across loci, 
  for pairwise population comparisons, and to plot these results interactively. 
  Parallel computing capabilities and pairwise results without 
  bootstrapping are provided. 
  Also calculates F-statistics from Weir and Cockerham (1984). 
  Various plotting features are provided, as well as Chi-square tests of 
  genetic heterogeneity. 
  Functionality for the calculation of various diversity parameters is 
  possible for RAD-seq derived SNP data sets containing thousands of marker loci.
  A shiny application for the development of microsatellite multiplexes is 
  also available.
Implements diversity forests as described in an
  upcoming paper by the author of the package.
  This package is a fork of the R package 'ranger' (main author: 
  Marvin N. Wright) that implements random forests using an efficient C++ 
  implementation. More precisely, 'diversityForest' was written by modifying 
  the code of 'ranger', version 0.11.0. Therefore, details on further 
  functionalities of the code that are not presented in the help pages of 
  'diversityForest' are found in the help pages of 'ranger' (version 0.11.0). 
  The code in the example sections of the 'diversityForest' manual can be used 
  as a template for all basic application scenarios with respect to 
  classification, regression and survival prediction using univariate, 
  binary splitting. Some function arguments adopted from the 'ranger' 
  package are not be useable with diversity forests (for the current 
  package version).
Predictions of alpha diversity are fitted from presence data, first abundance is modeled from occupancy models and then, several diversity indices are calculated and finally GLM models are used to predict diversity in different environments and select priority areas.
Provides tools to sort DICOM-format medical image files, and
    convert them to NIfTI-1 format.
A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.
Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some 'Fortran' code has been included to the original dixon2002() function of the 'ecespa' package to improve speed.
For outlier detection in small and normally distributed 
    samples the ratio test of Dixon (Q-test) can be used. Density,
    distribution function, quantile function and random generation 
    for Dixon's ratio statistics are provided as wrapper functions.
    The core applies McBane's Fortran functions <doi:10.18637/jss.v016.i03> 
    that use Gaussian quadrature for a numerical solution.
Perform multistage deterministic linkages and apply case definitions to datasets.
    Records are linked by different matching criteria in a specified order of certainty. 
    The linkage process handles missing data and conflicting matches using this same order.
    Track events (e.g. sample collection) and period (e.g. hospital admission) to episodes. 
    This process permits several options such as episode lengths and recurrence.
    Record linkage and episode tracking assign unique group identifiers to matched records.
    Duplicate events or records can then be identified or sub-analyses performed within each group.
Utility functions used for the R package
    development infrastructure inside the data integration centers ('DIZ')
    to standardize and facilitate repetitive tasks such as setting up a
    database connection or issuing notification messages and to avoid
    redundancy.
Implements various decision support tools related to the Econometrics & Technometrics.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, etc.
Provides functions to facilitate access to the 'DKAN' API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the 'DKAN' REST API (metadata), and the 'DKAN' datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the 'DKAN' datastore.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.
An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.
The Dirichlet Laplace shrinkage prior in Bayesian linear regression and variable selection, featuring:
    utility functions in implementing Dirichlet-Laplace priors such as visualization; 
    scalability in Bayesian linear regression; 
    penalized credible regions for variable selection.
Interface for 'Rcpp' users to 'dlib' <http://dlib.net> which is a
    'C++' toolkit containing machine learning algorithms and computer vision tools.
    It is used in a wide range of domains including robotics, embedded devices,
    mobile phones, and large high performance computing environments. This package
    allows R users to use 'dlib' through 'Rcpp'.
Provides routines for Maximum likelihood,
    Kalman filtering and smoothing, and Bayesian
    analysis of Normal linear State Space models, also known as 
    Dynamic Linear Models.
A collection of data-limited management procedures that can be evaluated 
    with management strategy evaluation with the MSEtool package, or applied to 
    fishery data to provide management recommendations.
Collection of functions for distributed lag linear and non-linear models.
A collection of tools that support data diagnosis, exploration, and transformation. 
    Data diagnostics provides information and visualization of missing values and outliers and 
    unique and negative values to help you understand the distribution and quality of your data. 
    Data exploration provides information and visualization of the descriptive statistics of 
    univariate variables, normality tests and outliers, correlation of two variables, and 
    relationship between target variable and predictor. Data transformation supports binning 
    for categorizing continuous variables, imputates missing values and outliers, resolving skewness. 
    And it creates automated reports that support these three tasks.
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression with constrained lag shapes (Magrini, 2018 <doi:10.2478/bile-2018-0012>; Magrini et al., 2019 <doi:10.1007/s11135-019-00855-z>). DLSEMs account for temporal delays in the dependence relationships among the variables through a single parameter per covariate, thus allowing to perform dynamic causal inference in a feasible fashion. Endpoint-constrained quadratic, quadratic decreasing, linearly decreasing and gamma lag shapes are available.
Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <http://cranlogs.r-pkg.org>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.
Provides tools for working with multiple related
    tables, stored as data frames or in a relational database.  Multiple
    tables (data and metadata) are stored in a compound object, which can
    then be manipulated with a pipe-friendly syntax.
Dynamic model averaging for binary and continuous
        outcomes.
Computes measurement nonequivalence effect size indices described in Nye and Drasgow (2011) <doi:10.1037/a0022955>.
Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (<DOI:10.1016/0304-4076(80)90070-6>).
Functions for fitting a Bayesian model for grouping binary
    dissimilarity matrices in homogeneous clusters. Currently, it includes
    methods only for binary data.
DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015).
  Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.
  Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>.
  Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs),
  a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008).
  Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common
  features from within standard diffusion models with the addition of superimposed controlled and automatic activation.
  The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural
  conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality
  to fit the model to observed data.
Forms the core for developing own domain specific metadata schemes. 
                  It contains the basic functionality needed for all metadata schemes based on the
                  'dmdScheme'. See R.M. Krug and O.L. Petchey (2019) <DOI:10.5281/zenodo.3581970>.
The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Dyadic mixed model analysis with multi-trait responses and
  pedigree-based partitioning of individual variation into a range of
  environmental and genetic variance components for individual and 
  maternal effects. Method documented in dmmOverview.pdf; it is an 
  implementation of dispersion mean model described by
  Searle et al. (1992) "Variance Components", Wiley, NY.
Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.
The framework provides functions to generate ODEs of reaction
    networks, parameter transformations, observation functions, residual functions,
    etc. The framework follows the paradigm that derivative information should be
    used for optimization whenever possible. Therefore, all major functions produce
    and can handle expressions for symbolic derivatives.
Perform differential analysis for
        methylation array data. Detect differentially
        methylated regions (DMRs) from array M-values. 
        The core is a Non-homogeneous Hidden Markov Model
        for estimating spatial correlation and a novel Constrained 
        Gaussian Mixture Model for modeling the M-value pairs of each individual locus.
Model selection algorithms for regression and classification, where the predictors can be numerical and categorical and the number of regressors exceeds the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka (2015) <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk (2015) <http://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>.
For checking the dataset from EDC(Electronic Data Capture) in clinical trials.
             'dmtools' reshape your dataset in a tidy view and check events.
             You can reshape the dataset and choose your target to check, for example, the laboratory reference range.
Work within the 'dplyr' workflow to add random variates to your data frame. 
  Variates can be added at any level of an existing column.  Also, bounds can be specified 
  for simulated variates.
This package includes functions and data accompanying the book 
	     "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
Package for conducting differential network analysis from
        microarray data.
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.
Computationally efficient tools for comparing all pairs of profiles
    in a DNA database. The expectation and covariance of the summary statistic
    is implemented for fast computing. Routines for estimating proportions of
    close related individuals are available. The use of wildcards (also called F-
    designation) is implemented. Dedicated functions ease plotting the results. 
    See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. 
    Compute the distribution of the numbers of alleles in DNA mixtures. 
    See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.
The DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as "is.xxdistribution" are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt().
The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.
Provides density, distribution function, quantile function and random
             generation for the split normal and split-t distributions, and computes their
             mean, variance, skewness and kurtosis for the two distributions (Li, F,
             Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).
Using Local Moran's I for detection of differential network local consistency.
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Functions are provided to fit temporal lag models to dynamic
    networks. The models are build on top of exponential random graph models (ERGM) framework. There are
    functions for simulating or forecasting networks for future time points.
    Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, <DOI: 10.1080/10618600.2019.1594834>.
Flexibly convert data between long and wide format using just two
    functions: reshape_toLong() and reshape_toWide().
Provides Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains.
A dimension reduction technique for outlier detection. DOBIN: a Distance 
    based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier 
    detection. This is not an outlier detection method; rather it is a pre-processing 
    method for outlier detection. It brings outliers to the fore-front using fewer basis 
    vectors (Kandanaarachchi, Hyndman 2019) <doi:10.13140/RG.2.2.15437.18403>.
Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.
Contains:
    1) Facilities for working with grouped data: 'do' something to data
    stratified 'by' some variables.
    2) LSmeans (least-squares means), general linear contrasts. 
    3) Miscellaneous other utilities.
Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.
Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms,
    namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. 
    The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <arXiv:1405.4053>.
Generation of a document (.docx file) with descriptive analysis reporting of a selected dataset. REFERENCE: R. Sarmento, V. Costa (2017) <DOI:10.4018/978-1-68318-016-6>.
Generation of a document (.docx file) with inference analysis reporting of a selected dataset. REFERENCE: R. Sarmento, V. Costa (2017) <DOI:10.4018/978-1-68318-016-6>.
Build a Dockerfile straight from your R session. 'dockerfiler' allows 
    you to create step by step a Dockerfile, and provide convenient tools to wrap 
    R code inside this Dockerfile.
Gives you the ability to use arbitrary Docker images
    (including custom ones) to process Rmarkdown code chunks.
Define a command-line interface by just giving it
    a description in the specific format.
A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.
Provides the ability to display something analogous to
    Python's docstrings within R.  By allowing the user to document
    their functions as comments at the beginning of their function
    without requiring putting the function into a package we allow
    more users to easily provide documentation for their functions.
    The documentation can be viewed just like any other help files
    for functions provided by packages as well.
Production of R packages from tagged comments introduced within the code 
             and a minimum of additional documentation files.
Have you ever been tempted to create 'roxygen2'-style documentation
    comments for one of your functions that was not part of one of your
    packages (yet)?
    This is exactly what this package is about: running 'roxygen2' on
    (chunks of) a single code file.
It is sometimes necessary to create documentation for all files in a directory. Doing so by hand can be very tedious. This task is made fast and reproducible using the functionality of 'documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion.
Connect to the 'DocuSign' Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, 
  which supports embedded signing, and sending of documents.
A set of helper functions for using R Markdown to create documents
    in docx format, especially documents for use in a classroom or workshop
    setting.
'Microsoft Word' 'docx' files provide an 'XML' structure that is fairly
    straightforward to navigate, especially when it applies to 'Word' tables and
    comments. Tools are provided to determine table count/structure, comment count
    and also to extract/clean tables and comments from 'Microsoft Word' 'docx' documents.
    There is also nascent support for '.doc' and '.pptx' files.
A variety of sampling plans are able to be compared using evaluations of their operating characteristics (OC), average outgoing quality (OQ), average total inspection (ATI) etc.
Distances on dual-weighted directed graphs using priority-queue
    shortest paths (Padgham (2019) <doi:10.32866/6945>). Weighted directed
    graphs have weights from A to B which may differ from those from B to A.
    Dual-weighted directed graphs have two sets of such weights. A canonical
    example is a street network to be used for routing in which routes are
    calculated by weighting distances according to the type of way and mode of
    transport, yet lengths of routes must be calculated from direct distances.
Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.
'CRAN' packages 'DoE.base' and 'Rmosek' and non-'CRAN' package 'gurobi' are enhanced with functionality for the creation of optimized arrays for experimentation, where optimization is in terms of generalized minimum aberration. It is also possible to optimally extend existing arrays to larger run size. Optimization requires the availability of at least one of the commercial products 'Gurobi' or 'Mosek' (free academic licenses available for both). For installing 'Gurobi' and its R package 'gurobi', follow instructions at <http://www.gurobi.com/downloads/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.5/refman/r_api_overview.html> (or higher version). For installing 'Mosek' and its R package 'Rmosek', follow instructions at <https://www.mosek.com/downloads/> and <http://docs.mosek.com/8.1/rmosek/install-interface.html>, or use the functionality in the stump CRAN R package 'Rmosek'.
Construct multi-response experimental designs, such as a Unique Factor 
    Central Composite Design (UF-CCD), given information (from screening or expert knowledge) about 
    which factors are related to each response variable (Wilmina M. Marget & Max D. Morris, 2019
    <doi:10.1080/00401706.2018.1549102>).
Various kinds of designs for
 (industrial) experiments can be created. The package uses, and sometimes enhances,
        design generation routines from other packages. 
        So far, response surface designs from package rsm, latin hypercube
        samples from packages lhs and DiceDesign, and 
        D-optimal designs from package AlgDesign have been implemented.
Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples.
Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017)  <doi:10.1371/journal.pone.0179364>.
Provides a '%dopar%' adapter such that any type of futures can
    be used as backends for the 'foreach' framework.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
Functions to calculate ADI (Average Dominance Index) and FDI (Frequency-Based Dominance Index). Functions to visualize the Data with Social Network Graphs with Dual
    Directions and Music Notation Graph.
Dominance analysis is a method that allows to compare the
             relative importance of predictors in multiple regression models:
             ordinary least squares, generalized linear models, 
             hierarchical linear models, beta regression and dynamic linear models. 
             The main principles and methods of 
             dominance analysis are described in
             Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and  
             Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>
             for ordinary least squares regression. Subsequently, the extensions 
             for multivariate regression, logistic regression and
             hierarchical linear models were described in 
             Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>,
             Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and
             Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>,
             respectively.
A wrapper on top of the 'Domino Command-Line Client'. It lets you
    run 'Domino' commands (e.g., "run", "upload", "download") directly from your
    R environment. Under the hood, it uses R's system function to run the 'Domino'
    executable, which must be installed as a prerequisite. 'Domino' is a service
    that makes it easy to run your code on scalable hardware, with integrated
    version control and collaboration features designed for analytical workflows
    (see <http://www.dominodatalab.com> for more information).
Provides a parallel backend for the %dopar% function using
        the Rmpi package.
Finds the k nearest neighbours in a dataset of specified points, 
  adding the option to wrap certain variables on a torus.  The user chooses
  the algorithm to use to find the nearest neighbours. Two such algorithms,
  provided by the packages 'RANN' <https://cran.r-project.org/package=RANN>, 
  and 'nabor' <https://cran.r-project.org/package=nabor>, are suggested.
Provides a parallel backend for the %dopar% function using
        the parallel package.
Provides information on drug names (brand, 
    generic and street) for drugs tracked by the DEA. There are functions that 
    will search synonyms and return the drug names and types. The vignettes 
    have extensive information on the work done to create the data for the package.
Provides functions to perform
    reproducible parallel foreach loops, using independent
    random streams as generated by L'Ecuyer's combined
    multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>].
    It enables to easily convert standard %dopar% loops into
    fully reproducible loops, independently of the number
    of workers, the task scheduling strategy, or the chosen
    parallel environment and associated foreach backend.
Contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.
Contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010)  <doi:10.1007/978-1-4419-1213-8>.
Identification of causal effects from arbitrary observational and experimental probability 
    distributions via do-calculus and standard probability manipulations using a search-based algorithm. 
    Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>), missing data (Mohan, K. and Pearl, J. and Tian., J. 
    (2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations of these. Also supports
    identification in the presence of context-specific independence (CSI) relations through labeled directed
    acyclic graphs (LDAG). For details on CSIs see Corander et al. (2019) <doi:10.1016/j.apal.2019.04.004>.
    For further information on the search-based approach see Tikka et al. (2019) <arXiv:1902.01073>.
Provides the user with an interactive application which can be used to facilitate the planning of dose finding studies by applying the theory of optimal experimental design (e.g., Ling (2006) <doi:10.1007/0-387-33706-7>).
The DoseFinding package provides functions for the design and analysis
	     of dose-finding experiments (with focus on pharmaceutical Phase
	     II clinical trials). It provides functions for: multiple contrast
	     tests, fitting non-linear dose-response models (using Bayesian and
	     non-Bayesian estimation), calculating optimal designs and an
	     implementation of the MCPMod methodology (Pinheiro et al. (2014)
	     <doi:10.1002/sim.6052>).
Provides a parallel backend for the %dopar% function using
        the snow package of Tierney, Rossini, Li, and Sevcikova.
Estimates dose-response relations from summarized dose-response
    data and to combines them according to principles of (multivariate)
    random-effects models.
A small package containing helper utilities for creating function
    for computing statistics.
Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.
Calculation of cluster typicality coefficients as being generated by fuzzy k-means clustering.
Provides .C64(), which is an enhanced version of .C()
    and .Fortran() from the foreign function interface. .C64() supports long
    vectors, arguments of type 64-bit integer, and provides a mechanism to
    avoid unnecessary copies of read-only and write-only arguments. This
    makes it a convenient and fast interface to C/C++ and Fortran code.
Use '..' on the right hand side of the ':=' operator as a shorthand for the 
    left hand side, so that 'var := f(..) + ..' is equivalent to
    'var <- f(var) + var'. This permits the user to be explicit about growing an
    object or overwriting it using its previous value, avoids repeating a 
    variable name, and saves keystrokes, time, visual space and cognitive load.
Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.
Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.
Quick and easy dot-and-whisker plots of regression results.
Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola  & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].
Likelihood-based inference methods with doubly-truncated data are developed under various models.
 Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and
 Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>.
 Parametric models from the special exponential family (SEF) are based on
 Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and
 Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>.
 The parametric location-scale models are based on Dorre et al. (2020) <doi:10.1007/s00180-020-01027-6>.
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
Implementation of the double/debiased machine learning framework of
    Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear
    regression models, partially linear instrumental variable regression models,
    interactive regression models and interactive instrumental variable
    regression models. 'DoubleML' allows estimation of the nuisance parts in
    these models by machine learning methods and computation of the Neyman
    orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the
    'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on
    the 'R6' package is very flexible.
Overload utils::'?' to build unary and binary operators from existing functions, 
    piping operators of different precedence, and flexible syntaxes.
Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.
'Kevin Dowd's' book Measuring Market Risk is a widely read book 
          in the area of risk measurement by students and 
          practitioners alike. As he claims, 'MATLAB' indeed might have been the most 
          suitable language when he originally wrote the functions, but,
          with growing popularity of R it is not entirely 
	  valid. As 'Dowd's' code was not intended to be error free and were mainly 
	  for reference, some functions in this package have inherited those 
	  errors. An attempt will be made in future releases to identify and correct 
	  them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. 
          It should be noted that 'Dowd' offers both
          'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more 
          recent version of 'MMR1' toolbox and they both have mostly similar 
          function. The toolbox mainly contains different parametric and non 
	  parametric methods for measurement of market risk as well as 
	  backtesting risk measurement methods.
Syntax highlighting of R code, specifically designed
    for the needs of 'RMarkdown' packages like 'pkgdown', 'hugodown', and
    'bookdown'. It includes linking of function calls to their documentation
    on the web, and automatic translation of ANSI escapes in output to the
    equivalent HTML.
Provides a wrapper for the download.file function,
    making it possible to download files over HTTPS on Windows, Mac OS X, and
    other Unix-like platforms. The 'RCurl' package provides this functionality
    (and much more) but can be difficult to install because it must be compiled
    with external dependencies. This package has no external dependencies, so
    it is much easier to install.
Implement download buttons in HTML output from 'rmarkdown' without the need for 'runtime:shiny'.
Downscaling the coarse-resolution remote sensing 
       images into fined-resolution images is important for 
       preprocessing. This package provides the functions for this
       with high accuracy. Currently, the resautonet is used 
       as the deep learning algorithm for downscaling. 
       This work was done based on this paper, 
       Lianfa Li (2019) <doi:10.3390/rs11111378>.
Toggles the test and production versions of a large workflow.
A GUI or command-line operated data analysis tool, for
        analyzing time-dependent simulation data in which multiple
        instantaneous or time-lagged relations are assumed. This
        package uses Structural Equation Modeling (the sem package). It
        is aimed to deal with time-dependent data and estimate whether
        a causal diagram fits data from an (agent-based) simulation
        model.
A Scannerless GLR parser/parser generator.  Note that GLR standing for "generalized LR", where L stands for "left-to-right" and
   R stands for "rightmost (derivation)".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita
   (1987) algorithm. (Paper can be found at <http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/J/J87/J87-1004.pdf>).
   The original 'dparser' package documentation can be found at <http://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like
   RxODE's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate
   them to R code.   To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use
   #include <dparser.h>.  This also provides a R-based port of the make_dparser <http://dparser.sourceforge.net/d/make_dparser.cat> command called
   mkdparser().  Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar
   testing.  The fastest parsing, of course, occurs at the C level, and is suggested.
Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.
Differential partial correlation identification with the ridge and the fusion penalties.
The automated clustering and quantification of the digital PCR
            data is based on the combination of 'DBSCAN' (Hahsler et al. (2019)
            <doi:10.18637/jss.v091.i01>) and 'c-means' (Bezdek et l. (1981) 
            <doi:10.1007/978-1-4757-0450-1>) algorithms.
            The analysis is independent of multiplexing geometry, dPCR system, 
            and input amount.
            The details about input data and parameters are available in the
            vignette.
Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.
Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.
The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in  Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
A fast, consistent tool for working with data frame
    like objects, both in memory and out of memory.
A 'Teradata' backend for 'dplyr'. It makes it possible to operate
  'Teradata' database <https://www.teradata.com/products-and-services/teradata-database/>
  in the same way as manipulating data frames with 'dplyr'.
An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
    You can learn each steps of data manipulation by clicking your mouse without coding.
    You can get resultant data (as a 'tibble') and the code for data manipulation.
Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.
This MCMC method takes a data numeric vector (Y) and assigns the elements of Y
  to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred.
  Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
Multivariate Gaussian mixture model with a determinant point 
  process prior to promote the discovery of parsimonious components from 
  observed data. See Xu, Mueller, Telesca (2016) <doi:10.1111/biom.12482>.
Computations for approximations and alternatives for the 'DPQ'
  (Density (pdf), Probability (cdf) and Quantile) functions for probability
  distributions in R.
  Primary focus is on (central and non-central) beta, gamma and related
  distributions such as the chi-squared, F, and t.
  --
  This is for the use of researchers in these numerical approximation
  implementations, notably for my own use in order to improve R`s own
   pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.
  -- We plan to complement with 'DPQmpfr' to be suggested later.
An extension to the 'DPQ' package with computations for 'DPQ'
  (Density (pdf), Probability (cdf) and Quantile) functions, where
  the functions here partly use the 'Rmpfr' package and hence the
  underlying 'MPFR' and 'GMP' C libraries.
Provides a generalized method for printing tabular data within 
	the R environment in order to make the process of presenting high quality 
	tabular output seamless for the user.  Output is directed to the R graphics 
	device so that tables can be exported to any file format supported by the 
	graphics device. Utilizes a formula interface to specify the contents of 
	tables often found in manuscripts or business reports.  In addition, formula 
	interface provides inline formatting of the numeric cells of a table and 
	renaming column labels.
Piecewise linear segmentation of ordered data by a
        dynamic programming algorithm. The algorithm was developed for time
	series data, e.g. growth curves, and for genome-wide read-count data
        from next generation sequencing, but is broadly applicable.
        Generic implementations of dynamic programming routines allow
        to scan for optimal segmentation parameters and test custom
        segmentation criteria ("scoring functions").
Contains functions to perform copula estimation 
	by the non-parametric Bayesian method, 
	Dirichlet-based Polya Tree. See Ning (2018) <doi:10.1080/00949655.2017.1421194>.
Use Dirichlet process Weibull mixture model and dependent Dirichlet process Weibull mixture model for survival data with and without competing risks. Dirichlet process Weibull mixture model is used for data without covariates and dependent Dirichlet process model is used for regression data. The package is designed to handle exact/right-censored/ interval-censored observations without competing risks and exact/right-censored observations for data with competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates as in Cox model and Fine and Gray model. For wrapper of the DPdensity function from the R package DPpackage (already archived by CRAN) that uses the Low Information Omnibus prior, please check (<https://github.com/mjmartens/DPdensity-wrapper-with-LIO-prior>).
Several fast random number generators are provided as C++
  header only libraries: The PCG family by O'Neill (2014
  <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>) as well as
  Xoroshiro128+ and Xoshiro256+ by Blackman and Vigna (2018
  <arXiv:1805.01407>). In addition fast functions for generating random
  numbers according to a uniform, normal and exponential distribution
  are included. The latter two use the Ziggurat algorithm originally
  proposed by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>).
  These functions are exported to R and as a C++ interface and are
  enabled for use with the default 64 bit generator from the PCG family,
  Xoroshiro128+ and Xoshiro256+ as well as the 64 bit version of the 20 rounds
  Threefry engine (Salmon et al., 2011 <doi:10.1145/2063384.2063405>) as
  provided by the package 'sitmo'.
Functions, methods, and datasets for fitting dimension
 reduction regression, using slicing (methods SAVE and SIR), Principal
 Hessian Directions (phd, using residuals and the response), and an
 iterative IRE.  Partial methods, that condition on categorical
 predictors are also available.  A variety of tests, and stepwise
 deletion of predictors, is also included.  Also included is
 code for computing permutation tests of dimension.  Adding additional
 methods of estimating dimension is straightforward.
 For documentation, see the vignette in the package.   With version 3.0.4,
 the arguments for dr.step have been modified.
Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems.  Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.
Decodes meshes and point cloud data encoded by the Draco mesh
    compression library from Google. Note that this is only designed for basic
    decoding and not intended as a full scale wrapping of the Draco library.
Create, visualize, manipulate, and analyze bipartite mineral-chemistry
    networks for set of focal element(s) across deep-time on Earth. The method is 
    described in Spielman and Moore (2020) <doi:10.3389/feart.2020.585087>.
Statistical tests and test statistics to identify events in a
    dataset that are dragon kings (DKs). The statistical methods in this
    package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>.
These are data sets for the hit TV show, RuPaul's Drag Race. 
    Data right now include episode-level data, contestant-level data, and 
    episode-contestant-level data. This is a work in progress, and a love letter
    of a kind to RuPaul's Drag Race and the performers that have appeared on the show.
    This may not be the most productive use of my time, but I have tenure and what 
    are you going to do about it? I think there is at least some value in this package
    if it allows the show's fandom to learn more about the R programming language 
    around its contents.
Move elements between containers in 'Shiny' without explicitly using 'JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables.
A general-purpose computational engine for data
  analysis, drake rebuilds intermediate data objects when their
  dependencies change, and it skips work when the results are already up
  to date.  Not every execution starts from scratch, there is native
  support for parallel and distributed computing, and completed projects
  have tangible evidence that they are reproducible.  Extensive
  documentation, from beginner-friendly tutorials to practical examples
  and more, is available at the reference website
  <https://docs.ropensci.org/drake/> and the online manual
  <https://books.ropensci.org/drake/>.
Analysis of preprocessed dramatic texts, with respect to literary research. 
  The package provides functions to analyze and visualize information about characters,
  stage directions, the dramatic structure and the text itself.
  The dramatic texts are expected to be in CSV format, which can be installed from within 
  the package, sample texts are provided. The package and the reasoning behind it are described in 
  Reiter et al. (2017) <doi:10.18420/in2017_119>.
Creation and use of R Repositories via helper functions 
 to insert packages into a repository, and to add repository information 
 to the current R session. Two primary types of repositories are support:
 gh-pages at GitHub, as well as local repositories on either the same machine
 or a local network. Drat is a recursive acronym: Drat R Archive Template.
A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings.
  Compatible with and based on the R 'grid' package.
We present DRaWR, a network-based method for ranking genes or
    properties related to a given gene set. Such related genes or properties are
    identified from among the nodes of a large, heterogeneous network of biological
    information. Our method involves a random walk with restarts, performed on
    an initial network with multiple node and edge types, preserving more of the
    original, specific property information than current methods that operate
    on homogeneous networks. In this first stage of our algorithm, we find the
    properties that are the most relevant to the given gene set and extract a
    subnetwork of the original network, comprising only the relevant properties. We
    then rerank genes by their similarity to the given gene set, based on a second
    random walk with restarts, performed on the above subnetwork.
A tool to sample data with the desired properties.Samples can be 
    drawn by purposive sampling with determining distributional 
    conditions, such as deviation from normality (skewness and kurtosis), 
    and sample size in quantitative research studies.
    For purposive sampling, a researcher has something in mind and participants that 
    fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) 
    <doi:10.11648/j.ajtas.20160501.11>.Purposive sampling can be useful for answering
    many research questions (Klar & Leeper, 2019) 
    <doi:10.1002/9781119083771.ch21>.
We offer an implementation of the series representation put 
  forth in "A series representation for multidimensional 
  Rayleigh distributions" by Wiegand and Nadarajah <DOI: 
  10.1002/dac.3510>. Furthermore we have implemented an 
  integration approach proposed by Beaulieu et al. for 3 and 
  4-dimensional Rayleigh densities (Beaulieu, Zhang,  "New 
  simplest exact forms for the 3D and 4D multivariate Rayleigh 
  PDFs with applications to antenna array geometrics", <DOI: 
  10.1109/TCOMM.2017.2709307>).
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
Implements the locally efficient doubly robust difference-in-differences (DiD)
    estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020)
    <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome
    regression estimators (also implemented in the package) to form estimators with
    more attractive statistical properties. Two different estimation methods can be used
    to estimate the nuisance functions.
Set of tools to facilitate package development and make R a more user-friendly place. Mostly for developers (or anyone who writes/shares functions). Provides a simple, powerful and flexible way to check the arguments passed to functions. 
    The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.
A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
An implementation of distributional random forests as introduced in Cevid & Michel & Meinshausen & Buhlmann (2020) <arXiv:2005.14458>.
A somewhat outdated package of basic and easy-to-use functions for
  fitting dose-response curves to continuous dose-response data, calculating some
  toxicological parameters and plotting the results. Please consider using
  the more powerful and actively developed 'drc' package.  Functions that are
  fitted are the cumulative density function of the log-normal distribution
  ('probit' fit), of the logistic distribution ('logit' fit), of the Weibull
  distribution ('weibull' fit) and a linear-logistic model ('linlogit' fit),
  derived from the latter, which is used to describe data showing stimulation at
  low doses (hormesis). In addition, functions checking, plotting and retrieving
  dose-response data retrieved from a database accessed via 'odbc' are included.
  As an alternative to the original fitting methods, the algorithms from the 'drc'
  package can be used.
Fit restricted mean models for the conditional association
  between an exposure and an outcome, given covariates. Three methods
  are implemented: O-estimation, where a nuisance model for the
  association between the covariates and the outcome is used;
  E-estimation where a nuisance model for the association
  between the covariates and the exposure is used, and doubly robust (DR)
  estimation where both nuisance models are used. In DR-estimation,
  the estimates will be consistent when at least one of the nuisance
  models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) <doi:10.1515/em-2014-0021>.
Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) <doi:10.1016/j.aap.2019.105278> along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) <doi:10.1111/sjos.12255> to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) <doi:10.2307/3318678>. The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented.
A fast, interactive tool built upon the 'learnr' function which will open a shiny app for learners to interact with the instructions and tasks. The best way to learn these skills together with the “Learning R with Dr. Hu” online/offline workshops.
Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors' MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>.
Concept drift refers to the change in the data distribution or 
  in the relationships between variables over time.
  'drifter' calculates distances between variable distributions or 
  variable relations and identifies both types of drift. 
  Key functions are: 
  calculate_covariate_drift() checks distance between corresponding variables in two datasets,
  calculate_residuals_drift() checks distance between residual distributions for two models,
  calculate_model_drift() checks distance between partial dependency profiles for two models,
  check_drift() executes all checks against drift.
  'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.
This is a connectivity mapping-based drug repurposing tool that identifies drugs that can potentially reverse query disease phenotype or have similar functions with query drugs.
This is a collection of functions for discontinuous regression
	     analysis and image processing (DRIP). This version removes the
	     dependence on the GNU Scientific Library.
Implementing some of the pillars of an automated machine learning pipeline such as (i) Automated data preparation, (ii) Feature engineering, (iii) Model building in classification context that includes techniques such as (a) Regularised regression [1], (b) Logistic regression [2], (c) Random Forest [3], (d) Decision tree [4] and (e) Extreme Gradient Boosting (xgboost) [5], and finally, (iv) Model explanation (using lift chart and partial dependency plots). Accomplishes the above tasks  by running the function instead of writing lengthy R codes. Also provides some additional features such as generating missing at random (MAR) variables and automated exploratory data analysis. Moreover, function exports the model results with the required plots in an HTML vignette report format that follows the best practices of the industry and the academia. [1] Gonzales G B and De Saeger (2018) <doi:10.1038/s41598-018-21851-7>, [2] Sperandei S (2014) <doi:10.11613/BM.2014.003>, [3] Breiman L (2001) <doi:10.1023/A:1010933404324>, [4] Kingsford C and Salzberg S (2008) <doi:10.1038/nbt0908-1011>, [5] Chen Tianqi and Guestrin Carlos (2016) <doi:10.1145/2939672.2939785>.
Cancer genomes contain large numbers of somatic alterations but few
    genes drive tumor development. Identifying cancer driver genes is critical 
    for precision oncology. Most of current approaches either identify driver 
    genes based on mutational recurrence or using estimated scores predicting 
    the functional consequences of mutations. 'driveR' is a tool for 
    personalized or batch analysis of genomic data for driver gene prioritization 
    by combining genomic information and prior biological knowledge. As features, 
    'driveR' uses coding impact metaprediction scores, non-coding impact scores, 
    somatic copy number alteration scores, hotspot gene/double-hit gene 
    condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG 
    pathways. It uses these features to estimate cancer-type-specific 
    probability for each gene of being a cancer driver using the related task of 
    a multi-task learning classification model. The method is described in detail 
    in Ulgen E, Sezerman OU. 2020. driveR: A Novel Method for Prioritizing Cancer 
    Driver Genes Using Somatic Genomics Data. bioRxiv 
    <doi:10.1101/2020.11.10.376707>.
Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of three replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated metabolomic data in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a Gaussian distribution for each dose or concentration, with a common standard error. For further details see Larras et al (2018) <DOI:10.1021/acs.est.8b04752> at <https://hal.archives-ouvertes.fr/hal-02309919>.
Drop out analysis for psychologists in a R based web application.
    Shiny is used to visualize and analyze drop outs tailored to the methods of
    online survey methodology. Concept and app presented at the SCIP Conference
    in Long Beach, California.
Generates simulated data representing the LOX drop testing
    process (also known as impact testing). A simulated process allows for
    accelerated study of test behavior. Functions are provided to simulate
    trials, test series, and groups of test series. Functions for creating plots
    specific to this process are also included. Test attributes and criteria can
    be set arbitrarily. This work is not endorsed by or affiliated with NASA.
    See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity
    of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized
    Liquid and Gaseous Oxygen Environments" <doi:10.1520/G0086-17>.
Efficient covariate-adjusted estimators of quantities that are useful for 
    establishing the effects of treatments on ordinal outcomes.
Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP) and drought risk assessments are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>.
An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.
Targeted minimum loss-based estimators of counterfactual means and
    causal effects that are doubly-robust with respect both to consistency and
    asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
    van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
An implementation of a Machine Learning Framework for prediction of new drugs Side Effects.
    Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores.
    Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm.
    This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways.
    Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).
Includes various functions for playing drum sounds. beat() plays a drum sound
  from one of the six included drum kits. tempo() sets spacing between calls to beat()
  in bpm. Together the two functions can be used to create many different drum patterns.
Performs various analyzes of descriptive statistics, including correlations, graphics and tables.
All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2020), available at <https://bookdown.org/hneth/ds4psy/>. The book and course introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in 'ds4psy' are designed to be explicit and instructive, rather than elegant or efficient.
Seasonal- and calendar adjustment of time series
    with daily frequency using the DSA approach developed by Ollech,
    Daniel (2018): Seasonal adjustment of daily time series. Bundesbank
    Discussion Paper 41/2018.
Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios.
    The purpose of the package is to help individuals learn 
    about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on what to do with the models.
Simulation models (apps) of various within-host immune response scenarios.
    The purpose of the package is to help individuals learn 
    about within-host infection and immune response modeling from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on what to do with the models. 
	  The development of this package was partially supported by NIH grant U19AI117891.
Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure.  The normalizing constant of the target distribution needs not to be known.
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.
The D-score is a quantitative measure of child development. 
    The D-score follows the Rasch model. See Jacobusse, van Buuren and 
    Verkerk (2006) <doi:10.1002/sim.2351>. The user can convert 
    milestone scores from 19 assessment instruments into the D-score 
    and the DAZ (D-score adjusted for age). Several tools assist in 
    mapping milestone names into the 9-position Global Scale of Early 
    Development (GSED) convention. Supports calculation of the D-score 
    using 'dutch' <doi:10.1177/0962280212473300>, 
    'gcdg' <doi:10.1136/bmjgh-2019-001724> and 'gsed' conversion keys.
    The user can calculate DAZ using 'dutch' and 'gcdg' age-conditional 
    references.
Tools for multivariate, linear, time-invariant,
	time series models. This includes ARMA and state-space representations,
	and methods for converting between them. It also includes simulation
	methods and several estimation functions. The package has functions 
	for looking at model roots, stability, and forecasts at different 
	horizons. The ARMA model representation is general, so that VAR, VARX, 
	ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
	filter and smoother estimates can be obtained from the state space
	model, and state-space model reduction techniques are implemented. 
	An introduction and User's Guide is available in a vignette.
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data. 
    This package defines the API that is to be implemented by 'DataSHIELD' compliant 
    data repositories.
Dataset containing information about job listings for data science job roles.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This 'DataSHIELD Interface' implementation is for analyzing datasets living
    in the current R session. The purpose of this is primarily for lightweight
    'DataSHIELD' analysis package development.
Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.
Tool collection for common and not so common data science use cases. This includes 
    custom made algorithms for data management as well as value calculations that are hard to find 
    elsewhere because of their specificity but would be a waste to get lost nonetheless. 
    Currently available functionality: find
    sub-graphs in an edge list data.frame, find mode or modes in a vector of values, extract 
    (a) specific regular expression group(s), generate ISO time stamps that play well with 
    file names, or generate URL parameter lists by expanding value combinations.
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This package is the 'DataSHIELD' interface implementation to analyze data
    shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a
    light-weight 'DataSHIELD' server using a file store and an 'RServe' server.
'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data.
    This package is the 'DataSHIELD' interface implementation for 'Opal', which is
    the data integration application for biobanks by 'OBiBa'. Participant data, once
    collected from any data source, must be integrated and stored in a central
    data repository under a uniform model. 'Opal' is such a central repository.
    It can import, process, validate, query, analyze, report, and export data.
    'Opal' is the reference implementation of the 'DataSHIELD' infrastructure.
Fits inhomogeneous Poisson process spatial models
    to line transect sampling data and provides estimate of
    abundance within a region.
You can retrieve 'Spotify' API Information such as artists, albums, tracks, features tracks, recommendations or related artists.
    This package allows you to search all the information by name and also includes a distance based algorithm to find similar songs.
	More information: <https://developer.spotify.com/documentation/web-api/> .
Perform a test of a simple null hypothesis about a 
    directly standardized rate and obtain the matching confidence 
    interval using a choice of methods.
The purpose of this package is to provide a comprehensive
    R interface to the Decision Support System for Agrotechnology
    Transfer Cropping Systems Model (DSSAT-CSM) documented by
    Jones et al (2003) <doi:10.1016/S1161-0301(02)00107-7>. The package
    provides cross-platform functions to read and write input files,
    run DSSAT-CSM, and read output files.
Creates survey designs for distance sampling surveys. These
    designs can be assessed for various effort and coverage statistics.
    Once the user is satisfied with the design characteristics they can 
    generate a set of transects to use in their distance sampling survey.
    Many of the designs implemented in this R package were first made 
    available in our 'Distance' for Windows software and are detailed in 
    Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, 
    ISBN-13: 978-0199225873). Find out more about estimating animal/plant 
    abundance with distance sampling at <http://distancesampling.org/>.
Performs distance sampling simulations.It repeatedly generates
    instances of a user defined population within a given survey region, generates
    realisations of a survey design (currently these must be generated using
    Distance software in advance <http://distancesampling.org/>) and simulates
    the detection process. The data are then analysed so that the results can
    be compared for accuracy and precision across all replications. This will
    allow users to select survey designs which will give them the best accuracy
    and precision given their expectations about population distribution. Any
    uncertainty in population distribution or population parameters can be
    included by running the different survey designs for a number of different
    population descriptions. An example simulation can be found in the help file for
    make.simulation.
Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
A native R package that allows to publish, share and track revisions 
  of plots using your favorite plotting package, e.g. 'ggplot2'. It also provides
  a kind of interactivity for such plots by specifying certain parameters for any 
  specific plot view. See <https://docs.dstack.ai> for more information.
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments.  A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>).  There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.
Building discrete-time survival trees and bagged trees based on
    the functionalities of the rpart package. Splitting criterion maximizes the
    likelihood of a covariate-free logistic discrete time hazard model.
Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. This package was designed and implemented for identifying disease similarities. It contains five functions which are 'DCEA', 'DCpathway', 'DS', 'comDCGL' and 'comDCGLplot'. In 'DCEA' function, differentially co-expressed genes and differentially co-expressed links are extracted from disease vs. health samples. Then 'DCpathway' function assigns differential co-expression values of pathways to be the average differential co-expression value of their component genes. Then 'DS' employs partial correlation coefficient of pathways as the disease similarity for each disease pairs. And 'DS' contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, 'comDCGL' and 'comDCGLplot' sort out shared differentially co-expressed genes and differentially co-expressed links with regulation information and visualize them.
Data science methods used in wind energy applications. 
              Current functionalities include creating a multi-dimensional power curve model, 
              performing power curve function comparison, and covariate matching. 
              Relevant works for the developed functions are: 
              funGP() - Prakash et al. (2020) <arxiv:2003.07899>, 
              AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, 
              tempGP() - Prakash et al. (2020) <arxiv:2012.01349>, 
              ComparePCurve() - Ding et al. (2020) <arxiv:2005.08652>,
              All other functions - Ding (2019, ISBN:9780429956508).
Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny). The
    'DataTables' library has been included in this R package. The package name
    'DT' is an abbreviation of 'DataTables'.
Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.
Dose Titration Algorithm Tuning (DTAT) is a methodologic framework
             allowing dose individualization to be conceived as a continuous
             learning process that begins in early-phase clinical trials and
             continues throughout drug development, on into clinical practice.
             This package includes code that researchers may use to reproduce
             or extend key results of the DTAT research programme, plus tools
             for trialists to design and simulate a '3+3/PC' dose-finding study.
             Please see Norris (2017) <doi:10.12688/f1000research.10624.3> and
             Norris (2017) <doi:10.1101/240846>.
To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method.
    The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.
This package contains functions to compare the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
Provides fast methods to work with Merton's distance to default 
  model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. 
  The methods includes simulation and estimation of the parameters.
Implementation of different algorithms for analyzing
        randomly truncated data, one-sided and two-sided (i.e. doubly)
        truncated data. Two real data sets are included.
Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.
Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-Álvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
             modality, that measures diffusion of water in tissues like the human
             brain. The package contains R-functions to process diffusion-weighted
             data. The functionality includes diffusion tensor imaging (DTI),
             diffusion kurtosis imaging (DKI), modeling for high angular resolution
             diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
             tensor mixture models, several methods for structural adaptive
             smoothing including POAS and msPOAS, and a streamline fiber tracking
             for tensor and tensor mixture models.
             The package provides functionality to manipulate and visualize results
             in 2D and 3D.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Dunnett modified Tukey-Kramer test (a.k.a. the
        T3 Procedure), create two vectors: one for your observations
        and one for the factor level of each observation. The function,
        gl.unequal, provides a means to more conveniently produce a
        factor vector with unequal sample sizes. Next, use the DTK.test
        function to conduct the test and save the output as an object
        to input into the DTK.plot function, which produces a
        confidence interval plot for each of the pairwise comparisons.
        Lastly, the function TK.test conducts the original Tukey-Kramer
        test.
A series of functions which aid in both simulating and
        determining the properties of finite, discrete-time, discrete
        state markov chains.  Two functions (DTMC, MultDTMC) produce n
        iterations of a Markov Chain(s) based on transition
        probabilities and an initial distribution.  The function FPTime
        determines the first passage time into each state.  The
        function statdistr determines the stationary distribution of a
        Markov Chain.
Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.
Provides the dose transition pathways (DTP) to project in advance
    the doses recommended by a model-based design for subsequent patients (stay,
    escalate, deescalate or stop early) using all the accumulated toxicity
    information; See Yap et al (2017) <doi: 10.1158/1078-0432.CCR-17-0582>. DTP
    can be used as a design and an operational tool and can be displayed as a
    table or flow diagram. The 'dtpcrm' package also provides the modified
    continual reassessment method (CRM) and time-to-event CRM (TITE-CRM) with
    added practical considerations to allow stopping early when there is
    sufficient evidence that the lowest dose is too toxic and/or there is a
    sufficient number of patients dosed at the maximum tolerated dose.
Provides a data.table backend for 'dplyr'. The goal of 'dtplyr' 
    is to allow you to write 'dplyr' code that is automatically translated to 
    the equivalent, but usually much faster, data.table code.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
Combines various decision tree algorithms, plus both
             linear regression and ensemble methods into one package.
             Allows for the use of both continuous and categorical outcomes.
             An optional feature is to quantify the (in)stability to the
             decision tree methods, indicating when results can be trusted
             and when ensemble methods may be preferential.
We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.
Dynamic treatment regime estimation and inference via G-estimation, dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via bootstrap and (for G-estimation) recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). Extension to continuous treatment variables (gdwols). Wallace et al. (2017) <DOI:10.18637/jss.v080.i02>; Simoneau et al. (2020) <DOI:10.1080/00949655.2020.1793341>.
Provides methods for estimating multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring. Cho, H., Holloway, S. T., and Kosorok, M. R. (2020) <arXiv:2012.03294>.
Basic time series functionalities such as listing of missing
    values, application of arbitrary aggregation as well as rolling (asymmetric)
    window functions and automatic detection of periodicity. As it is mainly
    based on 'data.table', it is fast and - in combination with the 'R6'
    package - offers reference semantics. In addition to its native R6
    interface, it provides an S3 interface inclusive an S3 wrapper method 
    generator for those who prefer the latter. Finally yet importantly, its
    functional approach allows incorporating functionalities from many other
    packages.
This package provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).
Manipulates date ('Date'), date time ('POSIXct') and
    time ('hms') vectors.  Date/times are considered discrete and are
    floored whenever encountered.  Times are wrapped and time zones are
    maintained unless explicitly altered by the user.
A comprehensive implementation of dynamic time warping
    (DTW) algorithms in R.  DTW computes the optimal (least cumulative
    distance) alignment between points of two time series.  Common DTW
    variants covered include local (slope) and global (window)
    constraints, subsequence matches, arbitrary distance definitions,
    normalizations, minimum variance matching, and so on.  Provides
    cumulative distances, alignments, specialized plot styles, etc.,
    as described in Giorgino (2009) <doi:10.18637/jss.v031.i07>.
Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).
Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions. Implementations of
    DTW barycenter averaging, a distance based on global alignment kernels,
    and the soft-DTW distance and centroid routines are also provided. 
    All included distance functions have custom loops optimized for the 
    calculation of cross-distance matrices, including parallelization support.
    Several cluster validity indices are included.
Provides an implementation of the Time-Weighted Dynamic Time
    Warping (TWDTW) method for land cover mapping using satellite image time series.
    TWDTW compares unclassified satellite image time series with a set of known
    temporal patterns (e.g. phenological cycles associated with the vegetation).
    Using 'dtwSat' the user can build temporal patterns for land cover types, apply
    the TWDTW analysis for satellite datasets, visualize the results of the time
    series analysis, produce land cover maps, create temporal plots for land cover
    change, and compute accuracy assessment metrics.
Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) <DOI:10.1109/MLSP.2017.8168165>: a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided.
Automatic differentiation is achieved by using dual numbers without
  providing hand-coded gradient functions. The output value of a mathematical 
  function is returned with the values of its exact first derivative 
  (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind
  (2018) <http://jmlr.org/papers/volume18/17-468/17-468.pdf>.
An implementation of the decimated two-dimensional complex dual-tree wavelet transform as described in Kingsbury (1999) <doi:10.1098/rsta.1999.0447> and Selesnick et al. (2005) <doi:10.1109/MSP.2005.1550194>. Also includes the undecimated version and spectral bias correction described in Nelson et al. (2018) <doi:10.1007/s11222-017-9784-0>. The code is partly based on the 'dtcwt' Python library.
Create shareable data sets from raw data files that
	     contain protected elements. Relying on master crosswalk
	     files that list restricted variables, package functions
	     warn users about possible violations of data usage
	     agreement and prevent writing protected elements.
Provides an operator for assigning nested components of a list to
  names via a concise pattern matching syntax. This is especially convenient for
  assigning individual names to the multiple values that a function may return
  in the form of a list, and for extracting deeply nested list components.
The DuckDB project is an embedded analytical data
    management system with support for the Structured Query Language (SQL). This package includes all of
    DuckDB and a R Database Interface (DBI) connector.
Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.
Expands factors, characters and other eligible classes
        into dummy/indicator variables.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
Provides functions to randomly select, return, and print
  quotes or entire scenes from the American version of
  the show the Office. Receive laughs from one of of the 
  greatest sitcoms of all time on demand. Add these functions 
  to your  '.Rprofile' to get a good laugh everytime you start
  a new R session.
Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.
For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Identifies code blocks that have a high level of similarity
  within a set of R files.
Estimation of piecewise constant mixed proportional hazard competing risk model with NPMLE.
 The model is described in S. Gaure et al. (2007) <doi:10.1016/j.jeconom.2007.01.015>,
 J. Heckman and B. Singer (1984) <doi:10.2307/1911491>, and
 B.G. Lindsay (1983) <doi:10.1214/aos/1176346059>.
Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.
Contains functions that do something convenient (e.g. create BMI categories), functions for calculating moving-window statistics efficiently, and functions for generating various figures (e.g. histograms with fitted probability mass/density function).
A set of control charts for batch processes based on the VAR model. The package contains the implementation of T2.var and W.var control charts based on VAR model coefficients using the couple vectors theory. In each time-instant the VAR coefficients are estimated from a historical in-control dataset and a decision rule is made for online classifying of a new batch data. Those charts allow efficient online monitoring since the very first time-instant. The offline version is available too. In order to evaluate the chart's performance, this package contains functions to generate batch data for offline and online monitoring.See in Danilo Marcondes Filho and Marcio Valk (2020) <doi:10.1016/j.ejor.2019.12.038>.
A tool to hydrologic modelling using the Budyko 
    framework and the Dynamic Water Balance model with Dynamical Dimension
    Search algorithm to calibrate the model and analyze the outputs from
    interactive graphics. It allows to calculate the water availability
    in basins and also some water fluxes represented by
    the structure of the model.
    See Zhang, L., N., Potter, K., Hickel, Y., Zhang, Q., Shao (2008) <DOI:10.1016/j.jhydrol.2008.07.021> "Water balance modeling over variable time scales based on the Budyko framework - Model development and testing", Journal of Hydrology, 360, 117–131.
    See Tolson, B., C., Shoemaker (2007) <DOI:10.1029/2005WR004723> "Dynamically dimensioned search algorithm for computationally efficient watershed model calibration", Water Resources Research, 43, 1–16.
Solving large scale distance weighted discrimination.
  The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details.
The 'DWD' provides gridded radar data for Germany in binary format. 
  'dwdradar' reads these files and enables a fast conversion into numerical format.
Infers networks with hubs using degree weighted Lasso method.
This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) <doi:10.1119/1.15963> and Ripley & Thompson(1987) <doi:10.1039/AN9871200377>.
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>, the (multilevel) b2 model.
Tools for DYRESM-CAEDYM model development, including auto-calibrate selected model parameters and visualise model output through time series plot, profile plot, contour plot, and scatter plot.
Finds regular and chaotic intervals in the data using 
    the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) 
	<DOI:10.1137/080718851>.
An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.
Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.
Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.
Time series regression.  The dyn class interfaces ts,
        irts(), zoo() and zooreg() time series classes to lm(), glm(),
        loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(),
        quantreg::rq(), randomForest::randomForest() and other regression
        functions allowing those functions to be used with time series
        including specifications that may contain lags, diffs and
        missing values.
While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).
The package is aimed at dynamically visualizing probability
    distributions and their moments and all the commonly used distributions are
    included.
Emulating and solving inverse problems for dynamic computer experiments.
	     It contains two major functionalities: (1) localized GP model for large-scale
	     dynamic computer experiments using the algorithm proposed by
	     Zhang et al. (2018) <arXiv:1611.09488>; (2) solving inverse problems
	     in dynamic computer experiments. The current version only supports 64-bit
	     version of R.
Interactive graphical tool for manipulating graphs
Contains functions that lets you fit dynamic hazard models using 
  state space models. The first implemented model is described in Fahrmeir 
  (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994) 
  <doi:10.1093/biomet/81.2.317>. Extensions hereof are available where the  
  Extended Kalman filter is replaced by an unscented Kalman filter and other 
  options including particle filters. The implemented particle filters support
  more general state space models.
Contains methods for detection of clusters in hierarchical clustering dendrograms.
Provides functions for inferring longitudinal dominance hierarchies, which describe dominance relationships and their dynamics in a single latent hierarchy over time. Strauss & Holekamp (in press).
It allows running 'Dynare' program from R Markdown. 'Dynare' is a software platform for handling a wide class of economic models, in particular dynamic stochastic general equilibrium ('DSGE') and overlapping generations ('OLG') models.  This package serves as a 'Dynare' Knit-Engine for 'knitr' package. The package requires 'Dynare' 4.6.1 (<https://www.dynare.org/>) and 'Octave' 5.2.0 (<https://www.gnu.org/software/octave/download.html>).  Write all your 'Dynare' commands in R Markdown chunk.
A set of tools to generate dynamic spectrogram visualizations in video format.
A native R implementation and enhancement of the Dynamic TOPMODEL
    semi-distributed hydrological model. Includes some preprocessing, utility and
    routines for displaying outputs.
Inference by sequential Monte Carlo for 
  dynamic tree regression and classification models
  with hooks provided for sequential design and optimization, 
  fully online learning with drift, variable selection, and 
  sensitivity analysis of inputs.  Illustrative 
  examples from the original dynamic trees paper are facilitated
  by demos in the package; see demo(package="dynaTree").
A GUI to solve dynamic biplots and classical biplot. Try matrices
    of 2-way and 3-way. The GUI can be run in multiple languages.
DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
Used for evolving network analysis regarding 
  community detection. Implements several algorithms that calculate communities 
  for graphs whose nodes and edges change over time.
  Edges, which can have new nodes, can be added or deleted. Changes in the 
  communities are calculated without recalculating communities for the entire 
  graph.
  REFERENCE: M. Cordeiro et al. (2016) <DOI:10.1007/s13278-016-0325-1>
	G. Rossetti et al. (2017) <DOI:10.1007/s10994-016-5582-8>
	G. Rossetti (2017) <DOI:10.1093/comnet/cnx016>
	R. Sarmento (2019) <arXiv:1904.12593>.
While there are many well-established measures for identifying critical fluctuations and phase transitions, these measures only work with many points of measurement and thus are unreliable when studying short and coarse-grained time series. This package provides a measure for complexity in a time series that does not rely on long time series (Kaiser (2017), <doi:10.17605/OSF.IO/GWTKX>).
Computes dynamical correlation estimates and percentile
        bootstrap confidence intervals for pairs of longitudinal
        responses, including consideration of lags and derivatives.
Provides a common interface for applying dimensionality reduction methods,
  such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, 
  Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), 
  and Uniform Manifold Approximation and Projection ('UMAP'). 
  Has built-in support for sparse matrices.
An anonymization algorithm to resist neighbor label attack in a dynamic network.
A novel, multi-modal simulation engine for
    studying dynamic cellular processes at single-cell resolution. 'dyngen'
    is more flexible than current single-cell simulation engines. It
    allows better method development and benchmarking, thereby stimulating
    development and testing of novel computational methods. Cannoodt et
    al. (2020) <doi:10.1101/2020.02.06.936971>.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) <doi:10.1016/j.apm.2013.05.038>; Nagy et al. (2011) <doi:10.1002/acs.1239>.
Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, mgcv::gam and gam::gam model objects.
Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.
Provides tools for describing parameters of algorithms in an abstract way.
  Description can include an id, a description, a domain (range or list of values),
  and a default value. 'dynparam' can also convert parameter sets to a 'ParamHelpers' format, 
  in order to be able to use 'dynparam' in conjunction with 'mlrMBO'.
The dynpred package contains functions for dynamic prediction in survival analysis.
A domain-specific language for specifying translating recursions
    into dynamic-programming algorithms. See 
    <https://en.wikipedia.org/wiki/Dynamic_programming> for a description
    of dynamic programming.
Intensive longitudinal data have become increasingly prevalent in
    various scientific disciplines. Many such data sets are noisy, multivariate,
    and multi-subject in nature. The change functions may also be continuous, or
    continuous but interspersed with periods of discontinuities (i.e., showing
    regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package
    that implements a set of computationally efficient algorithms for handling a
    broad class of linear and nonlinear discrete- and continuous-time models with
    regime-switching properties under the constraint of linear Gaussian measurement
    functions. The discrete-time models can generally take on the form of a state-
    space or difference equation model. The continuous-time models are generally
    expressed as a set of ordinary or stochastic differential equations. All
    estimation and computations are performed in C, but users are provided with the
    option to specify the model of interest via a set of simple and easy-to-learn
    model specification functions in R. Model fitting can be performed using single-
    subject time series data or multiple-subject longitudinal data.
Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.
Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in  Matias and Miele (2016) <doi:10.1111/rssb.12200>.
Dynamic simulations and graphical depictions of autoregressive
    relationships.
Time-varying coefficient models for interval censored and
    right censored survival data including
    1) Bayesian Cox model with time-independent, time-varying or
    dynamic coefficients for right censored and interval censored data studied by
    Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and
    Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>,
    2) Spline based time-varying coefficient Cox model for right censored data
    proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and
    3) Transformation model with time-varying coefficients for right censored data
    using estimating equations proposed by
    Peng and Huang (2007) <doi:10.1093/biomet/asm058>.
Methods to estimate dynamic treatment regimes using Interactive
  Q-Learning, Q-Learning, weighted learning, and value-search methods based on 
  Augmented Inverse Probability Weighted Estimators and Inverse Probability
  Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for 
  Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., 
  Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.
Provides common functionality for the 'dynverse' packages. 
  'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods.
  For more information, check out <https://dynverse.org>.
Provides functionality to infer trajectories from single-cell data,
  represent them into a common format, and adapt them. Other biological information
  can also be added, such as cellular grouping, RNA velocity and annotation.
  Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.
Functions for dyadic binary/dichotomous sequence analyses are implemented in this contribution.
             The focus is on estimating actor-partner-interaction models using various approaches, for  
             instances the approach of Bakeman & Gottman's (1997) <DOI:10.1017/cbo9780511527685>,
             generalized multi-level models, and basic Markov models. Moreover, coefficients of one model 
             can be translated into those of the other models. Finally, simulation-based power analyses are
             provided.
It is used to identify dysregulated pathways based on a pre-ranked gene pair list. A fast algorithm is used to make the computation really fast. The data in package 'DysPIAData' is needed.
This dataset includes Background and Pathway data used in package 'DysPIA'.
Functions for latent class analysis, short time Fourier
	     transform, fuzzy clustering, support vector machines,
	     shortest path computation, bagged clustering, naive Bayes
	     classifier, ...
Process and manage the data from the Empatica E4. All functions operate on the EDA data stream, but other streams will be added soon. The Empatica E4 is a wearable physiological monitor made by Empatica (Empatica is not associated with any of this code). You can find more information about the E4 at Empatica's website <https://www.empatica.com/research/e4/>.
Fits dose-response models using an evolutionary
    algorithm to estimate the model parameters. The procedure currently
    can fit 3-parameter, 4-parameter, and 5-parameter log-logistic models
    as well as exponential models. Functions are also provided to plot,
    make predictions, and calculate confidence intervals for the resulting
    models. For details see "Nonlinear Dose-response Modeling of
    High-Throughput Screening Data Using an Evolutionary Algorithm",
    Ma, J., Bair, E., Motsinger-Reif, A.; Dose-Response (In press).
Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. Lopez-Ibanez, L. Paquete, and T. Stuetzle (2010) <doi:10.1007/978-3-642-02538-9_9>.
An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and  outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This  opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
Estimator augmentation methods for statistical inference on high-dimensional data, 
    as described in Zhou, Q. (2014) <arXiv:1401.4425v2>
    and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>.
    It provides several simulation-based inference methods: (a) Gaussian and 
    wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
    lasso and their de-biased estimators, (b) importance sampler for approximating
    p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with 
    applications in post-selection inference.
A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.
Computes the most important properties of four 'Bayesian' early gating 
             designs (two single arm and two randomized controlled designs), such 
             as minimum required number of successes in the experimental group to 
             make a GO decision, operating characteristics and average operating 
             characteristics with respect to the sample size. 
             These might aid in deciding what design to use for the early phase trial.
Implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) <doi:10.1093/aje/kwt133>.
The Early-Warning-Signals Toolbox provides methods for estimating
    statistical changes in timeseries that can be used for identifying nearby
    critical transitions. Based on Dakos et al (2012) Methods for Detecting
    Early Warnings of Critical Transitions in Time Series Illustrated Using
    Simulated Ecological Data. PLoS ONE 7(7):e41010
Build regression models using the techniques in Friedman's
    papers "Fast MARS" and "Multivariate Adaptive Regression
    Splines" <doi:10.1214/aos/1176347963>.
    (The term "MARS" is trademarked and thus not used in
    the name of the package.)
This is a port of 'Fortran ETERNA 3.4' 
             <http://igets.u-strasbg.fr/soft_and_tool.php> by H.G. Wenzel
             for calculating synthetic Earth tides using the 
             Hartmann and Wenzel (1994) <doi:10.1029/95GL03324> or 
             Kudryavtsev (2004) <doi:10.1007/s00190-003-0361-2> tidal catalogs.
Downloads a satellite image via Google Maps/Earth (these are
    originally from a variety of aerial photography sources), 
    translates the image into a perceptually uniform color space,
    runs one of a few different clustering algorithms on the colors in the image 
    searching for a user-supplied number of colors,
    and returns the resulting color palette.
Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Given the scores from decision makers, the analytic hierarchy process can be conducted easily.
Alluvial plots are similar to sankey diagrams and visualise categorical data 
    over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in 
    Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694> 
    Their graphical grammar however is a bit more complex then that of a regular x/y 
    plots. The 'ggalluvial' package made a great job of translating that grammar into 
    'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial 
    plot, however there still remains a multi-layered complexity that makes it difficult
    to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple 
    interface to this package that allows you to produce a decent alluvial plot from any 
    dataframe in either long or wide format from a single line of code while also handling 
    continuous data. It is meant to allow a quick visualisation of entire dataframes 
    with a focus on different colouring options that can make alluvial plots a great 
    tool for data exploration.
Perform analysis of variance and other important complementary
  analyses.  The functions are easy to use.  Performs analysis in various
  designs, with balanced and unbalanced data.
Univariate and multivariate methods for compositional data 
    analysis, based on logratios. The package implements the approach in the
    book Compositional Data Analysis in Practice by Michael Greenacre (2018),
    where accent is given to simple pairwise logratios. Selection can be made
    of logratios that account for a maximum percentage of logratio variance.
    Various multivariate analyses of logratios are included in the package.
Allows users to easily read multiple comma separated tables and create a data frame under the same name.
    Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory.
Descriptive analysis is essential for publishing medical articles.
  This package provides an easy way to conduct the descriptive analysis.
  1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test.
  2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, 'Tukey' for the numeric variables and 'FDR' for the factor variables.
  3. T test, ANOVA or Fisher test can be forced to apply.
  4. Mean and standard deviation can be forced to display.
It is a package that can be used to send HTML reports easily.
Contains a few utility functions for working with capture-history data, a function for simulating capture-history data, and a function to fit this data, using a Gibbs sampler.
Utilities for building certain kinds of common matrices and models in 
    the extended structural equation modeling package, 'OpenMx'.
Set of wrappers for the 'ncdf4' package to simplify and extend its
    reading/writing capabilities into/from multidimensional R arrays.
Easily load and install multiple packages from different sources, 
    including CRAN and GitHub. The libraries function allows you to load or attach 
    multiple packages in the same function call. The packages function will load one 
    or more packages, and install any packages that are not installed on your system 
    (after prompting you). Also included is a from_import function that allows you 
    to import specific functions from a package into the global environment.
Power analysis is used in the estimation of sample sizes for
    experimental designs. Most programs and R packages will only output the highest
    recommended sample size to the user. Often the user input can be complicated
    and computing multiple power analyses for different treatment comparisons can
    be time consuming. This package simplifies the user input and allows the user
    to view all of the sample size recommendations or just the ones they want to see.
    The calculations used to calculate the recommended sample sizes are from the
    'pwr' package.
Provides various functions for reading and preparing the Panel Study of Income Dynamics (PSID) for longitudinal analysis, including functions that read the PSID's fixed width format files directly into R, rename all of the PSID's longitudinal variables so that recurring variables have consistent names across years, simplify assembling longitudinal datasets from cross sections of the PSID Family Files, and export the resulting PSID files into file formats common among other statistical programming languages ('SAS', 'STATA', and 'SPSS').
Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Makes difficult operations easy. Includes these types of functions: 
    shorthand, type conversion, data wrangling, and work flow. 
    Also includes some helpful data objects: NA strings, U.S. state list, color blind charting colors. 
    Built and shared by Oliver Wyman Actuarial Consulting. Accepting proposed contributions through GitHub.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models.
The main function, ProtectTable(), performs table suppression according to a 
 frequency rule with a data set as the only required input. Within this function, 
 protectTable(), protectLinkedTables() or runArgusBatchFile() in package 'sdcTable' is called. 
 Lists of level-hierarchy (parameter 'dimList') and other required input to these functions 
 are created automatically. 
 The function, PTgui(), starts a graphical user interface based on the shiny package.
This SVG elements generator can easily generate 
    SVG elements such as rect, line, circle, ellipse, polygon, 
    polyline, text and group. Also, it can combine and 
    output SVG elements into a SVG file.
Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.
Provides simple functions to create constraints for small test assembly problems 
    (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, 
    'GLPK', 'lpSolve', and 'Gurobi' are supported as solvers. The 'gurobi' package is not available from 
    any mainstream repository; see <https://www.gurobi.com/downloads/>.
Use 'SQLite3' as a database system via a complete SQL free R interface, treating the data as if it was a single spreadsheet.
Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.
Replication methods to compute some basic statistic operations (means, standard deviations,
  frequency tables, percentiles and generalized linear models) in complex survey designs comprising multiple
  imputed variables and/or a clustered sampling structure which both deserve special procedures at least in
  estimating standard errors. See the package documentation for a more detailed description along with references.
Miscellaneous functions for data cleaning and data analysis of educational assessments. Includes functions for descriptive 
   analyses, character vector manipulations and weighted statistics. Mainly a lightweight dependency for the packages 'eatRep', 
   'eatGADS', 'eatPrep' and 'eatModel' (which will be subsequently submitted to 'CRAN').
   The function for defining (weighted) contrasts in weighted effect coding refers to
   te Grotenhuis et al. (2017) <doi:10.1007/s00038-016-0901-1>.
   Functions for weighted statistics refer to
   Wolter (2007) <doi:10.1007/978-0-387-35099-8>.
Fitting and testing multi-attribute probabilistic choice
  models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
  Terry, 1952 <doi:10.1093/biomet/39.3-4.324>; Luce, 1959),
  elimination-by-aspects (EBA) models (Tversky, 1972 <doi:10.1037/h0032955>),
  and preference tree (Pretree) models (Tversky & Sattath, 1979
  <doi:10.1037/0033-295X.86.6.542>).
Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
We propose a new sample size calculation method for trial-based
    cost-effectiveness analyses. Our strategy is based on the value of perfect
    information that would remain after the completion of the study.
Empirical Bayes thresholding using the methods developed
    by I. M. Johnstone and B. W. Silverman. The basic problem is to
    estimate a mean vector given a vector of observations of the mean
    vector plus white noise, taking advantage of possible sparsity in
    the mean vector. Within a Bayesian formulation, the elements of
    the mean vector are modelled as having, independently, a
    distribution that is a mixture of an atom of probability at zero
    and a suitable heavy-tailed distribution. The mixing parameter can
    be estimated by a marginal maximum likelihood approach. This leads
    to an adaptive thresholding approach on the original data.
    Extensions of the basic method, in particular to wavelet
    thresholding, are also implemented within the package.
Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.
Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
  package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.
Tools to download, map, plot and analyze eBird
    Status and Trends data
    (<https://ebird.org/science/status-and-trends>). eBird
    (<https://ebird.org/home>) is a global database of bird observations
    collected by citizen scientists. eBird Status and Trends uses these
    data to analyze continental bird abundances, range boundaries,
    habitats, and trends.
Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.
Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.
Implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) <doi:10.1093/biomet/78.4.719>.
EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy.  No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for  each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.
Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors.  A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.
Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.
Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Serra", "Krivobokova" and "Rosales" (2018) <arXiv:1812.06948> for details.
Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
An implementation for estimating Effective control to 50% of growth
    inhibition (EC50) for multi isolates and stratified datasets. It implements 
    functions from the drc package in a way that is displayed a tidy data.frame 
    as output. Info about the drc package is available in Ritz C, Baty F, Streibig JC,
    Gerhard D (2015) <doi:10.1371/journal.pone.0146021>.
Implements the Excess Certainty Adjusted Probability adjustment procedure as described in the paper "Irrational Exuberance: Correcting Bias in Probability Estimates" by Gareth James, Peter Radchenko, and Bradley Rava (Journal of the American Statistical Association, 2020;  <doi:10.1080/01621459.2020.1787175>). The package includes a function that preforms the ECAP adjustment and a function that estimates the parameters needed for implementing ECAP. For testing and reproducibility, the ESPN and FiveThirtyEight data used in the paper are also included.
Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in 
             Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) <arXiv:2012.11767>.  
             Both the parametric and semi-parametric models are available.  The semi-parametric model 
             relies on 'INLA'.  The 'INLA' package can be obtained from <https://www.r-inla.org/>.
Provides an interface to the European Central Bank's Statistical
    Data Warehouse API, allowing for programmatic retrieval of a vast quantity
    of statistical data.
If translate English words into Chinese, there is a faster 
    way for R user. 'RYoudaoTranslate' package provides interface to 
    'Youdao' <http://youdao.com/> translation open API for R user. 'entcn' 
    package also provides similar features. But it does not support Chinese 
    words translation into English, I have made some improvements on the basis 
    of this software. You can pass in a words or a vector consisting of multiple 
    words, ecce package support both English and Chinese translation. It also 
    support browse translation results in website.
Draw sample paths for endpoint-conditioned continuous time Markov chains via modified rejection sampling or uniformization.
Elliptic lambda distribution and lambda option pricing model
    have been evolved into a framework of
    stable-law inspired distributions,
    such as the extended stable lambda distribution for asset return,
    stable count distribution for volatility,
    and Lihn-Laplace process as a leptokurtic extension of Wiener process.
    This package contains functions for the computation of
    density, probability, quantile, random variable, fitting procedures,
    option prices, volatility smile. It also comes with sample financial data,
    and plotting routines.
Data sets for econometrics, including political science.
Computes and plots a transformed empirical CDF (ecdf) as a
    diagnostic for heavy tailed data, specifically data with power law decay on the
    tails.  Routines for annotating the plot, comparing data to a model, fitting a
    nonparametric model, and some multivariate extensions are given.
Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on 'spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.
Functions and vignettes to update 
    data sets in 'Ecdat' and to create, manipulate, 
    plot, and analyze those and similar data sets.
A goodness-of-fit test for elliptical distributions with diagnostic
  capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2019) <arXiv:1902.03622>.
Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
Easily create interactive charts by leveraging the 'Echarts Javascript' library which includes
    36 chart types, themes, 'Shiny' proxies and animations.
Functions for the echelon analysis developed proposed by Myers et al. (1997) <doi:10.1023/A:1018518327329>, and the detection of spatial clusters using echelon scan method proposed by Kurihara (2003) <doi:10.20551/jscswabun.15.2_171>.
Simulates cyclic voltammetry, linear-sweep voltammetry 
    (both with and without stirring of the solution), and single-pulse 
    and double-pulse chronoamperometry and chronocoulometry 
    experiments using the implicit finite difference method outlined in 
    Gosser (1993, ISBN: 9781560810261) and in Brown (2015) 
    <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide 
    ways to display and to examine the results of these simulations.
    The primary purpose of this package is to provide tools for
    use in courses in analytical chemistry.
Provides a function (echo_find()) designed to find rhythms 
    from data using extended harmonic oscillators. For more information,
    see H. De los Santos et al. (2020) <doi:10.1093/bioinformatics/btz617> .
Easily import multi-frequency acoustic data stored in 'HAC' files (see <http://biblio.uqar.ca/archives/30005500.pdf> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.
An R interface to United States Environmental 
    Protection Agency (EPA) Environmental Compliance 
    History Online ('ECHO') Application Program Interface
    (API). 'ECHO' provides information about EPA permitted 
    facilities, discharges, and other reporting info 
    associated with permitted entities. Data are obtained 
    from <https://echo.epa.gov/>.
Provides a function that quickly computes the fine structure
    isotope patterns of a set of chemical formulas to a given degree of
    accuracy (up to the limit set by errors in floating point arithmetic). A
    data-set comprising the masses and isotopic abundances of individual
    elements is also provided and calculation of isotopic gross structures
    is also supported.
Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.
Companion package to the paper: An analytic approach for 
    interpretable predictive models in high dimensional data, in the presence of 
    interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. 
    This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.
Functions for easy building of error correction models (ECM) for time series regression.
Programmatic interface to the European Centre for Medium-Range
    Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>)
    and Copernicus's Climate Data Store (CDS; 
    <https://cds.climate.copernicus.eu>). Allows for easy downloads of weather 
    forecasts and climate reanalysis data in R.
Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that can document biodiversity change. Freely available remote sensing products (RSP) are downloaded and integrated with data for national or regional domains to derive EBV metrics related to horizontal ecosystem extent, forest fragmentation, species distribution ranges, among others. To process the spatial data, the users must provide at least a region of interest (polygon or geographic administrative unit). Downloadable RSP include Global Surface Water (Peckel et al., 2016) <doi:10.1038/nature20584>, Forest Change (Hansen et al., 2013) <doi:10.1126/science.1244693>, and Continuous Tree Cover data (Sexton et al., 2013) <doi:10.1080/17538947.2013.786146>.
Creates 'graphs' of species associations (interactions) and ordination biplots from 
    co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in
    Popovic, GC., Hui, FKC., Warton, DI., (2018) <doi:10.1016/j.jmva.2017.12.002>.
Biotracers and stomach content analyses are combined in a Bayesian hierarchical model
    to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix 
    (all diet proportions).
    The package relies on the JAGS software and the 'rjags' package to run a Markov chain Monte Carlo 
    approximation of the different variables.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.
Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.
Provides a flexible foundation for scientists, 
  engineers, and policy makers to base teaching exercises as well as for 
  more applied use to model complex eco-hydrological interactions.
Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.
Provides measures to characterize the complexity of classification 
    and regression problems based on aspects that quantify the linearity of the 
    data, the presence of informative feature, the sparsity and dimensionality 
    of the datasets. This package provides bug fixes, generalizations and 
    implementations of many state of the art measures. The measures are 
    described in the papers: Lorena et al. (2019) <doi:10.1145/3347711> and 
    Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
  All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. 
  The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
  Solutions to model projects are in the same subdirectories.
Coalescent-Based Simulation of Ecological Communities as proposed
    by Munoz et al. (2017) <doi:10.13140/RG.2.2.31737.26728>. The package includes
    a tool for estimating parameters of community assembly by using Approximate 
    Bayesian Computation.
Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette.
Randomly generate a wide range of interaction networks with
             specified size, average degree, modularity, and topological
             structure. Sample nodes and links from within simulated networks
             randomly, by degree, by module, or by abundance. Simulations
             and sampling routines are implemented in 'FORTRAN', providing
             efficient generation times even for large networks. Basic
             visualization methods also included. Algorithms implemented
             here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.
A collection of advanced tools, methods and models specifically  designed  for  analyzing different  types  of  ecological  networks - especially  antagonistic  (food  webs,  host-parasite),  mutualistic (plant-pollinator, plant-fungus, etc)  and competitive networks, as well as their variability in time and space. Statistical models are developed to describe and understand the mechanisms that determine species interactions, and to decipher the organization of these (multi-layer) ecological networks.
A wrapper of different methods from Linear Algebra for the equations
  introduced in The Atlas of Economic Complexity and related literature. This
  package provides standard matrix and graph output that can be used seamlessly
  with other packages.
Estimating individual-level covariate-outcome associations 
 using aggregate data ("ecological inference") or a combination of 
 aggregate and individual-level data ("hierarchical related regression").
Three sets of data and functions for informing ecosystem restoration
    decisions, particularly in the context of the U.S. Army Corps of Engineers.
    First, model parameters are compiled as a data set and associated metadata 
    for over 500 habitat suitability models developed by the U.S. Fish and 
    Wildlife Service (USFWS 1980) <https://www.fws.gov/policy/ESMindex.html>. 
    Second, functions for conducting habitat suitability analyses both for the
    models described above as well as generic user-specified model 
    parameterizations. Third, a suite of decision support tools for conducting 
    cost-effectiveness and incremental cost analyses (Robinson et al. 1995)
    <https://www.iwr.usace.army.mil/Portals/70/docs/iwrreports/95-R-1.pdf>.
Creates an R interface to the Bacen <http://api.bcb.gov.br/> and Sidra <http://api.sidra.ibge.gov.br> APIs and IPEA data <http://www.ipeadata.gov.br/Default.aspx>.
Classes and methods for implementing aquatic ecosystem models,
  for running these models, and for visualizing their results.
Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org.
R interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.
Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness.
    Functions are written so they will work in a parallel-computing environment.
Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.
Functions and data supporting the Eco-Stats text (Warton, forthcoming, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and vignette solutions to textbook exercises will be forthcoming at a later time.
Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) <doi:10.1007/BF00048870>), fuzzy clustering (De Cáceres et al. (2010) <doi:10.1080/01621459.1963.10500845>), biodiversity indices (Jost (2006) <doi:10.1111/j.2006.0030-1299.14714.x>), and network analyses (Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided.
A simple approach to using a probit or logit analysis to calculate
        lethal concentration (LC) or time (LT) and the appropriate fiducial 
        confidence limits desired for selected LC or LT for
        ecotoxicology studies (Finney 1971; Wheeler et al. 2006; 
        Robertson et al. 2007). The simplicity of 'ecotox' comes from the 
        syntax it implies within its functions which are similar to functions 
        like glm() and lm(). In addition to the simplicity of the syntax, 
        a comprehensive data frame is produced which gives the user a 
        predicted LC or LT value for the desired level and a suite of important 
        parameters such as fiducial confidence limits and slope.   
        Finney, D.J. (1971, ISBN: 052108041X);
        Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>;
        Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K.
        (2007, ISBN: 0849323312).
Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis.
    Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book.
    Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included.
    Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard.
    Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
Functions for evaluating and visualizing
  ecological assessment procedures for surface waters
  containing physical, chemical and biological assessments
  in the form of value functions.
Computer simulations of classical ecological models as a
    learning resource.
Implements various procedures for finding 
	     multiple change-points from Matteson D. et al (2013)
	     <doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017) 
	     <doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019).
	     Two methods make use of dynamic  
	     programming and pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Easy and rapid quantitative estimation of small terrestrial ectotherm temperature regulation effectiveness in R. ectotemp is built on classical formulas that evaluate temperature regulation by means of various indices, inaugurated by Hertz et al. (1993) <doi: 10.1086/285573>. Options for bootstrapping and permutation testing are included to test hypotheses about divergence between organisms, species or populations.
Functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively
    Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by
    us, as well as logistic regression and isotonic regression. Besides, the package also supports
    comparison between two estimation results.
Energy decomposition analysis for measuring contributions of components
             and factors on energy or carbon emission changes, as described in 
             B.W. Ang (2005) <doi:10.1016/j.enpol.2003.10.010>. Includes Log Mean 
             Devisia Index method and multi-year energy decomposition analysis using 
             five methods. Please refer P. Wu (2019) <doi:10.1016/j.jclepro.2019.02.200> 
             to use the package.
Provides data sets and R Codes for Williams, E.R., Matheson, A.C. and Harwood, C.E. (2002). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.
Import US Census Bureau, Education Demographic and Geographic Estimates Program,
  Composite School District Boundaries Files for 2013-2018 with the option to attach the 'EdBuild'
  master dataset of school district finance, student demographics, and community economic
  indicators for every school district in the United States. The master dataset is built
  from the US Census, Annual Survey of School System Finances (F33) and joins data from the
  National Center for Education Statistics, Common Core of Data; the US Census, Small Area
  Income and Poverty Estimates; and the US Census, Education Demographic and Geographic
  Estimates. Additional functions in the package create a dataset of all pairs of school
  district neighbors as either a dataframe or a shapefile and create formatted maps of
  selected districts at the state or neighbor level, symbolized by a selected variable
  in the 'EdBuild' master dataset. For full details about 'EdBuild' data processing please
  see 'EdBuild' (2020) <https://data.edbuild.org/>.
Import the 'EdBuild' master dataset of school district finance,
  student demographics, and community economic indicators for every school district in the United States.  
  The master dataset is built from the US Census, Annual Survey of School System Finances (F33)
  and joins data from the National Center for Education Statistics, Common Core of Data;
  the US Census, Small Area Income and Poverty Estimates; and the US Census,
  Education Demographic and Geographic Estimates. We apply 'EdBuild' standard processing to the
  dataset and provide the option to select from four different exclusion criteria - see the masterpull() help file for more details.
  The master dataset is available for any school year from 2013 to 2018 or longitudinally for all years 2013-2018.
  School year is identified by the end year. For example, the 2017-18 school year is 2018.
  Additional functions in the package use 'EdBuild' master data to analyze the difference 
  between neighboring school districts and create formatted excel tables of school district data. For full details about
  'EdBuild' data processing please see 'EdBuild' (2020) <http://data.edbuild.org>.
This package provides a unified approach for Economic
        Design of Control Charts. The main purpose of this package is
        to find out the optimal parameters to minimize the ECH
        (Expected Cost per Hour) of the process.
Detection of edge points in images based on the difference
        of two asymmetric M-kernel estimators. Linear and circular
        regression clustering based on redescending M-estimators.
        Detection of linear edges in images.
Finds and downloads raw Evaporative Demand Drought
    Index (EDDI) data, then reads the data into 'R' using the 'raster'
    package. The EDDI product detects drought at multiple time scales,
    from weekly "flash droughts" to long-term droughts. More information
    about the EDDI data product can be found at
    <https://www.esrl.noaa.gov/psd/eddi/>.
Compute a cyclist's Eddington number, including efficiently
    computing cumulative E over a vector. A cyclist's Eddington number
    <https://en.wikipedia.org/wiki/Arthur_Eddington#Eddington_number_for_cycling>
    is the maximum number satisfying the condition such that a cyclist has
    ridden E miles or greater in E days. The algorithm in this package is an
    improvement over the conventional approach because both summary statistics
    and cumulative statistics can be computed in linear time, since it does not
    require initial sorting of the data. These functions may also be used for
    computing h-indices for authors, a metric described by Hirsch (2005)
    <doi:10.1073/pnas.0507655102>. Both are specific applications of computing
    the side length of a Durfee square 
    <https://en.wikipedia.org/wiki/Durfee_square>.
Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package 'bupaR'.
This package will allow to connect with email server through Internet Message Access Protocol (IMAP) and extract header information e.g. from, to, cc, subject, date and time. User will supply their email address and password along with other options. Initially this package is developed only for Gmail. To run the functions from this package user have to have IMAP enabled Gmail account.
Import physiologic data stored in
    the European Data Format (EDF and EDF+) into R.
    Both EDF and EDF+ files are supported. Discontinuous
    EDF+ files are not yet supported.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>,
    BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>,
    and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>.
    The files are read in two steps: first the header is read
    and then the signals (using the header object as a parameter).
Easily creating empirical distribution functions from data: 'dfun', 'pfun',
    'qfun' and 'rfun'.
In the USA, companies file different forms with the U.S. 
    Securities and Exchange Commission (SEC) through EDGAR (Electronic 
    Data Gathering, Analysis, and Retrieval system). The EDGAR 
    database automated system collects all the different necessary 
    filings and makes it publicly available. This package facilitates
    retrieving, storing, searching, and parsing of all the available 
    filings on the EDGAR server. It downloads filings from SEC 
    server in bulk with a single query. Additionally, it provides 
    various useful functions: extracts 8-K triggering events, extract
    "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)"
    sections of annual statements, searches filings for desired 
    keywords, provides sentiment measures, parses filing header 
    information, and provides HTML view of SEC filings.
A set of methods to access and parse live filing information from the
    U.S. Securities and Exchange Commission (SEC - <https://www.sec.gov/>) including
    company and fund filings along with all associated metadata.
Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.
Facilitates basic spatial edge correction to point pattern data.
Datasets from (Excel download)
  <https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/CY2019-DIY-tables.01.17.20.xlsx>
	and (ZIP download)
	<https://www.cms.gov/CCIIO/Resources/Regulations-and-Guidance/Downloads/HHS-HCC-2019-software.01.17.2020.zip>
  in a tidy format. These support the Centers for Medicare and Medicaid
  Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows
  health insurance issuers to calculate member risk profiles under the Health
  and Human Services-Hierarchical Condition Categories (HHS-HCC) regression
  model. This regression model is used to calculate risk adjustment transfers.
  Risk adjustment is a selection mitigation program implemented under the
  Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA.
  Under the ACA, health insurance issuers submit claims data to CMS	in order
  for CMS to calculate a risk score under the HHS-HCC regression model.
  However, CMS does not inform issuers of their average risk score until after
  the data submission deadline. These data sets can be used by issuers to
  calculate their average risk score mid-year. More information about risk
  adjustment and the HHS-HCC model can be found here:
  <https://www.cms.gov/mmrr/Articles/A2014/MMRR2014_004_03_a03.html>.
Extends edgeR functionality by improving on exactTest using an unconditional exact test of negative binomial means.
Perform a Bayesian estimation of the exploratory 
    deterministic input, noisy and gate (EDINA)
    cognitive diagnostic model described by Chen et al. (2018)
    <doi:10.1007/s11336-017-9579-4>.
Package EDISON (Estimation of Directed Interactions from
    Sequences Of Non-homogeneous gene expression) runs an MCMC
    simulation to reconstruct networks from time series data, using
    a non-homogeneous, time-varying dynamic Bayesian network.
    Networks segments and changepoints are inferred concurrently,
    and information sharing priors provide a reduction of the
    inference uncertainty.
An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame'
    without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. 
    These modules are intended for reuse across applications.
An integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach as described in Kim et al. (2015) <doi:10.1080/01621459.2015.1040881>. In this approach, the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
Facilitates reading and manipulating (multivariate) data restrictions
    (edit rules) on numerical and categorical data. Rules can be defined with common R syntax
    and parsed to an internal (matrix-like format). Rules can be manipulated with
    variable elimination and value substitution methods, allowing for feasibility checks
    and more. Data can be tested against the rules and erroneous fields can be found based
    on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with 
    using the 'igraph' package.
Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
Collection of data sets from various assessments that can be used to 
    evaluate psychometric models. These data sets have been analyzed in the
    following papers that introduced new methodology as part of the application section:
    Chen, Y., Culpepper, S. A., & Liang, F. (2020) <doi:10.1007/s11336-019-09693-2>
    Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>,
    Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>,
    Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>,
    Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>,
    and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.
Implementations of (1) mutual dependence measures and mutual independence tests in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>;
    (2) independent component analysis methods based on mutual dependence measures in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532> 
    and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>;
    (3) conditional mean dependence measures and conditional mean independence tests in 
    Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012>,
    Park, T., et al. (2015) <doi:10.1214/15-EJS1047>,
    and Lee, C. E., and Shao, X. (2017) <doi:10.1080/01621459.2016.1240083>.
Package implements the EDNE-test for equivalence
    according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
    "EDNE" abbreviates "Euclidean Distance between the 
    Non-standardized Expected values".
    The EDNE-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Euclidean distance.
    The test is an asymptotically valid test for the family of distributions 
    fulfilling the assumptions of the multivariate central limit theorem 
    (see Hoffelder et al.,2015).
    The function EDNE.EQ() implements the EDNE-test for equivalence 
    according to Hoffelder et al. (2015).
    The function EDNE.EQ.dissolution.profiles() implements a variant 
    of the EDNE-test for equivalence analyses of dissolution profiles
    (see Suarez-Sharp et al.,2020 <DOI:10.1208/s12248-020-00458-9>).
    EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the 
    differences of the expected values of both dissolution profile populations 
    is statistically significantly smaller than 10 [\% of label claim]. 
    The current regulatory standard approach for equivalence analyses of 
    dissolution profiles is the similarity factor f2.
    The statistical hypotheses underlying EDNE.EQ.dissolution.profiles()
    coincide with the hypotheses for f2 (see Hoffelder et al.,2015, 
    Suarez-Sharp et al., 2020).
A non-parametric framework based on  estimation statistics principle. Its main purpose is to  infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of  domination  of  categories  and  representing  orders  in  the form of a graph; 2) estimating  magnitude  of  difference  between  a  pair  of categories in forms of mean-difference confidence intervals; and 3) visualizing  domination  orders  and  magnitudes  of  difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2020) <doi:10.1016/j.heliyon.2020.e05435>.
Reduction methods through slice inverse regression approaches. It mainly designed for illustrating the articles "A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches" (Liquet, B., Saracco, J. (2012) <doi:10.1007/s00180-011-0241-9>) and "Comparison of sliced inverse regression approaches for underdetermined cases”.
Provides convenience functions and pre-programmed Stan models
    related to item response theory. Its purpose is to make fitting
    common item response theory models using Stan easy.
Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd).
Easily compute education inequality measures and the distribution 
  of educational attainments for any group of countries, using the data set 
  developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>. 
  The package offers the possibility to compute not only the Gini index, but 
  also generalized entropy measures for different values of the sensitivity 
  parameter. In particular, the package includes functions to compute the 
  mean log deviation, which is more sensitive to the bottom part of the 
  distribution; the Theil’s entropy measure, equally sensitive to all parts 
  of the distribution; and finally, the GE measure when the sensitivity 
  parameter is set equal to 2, which gives more weight to differences in 
  higher education. The decomposition of these measures in the components 
  between-country and within-country inequality is also provided. Two 
  graphical tools are also provided, to analyse the evolution of the
  distribution of educational attainments: The cumulative distribution 
  function and the Lorenz curve.
Implements the Edwards (1997) <doi:10.1002/j.1551-8833.1997.tb08229.x>
    Langmuir-based semi-empirical coagulation model, which predicts the concentration
    of organic carbon remaining in water after treatment with an Al- or Fe-based
    coagulant. Data and methods are provided to optimise empirical coefficients.
Data from the seven Australian Federal Elections (House of
    Representatives) between 2001 and 2019, and from the four Australian
    Censuses over the same period. Includes tools for visualizing and
    analysing the data, as well as imputing Census data for years in
    which a Census does not occur. This package incorporates
    data that is copyright Commonwealth of Australia (Australian
    Electoral Commission and Australian Bureau of Statistics) 2019.
The eefAnalytics provides tools for analysing data from evaluations of educational interventions using a randomised controlled trial designs. It provides analytical tools to perform sensitivity analysis using different
    methods(e.g. frequentist models with bootstrapping and permutations options, Bayesian models). The functions contained in this package can be used for simple individual randomised trials, cluster randomised trials and multisite trials.
    The methods can also be used more widely beyond education trials. This package can be used to evaluate other interventions designs using Frequentist and Bayesian multilevel models.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Compute the extended empirical log likelihood ratio (Tsao & Wu, 2014) for the mean and parameters defined by estimating equations.
Read raw EEM data and prepares them for further analysis.
Forecasting univariate time series with different decomposition based Extreme Learning Machine models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>, Parida M, Behera MK, Nayak N (2018). <doi:10.1109/ICSESP.2018.8376723>.
Forecasting univariate time series with different decomposition based time delay neural network models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.
Provides various tools for preprocessing Emission-Excitation-Matrix (EEM) for Parallel Factor Analysis (PARAFAC). Different
  methods are also provided to calculate common metrics such as humification index and fluorescence index.
Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, and identify cut points. Also includes three example data 
    sets of administrative education records for learning how to process records 
    with errors.
Provides functions to create simulated time series of environmental
    exposures (e.g., temperature, air pollution) and health outcomes for use in
    power analysis and simulation studies in environmental epidemiology. This
    package also provides functions to evaluate the results of simulation studies
    based on these simulated time series. This work was supported by a grant
    from the National Institute of Environmental Health Sciences (R00ES022631) and
    a fellowship from the Colorado State University Programs for Research and
    Scholarly Excellence.
A set of functions to estimate capture probabilities and
  densities from multipass pass removal data.
Functions for seven different procedures for determining the number of 
    factors, including functions for parallel analysis and the minimum average partial 
    test. There are functions for conducting principal components analysis, principal 
    axis factor analysis, maximum likelihood factor analysis, image factor analysis, 
    and extension factor analysis, all of which can take raw data or correlation matrices 
    as input and with options for conducting the analyses using Pearson correlations, 
    Kendall correlations, Spearman correlations, or polychoric correlations. Varimax 
    rotation, promax rotation, and Procrustes rotations can be performed. Additional 
    functions focus on the factorability of a correlation matrix, the congruences between 
    factors from different datasets, and for assessing local independence. 
    O'Connor (2000, <doi:10.3758/bf03200807>);
    O'Connor (2001, <doi:10.1177/01466216010251011>);
    Fabrigar & Wegener (2012, ISBN:978-0-19-973417-7);
    Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2).
Performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 
    <doi:10.1037/a0023353>) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 
    2011 <doi:10.1080/00273171.2011.564527>) for assessing the dimensionality
    of a set of variables using minimum rank factor analysis (see ten Berge & 
    Kiers, 1991 <doi:10.1007/BF02294464> for more information). The package 
    also includes the option to compute minimum rank factor analysis by 
    itself, as well as the greater lower bound calculation.
Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++.
A number of utility function for exploratory factor analysis
    are included in this package. In particular, it computes standard errors for
    parameter estimates and factor correlations under a variety of conditions.
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
    in an image. The image is first transformed into the wavelet domain in
    order to decorrelate any noise components, following which the coefficients
    at each resolution are standardised. Statistical tests (in a multiple
    hypothesis testing setting) are then carried out to find the anomalies. The
    power of EFDR exceeds that of standard FDR, which would carry out tests on
    every wavelet coefficient: EFDR choose which wavelets to test based on a
    criterion described in Shen et al. (2002). The package also provides
    elementary tools to interpolate spatially irregular data onto a grid of the
    required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
    'Nonparametric hypothesis testing for a spatial signal.' Journal of the
    American Statistical Association 97.460 (2002): 1122-1140.
Variable selection and Bayesian effect fusion for categorical predictors in linear and 
    logistic regression models. Effect fusion aims at the question which categories have a similar 
	effect on the response and therefore can be fused to obtain a sparser representation of the model. 
    Effect fusion and variable selection can be obtained either with a prior that has an interpretation 
	as spike and slab prior on the level effect differences or with a sparse finite mixture prior on 
	the level effects. The regression coefficients are estimated with a flat uninformative prior after 
	model selection or by taking model averages. Posterior inference is accomplished by an MCMC 
	sampling scheme which makes use of a data augmentation strategy (Polson, Scott & Windle 
	(2013)) based on latent Polya-Gamma random variables in the case of logistic regression. 
	The code for data augmentation is taken from Polson et al. (2013), who own the copyright.
Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.
Predicts cytoplasmic effector proteins using genomic data by searching for motifs of interest using regular expression searches and hidden Markov models (HMM) based in Haas et al. (2009) <doi:10.1038/nature08358>.
Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.
Provide utilities to work with indices of effect size and standardized parameters for a wide variety of models (see support list of insight; Lüdecke, Waggoner & Makowski (2019) <doi:10.21105/joss.01412>), allowing computation and conversion of indices such as Cohen's d, r, odds, etc.
Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.
Functions to test for changes of a response to a stimulus grouping relative 
  to a background or baseline response.
Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.
Provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package.
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
An implementation for using efficient initials to compute the
    maximal eigenpair in R. It provides three algorithms to find the efficient
    initials under two cases: the tridiagonal matrix case and the general matrix
    case. Besides, it also provides two algorithms for the next to the maximal eigenpair under
    these two cases.
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.
A collection of functions to compute the standardized 
  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). 
  The computation algorithms have been optimized to allow efficient computation even 
  with very large data sets.
The European Eddy Fluxes Database Cluster distributes fluxes of different Green House Gases measured mainly using the eddy covariance technique acquired in sites involved in EU projects but also single sites in Europe, Africa and others continents that decided to share their measurements in the database <http://gaia.agraria.unitus.it>. The package provides two functions to load and row-wise bind CSV files distributed by the database. Currently only L2, L3, and L4 (L=Level), half-hourly and daily (aggregation) files are supported.
Interact with the FRED API, <https://fred.stlouisfed.org/docs/api/fred/>, 
      to fetch observations across economic series;
      find information about different economic sources, releases, series, etc.;
      conduct searches by series name, attributes, or tags; and determine the 
      latest updates. Includes functions for creating panels of related variables 
      with minimal effort and datasets containing data sources, releases, and 
      popular FRED tags.
Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().
The binary file format 'netCDF' is developed primarily for climate, ocean and
    meteorological data, and 'efts' is a package to read and write Ensemble Forecast Time 
    Series data in 'netCDF'. 'netCDF' has traditionally been used to store time slices 
    of gridded data, rather than complete time series of point data. 
    'efts' facilitates data handling stored in 'netCDF' files that follow a convention
    devised in the domain of ensemble hydrologic forecasting, but possibly applicable 
    in other domains. 'efts' uses reference class objects to 
    provide a high level interface to read and write such data, wrapping 
    lower level operations performed using 'ncdf4'.
Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids
    in both mg/mL and mmol/L units.
An implementation of the Exploratory Graph Analysis (EGA) framework for dimensionality assessment. EGA is part of a new area called network psychometrics that focuses on the estimation of undirected network models in psychological datasets. EGA estimates the number of dimensions or factors using graphical lasso or Triangulated Maximally Filtered Graph (TMFG) and a weighted network community analysis. A bootstrap method for verifying the stability of the estimation is also available. The fit of the structure suggested by EGA can be verified using confirmatory factor analysis and a direct way to convert the EGA structure to a confirmatory factor model is also implemented. Documentation and examples are available. Golino, H. F., & Epskamp, S. (2017) <doi:10.1371/journal.pone.0174035>. Golino, H. F., & Demetriou, A. (2017) <doi:10.1016/j.intell.2017.02.007> Golino, H., Shi, D., Garrido, L. E., Christensen, A. P., Nieto, M. D., Sadana, R., & Thiyagarajan, J. A. (2018) <doi:10.31234/osf.io/gzcre>. Christensen, A. P. & Golino, H.F. (2019)  <doi:10.31234/osf.io/9deay>.
An easy-to-use implementation of the Engle-Granger
  two-step procedure for identifying pairs of cointegrated series.  It is geared towards 
  the analysis of pairs of securities.  Summary and plot functions are provided, 
  and the package is able to fetch closing prices of securities from Yahoo.
  A variety of unit root tests are supported, and an improved unit root test is included.
Miscellaneous functions to help customise 'ggplot2' objects. High-level functions are provided to post-process 'ggplot2' layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom 'geom', and helper functions to enforce symmetric scales or add tags to facetted plots.
An implementation of Bayesian hierarchical models
    for faecal egg count data to assess anthelmintic
    efficacy. Bayesian inference is done via MCMC sampling using 'Stan' <https://mc-stan.org/>.
Tools for importing, analyzing and visualizing ego-centered
    network data. Supports several data formats, including the export formats of
    'EgoNet', 'EgoWeb 2.0' and 'openeddi'. An interactive (shiny) app for the
    intuitive visualization of ego-centered networks is provided. Also included
    are procedures for creating and visualizing Clustered Graphs 
    (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).
Statistics and graphics for streamflow history,
    water quality trends, and the statistical modeling algorithm: Weighted
    Regressions on Time, Discharge, and Season (WRTDS). The modeling
    method is introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>,
    and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>.
Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis. The WRTDS modeling
    method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>,
    and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The 
    paper describing the uncertainty and confidence interval calculations 
    is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.
Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.
Parametric proportional hazards fitting with left truncation and
        right censoring for common families of distributions, piecewise constant 
        hazards, and discrete models. Parametric accelerated failure time models
        for left truncated and right censored data. Proportional hazards
        models for tabular and register data. Sampling of risk sets in Cox 
        regression, selections in the Lexis diagram, bootstrapping. 
        Broström (2012) <doi:10.1201/9781315373942>.
Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE).
By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions. The package also provides additional functions to mimic the R basic example() function and the prototyping of packages.
Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman et al. 1993 Journal of Vegetation Science <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
Process and analyze Electronic Health Record (EHR) data. The 'EHR'
    package provides modules to perform diverse medication-related studies using
    data from EHR databases. Especially, the package includes modules to perform
    pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in
    Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, 
    Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional
    modules will be added in future. In addition, this package provides various
    functions useful to perform Phenome Wide Association Study (PheWAS) to explore
    associations between drug exposure and phenotypes obtained from EHR data, as
    outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) 
    <doi:10.1093/bioinformatics/bty306>.
Functions to delineate temporal dataset shifts in Electronic Health 
             Records through the projection and visualization of dissimilarities 
             among data temporal batches. This is done through the estimation of 
             data statistical distributions over time and their projection in 
             non-parametric statistical manifolds, uncovering the patterns of the 
             data latent temporal variability. 'EHRtemporalVariability' is 
             particularly suitable for multi-modal data and categorical variables 
             with a high number of values, common features of biomedical data where 
             traditional statistical process control or time-series methods may not 
             be appropriate. 'EHRtemporalVariability' allows you to explore and 
             identify dataset shifts through visual analytics formats such as 
             Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. 
             An additional 'EHRtemporalVariability' Shiny app can be used to load 
             and explore the package results and even to allow the use of these 
             functions to those users non-experienced in R coding. (Sáez et al. 2020)
             <doi:10.1093/gigascience/giaa079>.
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.
Provides API access to data from the 'US Energy Information Administration' ('EIA') <https://www.eia.gov/>. 
    Use of the API requires a free API key obtainable at <https://www.eia.gov/opendata/register.php>.
    The package includes functions for searching 'EIA' data categories and importing time series and geoset time series datasets. 
    Datasets returned by these functions are provided in a tidy format or alternatively in more raw form. 
    It also offers helper functions for working with 'EIA' date strings and time formats and for inspecting different summaries of series metadata.
    The package also provides control over API key storage and caching of API request results.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API <https://www.eia.gov/opendata/>.
Model fitting and species biotic interaction network topology selection for explicit
  interaction community models. Explicit interaction community models are an extension of binomial
  linear models for joint modelling of species communities, that incorporate both the effects of
  species biotic interactions and the effects of missing covariates. Species interactions are modelled
  as direct effects of each species on each of the others, and are estimated alongside the effects of
  missing covariates, modelled as latent factors. The package includes a penalized maximum likelihood
  fitting function, and a genetic algorithm for selecting the most parsimonious species interaction
  network topology.
Compares estimates from three ecological inference routines, based on King (1997) <ISBN: 0691012407>, <https://gking.harvard.edu/eicamera/kinroot.html>; King (2004) <ISBN: 0521542804>, <https://gking.harvard.edu/files/abs/ecinf04-abs.shtml>.
Solves the ``inverse eigenvalue problem'' which is to
        generate a real-valued matrix that has the specified real
        eigenvalue spectrum.  It can generate infinitely many dense
        matrices, symmetric or asymmetric, with the given set of
        eigenvalues.  Algorithm can also generate stochastic and doubly
        stochastic matrices.
Estimation of the parameters in a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accommodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification. See Hoff (2007) <arXiv:0711.1146> 
        for details on the model.
Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
Matrix algebra using the 'Eigen' C++ library: determinant, rank, inversion, kernel and image, QR decomposition, Cholesky decomposition, linear least-squares problems. Complex matrices are supported.
Implementation of Efficient Inference of Local Ancestry 
	     using fused quantile regression and k-means classifier
Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in <http://www.jmlr.org/papers/v11/mazumder10a.html>, which achieves highly computational advantage by truncated singular-value decomposition.
Methods and utilities for causal emergence.
    Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.
Provides methods for analyzing R by C ecological contingency
        tables using the extreme case analysis, ecological regression,
        and Multinomial-Dirichlet ecological inference models.  Also
        provides tools for manipulating higher-dimension data objects.
Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Stanke & Bulut (2019) <doi:10.21449/ijate.515085> and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.
Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.
This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression.  It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.
Emissions are the mass of pollutants released into the atmosphere. Air quality models need emissions data, with spatial and temporal distribution, to represent air pollutant concentrations. This package, eixport, creates inputs for the air quality models 'WRF-Chem' Grell et al (2005) <doi:10.1016/j.atmosenv.2005.04.027>, 'MUNICH' Kim et al (2018) <doi:10.5194/gmd-11-611-2018> , 'BRAMS-SPM' Freitas et al (2005) <doi:10.1016/j.atmosenv.2005.07.017> and 'RLINE' Snyder et al (2013) <doi:10.1016/j.atmosenv.2013.05.074>. See the 'eixport' website (<https://atmoschem.github.io/eixport/>) for more information, documentations and examples. More details in Ibarra-Espinosa et al (2018) <doi:10.21105/joss.00607>.
Functions for estimating catalytic constant and Michaelis-Menten constant
            for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs 
            sampler based on the Bayesian framework. Additionally, a function to create 
            plot to identify the goodness-of-fit is included.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Functions for computing critical values and implementing the one-sided/two-sided EL tests.
Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.
Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' API
    (<https://www.elastic.co/products/elasticsearch>), including functions for
    setting connection details to 'Elasticsearch' instances, loading bulk data,
    searching for documents with both 'HTTP' query variables and 'JSON' based body
    requests. In addition, 'elastic' provides functions for interacting with API's
    for 'indices', documents, nodes, clusters, an interface to the cat API, and
    more.
Description: Uses k-fold cross-validation and elastic-net regularization to estimate the
              Ising model on binary data. Produces 3D plots of the cost function as a function
              of the tuning parameter in addition to the optimal network structure.
Provides functions for fitting the entire
        solution path of the Elastic-Net and also provides functions
        for doing sparse PCA.
A lightweight R interface to 'Elasticsearch' - a NoSQL search-engine and 
    column store database (see <https://www.elastic.co/products/elasticsearch> for more 
    information). This package implements a simple Domain-Specific Language (DSL) for indexing, 
    deleting, querying, sorting and aggregating data using 'Elasticsearch'.
This is a bizarre collection of functions written to do
        various sorts of statistical election audits.  There are also
        functions to generate simulated voting data, and simulated
        "truth" so as to do simulations to check characteristics of
        these methods.
An extension of the elec package intended for use on
        election audits using stratified random samples.  Includes
        functions to obtain conservative and exact p-values, and
        functions that give sample sizes that may make election audits
        more efficient.
Digital simulation of electrochemical processes.
             Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.
             Bibliography regarding this methods can be found in the following texts.
             Dieter Britz, Jorg Strutwolf (2016) <ISBN:978-3-319-30292-8>.
             Allen J. Bard, Larry R. Faulkner (2000) <ISBN:978-0-471-04372-0>.
Functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the 'msm' package using age as the time-scale.
This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <http://vanderwalresearch.com/blog/15-elections>.
Offers a set of functions to easily download and clean 
    Brazilian electoral data from the Superior Electoral Court website. 
    Among others, the package retrieves data on local and
    federal elections for all positions (city councilor, mayor, state deputy,
    federal deputy, governor, and president) aggregated by
    state, city, and electoral zones.
Provides all electivity algorithms (including Vanderploeg and Scavia 
    electivity) that were examined in Lechowicz (1982) <doi:10.1007/BF00349007>, 
    plus the example data that were provided for moth resource utilisation.
Highest averages & largest remainders allocating seats methods and
    several party system scores.
    Implemented highest averages allocating seats methods are D'Hondt, Webster,
    Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague,
    equal proportions and Adams.
    Implemented largest remainders allocating seats methods are Hare, Droop,
    Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders.
    The main advantage of this package is that ties are always reported
    and not incorrectly allocated.
    Party system scores provided are competitiveness, concentration,
    effective number of parties, party nationalization score,
    party system nationalization score and volatility.
    References:
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>.
    Norris (2004, ISBN:0-521-82977-1).
    Consejo Nacional Electoral del Ecuador (2014)<http://cne.gob.ec/documents/Estadisticas/Atlas/ATLAS/CAPITULO%206%20web.pdf>.
    Laakso & Taagepera (1979) <http://journals.sagepub.com/doi/pdf/10.1177/001041407901200101>.
    Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>.
    Pedersen (1979) <http://janda.org/c24/Readings/Pedersen/Pedersen.htm>.
    Golosov (2010) <http://ppq.sagepub.com/content/16/2/171.abstract>.
    Golosov (2014) <http://ppq.sagepub.com/content/early/2014/09/08/1354068814549342.abstract>.
Aims to facilitate the reduction of elemental microchemistry data from solid-phase LAICPMS analysis (laser ablation inductive coupled plasma mass spectrometry). The 'elementR' package provides a reactive and user friendly interface (based on a 'shiny' application) and a set of 'R6' classes for conducting all steps needed for an optimal data reduction while leaving maximum control for user. For more details about the methods used in 'elementR', see Sirot et al (2017) <DOI:10.1111/2041-210X.12822>.
Several web services are available that provide access to elevation
             data. This package provides access to several of those services and 
             returns elevation data either as a SpatialPointsDataFrame from 
             point elevation services or as a raster object from raster 
             elevation services.  Currently, the package supports access to the
             Amazon Web Services Terrain Tiles <https://registry.opendata.aws/terrain-tiles/>, 
             the Open Topography Global Datasets API <https://opentopography.org/developers/>, 
             and the USGS Elevation Point Query Service <https://nationalmap.gov/epqs/>.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
Density, distribution function, quantile function
    and random generation for the Kumaraswamy Complementary Weibull
    Geometric (Kw-CWG) lifetime probability distribution proposed
    in Afify, A.Z. et al (2017) <doi:10.1214/16-BJPS322>.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
To run data analysis for enzyme-link immunosorbent assays (ELISAs). 
	Either the five- or four-parameter logistic model will be fitted for data of single ELISA. 
	Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. 
	Feng (2018) <doi:10.1101/483800>.
Create list comprehensions (and other types of comprehension) similar to those in
    'python', 'haskell', and other languages. List comprehension in 'R' converts a 
    regular for() loop into a vectorized lapply() function. Support for looping 
    with multiple variables, parallelization, and across non-standard objects included. Package 
    also contains a variety of functions to help with list comprehension and 
    higher order functions for manipulating lists.
Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name "elitism" is an acronym of "e"quipment for "l"ogarithmic and l"i"near "ti"me "s"tepwise "m"ultiple hypothesis testing. Version 1.0.0 was released on June 26, 2019. See Gou, J., and Zhang, F. (2020). Quick multiple test procedures and p-value adjustments. Technical report.
Correlation chart of two set (x and y) of data.  
  Using Quantiles.  Visualize the effect of factor.
Contains various routines for drawing
    ellipses and ellipse-like confidence regions, implementing the plots
    described in Murdoch and Chow (1996), A graphical display of large
    correlation matrices, The American Statistician 50, 178-180. There are
    also routines implementing the profile plots described in Bates and
    Watts (1988), Nonlinear Regression Analysis and its Applications.
The ellipsis is a powerful tool for extending functions. Unfortunately 
    this power comes at a cost: misspelled arguments will be silently ignored. 
    The ellipsis package provides a collection of functions to catch problems
    and alert the user.
A suite of elliptic and related functions including Weierstrass and
 Jacobi forms.  Also includes various tools for manipulating and
 visualizing complex functions.
Given the omnipresence of the assumption of elliptical symmetry, it is essential to be able to test whether that assumption actually holds true or not for the data
            at hand. This package provides several statistical tests for elliptical symmetry that are described in Babic et al. (2020) <arXiv:2011.12560>.
Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
An implementation of the algorithm described in "Efficient Large-
    Scale Internet Media Selection Optimization for Online Display Advertising"
    by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below
    for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf>
    for a full-text version of the paper). The algorithm here is designed to 
    allocate budget across a set of online advertising opportunities using a 
    coordinate-descent approach, but it can be used in any resource-allocation 
    problem with a matrix of visitation (in the case of the paper, website page-
    views) and channels (in the paper, websites). The package contains allocation
    functions both in the presence of bidding, when allocation is dependent on 
    channel-specific cost curves, and when advertising costs are fixed at each channel.
A flexible framework for calculating Elo ratings and resulting
    rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
    etc.). This implementation is capable of evaluating a variety of matchups,
    Elo rating updates, and win probabilities, all based on the basic Elo
    rating system. It also includes methods to benchmark performance,
    including logistic regression and Markov chain models.
Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.
Provides an implementation of the maximum likelihood methods for deriving
    Elo scores as published in Foerster, Franz et al. (2016) <DOI:10.1038/srep35404>.
Provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries' I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.
A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.
Flexible implementation of Elston-Stewart algorithm
Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) <doi:10.1016/j.fss.2011.05.022>.
We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.
Do data management functions common in real-time monitoring (also called: ecological momentary assessment, experience sampling, micro-longitudinal) data, including creating power curves for multilevel data, centering on participant means and merging event-level data into momentary data sets where you need the events to correspond to the nearest data point in the momentary data. This is VERY early release software, and more features will be added over time.
A light, simple tool for sending emails with minimal dependencies.
Analysis and visualization of an ensemble of boolean models for 
  biomarker discovery in cancer cell networks. The package allows to easily 
  load the simulation data results of the DrugLogics software pipeline which predicts synergistic drug 
  combinations in cancer cell lines (developed by the DrugLogics research group 
  in NTNU). It has generic functions that can be used to split a boolean model 
  dataset to model groups with regards to the models predictive performance (number of true 
  positive predictions/Matthews correlation coefficient score) or synergy prediction based on a given set 
  of gold standard synergies and find the average activity difference per network 
  node between all model group pairs. Thus, given user-specific thresholds,
  important nodes (biomarkers) can be accessed in the sense that they make the 
  models predict specific synergies (synergy biomarkers) or have better 
  performance in general (performance biomarkers). Lastly, if the 
  boolean models have a specific equation form and differ only in their link operator, 
  link operator biomarkers can also be found.
Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").
Predictors can be converted to one or more numeric representations using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737>. Most encoding methods are supervised.
Provides a smooth mapping of multidimensional points into
    low-dimensional space defined by a self-organizing map. Designed to work
    with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019)
    <doi:10.1101/496869>.
Tools to analyze the embryo growth and the sexualisation thermal reaction norms.
random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement
EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.
For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
The researchers can use this package to fit Empirical Mode Decomposition and Artificial Neural Network based hybrid model for nonlinear and non stationary time series data.
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Functions that support estimating, assessing and mapping regional
    disaggregated indicators. So far, estimation methods comprise direct estimation,
    the model-based unit-level approach Empirical Best Prediction (see "Small area
    estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), 
    the area-level model (see "Estimates of income for small places: An 
    application of James-Stein procedures to Census Data" by (Fay and Herriot 1979) 
    <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance estimation methods, 
    log and arcsin transformation, spatial, robust and measurement error models), 
    as well as their precision estimates. The assessment of the used model
    is supported by a summary and diagnostic plots. For a suitable presentation of
    estimates, map plots can be easily created. Furthermore, results can easily be
    exported to excel. For a detailed description of the package and the methods used
    see "The {R} Package {emdi} for Estimating and Mapping Regionally Disaggregated Indicators"
    by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>.
Package providing calculation of Earth Mover's Distance
        (EMD).
Researchers can fit Empirical Mode Decomposition and Support Vector Regression based hybrid model for nonlinear and non stationary time series data using this package.
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
Simulate and fitting exponential multivariate Hawkes model. 
    This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.1093/biomet/58.1.83>, with an exponential kernel and fits the parameters from the data. 
    Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. 
    The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with 'maxLik' package.
Various Expectation-Maximization (EM) algorithms are implemented for item response theory
				(IRT) models. The current implementation includes IRT models for binary and ordinal
				responses, along with dynamic and hierarchical IRT models with binary responses. The
				latter two models are derived and implemented using variational EM.  Subsequent edits
				also include variational network and text scaling models.
Creates emissions for use in air quality models. Vehicular emissions 
  are estimated by a top-down approach, total emissions are calculated using the 
  statistical description of the fleet of vehicles, the emission is spatially 
  distributed according to satellite images or openstreetmap data 
  <https://www.openstreetmap.org> and then distributed temporarily 
  (Vara-Vela et al., 2016, <doi:10.5194/acp-16-777-2016>).
Provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The 'EmiStatR' package implements modular R methods. This enables 
    to add new functionalities through the R framework.
Work with Ecological Metadata Language ('EML') files. 
    'EML' is a widely used metadata standard in the ecological and
    environmental sciences, described in Jones et al. (2006),
    <doi:10.1146/annurev.ecolsys.37.091305.110031>.
This is a utility for transforming Ecological Metadata Language
        ('EML') files into 'JSON-LD' and back into 'EML.'  Doing so creates a
        list-based representation of 'EML' in R, so that 'EML' data can easily
        be manipulated using standard 'R' tools. This makes this package an
        effective backend for other 'R'-based tools  working with 'EML.' By
        abstracting away the complexity of 'XML' Schema, developers can
        build around native 'R' list objects and not have to worry about satisfying
        many of the additional constraints of set by the schema (such as element
        ordering, which is handled automatically). Additionally, the 'JSON-LD' 
        representation enables the use of developer-friendly 'JSON' parsing and
        serialization that may facilitate the use of 'EML' in contexts outside of 'R,'
        as well as the informatics-friendly serializations such as 'RDF' and
        'SPARQL' queries.
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.
The evolutionary model-based multiresponse approach (EMMA)
        is a novel methodology to process optimisation and product
        improvement. The approach is suitable to contexts in which the
        experimental cost and/or time limit the number of implementable
        trials.
End-member modelling analysis of grain-size data is an approach 
    to unmix a data set's underlying distributions and their contribution to 
    the data set. EMMAgeo provides deterministic and robust protocols for 
    that purpose.
This package includes functions to read and write to an
        EMME/2 databank
Obtain estimated marginal means (EMMs) for many linear, generalized 
  linear, and mixed models. Compute contrasts or linear functions of EMMs,
  trends, and comparisons of slopes. Plots and other displays.
  Least-squares means are discussed, and the term "estimated marginal means"
  is suggested, 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>.
Provides unsupervised selection and clustering of microarray data
    using mixture models. Following the methods described in McLachlan, Bean and
    Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected
    based one the likelihood ratio statistic for the test of one versus two
    components when fitting mixtures of t-distributions to the expression data
    for each gene. The dimensionality of this gene subset is further reduced through
    the use of mixtures of factor analyzers, allowing the tissue samples to be
    clustered by fitting mixtures of normal distributions.
We provide functions to fit finite mixtures of multivariate normal or t-distributions to 
    data with various factor analytic structures adopted for the covariance/scale matrices. The 
    factor analytic structures available include mixtures of factor analyzers and mixtures of common 
    factor analyzers. The latter approach is so termed because the matrix of factor loadings is
    common to components before the component-specific rotation of the component factors to 
    make them white noise. Note that the component-factor loadings are not common after 
    this rotation. Maximum likelihood estimators of model parameters are obtained via the 
    Expectation-Maximization algorithm. See descriptions of the algorithms used in
    McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8>
    McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> 
    McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> 
    McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> 
    Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> 
    Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> 
    McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
        of evolutionary multiobjective optimization algorithms.
An implementation of using emoji and fontawesome for using in both
    base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.
Evaluates the empirical characteristic function of univariate and multivariate samples.
    This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level.
Routines for performing empirical calibration of observational
  study estimates. By using a set of negative control hypotheses we can
  estimate the empirical null distribution of a particular observational
  study setup. This empirical null distribution can be used to compute a
  calibrated p-value, which reflects the probability of observing an
  estimated effect size when the null hypothesis is true taking both random
  and systematic error into account. A similar approach can be used to
  calibrate confidence intervals, using both negative and positive controls.
Auxiliary functions for the DESeq2 package to simulate read counts according to the null hypothesis (i.e., with empirical sample size factors, per-gene total counts and dispersions, but without effects of predictor variables) and to compute the empirical false discovery rate.
Empirical likelihood ratio tests for means/quantiles/hazards
 	from possibly censored and/or truncated data. Now does regression too.
	This version contains some C code.
Calculates the p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with censored data.
Collection of functions for data analysis and
    editing of clinical and epidemiological data.
    Most of them are related to benchmark with prediction models.
Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.
Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EM's main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.
Fast and easily computes an Euclidean Minimum Spanning Tree (EMST) from data. 
    This package relies on 'RcppMLPACK' to provide an R interface to the Dual-Tree Boruvka 
    algorithm (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>) implemented in 
    'mlpack', the C++ Machine Learning Library (Curtin et. al., 2013). The Dual-Tree 
    Boruvka is theoretically and empirically the fastest algorithm for computing an EMST. 
    This package also provides functions and an S3 method for readily plotting Minimum 
    Spanning Trees (MST) using either the style of the 'base', 'scatterplot3d', or 
    'ggplot2' libraries.
The package provides functions to carry out a
        Goodness-of-fit test for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. A p-value can be
        calculated using different distance measures between observed
        and expected frequencies. A Monte Carlo method is provided to
        make the package capable of solving high-dimensional problems.
Allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a training set
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  A vignette is
 provided and the help pages include examples.
An R package that provides the EMU Speech 
    Database Management System (EMU-SDMS) with database management, data 
    extraction, data preparation and data visualization facilities.
An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization paths for linear regression. Rockova and George (2014) <doi:10.1080/01621459.2013.869223>.
Implements an S3 class for storing 'UTF-8' strings, based on regular character vectors.
    Also contains routines to portably read and write 'UTF-8' encoded text files,
    to convert all strings in an object to 'UTF-8',
    and to create character vectors with various encodings.
We describe fifteen different splice site sequence encoding schemes that have been used in earlier studies for mapping of splice site sequences into numeric feature vectors. These encoding schemes will also be helpful for transforming other nucleotide sequences into numeric forms, provided they are of equal length. These encoding schemes will help the computational biologist working in the field of classification (binary or multiclass) or prediction involving nucleic acid sequences of equal length.
Implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2020) <arXiv:2003.03649>.
Interconverts between ordered lists and compact string notation.  
 Useful for capturing code lists, and pair-wise codes and decodes, for text storage.
 Analogous to factor levels and labels. Generics encode() and decode()
 perform interconversion, while codes() and decodes() extract components of an encoding.
 The function encoded() checks whether something is interpretable as an encoding.
 If a vector has an encoded 'guide' attribute, as_factor() uses it to coerce to factor.
Create encrypted html files that are fully self contained and do
  not require any additional software. Using the package you can encrypt
  arbitrary html files and also directly create encrypted 'rmarkdown' html reports.
It is important to ensure that sensitive data is protected. 
    This straightforward package is aimed at the end-user. 
    Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns.
    A public key can be shared to allow others to encrypt data to be sent to you. 
    This is particularly aimed a healthcare settings so patient data can be pseudonymised.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
endogMNP is an R package that fits a Bayesian multinomial
        probit model with endogenous selection, which is sometimes
        called an endogenous switching model.  This can be used to
        model discrete choice data when respondents select themselves
        into one of several groups.  This package is based on the MNP
        package by Kosuke Imai and David A. van Dyk.  This package
        modifies their code.
Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Maximum likelihood estimation of endogenous switching regression models from Heckman (1979) <doi:10.2307/1912352> and estimation of treatment effects.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy 
             distance, testing for multivariate normality, distance components (disco) for non-parametric 
             analysis of structured data, and other energy statistics/methods are implemented.
Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) <https://github.com/YafeiXu/working_paper/raw/master/CPM102.pdf>.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <DOI:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.
Allow numbers to be presented in an English language
        version, one, two, three, ...  Ordinals are also available,
        first, second, third, ... and indefinite article choice, "a" or "an".
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Analyzing English & European soccer results data from 1871-2016.
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006) or maxnet (Phillips et al. 2017).
Description: Tools for constructing niche models and analyzing patterns of niche evolution.  Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography.  Warren, D.L., R.E. Glor, and M. Turelli (2008) <doi:10.1111/j.1558-5646.2008.00482.x> Glor, R.E., and D.L. Warren (2011) <doi:10.1111/j.1558-5646.2010.01177.x>  Warren, D.L., R.E. Glor, and M. Turelli (2010) <doi:10.1111/j.1600-0587.2009.06142.x> Cardillo, M., and D.L. Warren (2016) <doi:10.1111/geb.12455> D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) <doi:10.1111/ecog.03900>.
The Emergency Nutrition Network or en-net forum is the go to online 
    forum for field practitioners requiring prompt technical advice for 
    operational challenges for which answers are not readily accessible in 
    current guidelines. The questions and the corresponding answers raised 
    within en-net can provide insight into what the key topics of discussion are 
    within the nutrition sector. This package provides utility functions for the 
    extraction, processing and analysis of text data from the online forum.
An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.
Joint statistical modelling of ChIP-seq data, accounting for technical/biological replicates, multiple conditions and different ChIP efficiencies of the individual experiments. <DOI:10.1186/1471-2105-14-169>. <DOI:10.1093/biostatistics/kxt047>.
Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See (<http://amp.pharm.mssm.edu/Enrichr/>) for further details.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.
An implementation of the clustering methods of categorical data
    discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical 
    data via ensembling dissimilarity matrices.  Preprint <arXiv:1506.07930>.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
        from ensemble forecasts and weather observations.
Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.
Functions to use ensembles of several machine learning models
    specified in caret package.
Creates ensemble taxonomic assignments of amplicon sequencing data 
  in R using outputs of multiple taxonomic assignment algorithms and/or
	reference databases. Includes flexible algorithms for mapping taxonomic 
	nomenclatures onto one another and for computing ensemble taxonomic 
	assignments.
Elastic net regression models are controlled by two parameters,
  lambda, a measure of shrinkage, and alpha, a metric defining the model's
  location on the spectrum between ridge and lasso regression.  
  glmnet provides tools for selecting lambda via cross
  validation but no automated methods for selection of alpha.  Elastic Net
  SearcheR automates the simultaneous selection of both lambda and alpha.
  Developed, in part, with support by NICHD R03 HD094912.
Add simple runtime contracts to R values. These ensure that values
    fulfil certain conditions and will raise appropriate errors if they do not.
If translate English words into Chinese, you might consider 
    looking up a dictionary or online query, in fact, there is a faster 
    way for R user. Ke-Hao Wu (2014) <RYoudaoTranslate: R package provide 
    functions to translate English words into Chinese.> provides interface 
    to Youdao translation open API for R user. But this software is not 
    very friendly to use, I have made some improvements on the basis of 
    this software. You can pass in a words or a vector consisting of multiple 
    words, which will return the corresponding type of Chinese representation 
    and be easy to reuse.
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>.
             'entropart' provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
             Estimation-bias corrections are available.
This package implements various estimators of entropy, such
  as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood 
  and the Millow-Madow estimator, various Bayesian estimators, and the 
  Chao-Shen estimator.  It also offers an R interface to the NSB estimator.
  Furthermore, it provides functions for estimating Kullback-Leibler divergence,
  chi-squared, mutual information, and chi-squared statistic of independence.
  In addition there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Rows of two matrices are compared for Shannon entropy,
     coefficient of variation, and expression. P-values can be requested for all metrics.
Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) <doi:10.1051/ps/2012004> and next articles.
Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991) <https://www.jstor.org/stable/2937655>.
Small toolbox for data analyses in environmental chemistry and
    ecotoxicology. Provides, for example, calibration() to calculate calibration
    curves and corresponding limits of detection (LODs) and quantification
    (LOQs) according to German DIN 32645:2008-11. texture() makes it easy to
    estimate soil particle size distributions from hydrometer measurements (ASTM
    D422-63(2007)e2).
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
Prints out information about the R working environment
    (system, R version,loaded and attached packages and versions) from a single
    function "env_doc()".  Optionally adds information on git repository,
    tags, commits and remotes (if available).
A collection of functions for microbial ecology and other
   applications of genomics and metagenomics. Companion package for the
   Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016
   <DOI:10.7287/peerj.preprints.1900v1>).
Tools for the assessment of the environmental exposure. The package provides three methods (nearest monitoring site, inverse distance weighted as described in Li Wu (2017) <doi:10.1016/j.envint.2016.11.013>,and ordinary kriging) to calculate the environmental exposure (e.g. air pollution) on the individual level.
Estimates an ecological niche using occurrence data, covariates, and kernel
        density-based estimation methods. For a single species with presence and absence data,
        the 'envi' package uses the spatial relative risk function that is estimated using the
        'sparr' package. Details about the 'sparr' package methods can be found in the tutorial:
        Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can
        be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.  More information about
        relative risk functions using kernel density estimation can be found in J. F. Bithell
        (1991) <doi:10.1002/sim.4780101112>.
Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.
Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing. 
	Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) 
	<doi:10.1021/acs.analchem.5b00941>.
Sequential partitioning, clustering and peak detection of
    centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
    data plot.
Provides a small set of functions for managing R environments, with defaults designed to encourage usage patterns that scale well to larger code bases. It provides: import_from(), a flexible way to assign bindings that defaults to the current environment; include(), a vectorized alternative to base::source() that also default to the current environment; and attach_eval() and attach_source(), a way to evaluate expressions in attached environments. Together, these (and other) functions pair to provide a robust alternative to base::library() and base::source().
Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.
Methods to perform a Probabilistic Environmental Risk assessment from exposure to toxic substances - i.e. USEPA (1997) <https://www.epa.gov/risk/guiding-principles-monte-carlo-analysis> -.
Functions and datasets to support the book by Nhu D Le and James V Zidek, Springer (2006).
Envelope methodology and aster modeling are combined to provide users with precise estimation of expected Darwinian fitness.
Set of functions to keep track and find objects in user-defined environments
    by identifying environments by name --which cannot be retrieved with the built-in function environmentName().
    The package also provides functionality to obtain simplified information about function calling chains
    and to get an object's memory address.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).
Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, 
  <https://www.springer.com/book/9781461484554>).
Provides wrap functions to export and import graphics and
             data frames in R to 'microsoft' office. And This package also 
             provide write out figures with lots of different formats.
             Since people may work on the platform without GUI support, the package 
             also provide function to easily write out figures
             to lots of different type of formats. Now this package provide function 
             to extract colors from all types of figures and pdf files.
This data management package provides some helper
    classes for publicly available data sources (HMD, DESTATIS) in
    Demography. Similar to ideas developed in the Bioconductor
    project <https://bioconductor.org> we strive to encapsulate data in easy
    to use S4 objects.
    If original data is provided in a text file, the resulting S4
    object contains all information from that text file. But the
    information is somehow structured (header, footer, etc).
    Further the classes provide methods to make a subset for selected
    calendar years or selected regions. The resulting subset
    objects still contain the original header and footer information.
A collection of nice plotting functions directly from a
        data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Reads water network simulation data in 'Epanet' text-based
    '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'.
    Provides basic summary information and plots.  The README file has a 
    quick introduction. See <http://www2.epa.gov/water-research/epanet> 
    for more information on the Epanet software for modeling
    hydraulic and water quality behavior of water piping systems.
Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation.
The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.
Everglades Phosphorus Gradient Model predicts variations in water-column P concentration, peat accretion rate, and soil P concentration along a horizontal gradient imposed by an external phosphorus load and sheet-flow conditions. Potential biological responses are expressed in terms of marsh surface area exceeding threshold criteria for water-column and soil phosphorus concentrations. More information of the model can be found at <http://www.wwwalker.net/epgm/>.
Tools to download and manipulate the Permanent Household Survey from Argentina
    (EPH is the Spanish acronym for Permanent Household Survey).
    e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets,
    calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology.
    organize_panels() is used to concatenate observations from different periods, and organize_labels()
    adds the official labels to the data. The implemented methods are based on INDEC (2016) <http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf>.
    As this package works with the argentinian Permanent Household Survey and its main audience is from this country,
    the documentation was written in Spanish.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data, in
  particular representation, manipulation and simulation of multistate 
  data - the Lexis suite of functions, which includes interfaces to
  'mstate', 'etm' and 'cmprsk' packages. 
  Also contains functions for Age-Period-Cohort and Lee-Carter
  modeling and a function for interval censored data and some useful
  functions for tabulation and plotting, as well as a number of
  epidemiological data sets.
Contains elementary tools for analysis of
        common epidemiological problems, ranging from sample size
        estimation, through 2x2 contingency table analysis and basic
        measures of agreement (kappa, sensitivity/specificity).
        Appropriate print and summary statements are also written to
        facilitate interpretation wherever possible.  Source code is 	commented throughout to facilitate modification.  The target audience includes advanced undergraduate and graduate students
        in epidemiology or biostatistics courses, and clinical researchers.
A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.
Routines for epidemiological contact tracing
    and visualisation of network of contacts.
Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.
The Economic Policy Institute (<https://www.epi.org/>) provides
    researchers, media, and the public with easily accessible, up-to-date, and
    comprehensive historical data on the American labor force. It is compiled
    from Economic Policy Institute analysis of government data sources. Use
    it to research wages, inequality, and other economic indicators over time
    and among demographic groups. Data is usually updated monthly.
Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<http://medipe.psu.ac.th/epicalc>'.
Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.
Tools to quantify transmissibility throughout
    an epidemic from the analysis of time series of incidence as described in
    Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) 
    <doi:10.1093/aje/kwh255>.
Analysis and visualization of plant disease progress curve data. Functions for
    fitting two-parameter population dynamics models (exponential, monomolecular, logistic
    and Gompertz) to proportion data for single or multiple epidemics using either linear
    or no-linear regression.  Statistical and visual outputs are provided to aid in model
    selection. Synthetic curves can be simulated for any of the models given the parameters.
    See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>
    for further information on the methods.
Provides functions and classes designed to handle and visualise
    epidemiological flows between locations. Also contains a statistical method
    for predicting disease spread from flow data initially described in
    Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>.
    This package is part of the RECON (<http://www.repidemicsconsortium.org/>)
    toolkit for outbreak analysis.
The interface package to access data from the
    'EpiGraphDB' <https://epigraphdb.org> platform.
    It provides easy access to the 'EpiGraphDB' platform with functions that
    query the corresponding REST endpoints on the API <https://api.epigraphdb.org>
    and return the response data in the 'tibble' data frame format.
Functions to perform robust epistasis detection in genome-wide
  association studies, as described in Slim et al. (2018) <doi:10.1101/442749>.
  The implemented methods identify pairwise interactions between a particular
  target variant and the rest of the genotype, using a propensity score
  approach. The propensity score models the linkage disequilibrium between the
  target and the rest of the genotype. All methods are penalized regression
  approaches, which differently incorporate the propensity score to only
  recover the synergistic effects between the target and the genotype.
Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. An overview of the implemented continuous-time individual level models for epidemics is given by Almutiry and Deardon (2019) <doi:10.1515/ijb-2017-0092>.
Contains tools for formatting inline code, renaming redundant
  columns, aggregating age categories, and calculating proportions with
  confidence intervals. This is part of the 'R4Epis' project
  <https://r4epis.netlify.com>.
Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.
Tools for simulating mathematical models of infectious disease dynamics. 
    Epidemic model classes include deterministic compartmental models, stochastic 
    individual-contact models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs) 
    from the Statnet suite of software packages in R. Standard templates for epidemic 
    modeling include SI, SIR, and SIS disease types. EpiModel features an API for 
    extending these templates to address novel scientific research aims. Full 
    methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
Allows for forward-in-time simulation of epistatic networks with associated
    phenotypic output.
Estimates the time-varying reproduction number, rate of spread,
             and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>),
             and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>).
             It aims to help users avoid some of the limitations of naive implementations in a framework
             that is informed by community feedback and is under active development.
A toolbox to make it easy to analyze plant disease epidemics. It
    provides a common framework for plant disease intensity data recorded over
    time and/or space. Implemented statistical methods are currently mainly
    focused on spatial pattern analysis (e.g., aggregation indices, Taylor and
    binary power laws, distribution fitting, SADIE and mapcomp methods). See
    Madden LV, Hughes G, van den Bosch F (2007, ISBN: 978-089054-354-2) for
    further information on these methods. Several data sets that were mainly
    published in plant disease epidemiology literature are also included in this
    package.
Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.
Drafting an epidemiological report in 'Microsoft Word' format for a given disease,
  similar to the Annual Epidemiological Reports published by the European Centre 
  for Disease Prevention and Control. Through standalone functions, it is specifically 
  designed to generate each disease specific output presented in these reports and includes:
  - Table with the distribution of cases by Member State over the last five years;
  - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Trend plot with the trend and number of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level.
  Two types of datasets can be used:
  - The default dataset of dengue 2015-2019 data;
  - Any dataset specified as described in the vignette.
Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). <doi:10.1038/ejhg.2010.196>. 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011).  <doi:10.1093/bioinformatics/btr218>.)
Basic sensitivity analysis of the observed relative risks adjusting
    for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Lash T.L, Fox M.P, and Fink A.K.
    "Applying Quantitative Bias Analysis to Epidemiologic Data",
    ('Springer', 2009).
Exploring time series for signal detection. It is specifically designed
    to detect possible outbreaks using infectious disease surveillance data
    at the European Union / European Economic Area or country level.
    Automatic detection tools used are presented in the paper
    "Monitoring count time series in R: aberration detection in public health surveillance",
    by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>.
    The package includes:
    - Signal Detection tool, an interactive 'shiny' application
      in which the user can import external data and perform basic signal detection analyses;
    - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs.
      This report can also be stratified by population characteristics (see 'Population' variable).
    This project was funded by the European Centre for Disease Prevention and Control.
Produce one-dimensional density estimates using 
 exponential epi-splines. The user may incorporate soft information, by 
 imposing constraints that (i) require unimodality; (ii) require that the 
 density be monotone non-increase or non-decreasing; (iii) put upper bounds
 on first or second moments; (iv) bound the density's values at mesh points;
 (v) require that the estimate be continuous or continuously differentiable;
 and more.
An efficient multi-core package to reconstruct an underlying network of
             genomic signatures of high-dimensional epistatic selection from 
             partially observed genotype data. The phenotype that we consider is viability. 
			 The network captures the conditional dependent short- and long-range linkage 
			 disequilibrium structure of genomes and thus reveals aberrant marker-marker 
			 associations that are due to epistatic selection. We target on high-dimensional
			 genotype data where number of variables (markers) is larger than number of 
			 sample sizes (p >> n). The computations is memory-optimized using the sparse 
			 matrix output.
Provides set of functions aimed at epidemiologists. 
 They include commands for measures of association and impact for case control studies and cohort studies. 
 They may be particularly useful for outbreak investigations and include univariate and stratified analyses.
 The functions for cohort studies include the CS(), CSTable() and CSInter() commands. 
 The functions for case control studies include the CC(), CCTable() and CCInter() commands.
Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.
Builds contingency tables that cross-tabulate multiple 
    categorical variables and also calculates various summary measures.
    Export to a variety of formats is supported, including: 'HTML', 
    'LaTeX', and 'Excel'.
Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymise data, and fitting discretised Gamma distributions.
It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords.
Aim: Adjust the graphical parameters to create nicer
    longitudinal series plots. The default set of graphical parameters is very
    general, and can be improved upon when we are interested in plotting data
    points observed over time. Functions facilitate plotting those kind of
    series, univariate plots, bivariate plots (with vertical axis on both left
    and right hand sides), multivariate plots and plots which allow to examine
    whether a new observation is 'unusual' via construction and visualization
    of prediction intervals around it.
A rich toolkit of using the whole building
    simulation program 'EnergyPlus'(<https://energyplus.net>), which
    enables programmatic navigation, modification of 'EnergyPlus' models
    and makes it less painful to do parametric simulations and analysis.
Evaluate a function over a data frame of expressions.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
      AY + U + R = 0
  and EPoC G where
      Y = GU + E,
  the matrices R and E are so far treated as noise. For
  details see the manual page of 'lassoshooting' and the
  article Rebecka Jörnsten, Tobias Abenius, Teresia Kling, 
  Linnéa Schmidt, Erik Johansson, Torbjörn E M Nordling, 
  Bodil Nordlander, Chris Sander, Peter Gennemark, 
  Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander
  (2011) <doi:10.1038/msb.2011.17>.
Provides a set of functions, which facilitates removing objects from an environment. 
             It allows to delete objects specified with regular expression or with other conditions (e.g. if object is numeric),
             using one function call.
Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the corpus of LIVIVO. The ranked result lists of neurological drug terms co-occurring with named entities from the epilepsy ontologies EpSO, ESSO, and EPILONT are aggregated in order to generate two different results: an overview table of drugs that are relevant to epilepsy created with the method createNeuroTable, and a plot of tanimoto similarity coefficients between the aggregated list of drug terms against the list of drug terms from each of the ontologies created with the method createTanimotoBaseline(). The alignment of the Top-K Ranked Lists is conducted using the R-package TopKLists <https://cran.r-project.org/package=TopKLists>. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558> and Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>.
Performs analysis of polynomial regression in simple designs with quantitative treatments.
Implements the conditional estimation procedure of
  Lee, Sun, Sun and Taylor (2016) <doi:10.1214/15-AOS1371>.
  This procedure allows hypothesis testing on the mean of
  a normal random vector subject to linear constraints.
For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Donghoh Kim, Guebin Choi and Hee-Seok Oh (2019) <arXiv:1904.03643>.
Provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    The 'epubr' package provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. 
    E-book formatting is not completely standardized across all literature. 
    It can be challenging to curate parsed e-book content across an arbitrary collection of e-books 
    perfectly and in completely general form, to yield a singular, consistently formatted output. 
    Many EPUB files do not even contain all the same pieces of information in their respective metadata. 
    EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. 
    However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. 
    There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with 'epubr'. 
    Text is read 'as is' for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. 
    Substantive changes are expected to be performed subsequently by the user as part of their text analysis. 
    Additional text cleaning can be performed at the user's discretion, such as with functions from packages like 'tm' or 'qdap'.
Query, download climate change projection data from the 'CMIP6' (Coupled
    Model Intercomparison Project Phase 6) project
    <https://pcmdi.llnl.gov/CMIP6/> in the 'ESGF' (Earth System Grid Federation)
    platform <https://esgf.llnl.gov>, and create future 'EnergyPlus'
    <https://energyplus.net> Weather ('EPW') files adjusted from climate changes
    using data from Global Climate Models ('GCM').
An ensemble method for the statistical detection of
    a rare class in two-class classification problems. The method uses an
    ensemble of classifiers where the constituent
    models of the ensemble use disjoint subsets (phalanxes) of explanatory
    variables. We provide an implementation of the phalanx-formation algorithm.
    Please see Tomal et al. (2015) <doi:10.1214/14-AOAS778>,
    Tomal et al. (2016) <doi:10.1021/acs.jcim.5b00663>, and
    Tomal et al. (2019) <arXiv:1706.06971> for more details.
Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
EQ-5D is a popular health related quality of life instrument used 
    in the clinical and economic evaluation of health care. Developed by the 
    EuroQol group <https://euroqol.org/>, the instrument consists of two 
    components: health state description and evaluation. For the description 
    component a subject self-rates their health in terms of five dimensions; 
    mobility, self-care, usual activities, pain/discomfort, and 
    anxiety/depression using either a three-level (EQ-5D-3L,
    <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/>) or a five-level
    (EQ-5D-5L, <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/>) 
    scale. Frequently the scores on these five dimensions are converted to a 
    single utility index using country specific value sets, which can be used
    in the clinical and economic evaluation of health care as well as in 
    population health surveys. The eq5d package provides methods to calculate 
    index scores from a subject's dimension scores. 26 TTO and 11 VAS EQ-5D-3L value 
    sets including those for countries in Szende et al (2007) 
    <doi:10.1007/1-4020-5511-0> and Szende et al (2014) 
    <doi:10.1007/978-94-007-7596-1>, 25 EQ-5D-5L EQ-VT value sets from the EuroQol
    website, and the EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) 
    <doi:10.1016/j.jval.2012.02.008> are included. Additionally, a shiny web tool is 
    included to enable the calculation, visualisation and automated statistical 
    analysis of EQ-5D index values via a web browser using EQ-5D dimension scores 
    stored in CSV or Excel files.
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials).
Transitioning from EQS to R for structural equation modeling (SEM)
    is made easier with a set of functions to convert .out files into R code.
    The EQS output can be converted into lavaan syntax and run in the R
    environment.  Other functions parse descriptive statistics and the covariance matrix
    from an EQS .out file.  A heat map plot of a covariance matrix is also
    included.
Analysis of experimental crosses to identify genes (called
        quantitative trait loci, QTLs) contributing to variation in
        quantitative traits.
Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.
The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.
Functions for examining measurement invariance via equivalence testing are included in this package. The traditionally used RMSEA (Root Mean Square Error of Approximation) cutoff values are adjusted based on simulation results. In addition, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts. For more information, see Yuan, K. H., & Chan, W. (2016) <doi:10.1037/met0000080>, Deng, L., & Yuan, K. H. (2016) <doi:10.1007/s11336-015-9491-8>, and Jiang, G., Mai, Y., & Yuan, K. H. (2017) <doi:10.3389/fpsyg.2017.01823>.
Contains methods for observed-score linking
  and equating under the single-group, equivalent-groups,
  and nonequivalent-groups with anchor test(s) designs.
  Equating types include identity, mean, linear, general
  linear, equipercentile, circle-arc, and composites of
  these. Equating methods include synthetic, nominal
  weights, Tucker, Levine observed score, Levine true
  score, Braun/Holland, frequency estimation, and chained
  equating. Plotting and summary methods, and methods for
  multivariate presmoothing and bootstrap error estimation
  are also provided.
Computation of direct, chain and average (bisector) equating coefficients with 
  standard errors using Item Response Theory (IRT) methods for dichotomous items 
  (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, 
  Battauz (2015) <doi:10.18637/jss.v068.i07>). 
  Test scoring can be performed by true score equating and observed score equating methods. 
  DIF detection can be performed using a Wald-type test  
  (Battauz (2018) <doi:10.1007/s10260-018-00442-w>).
Equating of multiple forms using Item Response Theory (IRT) methods (Battauz M. (2017) <doi:10.1007/s11336-016-9517-x> and Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>).
The goal of 'equatiomatic' is to reduce the pain associated with
             writing 'LaTeX' formulas from fitted models. The primary function of
             the package, extract_eq(), takes a fitted model object as its input
             and returns the corresponding 'LaTeX' code for the model.
We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points.
    For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula.
    The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration.
    All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.
Provides statistical tests and graphics for assessing tests
        of equivalence.  Such tests have similarity as the alternative
	hypothesis instead of the null.  Sample data sets are included.
Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results.
Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph
             ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition''
             (Chapman&Hall/CRC).
Implementation of uniformly most powerful invariant equivalence
    tests for one- and two-sample problems (paired and unpaired) as described 
    in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives 
    (non-inferiority and non-superiority tests) are supported. Basically a variant 
    of a t-test with (relaxed) null and alternative hypotheses exchanged.
Multivariate modeling of data after deflation of interfering effects (EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>). The effects can be estimates from designed experiments, non-overlapping clusters or continuous effects.
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) <doi:10.1021/acs.analchem.6b02927>.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together
    with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks.
A template package to demonstrate the use of user-specified statistics for use in 'ergm' models as part of the Statnet suite of packages.
Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.
Simulation and estimation of Exponential Random Graph Models (ERGMs)
  for small networks using exact statistics as shown in Vega Yon et al. (2020)
  <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm'
  package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator
  (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs
  for small networks. As exhaustive enumeration is computationally feasible for
  small networks, this R package takes advantage of this and provides tools for
  calculating likelihood functions, and other relevant functions, directly,
  meaning that in many cases both estimation and simulation of ERGMs for
  small networks can be faster and more accurate than simulation-based
  algorithms.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).
A set of user-friendly functions to aid in organizing, plotting
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn
    method to explore ERP data. Should be useful to those without a background
    in computer programming, and to those who are new to ERPs (or new to the
    more advanced ERP software available). Emphasis has been placed on highly
    automated processes using functions with as few arguments as possible.
    Expects processed (cleaned) data.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Messages should provide users with readable information 
    about R objects without flooding their console. 
    'cc()' concatenates vector and data frame values 
    into a grammatically correct string using commas, an ellipsis and conjunction. 
    'cn()' allows the user to define a string which varies based on a count.
    'co()' combines the two to produce a customizable object aware string.
    The package further facilitates this process by providing five 'sprintf'-like 
    types such as '%n' for the length of an object and '%o' for its name as
    well as wrappers for pasting objects and issuing errors, warnings and messages.
Elegantly handle error and warning messages.
Builds and analyzes error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.
Provides a function to convert existing R functions into "errorized" versions 
    with added logging and handling functionality when encountering errors or warnings. 
    The errorize function accepts an existing R function as its first argument and 
    returns a R function with the exact same arguments and functionality. However, 
    if an error or warning occurs when running that "errorized" R function, it will save a 
    .Rds file to the current working directory with the relevant objects and information 
    required to immediately recreate the error.
Errors in data can be located and removed using validation rules from package 'validate'.
Support for measurement errors in R vectors, matrices and arrays:
    automatic uncertainty propagation and reporting.
    Documentation about 'errors' is provided in the paper by Ucar, Pebesma &
    Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a
    vignette; see 'citation("errors")' for details.
Perform a Bayesian estimation of the exploratory reduced
    reparameterized unified model (ErRUM) described by Culpepper and Chen (2018)
    <doi:10.3102/1076998618791306>.
Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.
Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories.
    Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>.
    Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.
Implementation of the Edge Selection Algorithm
Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges' d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality).
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).
Tools for fitting the Extended Empirical Saddlepoint (EES) density of Fasiolo et al. (2018) <doi:10.1214/18-EJS1433>.
It allows to construct two types of indicators used in the study of
    Electoral Systems and Party Systems starting from electoral results data.
    The Effective Number of Parties (Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>)
    and Electoral Volatility in its three versions (Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>,
    Powell and Tucker (2014) <doi:10.1017/S0007123412000531> and Torcal and Lago (2015, ISBN:9788415260356)).
Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> 
    as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the 
    expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk 
    of investments. A special feature of the backtests of  Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> 
    is that they only require forecasts of  the expected shortfall, which is in striking contrast to all other existing 
    backtests, making them particularly attractive for practitioners.
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>)
    in R. Based on the input, the effect size can be returned as standardized mean 
    difference, Cohen's f, Hedges' g, Pearson's r or Fisher's
    transformation z, odds ratio or log odds, or eta squared effect size.
Methods for working with dose-finding clinical trials. We provide 
    implementations of many dose-finding clinical trial designs, including the 
    continual reassessment method (CRM) by O'Quigley et al. (1990) 
    <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji
    et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design
    by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal 
    interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox
    by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of
    Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 
    described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs
    are implemented with a common interface. We also offer optional additional 
    classes to tailor the behaviour of all designs, including avoiding skipping 
    doses, stopping after n patients have been treated at the recommended dose,  
    stopping when a toxicity condition is met, or demanding that n patients are 
    treated before stopping is allowed. By daisy-chaining together these classes
    using the pipe operator from 'magrittr', it is simple to tailor the 
    behaviour of a dose-finding design so it behaves how the trialist wants.
    Having provided a flexible interface for specifying designs, we then provide
    functions to run simulations and calculate dose-paths for future cohorts of 
    patients.
Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.
A tool which allows users to create and evaluate ensembles 
    of species distribution model (SDM) predictions. 
    Functionality is offered through R functions or a GUI (R Shiny app). 
    This tool can assist users in identifying spatial uncertainties and 
    making informed conservation and management decisions. The package is 
    further described in Woodman et al (2019) <doi:10.1111/2041-210X.13283>.
The package can identify the dysregulated canonical pathways by investigating the changes of biological relationships of pathways in the context of gene expression data. (1) The ESEA package constructs a background set of edges by extracting pathway structure (e.g. interaction, regulation, modification, and binding etc.) from the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther) and the edge sets of pathways for each of the above databases. (2) The ESEA package can can quantify the change of correlation between genes for each edge based on gene expression data with cases and controls. (3) The ESEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an edge enrichment score (EES), which reflects the degree to which a given pathway is associated the specific phenotype. (4) The ESEA package can provide the visualization of the results.
Environmental seismology is a scientific field that studies the  
    seismic signals, emitted by Earth surface processes. This package 
    provides all relevant functions to read/write seismic data files, prepare, 
    analyse and visualise seismic data, and generate reports of the processing 
    history.
Presents a "Scenarios" class containing
        general parameters, risk parameters and projection results.
        Risk parameters are gathered together into a ParamsScenarios
        sub-object. The general process for using this package is to
        set all needed parameters in a Scenarios object, use the
        customPathsGeneration method to proceed to the projection, then
        use xxx_PriceDistribution() methods to get asset prices.
Computes shrinkage estimators for regression problems. Selects
    penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) <doi:10.1093/aje/kwx225> for more details.
Misc functions programmed by Eduard Szöcs. 
    Provides read_regnie() to read gridded precipitation data from German Weather 
    Service  (DWD, see <http://www.dwd.de/> for more information).
Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.
A 'shiny' gadget to create 'ggplot2' charts interactively with drag-and-drop to map your variables.
    You can quickly visualize your data accordingly to their type, export to 'PNG' or 'PowerPoint',
    and retrieve the code to reproduce the chart.
Simultaneous modeling of the quantile and the expected shortfall of a response variable given 
    a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.
An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis,
	     Michael I Jordan (2013) <arXiv:1301.2267>. The ESS algorithm
	     is used for model selection in decomposable graphical models.
Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
An implementation of sequential testing that uses evidence ratios
    computed from the weights of a set of models. These weights correspond either
    to Akaike weights computed from the Akaike Information Criterion (AIC) or the
    Bayesian Information Criterion (BIC) and following Burnham & Anderson
    (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA
    weights computed from the WAIC or the LOO-IC of models fitted
    with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
Provides tools for determining estimability of linear functions 
  of regression coefficients, and 'epredict' methods that handle 
  non-estimable cases correctly. Estimability theory is discussed in
  many linear-models textbooks including Chapter 3 of Monahan, JF (2008), 
  "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4).
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation.
Routines for parameter estimation for any probability density or
    mass function implemented in R via maximum likelihood (ML) given a data set. 
    The main routines 'maxlogL' and 'maxlogLreg' are  wrapper functions specifically 
    developed for ML estimation. There are included optimization procedures such as
    'nlminb' and 'optim' from base package, and 'DEoptim' Mullen (2011) 
    <doi: 10.18637/jss.v040.i06>. Standard errors are estimated with 'numDeriv' 
    Gilbert (2011) <https://CRAN.R-project.org/package=numDeriv>
    or the option 'Hessian = TRUE' of 'optim' function.
Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance.
Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.
Implements the methods of McGrath et al. (2020) 
    <doi:10.1177/0962280219889080> for estimating the sample mean and standard 
    deviation from commonly reported quantiles in meta-analysis. These methods 
    can be applied to studies that report the sample median, sample size, and 
    one or both of (i) the sample minimum and maximum values and (ii) the first 
    and third quartiles.
Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, <doi:10.1214/17-AOAS1026> for more details.
This package is intended to speedup the process of
        creating model-comparing tables common in Macroeconomics. The
        function collection stores the estimates of several models and
        formats it to a table of the form estimate starred and std.err.
        below. The default output is LaTeX but output to CSV for later
        editing in a spreadsheet tool is possible as well. It works for
        linear models (lm) and panel models from the "plm"-package
        (plm). Two further implemented functions "descsto" and
        "desctab" enable you to export descriptive statistics of
        data-frames and single variables to LaTeX and CSV.
Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. 
    The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. 
    The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>.
    The second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>.
	And the third by Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>.
This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
An implementation of a most commonly used event study methodology,
    including both parametric and nonparametric tests. It contains variety
    aspects of the rate of return estimation (the core calculation is done in
    C++), as well as three classical for event study market models: mean
    adjusted returns, market adjusted returns and single-index market models.
    There are 6 parametric and 6 nonparametric tests provided, which examine
    cross-sectional daily abnormal return (see the documentation of the
    functions for more information). Parametric tests include tests proposed by 
    Brown and Warner (1980) <DOI:10.1016/0304-405X(80)90002-1>, Brown and Warner
    (1985) <DOI:10.1016/0304-405X(85)90042-X>, Boehmer et al. (1991)
    <DOI:10.1016/0304-405X(91)90032-F>, Patell (1976) <DOI:10.2307/2490543>, and
    Lamb (1995) <DOI:10.2307/253695>. Nonparametric tests covered in estudy2 are
    tests described in Corrado and Zivney (1992) <DOI:10.2307/2331331>,
    McConnell and Muscarella (1985) <DOI:10.1016/0304-405X(85)90006-6>,
    Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Cowan (1992)
    <DOI:10.1007/BF00939016>, Corrado (1989) <DOI:10.1016/0304-405X(89)90064-0>,
    Campbell and Wasley (1993) <DOI:10.1016/0304-405X(93)90025-7>, Savickas (2003)
    <DOI:10.1111/1475-6803.00052>, Kolari and Pynnonen (2010)
    <DOI:10.1093/rfs/hhq072>. Furthermore, tests for the cumulative
    abnormal returns proposed by Brown and Warner (1985)
    <DOI:10.1016/0304-405X(85)90042-X> and Lamb (1995) <DOI:10.2307/253695>
    are included.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
Fits the space-time Epidemic Type Aftershock Sequence
    ('ETAS') model to earthquake catalogs using a stochastic 'declustering' 
    approach. The 'ETAS' model is a 'spatio-temporal' marked point process
    model and a special case of the 'Hawkes' process. The package is based 
    on a Fortran program by 'Jiancang Zhuang'
    (available at <http://bemlar.ism.ac.jp/zhuang/software.html>),
    which is modified and translated into C++ and C such that it 
    can be called from R. Parallel computing with 'OpenMP' is possible 
    on supported platforms.
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017)<doi:10.18637/jss.v076.i03>.
The package allows selecting those treatments of a one-way layout
 being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
 (TOST) and related simultaneous confidence intervals are given for both
 differences or ratios of means of normally distributed data. For the case of
 equal variances and balanced sample sizes for the treatment groups, the
 single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
 non-normal data, the Wilcoxon test is applied.
Interacts with the open-source, public 'Ethereum' <https://www.ethereum.org/> blockchain.
    It provides a distributed computing platform via smart contracts. This package provides
    functions which interrogate blocks and transactions in the 'Ethereum' blockchain.
An implementation of the quantitative ethnobotany indices in R. The goal is to provide an easy-to-use platform for ethnobotanists to assess the cultural significance of plant species based on informant consensus. The package closely follows the paper by Tardio and Pardo-de-Santayana (2008). Tardio, J., and M. Pardo-de-Santayana, 2008. Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain) 1. Economic Botany, 62(1), 24-39. <doi:10.1007/s12231-007-9004-5>.
Reliable and rapid ethnicity annotation from whole exome sequencing data.
A predictable and pipeable framework for performing ETL 
    (extract-transform-load) operations on publicly-accessible medium-sized data 
    set. This package sets up the method structure and implements generic 
    functions. Packages that depend on this package download specific data sets 
    from the Internet, clean them up, and import them into a local or remote 
    relational database management system.
Provides functions to facilitate the use of the 'ff' package
    in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL',
    'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects
    using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to
    do fast left outer join merging based on 'match', factorisation of data and a
    basic function for re-coding vectors.
The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 <doi:10.18637/jss.v038.i04>). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 <doi:10.1007/s00180-010-0200-x>).
Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).
Use R to interface with the 'ETRADE' API <https://developer.etrade.com/home>.
    Functions include authentication, trading, quote requests, account information, and option 
    chains. A user will need an ETRADE brokerage account and 'ETRADE' API approval. See README 
    for authentication process and examples.
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
The remit of the European Clinical Trials Data Base (EudraCT <https://eudract.ema.europa.eu/> ) is to provide open access to summaries of all registered clinical trial results; thus aiming to prevent non-reporting of negative results and provide open-access to results to inform future research. The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by EudraCT and formats them into the precise requirements to directly upload an XML file into the web portal, with no further data entry by hand.
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
Generate area-proportional Euler diagrams
    using numerical optimization. An Euler diagram is a generalization of a Venn
    diagram, relaxing the criterion that all interactions need to be
    represented. Diagrams may be fit with ellipses and circles via
    a wide range of inputs and can be visualized in numerous ways.
A sample dataset in the OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM) format. The CDM enables uniform storage of observational health care data, and is widely used for health care analytics. 'Eunomia' contains simulated data as well as a subset of the OMOP Vocabulary, and enables testing of additional packages and is used for educational and demonstration purposes.
Access to data on European Union laws and court decisions made easy with pre-defined 'SPARQL' queries and 'GET' requests.
An R Client for the Europe PubMed Central RESTful Web Service
    (see <https://europepmc.org/RestfulWebService> for more information). It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
This dataset contains population estimates of all European cities 
    with at least 10,000 inhabitants during the period 1500-1800. These data are
    adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).
Bayesian sample size calculation software and examples for EuroSARC
    clinical trials which utilise Bayesian methodology. These trials rely on
    binomial based endpoints so the majority of programs found here relate to this
    sort of endpoint. Developed as part of the EuroSARC FP7 grant.
Tools to download data from the Eurostat database
    <http://ec.europa.eu/eurostat> together with search and
    manipulation utilities.
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
    Bader, B., Yan, J. & Zhang, X. (2016) <doi:10.1007/s11222-016-9697-3>.
    Bader, B., Yan, J. & Zhang, X. (2018) <doi:10.1214/17-AOAS1092>.
Evaluates the performance of binary classifiers.
    Computes confusion measures (TP, TN, FP, FN), derived measures (TPR,
    FDR, accuracy, F1, DOR, ..), and area under the curve.  Outputs are
    well suited for nested dataframes.
Provides functions for evaluating (time series) model
	estimation methods. These facilitate Monte Carlo experiments of repeated
	simulations and estimations. Also provides methods for
	looking at the distribution of the results from these experiments,
	including model roots (which are an equivalence class invariant).
Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) <arXiv:1905.05389>.
Parsing and evaluation tools that make it easy to recreate the
    command line behaviour of R.
Implements various quality evaluation statistics to assess the
    value of plant germplasm core collections using qualitative and 
    quantitative phenotypic trait data according to Odong et al. (2015) 
    <doi:10.1007/s00122-012-1971-y>.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
An open source risk analysis toolkit based on the OpenFAIR ontology 
  <https://www2.opengroup.org/ogsys/catalog/C13K> and risk assessment standard 
  <https://www2.opengroup.org/ogsys/catalog/C13G>. Empowers an organization to 
  perform a quantifiable, repeatable, and data-driven risk review.
Conducts sensitivity analyses for unmeasured confounding,selection bias, and measurement error (individually or in combination) in individual studies. Also conducts sensitivity analyses for unmeasured confounding in meta-analyses.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Different evidential distance-based classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: the
    evidential K-nearest neighbor rule and the evidential neural network.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the objects.
    The algorithms are: Evidential c-Means (ECM), Relational Evidential c-Means (RECM),
    Constrained Evidential c-Means (CECM), EVCLUS, EK-NNclus and bootclus.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.
Provides functions for the bayesian analysis of extreme
        value models, using MCMC methods.
Functions for setting up and analyzing event history data.
Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages 'edeaR', 'processmapR', etc.
A graphical user interface for open source event detection.
Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.
A user friendly, easy to understand way of doing event
    history regression for marginal estimands of interest,
    including the cumulative incidence and the restricted mean
    survival, using the pseudo observation framework for 
    estimation. For a review of the methodology, see Andersen and
    Pohar Perme (2010) <doi:10.1177/0962280209105020>. The
    interface uses the well known formulation of a generalized
    linear model and allows for features including plotting of 
    residuals, the use of sampling weights, and corrected
    variance estimation.
Functions for analysis of rate changes in sequential events.
Event-driven programming is a programming paradigm where the flow of execution is defined by event.
    In this paradigm an event can be defined as "a change in the state" of an object. This package offers a set
    of functions for creating event-based architectures using three basic functions: events, dispatchers, and
    handlers. The handlers manage the events, the dispatchers are in charge of redirecting the events to each of
    the handlers, finally the events are the objects that carry the information about the change of state.
Stores, manipulates, aggregates and otherwise messes with event
    data from KEDS/TABARI or any other extraction tool with similar output
Implements event extraction and early classification of events in data streams in R. 
    It has the functionality to generate 2-dimensional data streams with events belonging to 
    2 classes. These events can be extracted and features computed. The event features extracted
    from incomplete-events can be classified using a partial-observations-classifier 
    (Kandanaarachchi et al. 2018) <doi:10.13140/RG.2.2.10051.25129>.
A platform for conducting event studies (Fama, Fisher,
             Jensen, Roll (1969) <doi:10.2307/2525569>) and for
             methodological research on event studies. The package
             supports market model, augmented market model, and excess
             returns methods for data modelling along with Wilcox,
             classical t-test, and Bootstrap as inference procedures.
Perform Event Studies from through our <http://EventStudyTools.com> Application Programming Interface, parse the results, visualize it, and / or use the results in further analysis.
Methods for fitting various extreme value distributions with parameters of 
      generalised additive model (GAM) form are provided. For details of distributions 
      see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.
Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package:
        Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) <doi:10.1038/ejhg.2010.47>.
        Strug, L. J., & Hodge, S. E. (2006) <doi:10.1159/000094709>.
        Royall, R. (1997) <ISBN:0-412-04411-0>.
Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach.  The functions in the package also make extensive use of graphical displays for data exploration and model comparison.
Provides tools for integrated sensitivity analysis of evidence factors in observational
	studies. When an observational study allows for multiple independent or nearly
	independent inferences which, if vulnerable, are vulnerable to different biases, we have 
	multiple evidence factors.  This package provides methods that respect type I error rate control. 
	Examples are provided of integrated evidence factors analysis in a longitudinal study with
	continuous outcome and in a case-control study. 
	Karmakar, B., French, B., and Small, D. S. (2019)<DOI:10.1093/biomet/asz003>.
Contains a collection of examples of evidence factors in observational studies; e.g., Rosenbaum (2017) <doi:10.1214/17-STS621>.  The examples are collected to aid readers of a book in preparation, "Replication and Evidence Factors in Observational Studies".
It allows running 'EViews'(<https://eviews.com>) program from R Markdown. 'EViews' (Econometric Views) is a statistical software for Econometric analysis.  This package serves as an 'EViews' Knit-Engine for 'knitr' package. Write all your 'EViews' commands in R Markdown chunk.
Functions for extreme value theory, which may be 
  divided into the following groups; exploratory data analysis, 
  block maxima, peaks over thresholds (univariate and bivariate), 
  point processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
Provides functions for covariance matrix comparisons, estimation
    of repeatabilities in measurements and matrices, and general evolutionary
    quantitative genetics tools.
A comprehensive set of tools to illustrate the
    core concepts of evolutionary game theory, such as
    evolutionary stability or various evolutionary dynamics, 
    for teaching and academic research.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible 
      package providing optimization driven parameter estimation methods using metaheuristics  and 
      evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization 
      for continuous domains, Tabu Search, Evolutionary Strategies, ...)  which could be more efficient and require, 
      in some cases, fewer model evaluations than alternatives relying on experimental design.  Currently there 
      are built in support for models developed with 'Repast Simphony'  Agent-Based framework (<https://repast.github.io/>)  
      and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP) 
	     and peaks over random threshold (PORT) Hill methodologies. 
             Besides, also computes moment, generalised Hill and mixed moment estimates for EVI. 
             Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.
Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.
Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.
Implements escalation with overdose control (EWOC) trial designs using two drug combinations described by Tighiouart, Li and Rogatko (2017) <doi:10.1002/sim.6961>. It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics.
Function and data sets in the book entitled "R ile Temel Ekonometri", S.Guris, E.C.Akay, B. Guris(2020). The book published in Turkish. It is possible to makes Durbin two stage method for autocorrelation, generalized differencing method for correction autocorrelation, Hausman Test for identification and computes LM, LR and Wald test statistics for redundant variable by using the functions written in this package.
Early Warning Systems (EWS) are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) <doi:10.1162/rest.90.4.777>. Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) <doi:10.1016/j.ijforecast.2014.03.015>. This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers.
Performs unconditional exact tests and power calculations for 2x2 contingency tables.  Calculates Barnard's test (1945) <doi:10.1038/156177a0> using the original CSM test (Barnard, 1947 <doi:10.1093/biomet/34.1-2.123>), using Fisher's p-value referred to as Boschloo's test (1970) <doi:10.1111/j.1467-9574.1970.tb00104.x>, or using a Z-statistic (Suissa and Shuster, 1985, <doi:10.2307/2981892>).  Calculations confidence intervals for the difference in proportion.
Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or  exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case (Fay, et al, 2015, <DOI:10.1111/biom.12231>). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, <DOI:10.1002/sim.7579>), an unconditional exact test for the situation where the controls are all expected to fail.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
        difference between two independent or dependent proportions.
Provides a replacement for the 'extract' function from the 'raster' package
    that is suitable for extracting raster values using 'sf' polygons.
Monte Carlo and MCMC goodness of fit tests for log-linear
        models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2020) <arXiv:2008.12682>. Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed.
Computes exact conditional p-values and quantiles using an
 implementation of the Shift-Algorithm by Streitberg & Roehmel.
Contains all data sets for Exam PA: Predictive Analytics at 
    <https://exampa.net/>.
Take the examples written in your documentation of
    functions and use them to create shells (skeletons which must be
    manually completed by the user) of test files to be tested with the
    'testthat' package. Sort of like python 'doctests' for R.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single-choice and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include standalone
	files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOLAT,
	ARSnova, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format
	(NOPS) is provided that can be printed, scanned, and automatically evaluated.
Randomized multiple-select and single-select
  question generation for the 'MyLearn' teaching and learning
  platform. Question templates
  in the form of the R/exams package (see <http://www.r-exams.org/>)
  are transformed into XML format required by 'MyLearn'.
Tools for constructing confidence regions for exceedance regions
    and contour lines.
Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) <arXiv:1803.03356> for more information.
Allows access to data in running instance of Microsoft Excel
    (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
    'xl[a1] = current.graphics()'. Additionally there are function for reading/writing 
    'Excel' files - 'xl.read.file'/'xl.save.file'. They are not fast but able to read/write 
    '*.xlsb'-files and password-protected files. There is an Excel workbook with 
    examples of calling R from Excel in the 'doc' folder. It tries to keep things as
    simple as possible - there are no needs in any additional
    installations besides R, only 'VBA' code in the Excel workbook.
    Microsoft Excel is required for this package.
Implements 'Excel' functions in 'R' for your calculation simplicity.You can use most of the aggregate functions, addressing functions,logical functions and text functions. Helps you a ton in learning how 'R' works as some 'Excel' users might be struggling with the program.
An R interface to 'jExcel' library to create web-based interactive tables and spreadsheets compatible with 'Excel' or any other spreadsheet software.
Removes metadata and summary formatting from Excel reports, so that 
    data from those reports can more easily be loaded into R. Currently works with 
    .xlsx and .xls format files.
This is an R interface to the
    python package 'excerpts' (<https://pypi.python.org/pypi/excerpts>).
Implementation of a function which calculates the empirical excess mass 
	for given \eqn{\lambda} and given maximal number of modes (excessm()). Offering 
	powerful plot features to visualize empirical excess mass (exmplot()). This 
	includes the possibility of drawing several plots (with different maximal 
	number of modes / cut off values) in a single graph.
Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.
Performs frequentist inference for the extremal index of a 
    stationary time series.  Two types of methodology are used.  One type is
    based on a model that relates the distribution of block maxima to the 
    marginal distribution of series and leads to the semiparametric maxima 
    estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and 
    Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>.  Sliding block maxima
    are used to increase precision of estimation. The other type of methodology
    uses a model for the distribution of threshold inter-exceedance times
    (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Two 
    versions of this type of approach are provided, following Suveges (2007) 
    <doi:10.1007/s10687-007-0034-2> and Suveges and Davison (2010)  
    <doi:10.1214/09-AOAS292>.
The goal of this package is to provide an easy to use, fast and
    scalable exhaustive search framework. Exhaustive feature selections 
    typically require a very large number of models to be fitted and evaluated. 
    Execution speed and memory management are crucial factors here. This package 
    provides solutions for both. Execution speed is optimized by using a 
    multi-threaded C++ backend, and memory issues are solved by by only storing 
    the best results during execution and thus keeping memory usage constant.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Reads, writes, and edits EXIF and other file metadata
    using ExifTool <https://exiftool.org/>,
    returning read results as a data frame. ExifTool supports many
    different metadata formats including EXIF, GPS, IPTC, XMP, JFIF,
    GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, as
    well as the maker notes of many digital cameras by Canon, Casio,
    DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf,
    Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson,
    Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung,
    Sanyo, Sigma/Foveon and Sony.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.
Provides a shiny-based front end (the 'ExPanD' app) and
    a set of functions for exploratory data analysis. Run as a web-based 
    app, 'ExPanD' enables users to assess the robustness of empirical evidence 
    without providing them access to the underlying data. You can export a 
    notebook containing the analysis of 'ExPanD' and/or use the functions of the 
    package to support your exploratory data analysis workflow. Refer to the 
    vignettes of the package for more information on how to use 'ExPanD' and/or 
    the functions of this package.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
Expanding Ploidy and Allele Frequency on Nested Subpopulations (expands) characterizes coexisting subpopulations in a single tumor sample using copy number and allele frequencies derived from exome- or whole genome sequencing input data (<http://www.ncbi.nlm.nih.gov/pubmed/24177718>). The model detects coexisting genotypes by leveraging run-specific tradeoffs between depth of coverage and breadth of coverage. This package predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation, tumor purity and phylogeny. The main function runExPANdS() provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of subpopulation predictions increases with the number of mutations provided. It is recommended that at least 200 mutations are used as input to obtain stable results. Updates in version 2.1 include: (i) new parameter ploidy in runExPANdS.R allows specification of non-diploid background ploidies (e.g. for near-triploid cell lines); (ii) parallel computing option is available. Further documentation and FAQ available at <http://dna-discovery.stanford.edu/software/expands>.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test  of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Pacote para análise de delineamentos experimentais (DIC, DBC e DQL), experimentos em esquema fatorial duplo (em DIC e DBC), experimentos em parcelas subdivididas (em DIC e DBC), experimentos em esquema fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em fatorial triplo (em DIC e DBC) e experimentos em esquema fatorial triplo com um tratamento adicional (em DIC e DBC), fazendo analise de variancia e comparacao de multiplas medias (para tratamentos qualitativos), ou ajustando modelos de regressao ate a terceira potencia (para tratamentos quantitativos); analise de residuos (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Distributes samples in batches while making batches homogeneous 
    according to their description. Allows for an arbitrary number of variables, 
    both numeric and categorical. For quality control it provides functions to 
    subset a representative sample.
Provides various statistical methods for
  designing and analyzing randomized experiments. One functionality
  of the package is the implementation of randomized-block and
  matched-pair designs based on possibly multivariate pre-treatment
  covariates. The package also provides the tools to analyze various
  randomized experiments including cluster randomized experiments,
  two-stage randomized experiments, randomized experiments with 
  noncompliance, and randomized experiments with missing data.
Expert opinion (or judgment) is a body of techniques to
  estimate the distribution of a random variable when data is scarce
  or unavailable. Opinions on the quantiles of the distribution are
  sought from experts in the field and aggregated into a final
  estimate. The package supports aggregation by means of the Cooke,
  Mendel-Sheridan and predefined weights models.
Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.
The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.
Generates explanations for classification and regression models and visualizes them.
 Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
 are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
 in the model. The IME method is slower as it samples from all feature subsets.
 For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, 
 and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
 All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided 
 and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Interactive data exploration with one line of code or use an easy to remember set of tidy functions for exploratory data analysis. Introduces three main verbs. explore() to graphically explore a variable or table, describe() to describe a variable or table and report() to create an automated report.
Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.
Computation of the matrix exponential, logarithm, sqrt,
  and related quantities, using traditional and modern methods.
Estimate individual and average joint effects of chemical mixtures, dose-response relationships, and potential interactions in environmental epidemiology.
    The visualization of interactions and the plotting of all the objects. For more information please, check next work: Youssef Oulhote, Marie-Abele Bind, Brent Coull, Chirag, Patel, Philippe Grandjean (2017) <https://www.biorxiv.org/content/early/2017/06/30/147413.article-info>.
A variety of descriptive multivariate analyses with the singular value decomposition,
    such as principal components analysis, correspondence analysis, and multidimensional scaling.
    See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Tools and data to accompany Schlicht, L., Valcu, M., & Kempenaers, B. (2015) <doi:10.1111/1365-2656.12293>. Spatial patterns of extra-pair paternity: beyond paternity gains and losses. Journal of Animal Ecology, 84(2), 518-531.
A set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. It has been written to accompany the forthcoming paper 'Computing expectations and marginal likelihoods for permutations'. Publication details will be updated as soon as they are finalized.
Allows to calculate the probabilities  of occurrences of an event in a 
 great number of repetitions of Bernoulli experiment, through the application of  
 the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. 
 Gives the possibility to show the results graphically and analytically, and to compare
 the results obtained by the application of the above  theorems with those calculated by
 the direct application of the Binomial formula. Is basically useful for educational
 purposes.
Given a continuous-time dynamic network, this package allows one to fit a stochastic blockmodel where nodes belonging to the same group create interactions and non-interactions of similar lengths. This package implements the methodology described by R. Rastelli and M. Fop (2019) <arXiv:1901.09828>.
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Package computes and displays tables with support for 'SPSS'-style 
        labels, multiple and nested banners, weights, multiple-response variables 
        and significance testing. There are facilities for nice output of tables 
        in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods 
        for labelled variables add value labels support to base R functions and to 
        some functions from other packages. Additionally, the package brings 
        popular data transformation functions from 'SPSS' Statistics and 'Excel': 
        'RECODE', 'COUNT', 'COMPUTE', 'DO IF', 'COUNTIF', 'VLOOKUP' and etc. 
        These functions are very useful for data processing in marketing research 
        surveys. Package intended to help people to move data 
        processing from 'Excel' and 'SPSS' to R.
Tests for the composite hypothesis of exponentiality
Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
Estimation for high conditional quantiles based on quantile regression.
The package provides tools for botanists, plant taxonomists,
    curators of plant genebanks and perhaps other biological collections.
A consistent, unified and extensible
    framework for estimation of parameters for probability distributions, including 
    parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.
If 'dplyr' is a grammar for data manipulation, 'extdplyr' is like
    a short paragraph written in 'dplyr'. 'extdplyr' extends 'dplyr' and
    'tidyr' verbs to some common "routines" that manipulate data sets. It uses
    the same interface and preserves all the features from 'dplyr', has good 
    performance, and supports various data sources.
Creates family objects identical to stats
    family but for new links.
If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.
This is a package containing the function extfunnel()
        which produces a funnel plot including additional augmentations
        such as statistical significance contours and heterogeneity
        contours.
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.
For multiple full/partial ranking lists, R package 'ExtMallows' can (1) detect whether the input ranking lists are over-correlated, and (2) use the Mallows model or extended Mallows model to integrate the ranking lists, and (3) use hierarchical extended Mallows model for rank integration if there are groups of over-correlated ranking lists.
This package tests for differences in minor allele
        frequency between groups and is based on an extra-binomial
        variation model for pooled sequencing data.
By using a multispectral image and ESRI shapefile (Point/ Line/ Polygon), a data table will be generated for classification, regression or other processing. The data table will be contained by band wise raster values and shapefile ids (User Defined).
Density, distribution function, quantile function
    and random generation for a number of univariate
    and multivariate distributions. This package implements the
    following distributions: Bernoulli, beta-binomial, beta-negative
    binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
    bivariate normal, bivariate Poisson, categorical, Dirichlet,
    Dirichlet-multinomial, discrete gamma, discrete Laplace,
    discrete normal, discrete uniform, discrete Weibull, Frechet,
    gamma-Poisson, generalized extreme value, Gompertz,
    generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t,
    Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy,
    Laplace, location-scale t, logarithmic, Lomax, multivariate
    hypergeometric, multinomial, negative hypergeometric, 
    non-standard beta, normal mixture, Poisson mixture, Pareto,
    power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam,
    slash, triangular, truncated binomial, truncated normal,
    truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
    zero-inflated negative binomial, zero-inflated Poisson.
Tools to using fonts other than the standard PostScript fonts.
    This package makes it easy to use system TrueType fonts and with PDF or
    PostScript output files, and with bitmap output files in Windows. extrafont
    can also be used with fonts packaged specifically to be used with, such as
    the fontcm package, which has Computer Modern PostScript fonts with math
    symbols. See https://github.com/wch/extrafont for instructions and
    examples.
Package for holding the database for the extrafont package
Speed up common tasks, particularly logical or
  relational comparisons and routine follow up tasks such as finding the
  indices and subsetting. Inspired by mathematics, where something like:
  3 < x < 6 is a standard, elegant and clear way to assert that
  x is both greater than 3 and less than 6
  (see for example <https://en.wikipedia.org/wiki/Relational_operator>),
  a chaining operator is implemented. The chaining operator, %c%,
  allows multiple relational operations to be used in quotes on the right
  hand side for the same object, on the left hand side.
  The %e% operator allows something like set-builder notation 
  (see for example <https://en.wikipedia.org/wiki/Set-builder_notation>) 
  to be used on the right hand side.
  All operators have built in prefixes defined for all, subset, and which
  to reduce the amount of code needed for common tasks, such as return those
  values that are true.
Functions to 'numericise' 'R' objects (coerce to numeric objects)
    and summarise 'MCMC' (Monte Carlo Markov Chain) samples
    as well as 'R' translations of 'BUGS' (Bayesian Using Gibbs Sampling)
    and 'JAGS' (Just Another Gibbs Sampler) functions.
Finding life outside the planet Earth several is the ultimate goal of an astrobiologist. Using known astronomical measurements and assumptions the probability of extraterrestrial life existence could be estimated. Equations such as the Drake equation (1961) as stated in the paper of Molina (2019) <arXiv:1912.01783>, Seager (2013) <https://www.space.com/22648-drake-equation-alien-life-seager.html> and Foucher et al, (2017) <doi:10.3390/life7040040> are included in the 'extraterrestrial' package.
Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method.
Provides summary statistics of local geospatial features within a given geographic area. 
	It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). 
	The geospatial features can be of any type of geospatial data, including point, polygon or line data.
Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) <arXiv:1910.13848>. These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures.
A set of procedures for modelling parametrically and non-parametrically the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. Adapts the methodologies derived in Beranger et al. (2019) <arxiv:1904.08251>, Beranger et al. (2017) <doi:10.1111/sjos.12240>, Beranger and Padoan (2015) <arxiv:1508.05561>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Marcon et al. (2016) <doi:10.1214/16-EJS1162>. It also refers to the works of Bortot (2010) <https://pdfs.semanticscholar.org/b0dc/1cb608d35bf515c76e39aacc14b4de82e281.pdf>, Padoan (2011) <doi:10.1016/j.jmva.2011.01.014>, Cooley et al. (2010) <doi:10.1016/j.jmva.2010.04.007>, Husler and Reiss (1989) <doi:10.1016/0167-7152(89)90106-5>, Engelke et al. (2015) <doi:10.1111/rssb.12074>, Coles and Tawn (1991) <doi:10.1111/j.2517-6161.1991.tb01830.x>, Nikoloulopoulos et al. (2011) <doi:10.1007/s10687-008-0072-4>, Opitz (2013) <doi:10.1016/j.jmva.2013.08.008>, Tawn (1990) <doi:10.2307/2336802>, Azzalini (1985) <https://www.jstor.org/stable/pdf/4615982.pdf>, Azzalini and Capitanio (2014) <doi:10.1017/CBO9781139248891>, Azzalini (2003) <doi:10.1111/1467-9469.00322>, Azzalini and Capitanio (1999) <doi:10.1111/1467-9868.00194>, Azzalini and Dalla Valle (1996) <doi:10.1093/biomet/83.4.715>, Einmahl et al. (2013) <doi:10.1007/s10687-012-0156-z>, Naveau et al (2009) <doi:10.1093/biomet/asp001> and Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Extreme value theory, nonparametric kernel estimation, tail
    conditional probabilities, extreme conditional quantile, adaptive estimation,
    quantile regression, survival probabilities.
An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.
A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations.  The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2020) <arxiv:2004.04078>, Padoan and Stupfler (2020) <arxiv:2007.08944>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>.
General functions for performing extreme value analysis.  In particular, allows for inclusion of covariates into the parameters of the extreme-value distributions, as well as estimation through MLE, L-moments, generalized (penalized) MLE (GMLE), as well as Bayes.  Inference methods include parametric normal approximation, profile-likelihood, Bayes, and bootstrapping.  Some bivariate functionality and dependence checking (e.g., auto-tail dependence function plot, extremal index estimation) is also included.  For a tutorial, see Gilleland and Katz (2016) <doi: 10.18637/jss.v072.i08> and for bootstrapping, please see Gilleland (2020) <doi: 10.1175/JTECH-D-20-0070.1>.
Code to fit, plot and compare several (extreme value)
    distribution functions. Can also compute (truncated) distribution quantile estimates and
    draw a plot with return periods on a linear scale.
Detect outliers in one-dimensional data.
Conducts inference in statistical models for extreme values (de Carvalho et al (2012), <doi:10.1080/03610926.2012.709905>; de Carvalho and Davison (2014), <10.1080/01621459.2013.872651>; Einmahl et al (2016), <doi:10.1111/rssb.12099>).
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
It implements the subjectively censored Weibull MLE and censored Weibull mixture methods for the lower quantile estimation. Quantile estimates from these two methods are robust to model misspecification in the lower tail. It also includes functions to evaluation the standard error of the resulting quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for the Type-I or Type-II right censored data.
Testing for and dating periods of explosive
    dynamics (exuberance) in time series using the univariate and panel
    recursive unit root tests proposed by Phillips et al. (2015)
    <doi:10.1111/iere.12132> and Pavlidis et al. (2016)
    <doi:10.1007/s11146-015-9531-2>.  The recursive least-squares
    algorithm utilizes the matrix inversion lemma to avoid matrix
    inversion which results in significant speed improvements. Simulation
    of a variety of periodically-collapsing bubble processes.
There is no ophthalmic researcher who has not had headaches from 
    the handling of visual acuity entries. Different notations, untidy entries. 
    This shall now be a matter of the past. Eye makes it as easy as pie to work
    with VA data - easy cleaning, easy conversion between 
    Snellen, logMAR, ETDRS letters, and qualitative visual acuity 
    shall never pester you again. The eye 
    package automates the pesky task to count number of patients and eyes, 
    and can help to clean data with easy re-coding for right and left eyes. 
    It also contains functions to help reshaping eye side specific variables 
    between wide and long format. Visual acuity conversion is based on 
    Schulze-Bonsel et al. (2006) <doi:10.1167/iovs.05-0981>, 
    Gregori et al. (2010) <doi:10.1097/iae.0b013e3181d87e04>, 
    Beck et al. (2003) <doi:10.1016/s0002-9394(02)01825-1> and 
    Bach (2007) <http:michaelbach.de/sci/acuity.html>.
Open source data allows for reproducible research and helps advance
    our knowledge. The purpose of this package is to collate open source 
    ophthalmic data sets curated for direct use. This is real life data of 
    people with intravitreal injections with anti-vascular endothelial growth
    factor (anti-VEGF), due to age-related macular degeneration or diabetic 
    macular edema. Associated publications of the data sets: 
    Fu et al. (2020) <doi:10.1001/jamaophthalmol.2020.5044>, 
    Moraes et al (2020) <doi:10.1016/j.ophtha.2020.09.025>,
    Fasler et al. (2019) <doi:10.1136/bmjopen-2018-027441>, 
    Arpa et al. (2020) <doi:10.1136/bjophthalmol-2020-317161>,
    Kern et al. 2020, <doi:10.1038/s41433-020-1048-0>.
Imports plain-text ASC data files from EyeLink eye trackers 
    into (relatively) tidy data frames for analysis and visualization.
Functions to prepare and analyse eye tracking data of reading
  exercises. The functions allow some basic data preparations and code fixations 
  as first and second pass. First passes can be further devided into forward and 
  reading. The package further allows for aggregating fixation times per AOI or 
  per AOI and per type of pass (first forward, first rereading, second). These 
  methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> 
  and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is 
  also possible to convert between metric length and visual degrees.
Misc function for working with eyetracking data
A set of functions for organising and analysing datasets from
    experiments run using 'Eyelink' eye-trackers. Organising functions help
    to clean and prepare eye-tracking datasets for analysis, and mark up
    key events such as display changes and responses made by participants.
    Analysing functions help to create means for a wide range of standard
    measures (such as 'mean fixation durations'), which can then be fed into
    the appropriate statistical analyses and graphing packages as necessary.
Facilitates easy analysis of factorial experiments, including
    purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss
    designs, and mixed within-and-between-Ss designs. The functions in this package
    aim to provide simple, intuitive and consistent specification of data analysis
    and visualization. Visualization functions also include design visualization for
    pre-analysis data auditing, and correlation matrix visualization. Finally, this
    package includes functions for non-parametric analysis, including permutation
    tests and bootstrap resampling. The bootstrap function obtains predictions
    either by cell means or by more advanced/powerful mixed effects models, yielding
    predictions and confidence intervals that may be easily visualized at any level
    of the experiment's design.
A tool to operate a batch of univariate or multivariate 
    Cox models and return tidy result.
Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.
Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.
Enables users to create visualizations using functions 
    based on the data analysis task rather than on plotting mechanics. It hides
    the details of the individual 'ggplot2' function calls and 
    allows the user to focus on the end goal. Useful for quick preliminary explorations. 
    Provides functions for common exploration patterns. Some of the ideas in this
    package are motivated by Fox (2015, ISBN:1938377052).
This package implements a simplified version of least
        squares, and logistic regression for efficiently selecting the
        significant non-additive interactions between two variables.
An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.
Serves as a platform for published fluorometric enzyme assay
 protocols. 'ezmmek' calibrates, calculates, and plots enzyme activities as they
 relate to the transformation of synthetic substrates. At present, 'ezmmek'
 implements two common protocols found in the literature, and is modular to accommodate
 additional protocols. Here, these protocols are referred to as the In-Sample
 Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>).
 protocols. By containing multiple protocols, 'ezmmek' aims to stimulate
 discussion about how to best optimize fluorometric enzyme assays. A standardized
 approach would make studies more comparable and reproducible.
Choosing any rectangular data file using interactive GUI dialog box, and seamlessly manipulating tidy data between an 'Excel' window and R session.
Wrapper for the 'ggplot2' package that creates a variety of common
  charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce
  typing.
Runs a Shiny App in the local machine for basic
    statistical and graphical analyses. The point-and-click interface 
    of Shiny App enables obtaining the same analysis outputs (e.g., plots and
    tables) more quickly, as compared with typing the required code in R,
    especially for users without much experience or expertise with coding.
    Examples of possible analyses include tabulating descriptive
    statistics for a variable, creating histograms by experimental groups,
    and creating a scatter plot and calculating the correlation between
    two variables.
ezsim provides a handy way to run simulation and examine its result
Contains two functions that are intended to make
    tuning supervised learning methods easy. The eztune function uses a
    genetic algorithm or Hooke-Jeeves optimizer to find the best 
    set of tuning parameters. The user can choose the optimizer, the 
    learning method, and if optimization will be based on accuracy 
    obtained through validation error, cross validation, or resubstitution. 
    The function eztune.cv will compute a cross validated error rate. The purpose 
    of eztune_cv is to provide a cross validated accuracy or MSE when 
    resubstitution or validation data are used for optimization because 
    error measures from both approaches can be misleading.
Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <DOI:10.1093/biomet/asy009> and  
    'Exact adaptive confidence intervals for linear regression coefficients'
    by Hoff and Yu <DOI:10.1214/18-EJS1517>.
Frequentist assisted by Bayes (FAB) p-values and confidence 
 interval construction. See 
 Hoff (2019) <arXiv:1907.12589> 
 "Smaller p-values via indirect information",
 Hoff and Yu (2019) <doi:10.1214/18-EJS1517> 
 "Exact adaptive confidence intervals for linear regression coefficients", and
 Yu and Hoff (2018) <doi:10.1093/biomet/asy009> 
 "Adaptive multigroup confidence intervals with constant coverage".
Provides a collection of commonly used univariate and multivariate
    time series forecasting models including automatically selected exponential 
    smoothing (ETS) and autoregressive integrated moving average (ARIMA) models.
    These models work within the 'fable' framework provided by the 'fabletools'
    package, which provides the tools to evaluate, visualise, and combine models 
    in a workflow consistent with the tidyverse.
Allows prophet models from the 'prophet' package to be used in a tidy workflow with the modelling interface of 'fabletools'. This extends 'prophet' to provide enhanced model specification and management, performance evaluation methods, and model combination tools.
Provides tools, helpers and data structures for
    developing models and time series functions for 'fable' and extension
    packages. These tools support a consistent and tidy interface for time
    series modelling and analysis.
Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <https://www.jstor.org/stable/25703571>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).
Helps you imagine your data before you collect it. Hierarchical data structures
   and correlated data can be easily simulated, either from random number generators or
   by resampling from existing data sources. This package is faster with 'data.table' and
   'mvnfast' installed.
Allows the user to implement easily canvas elements within a 'shiny' app or an 'RMarkdown' document. 
    The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes.
    The package relies on the 'fabricjs' 'JavaScript' library. See <http://fabricjs.com/>.
We implement the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing <doi: 10.1007/s11222-017-9744-8>.
Provides an interface to the 'Kairos' Face Recognition API <https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.
A tool for simulating a variety of spatial individual-based models of
    plant-plant interactions. User-created models can include any number of species, each of
    which can be structured in any number of life-stages, where each life-stage has specific
    death, growth and reproduction rates, as well as specific interaction radius, dispersal
    radius, and interaction effects over each other species/life-stage. Life stages were modeled
    so as to be a stochastic, individual-based version of differential Matrix Population
    Models (Caswell 2001, ISBN:0-87893-096-5). Interactions can be positive
    (facilitation) or negative (competition) and can affect death rates, growth rates or
    reproduction rates. Interactions from multiple numbers are additive, so as to best
    approximate classic population dynamics models such as the logistic model and
    Lotka-Volterra model (Britton 2004, ISBN:9781852335366). All models work in continuous time, implemented as an optimized version
    of the Gillespie algorithm (Gillespie 1976 <doi:10.1016/0021-9991(76)90041-3>) for independent exponential times, and continuous space.
This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.
The package estimates Factor Mixture Analysis via the EM
        algorithm
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.
Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. 
  First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. See, for example: 
      Lebart, L. and Piron, M. and Morineau, A.  (2006).
      Statistique Exploratoire Multidimensionnelle, Dunod, Paris.
  In order to permit to have different weights of the elements to be clustered, the function 'kmeansW', programmed in C++, is included. It is a modification of 'kmeans'.   
  Some graphical functions include the option: 'gg=FALSE'.  When 'gg=TRUE', they  use the 'ggplot2' and 'ggrepel' packages to avoid  the super-position of the labels.
Provides some easy-to-use functions to extract and visualize the
    output of multivariate data analyses, including 'PCA' (Principal Component
    Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence
    Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple
    Factor Analysis) functions from different R packages. It contains also functions
    for simplifying some clustering analysis steps and provides 'ggplot2' - based
    elegant data visualization.
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2020) <arXiv:2004.05105>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.
Tests for Kaiser-Meyer-Olkin (KMO) and
    communalities in a dataset. It provides a final sample by removing
    variables in a iterable manner while keeping account of the variables
    that were removed in each step. It follows the best practices and assumptions according to
    Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).
Estimation, model selection and goodness-of-fit of factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2019) <arXiv:1907.07395>.
Identifies change-points in the common and the idiosyncratic components via factor modelling.
Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2019+), and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.
Used for the design and analysis of a 2x2 factorial trial for
    a time-to-event endpoint.  It performs power calculations and significance
    testing as well as providing estimates of the relevant hazard ratios and the 
    corresponding 95% confidence intervals.  Important reference papers include
    Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609>
    Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507>
    Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020)
    <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.
The Merging Path Plot is a methodology for adaptive fusing of k-groups 
    with likelihood-based model selection. This package contains tools for 
    exploration and visualization of k-group dissimilarities. 
    Comparison of k-groups is one of the most important issues
    in exploratory analyses and it has zillions of applications. 
    The traditional approach is to use pairwise post hoc tests
    in order to verify which groups differ significantly. However, 
    this approach fails with a large number of groups in both interpretation 
    and visualization layer.
    The Merging Path Plot solves this problem by using an easy-to-understand 
    description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic (Sitko, Biecek 2017) <arXiv:1709.04412>.
    'factorMerger' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
    Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
Methods to calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models.
Package to fit Bayesian quantile regression models that
        assume a factor structure for at least part of the design
        matrix.
It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' (<http://www.ipez.es/ModestR>) with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Function factories are functions that make functions. They can be 
    confusing to construct. Straightforward techniques can produce functions 
    that are fragile or hard to understand. While more robust techniques exist 
    to construct function factories, those techniques can be confusing. This 
    package is designed to make it easier to construct function factories.
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
Allow clients to fetch 'analytics' through API for Portfolio 
    'Analytics'('PA'), Style Performance Risk('SPAR') and 'Vault' products of 
    'FactSet'. Visit 
    <https://github.com/factset/analyticsapi-engines-r-sdk/tree/master/Engines>
    for more information on the usage of package. Visit 
    <https://developer.factset.com/> for more information on products.
Generates 'RProtobuf' classes for 'FactSet' 'STACH' tabular 
    format which represents complex multi-dimensional array of data. These 
    classes help in the 'serialization' and 'deserialization' of 'STACH' 
    formatted data. See 'GitHub' repository documentation for more 
    information.
Per the Factual.com website, "Factual is a platform where
        anyone can share and mash open, living data on any subject."
        The data is in the form of tables and is accessible via REST
        API.  The factualR package is a thin wrapper around the
        Factual.com API, to make it even easier for people working with
        R to explore Factual.com data sets.
Compute maximum likelihood estimators of parameters in a Gaussian factor model using
  the the matrix-free methodology described in Dai et al. (2020) <doi:10.1080/10618600.2019.1704296>.
  In contrast to the factanal() function from 'stats' package, fad() can handle high-dimensional datasets where
  number of variables exceed the sample size and is also substantially faster than the EM algorithms.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a  
   factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
    (see Friedman et al. (2010)), sparse linear discriminant analysis (see
    Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
    analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Probability distributions that are sometimes useful in hydrology.
Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.
More comfortable interface to work with R data or source files
    in a key-value fashion.
An implementation of the fair data adaptation with quantile preservation described in
    Plecko & Meinshausen (2019) <arXiv:1911.06685>. 
    The adaptation procedure uses the specified causal graph to pre-process the 
    given training and testing data in such a way to remove the bias caused by 
    the protected attribute. The procedure uses tree ensembles for quantile 
    regression.
Fair machine learning regression models which take sensitive attributes into account in
  model estimation. Currently implementing Komiyama et al. (2018) 
  <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>.
Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented.
Offers calculation, visualization and comparison of algorithmic fairness metrics. Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) <doi:10.1007/s10618-010-0190-x>, Chouldechova (2017) <doi:10.1089/big.2016.0047>, Feldman et al. (2015) <doi:10.1145/2783258.2783311> , Friedler et al. (2018) <doi:10.1145/3287560.3287589> and Zafar et al. (2017) <doi:10.1145/3038912.3052660>. The package also offers convenient visualizations to help understand fairness metrics.
Allows user to obtain subsets of columns of data or vectors within a list.  These subsets will match the original data in terms of average and variation, but have a consistent length of data per column.  It is intended for use on automated data generation which may not always output the same N per replicate or sample.
It is used for systematic analysis of decisions based on attributes and its levels.
Use R as a minimal build system. This might come in
    handy if you are developing R packages and can not use a proper build
    system. Stay away if you can (use a proper build system).
Generates fake data from a dataset of different variable types.
    The package contains the functions simulate_dataset and simulate_dataset_ts 
    to simulate time-independent and time-dependent data. It randomly samples 
    character and factor variables from contingency tables and  numeric and 
    ordered factors from a multivariate normal distribution. It currently supports the 
    simulation of stationary and zero-inflated count time series.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data.  Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.  The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.
Functionality provided for conditional simulation, likelihoods and plotting of pedigrees, mostly as a wrapper for 'paramlink'. Users typically start by exporting from the Windows version of 'Familias'.
Simulates age-at-onset traits associated with a segregating major gene in family data 
     obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment 
     correction is utilized to estimate age-dependent penetrance functions either parametrically from 
     the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm 
     can infer missing genotypes and carrier probabilities estimated from family's genotype and
     phenotype information or from a fitted model. Plot functions include pedigrees of simulated 
     families and predicted penetrance curves based on specified parameter values.
An interface to the core Familias functions (www.familias.name), 
 which are programmed in C++.
Fits penalized linear and logistic regression models with pairwise interaction terms.
Estimate parameters of univariate probability distributions 
  with maximum likelihood and Bayesian methods.
Given a set of parameters describing model dynamics and a corresponding cost function,
    FAMoS performs a dynamic forward-backward model selection on a specified selection
    criterion. It also applies a non-local swap search method. Works on any cost function. 
    For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.
FamSKAT-RC is a family-based association kernel test for both 
    rare and common variants. This test is general and several special cases 
    are known as other methods: famSKAT, which only focuses on rare variants 
    in family-based data, SKAT, which focuses on rare variants in 
    population-based data (unrelated individuals), and SKAT-RC, which focuses 
    on both rare and common variants in population-based data. When one 
    applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes 
    famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the 
    kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one 
    applies famSKAT-RC and set the kinship matrix (fullkins) to the identity 
    matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also 
    include a small sample synthetic pedigree to demonstrate the method with.  
    For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used.  This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
A collection of R functions to perform nonparametric 
    analysis of covariance for regression curves or surfaces. 
    Testing the equality or parallelism of nonparametric curves 
    or surfaces is equivalent to analysis of variance (ANOVA) or 
    analysis of covariance (ANCOVA) for one-sample functional data. 
    Three different testing methods are available in the package, 
    including one based on L-2 distance, one based on an ANOVA 
    statistic, and one based on variance estimators.
Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the 'RStudio IDE' with 'RStudio' package 'manipulate' loaded. 'RStudio' is freely available (<https://rstudio.com/>), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only. For further information on the method see Fruth, J., Roustant, O., Kuhnt, S. (2014) <doi:10.1016/j.jspi.2013.11.007>.
Visualise sequential distributions using a range of plotting
    styles. Sequential distribution data can be input as either simulations or
    values corresponding to percentiles over time. Plots are added to
    existing graphic devices using the fan function. Users can choose from four
    different styles, including fan chart type plots, where a set of coloured
    polygon, with shadings corresponding to the percentile values are layered
    to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.
Counterparts to R string manipulation functions that account for
   the effects of ANSI text formatting control sequences.
Download Data from the FAOSTAT Database of the Food and Agricultural Organization (FAO) of the United Nations.
    A list of functions to download statistics from FAOSTAT (database of the FAO <http://www.fao.org/faostat/en/#data>) 
    and WDI (database of the World Bank <https://data.worldbank.org/>), and to perform some harmonization operations.
Tools for detecting and summarize influential cases that
    can affect exploratory and confirmatory factor analysis models as well as
    structural equation models more generally (Chalmers, 2015, <doi:10.1177/0146621615597894>; 
    Flora, D. B., LaBrish, C. & Chalmers, R. P., 2012, <doi:10.3389/fpsyg.2012.00055>).
A fast tool to create an interactive and toggle styling Frequently Asked Questions page by using R data.
Modelizations and previsions functions for
   Functional AutoRegressive processes using
   nonparametric methods: functional kernel,
   estimation of the covariance operator in
   a subspace, ...
Books are "Practical Regression and ANOVA in R" on CRAN, "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248.
Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.
Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See <http://weka.wikispaces.com/ARFF> for further info on 'ARFF' and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Note that the 'OpenML' package is currently only available from 'Github' at: <https://github.com/openml/openml-r>.
Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.
Performs robust multiple testing for means in the presence of known and unknown latent factors presented in Fan et al.(2019) "FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control" <doi:10.1080/01621459.2018.1527700>.
             Implements a series of adaptive Huber methods combined with fast data-drive tuning schemes proposed in Ke et al.(2019) "User-Friendly Covariance Estimation for Heavy-Tailed Distributions" <doi:10.1214/19-STS711> to estimate model parameters and construct test statistics that are robust against heavy-tailed and/or asymmetric error distributions. 
             Extensions to two-sample simultaneous mean comparison problems are also included. 
             As by-products, this package contains functions that compute adaptive Huber mean, covariance and regression estimators that are of independent interest.
Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, 
    select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. 
    When the model is executed a set of results are displayed which include efficiency scores, peers' determination, scale efficiencies' evaluation 
    and slacks' calculation. Fore more information about the theoretical background of the package, 
    please refer to Bogetoft & Otto (2011) <doi:10.1007/978-1-4419-7961-2>.
The encoding of colour can be handled in many different ways, using
    different colour spaces. As different colour spaces have different uses,
    efficient conversion between these representations are important. The 
    'farver' package provides a set of functions that gives access to very fast
    colour space conversion and comparisons implemented in C++, and offers 
    speed improvements over the 'convertColor' function in the 'grDevices' 
    package.
It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.
Provides functions for pricing
	and valuating Asian Options together with tools for analyzing
    and modeling Exponential Brownian Motion (EBM).
This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.
Provides a  collection of functions 
  to manage, to investigate and to analyze data sets of financial 
  assets from different points of view.
The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various 
    functions to tidy and screen daily stream discharge data; calculate and visualize various summary statistics
    and metrics; and compute annual trending (using 'zyp' package methods <https://CRAN.R-project.org/package=zyp>)
    and volume frequency analyses (using methods similar to HEC-SSP (2019) 
    <https://www.hec.usace.army.mil/software/hec-ssp/>). It features useful function arguments for filtering of and
    handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey
    of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).
A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in
    Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> 
    and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.
Implements Adaboost based on C++ backend code.
             This is blazingly fast and especially useful for large, in memory data sets. 
             The package uses decision trees as weak classifiers. Once the classifiers
             have been trained, they can be used to predict new data. 
             Currently, we support only binary classification tasks.
             The package implements the Adaboost.M1 algorithm and the real
             Adaboost(SAMME.R) algorithm.
The 'fastai' <https://docs.fast.ai/index.html> library 
             simplifies training fast and accurate neural networks 
             using modern best practices. It is based on research 
             in to deep learning best practices undertaken 
             at 'fast.ai', including 'out of the box' support
             for vision, text, tabular, audio, time series, and 
             collaborative filtering models.
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Provides a method of recovering the precision matrix efficiently 
        and solving for the dantzig selector by applying the parametric 
        simplex method.  The computation is based on a linear optimization
        solver. It also contains a generic LP solver and a parameterized LP 
        solver using parametric simplex method.
This is a two-in-one package which provides interfaces to
        both R and 'Python'. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the 'SciPy' package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the 'Python' files, see the file INSTALL in the source distribution.
        Based on the present package, Christoph Dalitz also wrote a pure 'C++'
        interface to 'fastcluster':
        <http://informatik.hsnr.de/~dalitz/data/hclust>.
A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net.
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) DOI: <doi:10.4310/SII.2013.v6.n2.a1>.
For ordinal rating data, consider the accelerated Expectation-Maximization algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis' identity for the observed information matrix.  Best-subset variable selection for CUB regression models is then implemented on such basis. The methods here implemented are illustrated and discussed in the preprint available from Researchgate by Simone R. (2020) <https://tinyurl.com/vvk563e>.
Provides an R interface to Bob Jenkin's streaming, 
 non-cryptographic 'SpookyHash' hash algorithm for use in digest-based 
 comparisons of R objects. 'fastdigest' plugs directly into R's internal 
 serialization machinery, allowing digests of all R objects the serialize() 
 function supports, including reference-style objects via custom hooks. Speed is
 high and scales linearly by object size; memory usage is constant and 
 negligible.
Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
If there is a need to recognise edges on a raster image or a bitmap or any kind of a matrix, one can find packages
    that does only 90 degrees vectorization. Typically the nature of artefact images is linear and can be vectorized in much more
    efficient way than draw a series of 90 degrees lines. The fasteraster package does recognition of lines using only one pass.
    It also allows to calculate mass and the mass centers for the recognized zones or polygons.
Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)(<doi:10.1214/009053604000000067 >) and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library.
Provides a drop-in replacement for rasterize() from the 'raster'
   package that takes 'sf'-type objects, and is much faster. There is support
   for the main options provided by the rasterize() function, including
   setting the field used and background value, and options for 
   aggregating multi-layer rasters. Uses the scan line algorithm attributed to
   Wylie et al. (1967) <doi:10.1145/1465611.1465619>.
Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise.  See the reference: Mengyang Gu and Yanxun Xu (2017) <arXiv:1711.11501>.
Fast, numerically-stable Gauss-Hermite quadrature rules and
    utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A.
    (1994) <doi:10.2307/2337136> for a reference on these methods.
Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares 
    implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard
    against convergence issues.
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
The FastHCS algorithm of Schmitt and Vakili (2015) for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
It implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
Implementation of FastICA algorithm to perform Independent
        Component Analysis (ICA) and Projection Pursuit.
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.
R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 <doi:10.1007/978-3-030-32047-8_16>.
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.
Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data
    and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two 
    datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, 
    tools for preparing, adjusting, and summarizing data merges are included. The package implements methods 
    described in Enamorado, Fifield, and Imai (2019) ''Using a Probabilistic Model to Assist Merging of 
    Large-scale Administrative Records'', American Political Science Review and is available 
    at <http://imai.fas.harvard.edu/research/linkage.html>.
Calculates marginal effects based on logistic model objects such as 'glm' or 'speedglm' at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.
A very fast Log-Rank-Test implementation that is several orders of magnitude faster than the implementation in the 'survival' package.
  Log-Rank-Tests can be computed individually or concurrently using threading.
Estimate spike times from calcium imaging data using an L0 penalty.
Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.
Fast implementation of a key-value store. Environments are commonly
    used as key-value stores, but every time a new key is used, it is added to
    R's global symbol table, causing a small amount of memory leakage. This can
    be problematic in cases where many different keys are used. Fastmap avoids
    this memory leak issue by implementing the map using data structures in C++.
Fast Marching Method (FMM) first developed by Sethian (1996) <http://www.pnas.org/content/93/4/1591.short>, and further extended by including a second-order approximation, the first-arrival rule, additive weights, and non-homogeneous domains following Silva and Steele (2012) <doi:10.1142/S0219525911003293> and Silva and Steele (2014) <doi:10.1016/j.jas.2014.04.021>.
Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.
Small set of functions to fast computation of some matrices and operations
  useful in statistics.
A mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138> to measure general dependence and the time complexity for our estimator is only squared to the sample size, which is faster than other statistics. Besides, an implementation of mutual information based independence test is provided for analyzing multivariate data in Euclidean space (T B. Berrett, et al. (2019) <doi:10.1093/biomet/asz024>); furthermore, we extend it to tackle datasets in metric spaces.
This is an extremely fast implementation of a Naive Bayes classifier. This 
    package is currently the only package that supports a Bernoulli distribution, a Multinomial 
    distribution, and a Gaussian distribution, making it suitable for both binary features, 
    frequency counts, and numerical features. Another feature is the support of a mix of 
    different event models. Only numerical variables are allowed, however, categorical variables 
    can be transformed into dummies and used with the Bernoulli distribution. 
    The implementation is largely based on the paper 
    "A comparison of event models for Naive Bayes anti-spam e-mail filtering" 
    written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be 
    submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
We present an implementation of the algorithms required to simulate
  large-scale social networks and retrieve their most relevant metrics. Details 
  can be found in the accompanying scientific paper on the Journal 
  of Statistical Software, <doi:10.18637/jss.v096.i07>.
The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.
Finds the critical sample size ("critical point of stability") for a 
    correlation to stabilize in Schoenbrodt and Perugini's definition of 
    sequential stability (see <doi:10.1016/j.jrp.2013.05.009>).
Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.
'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.
Data sets and utilities to accompany the second edition of
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2017), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet() function.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.
Computes fast (relative to other implementations) approximate 
    Shapley values for any supervised learning model. Shapley values help to 
    explain the predictions from any black box model using ideas from game 
    theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> 
    for details.
Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.
When we do statistic work, we need to see the structure of the data.
    list.str() function will help you see the structure of the data quickly. 
    list.plot() function can help you check every variable in your dataframe.
    table_one() function will make it easy to make a baseline table including 
    difference tests. uv_linear(), uv_logit(), uv_cox(), uv_logrank() will give
    you a hand to do univariable regression analysis, while mv_linear(), 
    mv_logit() and mv_cox() will carry out multivariable regression analysis.
An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	An example how to use 'fastTextR' can be found in the 'README' file.
Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
             defined by some design matrix.
Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Kiener distributions K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.
Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.
Provides a replacement for dplyr::na_if().  Allows
    you to specify multiple values to be replaced with NA using a single
    function.
HTTP error helpers. Methods included for general purpose HTTP 
    error handling, as well as individual methods for every HTTP status
    code, both via status code numbers as well as their descriptive names.
    Supports ability to adjust behavior to stop, message or warning.
    Includes ability to use custom whisker template to have any configuration
    of status code, short description, and verbose message. Currently 
    supports integration with 'crul', 'curl', and 'httr'.
Finds the URL to the 'favicon' for a website. This is useful if you
  want to display the 'favicon' in an HTML document or web application,
  especially if the website is behind a firewall.
A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos
             (http://pages.bloomsbury.com/favouritenumber).
Provides functions and datasets from the book "Forest Analytics with R".
A toolkit for Flux Balance Analysis and related
    metabolic modeling techniques. Functions are provided for: parsing
    models in tabular format, converting parsed metabolic models to input
    formats for common linear programming solvers, and
    evaluating and applying gene-protein-reaction mappings. In addition, there
    are wrappers to parse a model, select a solver, find the metabolic fluxes,
    and return the results applied to the original model. Compared to other
    packages in this field, this package puts a much heavier focus on
    providing reusable components that can be used in the design of new
    implementation of new techniques, in particular those that involve large
    parameter sweeps. For a background on the theory, see What is Flux Balance 
    Analysis <doi:10.1038/nbt.1614>.
Provides a collection of functions to 
    explore and to investigate basic properties of financial returns 
    and related quantities.
    The covered fields include techniques of explorative data analysis
    and the investigation of distributional properties, including
    parameter estimation and hypothesis testing. Even more there are
    several utility functions for data handling and management.
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
Normalizes the data from a file containing the raw values
        of the SNP probes of microarrray data by using the FISH probes
        and their corresponding CNs.
It implements the Nelson-Siegel and the Nelson-Siegel-Svensson
	term structures.
Wrapper functions around the Facebook Marketing 'API' to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.
This package uses time dependent Poisson regression and a record of goals scored in matches to rank teams via estimated attack and defense strengths.  The statistical model is based on Dixon and Coles (1997) Modeling Association Football Scores and Inefficiencies in the Football Betting Market, Applied Statistics, Volume 46, Issue 2, 265-280.  The package has a some webscrapers to assist in the development and updating of a match database.  If the match database contains unconnected clusters (i.e. sets of teams that have only played each other and not played teams from other sets), each cluster is ranked separately relative to the median team strength in the cluster.  The package contains functions for predicting and simulating tournaments and leagues from estimated models. The package allows fitting via the glm(), speedglm(), and glmnet() functions.  The latter allows fast and efficient fitting of very large numbers of teams.  The fitting algorithm will analyze the match data and determine which teams form a cluster (a set of teams where there is a path of matches connecting every team) and fit each cluster separately.
Implements a very fast C++ algorithm to quickly bootstrap receiver
    operating characteristics (ROC) curves and derived performance metrics,
    including the area under the curve (AUC) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of one second.
Provides access to a range of functions for computing and visualising the Full Bayesian Significance Test (FBST) and the e-value for testing a sharp hypothesis against its alternative. The methods are widely applicable as long as a posterior MCMC sample is available. For details on the computation and theory of the FBST see <arXiv:2005.13181>.
Provides a streamlined, standard evaluation-based approach to multivariate function composition. Allows for chaining commands via a forward-pipe operator, %>%.
Provides tools to perform fuzzy formal concept
    analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23>
    and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>.  It
    provides functions to load and save a formal context, extract its
    concept lattice and implications.  In addition, one can use the
    implications to compute semantic closures of fuzzy sets and, thus,
    build recommendation systems.
Efficient procedures for community detection in network studies, especially for sparse networks with not very obvious community structure. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Collection of pricing by duplication methods for popular structured products ("Zertifikate").
Fatigue Crack Growth in Reliability estimates the distribution
             of material lifetime due to mechanical fatigue efforts. The FCGR
             package provides simultaneous crack growth curves fitting to 
             different specimens in materials under mechanical stress efforts. 
             Linear mixed-effects models (LME) with smoothing B-Splines 
             and the linearized Paris-Erdogan law are applied. Once defined
             the fail for a determined crack length, the distribution function
             of failure times to fatigue is obtained. The density function is
             estimated by applying nonparametric binned kernel density estimate 
             (bkde) and the kernel estimator of the distribution function (kde). 
             The results of Pinheiro and Bates method based on nonlinear 
             mixed-effects regression (nlme) can be also retrieved. The package 
             contains the crack.growth, PLOT.cg, IB.F, and Alea.A (database) functions.
Extract full length circular RNA sequences and classify circular RNA
             using the output of circular RNA prediction tools, reference genome and the annotation file corresponding to the reference genome.
             This package uses the output of circular RNA prediction tools such as 'CIRI', 'CIRCExplorer' and the output of other state-of-the-art circular RNA prediction tools.
             Details about the circular RNA prediction procedure can be found in
             'Yuan Gao, Jinfeng Wang and Fangqing Zhao' (2015) <doi:10.1186/s13059-014-0571-3>
             and 'Zhang XO, Wang HB, Zhang Y, Lu X, Chen LL and Yang L' (2014) <doi:10.1016/j.cell.2014.09.001>.
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
Provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the 'fcm' package returns a data frame of the concepts' values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.
Provides several functions to create and manipulate fuzzy
    cognitive maps. It is based on 'FCMapper' for Excel, distributed at <http://
    www.fcmappers.net/joomla/>, developed by Michael Bachhofer and Martin Wildenberg.
    Maps are inputted as adjacency matrices. Attributes of the maps and the
    equilibrium values of the concepts (including with user-defined constrained
    values) can be calculated. The maps can be graphed with a function that calls
    'igraph'. Multiple maps with shared concepts can be aggregated.
Provides a  collection of functions to 
	manage, to investigate and to analyze bivariate financial returns by  
	Copulae. Included are the families of Archemedean, Elliptical, 
	Extreme Value, and Empirical Copulae.
Over fifty conventional clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.
Dynamic prediction in functional concurrent regression with an application to child growth. Extends the pffr() function from the 'refund' package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured.  Leroux et al. (2017), Statistics in Medicine, <doi:10.1002/sim.7582>.
A fold change rank based method is presented to search for genes with changing
             expression and to detect recurrent chromosomal copy number aberrations. This 
             method may be useful for high-throughput biological data (micro-array, sequencing, ...).
             Probabilities are associated with genes or probes in the data set and there is no
             problem of multiple tests when using this method. For array-based comparative genomic
             hybridization data, segmentation results are obtained by merging the significant
             probes detected.
This is a package for fluorescence fluctuation spectroscopy data analysis methods such as spFCS, FCCS, scanning-FCS, pCF, N&B and pCOMB, among others.
    In addition, several data detrending tools are provided. For an extensive user's guide for the use of FCSlib, please navigate to (<https://github.com/FCSlib/FCSlib/tree/master/Documentation>).
    Sample data can be found at (<https://github.com/FCSlib/FCSlib/tree/master/Sample%20Data>).
    The original paper where this package is presented can be found at (<doi:10.1093/bioinformatics/btaa876>).
Easy-to-use, very fast implementation of various functional bases. Easily used together with other packages.
    A functional basis is a collection of basis functions [\phi_1, ..., \phi_n] that can represent a smooth function, i.e. $f(t) = \sum c_k \phi_k(t)$.
    As of version 1.0, this package includes B-splines, Fourier bases and polynomials.
Automatically suggests a correction when a typo occurs.
FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.
These functions were developed to support functional data
 analysis as described in Ramsay, J. O. and Silverman, B. W.
 (2005) Functional Data Analysis. New York: Springer and in 
 Ramsay, J. O., Hooker, Giles, and Graves, Spencer (2009). 
 Functional Data Analysis with R and Matlab (Springer). 
 The package includes data sets and script files working many examples 
 including all but one of the 76 figures in this latter book.  Matlab versions 
 are available by ftp from 
 <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>.
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
Quantify the serial correlation across lags of a given functional 
    time series using the autocorrelation function and a partial autocorrelation
    function for functional time series proposed in 
    Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>.
    The autocorrelation functions are based on the L2 norm of the lagged covariance 
    operators of the series. Functions are available for estimating the 
    distribution of the autocorrelation functions under the assumption 
    of strong functional white noise.
An implementation of the methodology described in
    Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional
    data analysis of samples of density functions.  Densities are first
    transformed to their corresponding log quantile densities, followed by
    ordinary Functional Principal Components Analysis (FPCA).  Transformation
    modes of variation yield improved interpretation of the variability in the
    data as compared to FPCA on the densities themselves.  The standard
    fraction of variance explained (FVE) criterion commonly used for functional
    data is adapted to the transformation setting, also allowing for an
    alternative quantification of variability for density data through the
    Wasserstein metric of optimal transport.
It performs simultaneously clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.
Likelihood based analysis of 1-dimension functional data
        in a mixed-effects model framework. Matrix computation are
        approximated by semi-explicit operator equivalents with linear
        computational complexity. Markussen (2013) <doi:10.3150/11-BEJ389>.
Performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) <doi:10.1007/s11749-010-0185-3>, Gorecki and Smaga (2015) <doi:10.1007/s00180-015-0555-0>, Gorecki and Smaga (2017) <doi:10.1080/02664763.2016.1247791>, Zhang et al. (2018) <doi:10.1016/j.csda.2018.05.004>).
A collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness, MS-plot, total variation depth, and sequential transformations among others.
A versatile package that provides implementation of various
    methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this
    package is Functional Principal Component Analysis (FPCA), a key technique for
    functional data analysis, for sparsely or densely sampled random trajectories
    and time courses, via the Principal Analysis by Conditional Estimation
    (PACE) algorithm. This core algorithm yields covariance and mean functions,
    eigenfunctions and principal component (scores), for both functional data and
    derivatives, for both dense (functional) and sparse (longitudinal) sampling designs.
    For sparse designs, it provides fitted continuous trajectories with confidence bands,
    even for subjects with very few longitudinal observations. PACE is a viable and
    flexible alternative to random effects modeling of longitudinal data. There is also a
    Matlab version (PACE) that contains some methods not available on fdapace and vice
    versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry).
    References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>;
    Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L.M., Ramsay, J.O., Ramsay, T.O. (2013), Spatial spline regression models for an overview.
Integrated Depths for Partially Observed Functional Data (PoFD). Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2020), "Integrated Depth for Partially Observed Functional Data".
Performs alignment, PCA, and modeling of multidimensional and
            unidimensional functions using the square-root velocity framework
            (Srivastava et al., 2011 <arXiv:1103.3817> and
            Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework
            allows for elastic analysis of functional data through phase and
            amplitude separation.
Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
Regression models for functional data, i.e., scalar-on-function,
    function-on-scalar and function-on-function regression models, are fitted
    by a component-wise gradient boosting algorithm. 
	For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.
Provides a variety of tools for the analysis of covariance operators including k-sample tests for equality and classification and clustering methods found in the works of Cabassi et al (2017) <doi:10.1214/17-EJS1347>, Kashlak et al (2017) <arXiv:1604.06310>, Pigoli et al (2014) <doi:10.1093/biomet/asu008>, and Panaretos et al (2010) <doi:10.1198/jasa.2010.tm09239>.
Provides the probability density function (PDF) of the diffusion decision
  model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>)
  with across-trial variability in the drift rate.
  Because the PDF of the DDM contains an infinite sum, it needs to be
  approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009,
  <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014,
  <doi:10.1016/j.jmp.2014.05.002>) plus new approximations. All approximations
  are implemented purely in 'C++' providing faster speed than existing packages.
Provides functionality to generate a frequency 
    distribution table from a set of observations and plot the frequency
    distribution using a Pareto chart.
Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".
Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.
Forest data quality is a package that contains methods of 
    analysis of forest databases, the purpose of the analyzes is to evaluate 
    the quality of the data present in the databases focusing on the dimensions
    of consistency, pountuality and completeness. Databases can range from forest
    inventory data to growth model data. The package has methods to work with large 
    volumes of data quickly, in addition in certain analyzes it is possible to generate
    the graphs for a better understanding of the analysis and reporting of the analyzed analysis.
FDR functions for permutation-based estimators, including pi0 as well as FDR
             confidence intervals. The confidence intervals account for dependencies between
             tests by the incorporation of an overdispersion parameter, which is estimated
             from the permuted data.
It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.
The user can directly compute and display false discovery rates from inputted p-values or z-scores under a variety of assumptions. p.fdr() computes FDRs, adjusted p-values and decision reject vectors from inputted p-values or z-values. get.pi0() estimates the proportion of data that are truly null. plot.p.fdr() plots the FDRs, adjusted p-values, and the raw p-values points against their rejection threshold lines. Megan H. Murray and Jeffrey D. Blume (2020) <arXiv:2010.04680>.
Tools for FDR problems, including false discovery rate regression.
    See corresponding paper: "False discovery rate regression: application to
    neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
    C. Kelly, Matthew A. Smith, Robert E. Kass.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.
Estimate step functions via multiscale inference with controlled false discovery rate (FDR). For details see H. Li, A. Munk and H. Sieling (2016) <doi:10.1214/16-EJS1131>.
Estimates both tail area-based false 
   discovery rates (Fdr) as well as local false discovery rates (fdr) for a 
   variety of null models (p-values, z-scores, correlation coefficients,
   t-scores).  The proportion of null values and the parameters of the null 
   distribution are adaptively estimated from the data.  In addition, the package 
   contains functions for non-parametric density estimation (Grenander estimator), 
   for monotone regression (isotonic regression and antitonic regression with weights),
   for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), 
   for the half-normal and correlation distributions, and for computing
   empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.
Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2019) <arXiv:1912.04607v1>. The main algorithms of the paper are available as continuous, discrete and weighted versions.
For each feature, a score is computed that can be useful
        for feature selection. Several random subsets are sampled from
        the input data and for each random subset, various linear
        models are fitted using lars method. A score is assigned to
        each feature based on the tendency of LASSO in including that
        feature in the models.Finally, the average score and the models
        are returned as the output. The features with relatively low
        scores are recommended to be ignored because they can lead to
        overfitting of the model to the training data. Moreover, for
        each random subset, the best set of features in terms of global
        error is returned. They are useful for applying Bolasso, the
        alternative feature selection method that recommends the
        intersection of features subsets.
Comprises a pipeline for predicting microRNA/mRNA interactions, as detailed in Williams, Calinescu, Mohorianu (2020) <doi:10.1101/2020.12.23.424130>. Its input consists of [a] a messenger RNA (mRNA) dataset (either in fasta format, focused on 3' UTRs or in gtf format; for the latter, the sequences of the 3’ UTRs are generated using the genomic coordinates), [b] a microRNA dataset (in fasta format, retrieved from miRBase,  <http://www.mirbase.org/>) and [c] an interaction dataset (in csv format, from miRTarBase <http://mirtarbase.cuhk.edu.cn/php/index.php>). To characterise and predict microRNA/mRNA interactions, we use [a] statistical analyses based on Chi-squared and Fisher exact tests and [b] Machine Learning classifiers (decision trees, random forests and support vector machines). To enhance the accuracy of the classifiers we also employ feature selection approaches used in on conjunction with the classifiers. The feature selection approaches include a voting scheme for decision trees, a measure based on Gini index for random forests, forward feature selection and Genetic Algorithms on SVMs. The pipeline also includes a novel approach based on embryonic Genetic Algorithms which combines and optimises the forward feature selection and Genetic Algorithms. All analyses, including the classification and feature selection, are applicable on the microRNA seed features (default), on the full microRNA features and/or flanking features on the mRNA. The sets of features can be combined.
Provides a collection of features, decomposition methods, 
    statistical summaries and graphics functions for the analysing tidy time
    series data. The package name 'feasts' is an acronym comprising of its key
    features: Feature Extraction And Statistics for Time Series.
Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.
Local inferential feature significance for multivariate kernel density estimation.
Uses three different correlation coefficients to
    calculate measurement-level adequate correlations in a feature matrix: 
    Pearson product-moment correlation coefficient,
    Intraclass correlation and Cramer's V.
Finds modelling features through a detailed analysis of model residuals using 'rpart' classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model prediction differs from the actual target variable. S. Chatterjee, A. S. Hadi (2006) <doi:10.1002/0470055464>.
Feature hashing, also called as the hashing trick, is a method to transform 
  features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. 
  Without looking up the indices in an associative array, 
  it applies a hash function to the features and uses their hash values as indices directly.
  The method of feature hashing in this package was proposed in Weinberger et al. (2009) <arXiv:0902.2206>. 
  The hashing algorithm is the murmurhash3 from the 'digest' package. 
  Please see the README in <https://github.com/wush978/FeatureHashing> for more information.
Implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values.
Discretely-sampled function is first smoothed.  Features
        of the smoothed function are then extracted.  Some of the key
        features include mean value, first and second derivatives,
        critical points (i.e. local maxima and minima), curvature of
        cunction at critical points, wiggliness of the function, noise
        in data, and outliers in data.
A 'reticulate'-based interface to the 'Python' module 'Featuretools'.
  The package grants functionality to interact with 'Pythons' 'Featuretools' module, which allows 
  for automated feature engineering on any data frame. Valid features and new data sets can, after
  feature synthesis, easily be extracted.
A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.
Easily analyze relational data from the United States 2016 federal 
    election cycle as reported by the Federal Election Commission.
    This package contains data about candidates, committees, and a
    variety of different financial expenditures. Data is from <https://www.fec.gov/data/browse-data/?tab=bulk-data>.
Functions and example datasets for Fechnerian scaling of discrete
  object sets.  User can compute Fechnerian distances among objects representing
  subjective dissimilarities, and other related information.  See
  package?fechner for an overview.
Functions to automate downloading geospatial data available from
    several federated data sources (mainly sources maintained by the US Federal
    government). Currently, the package enables extraction from seven datasets:
    The National Elevation Dataset digital elevation models (1 and 1/3 arc-second;
    USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic
    (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is
    led by the Natural Resources Conservation Service (NRCS) under the USDA; the
    Global Historical Climatology Network (GHCN), coordinated by National Climatic
    Data Center at NOAA; the Daymet gridded estimates of daily weather parameters 
    for North America, version 3, available from the Oak Ridge National Laboratory's
    Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; 
    and the National Land Cover Database (NLCD).
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
The Code of Federal Regulations (CFR) annual edition is the codification 
    of the general and permanent rules published in the Federal Register by the departments
    and agencies of the Federal Government of the United States of America. Simply, the 
    'fedregs' package facilitates word processing and sentiment analysis of the CFR using tidy 
    principles. Note: According to the Code of Federal Regulations XML Rendition User Guide Document: 
    "In general, there are no restrictions on re-use of information in Code of Federal Regulations
    material because U.S. Government works are not subject to copyright. OFR and GPO do not
    restrict downstream uses of Code of Federal Regulations data, except that independent providers
    should be aware that only the OFR and GPO are entitled to represent that they are the providers
    of the official versions of the Code of Federal Regulations and related Federal Register
    publications."
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Provides the function feis() to estimate fixed effects individual 
    slope (FEIS) models. The FEIS model constitutes a more general version of 
    the often-used fixed effects (FE) panel model, as implemented in the 
    package 'plm' by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. 
    In FEIS models, data are not only person demeaned like in conventional 
    FE models, but detrended by the predicted individual slope of each 
    person or group. Estimation is performed by applying least squares lm() 
    to the transformed data. For more details on FEIS models see Bruederl and 
    Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; 
    Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; 
	Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>;
    Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE 
    and random effects estimators against heterogeneous slopes, the package 
    also provides the functions feistest() for an artificial regression test 
    and bsfeistest() for a bootstrapped version of the Hausman test.
A small package for determining if n-dimensional ellipses overlap.
Provides pseudo-postfix operators and more to enhance displaying documents.
    The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects.
    The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo).
Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported.
A structured profile likelihood algorithm for the logistic fixed effects model and an approximate expectation maximization (EM) algorithm for the logistic mixed effects model. Based on He, K., Kalbfleisch, J.D., Li, Y. and Li, Y. (2013) <doi:10.1007/s10985-013-9264-6>.
Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>.
    Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.
Data from various catalogs of astrophysical gamma-ray sources
    detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071,
    2009 June 1), on board the Fermi gamma-ray satellite. More information on
    Fermi and its data products is available from the Fermi Science Support Center
    (http://fermi.gsfc.nasa.gov/ssc/).
Quantify variability (such as confidence interval) of fertilizer response curves and optimum fertilizer rates using bootstrapping residuals with several popular non-linear and linear models.
Original idea was presented in the reference paper. Varghese et al. (2020, 74(1):35-42) "Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery". Marine fisheries governance and management practices are very essential to ensure the sustainability of the marine resources. A widely accepted resource management strategy towards this is to derive sustainable fish harvest levels based on the status of marine fish stock. Various fish stock assessment models that describe the biomass dynamics using time series data on fish catch and fishing effort are generally used for this purpose. In the scenario of complex multi-species marine fishery in which different species are caught by a number of fishing gears and each gear harvests a number of species make it difficult to obtain the fishing effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum of efforts expended by different fishing gears. This necessitates standardisation of fishing effort in unit base.
Provides a collection of functions to evaluate
	barrier options, Asian options, binary options, currency 
    translated options, lookback options, multiple asset options
	and multiple exercise options.
Provides pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables).
Provides functions for analysing
  and modelling extreme events in financial time Series. The
  topics include: (i) data pre-processing, (ii) explorative 
  data analysis, (iii) peak over threshold modelling, (iv) block
  maxima modelling, (v) estimation of VaR and CVaR, and (vi) the
  computation of the extreme index.
Extends the out of memory vectors of 'ff' with
    statistical functions and other utilities to ease their usage.
Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997, <doi:10.1016/s0167-5877(97)00081-0>). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002, <doi:10.1016/S0167-5877(01)00245-8>). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.
Downloads all the datasets (you can exclude the daily ones or specify a list of those you are targeting specifically) from Kenneth French's Website at <https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>, process them and convert them to list of 'xts' (time series).
Force field simulation of interaction of set of points.
        Very useful for placing text labels on graphs, such as
        scatterplots.
Format the raw data from the ESPN fantasy football
    API <https://fantasy.espn.com/apis/v3/games/ffl/> into tidy tables.
    Scrape data on public leagues, rosters, athletes, and matches.
General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.
A collection of functions that allows users to retrieve 
  metadata for the Fragile Families challenge via a Web API 
  (<http://api.metadata.fragilefamilies.princeton.edu>).  
  Users can select and search metadata for relevant variables by 
  filtering on different attribute names.
Helps access various Fantasy Football APIs by handling authentication and
    rate-limiting, forming appropriate calls, and returning tidy
    dataframes which can be easily connected to other data sources.
An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in C++ and uses Rcpp. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic CUSUM and EWMA methods, are included.
Provides a simple and efficient wrapper around the fastest
        Fourier transform in the west (FFTW) library <http://www.fftw.org/>.
Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
Bi-variate data fitting is done by two stochastic
        components: the marginal distributions and the dependency
        structure. The dependency structure is modeled through a
        copula. An algorithm was implemented considering seven families
        of copulas (Generalized Archimedean Copulas), the best fitting
        can be obtained looking all copula's options (totally positive
        of order 2 and stochastically increasing models).
This is a package for implementation of Flury-Gautschi
        algorithms.
Provides a collection of functions to 
  analyze and model heteroskedastic behavior in financial time
  series models.
Read and Parse for Fundamental Geo-Spatial Data (FGD) which downloads XML file 
    from providing site (<https://fgd.gsi.go.jp/download/menu.php>). The JPGIS format file 
    provided by FGD so that it can be handled as an R spatial object such as 'sf' and 
    'raster', 'terra' or 'stars'.
    Supports the FGD version 4.1, and accepts fundamental items and digital elevation models.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to install and load the entire package-collection with a
    single R command, and provides convenient ways to find relevant
    documentation. Most commonly, you should not worry about the
    individual packages that make up the package-collection as you can
    access all features via this package. To learn more about ForestGEO
    visit <http://www.forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to implement analyses of plot species distributions,
    topography, demography, and biomass. It also includes a torus
    translation test to determine habitat associations of tree species as
    described by Zuleta et al. (2018) <doi:10.1007/s11104-018-3878-0>. To
    learn more about ForestGEO visit <https://forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to plot ForestGEO data. To learn more about ForestGEO visit
    <https://forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to easily import, filter, and modify ForestGEO data. To
    learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.
Access small example datasets from Luquillo, a
    ForestGEO site in Puerto Rico
    (<https://forestgeo.si.edu/sites/north-america/luquillo>).
Estimates a functional graphical model and a partially separable Karhunen-Loève decomposition for a multivariate Gaussian process. See Zapata J., Oh S. and Petersen A. (2019) <arXiv:1910.03134>.
Growth models and forest production require existing data
    manipulation and the creation of new data, structured from basic forest
    inventory data. The purpose of this package is provide functions to support
    these activities.
Goodness-of-fit test with multiplier or parametric
        bootstrap.
A permutation technique to explore and control for spatial autocorrelation. This package contains low level functions for performing permutations and calculating statistics as well as higher level functions. Higher level functions are an easy to use function for performing spatially restricted permutation tests and summarize and plot results.
Implement algorithms for feature grouping and selection over an undirected graph, solves problems like graph fused lasso, graph OSCAR and so on.
Rapidly create a GUI interface for a function you created by automatically creating widgets for arguments of the function. Automatically parses help routines for context-sensitive help to these arguments. The interface essentially a wrapper to some Tcl/Tk routines to both simplify and facilitate GUI creation. More advanced Tcl/Tk routines/GUI objects can be incorporated into the interface for greater customization for the more experienced.
Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.
Provides structural data for Norway. Datasets relating to maps, population in municipalities, vaccination coverage for childhood vaccines, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2019.
Useful tools for conveniently downloading FHIR resources in xml format 
    and converting them to R data frames. The package uses FHIR-search to download bundles 
    from a FHIR server, provides functions to save and read xml-files containing such bundles 
    and allows flattening the bundles to data.frames using XPath expressions.
Functions to compare two or more survival curves with:
             a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
             b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
        Common volumetric equations (Smalian, Newton and Huber) as well
        stacking factor and form
Routines for estimating tree fiber (tracheid) length distributions in the standing tree
           based on increment core samples. Two types of data can be used with the package, increment 
           core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani 
          Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell
          lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells)
           without being able to identify which cells are cut or if they are fines or fibres. The
          microscopy measured data consist of the observed lengths of the uncut fibres in the increment
         core. A censored version of a mixture of the fine and fiber length distributions is proposed to
         fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the
        assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers
        (fines) that at least partially appear in the increment core, being the generalized gamma and 
        the log normal densities.
Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes.  Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models.  The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>. The package is described in Miettinen, Nordhausen and Taskinen (2018) <doi:10.32614/RJ-2018-046>.
In remote sensing, designing a field campaign to collect ground-truth data can be a challenging task. We need to collect representative samples while accounting for issues such as budget constraints and limited accessibility created by e.g. poor infrastructure. As suggested by Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015>, this demands the establishment of best-practices to collect ground-truth data that avoid the waste of time and funds. 'fieldRS' addresses this issue by helping scientists and practitioners design field campaigns through the identification of priority sampling sites, the extraction of potential sampling plots and the conversion of plots into consistent training and validation samples that can be used in e.g. land cover classification.
For curve, surface and function fitting with an emphasis
 on splines, spatial data, geostatistics,  and spatial statistics. The major methods
 include cubic, and thin plate splines, Kriging, and compactly supported
 covariance functions for large data sets. The splines and Kriging methods are
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance function parameters by cross
 validation and also by restricted maximum likelihood. For Kriging
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple format can be used for
 spatial prediction. There are also many useful functions for plotting
 and working with spatial data as images. This package also contains
 an implementation of sparse matrix methods for large spatial data
 sets and currently requires the sparse matrix (spam) package. Use
 help(fields) to get started and for an overview.  The fields source
 code is deliberately commented and provides useful explanations of
 numerical details as a companion to the manual pages. The commented
 source code can be viewed by expanding the source code version
 and looking in the R subdirectory. The reference for fields can be generated
 by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
 of this package was supported in part by the National Science Foundation  Grant
 1417857,  the National Center for Atmospheric Research, and Colorado School of Mines.
 See the Fields URL
 for a vignette on using this package and some background on spatial statistics.