Tools for calculating Laspeyres, Paasche, and Fisher
  price and quantity indices.
Tools for econometric production analysis
   with the Symmetric Normalized Quadratic (SNQ) profit function,
   e.g. estimation, imposing convexity in prices,
   and calculating elasticities and shadow prices.
Fast imputations under the object-oriented programming paradigm. 	
  Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'.
  The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used.
  A single evaluation of a quantitative model for the multiple imputations is another major enhancement.
  A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.
Multiple Imputation has been shown to 
  be a flexible method to impute missing values by 
  Van Buuren (2007) <doi:10.1177/0962280206074463>. 
  Expanding on this, random forests have been shown 
  to be an accurate model by Stekhoven and Buhlmann 
  <arXiv:1105.0828> to impute missing values in datasets. 
  They have the added benefits of returning out of bag 
  error and variable importance estimates, as well as 
  being simple to run in parallel.
Michel Rodange was a Luxembourguish writer and poet who lived in the 19th century. 
    His most notable work is Rodange (1872, ISBN:1166177424), ("Renert oder de Fuuß am Frack an a Ma'nsgrëßt"),
    but he also wrote many more works, including Rodange, Tockert (1928) <https://www.autorenlexikon.lu/page/document/361/3614/1/FRE/index.html> 
    ("D'Léierchen - Dem Léiweckerche säi Lidd") and Rodange, Welter (1929) <https://www.autorenlexikon.lu/page/document/361/3615/1/FRE/index.html>
    ("Dem Grow Sigfrid seng Goldkuommer"). This package contains three datasets, each made from the
    plain text versions of his works available on 
    <https://data.public.lu/fr/datasets/the-works-in-luxembourguish-of-michel-rodange/>.
Implementation of a framework for cluster analysis with selection of the
    final number of clusters and an optional variable selection procedure. The package
    is designed to integrate the results of multiple imputed datasets while accounting
    for the uncertainty that the imputations introduce in the final results. In addition,
    the package can also be used for a cluster analysis of the complete cases of a single
    dataset. The package also includes specific methods to summarize and plot the results.
    The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.
A procedure for comparing multivariate samples associated with
    different groups. It uses principal component analysis to convert
    multivariate observations into a set of linearly uncorrelated statistical
    measures, which are then compared using a number of statistical methods. The
    procedure is independent of the distributional properties of samples and
    automatically selects features that best explain their differences, avoiding
    manual selection of specific points or summary statistics. It is appropriate
    for comparing samples of time series, images, spectrometric measures or
    similar multivariate observations. This package is described in Fachada et
    al. (2016) <doi:10.32614/RJ-2016-055>.
Fits Semiparametric Promotion Time Cure Models, taking into account (using a 
			 corrected score approach or the SIMEX algorithm) or not the measurement error
			 in the covariates, using a backfitting approach to maximize the likelihood.
A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
Provides infrastructure to accurately measure and compare
        the execution time of R expressions.
Provides functions to enhance the available 
            statistical analysis procedures in R by providing simple functions to 
            analysis and visualize the 16S rRNA data.Here we present a tutorial 
            with minimum working examples to demonstrate usage and dependencies.
A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2021)<arXiv:2104.00242>).  The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.
Functions for assigning 16S sequence data to a
    taxonomic level in the tree-of-life for prokaryotes.
The consensus taxonomy for prokaryotes is a set of data-sets for
    best possible taxonomic classification based on 16S rRNA sequence data.
Provides utilities for reading and processing microdata from Spanish official statistics with R.
A series of statistical and plotting approaches in microbial community ecology based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis.
A downstream bioinformatics tool to construct and assist 
    curation of microhaplotypes from short read sequences.
Most multilevel methodologies can only model macro-micro
    multilevel situations in an unbiased way, wherein group-level predictors
    (e.g., city temperature) are used to predict an individual-level
    outcome variable (e.g., citizen personality). In contrast,
    this R package enables researchers to model micro-macro situations, wherein
    individual-level (micro) predictors (and other group-level predictors) are
    used to predict a group-level (macro) outcome variable in an unbiased way.
This group of functions simplifies the creation of linked micromap plots. Please
  see <https://www.jstatsoft.org/v63/i02/> for additional details.
Provides the users with the ability to quickly create
     linked micromap plots for a collection of geographic areas.
     Linked micromap plots are visualizations of geo-referenced data
     that link statistical graphics to an organized series of small
     maps or graphic images. The Help description contains examples
     of how to use the 'micromapST' function. Contained in this
     package are border group datasets to support creating linked
     micromap plots for the 50 U.S. states and District of Columbia
     (51 areas), the U. S. 20 Seer Registries, the 105 counties in
     the state of Kansas, the 62 counties of New York, the 24
     counties of Maryland, the 29 counties of Utah, the 32
     administrative areas in China, the 218 administrative areas in
     the UK and Ireland (for testing only), the 25 districts in the
     city of Seoul South Korea, and the 52 counties on the Africa
     continent.
     A border group dataset contains the boundaries related to the
     data level areas, a second layer boundaries, a top or third
     layer boundary, a parameter list of run options, and a cross
     indexing table between area names, abbreviations, numeric
     identification and alias matching strings for the specific
     geographic area.  By specifying a border group, the package
     create linked micromap plots for any geographic region.  The
     user can create and provide their own border group dataset for
     any area beyond the areas contained within the package.
     References: Carr and Pickle, Chapman and Hall/CRC, Visualizing
     Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson,
     and Carr (2015), micromapST: Exploring and Communicating
     Geospatial Patterns in US State Data., Journal of Statistical
     Software, 63(3), 1-25., <https://www.jstatsoft.org/v63/i03/>.
     Copyrighted 2013, 2014, 2015, 2016, and 2022 by Carr, Pearson
     and Pickle.
Provides a framework based on S3 dispatch for constructing models
  of mosquito-borne pathogen transmission which are constructed from submodels of various
  components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical
  expression for the distribution of bites on hosts means that different models
  (stochastic, deterministic, etc.) can be coherently incorporated and updated
  over a discrete time step.
Measures niche breadth and overlap of microbial taxa from large matrices.
			Niche breadth measurements include Levins' niche breadth (Bn) index, Hurlbert's Bn 
			and Feinsinger's proportional similarity (PS) index. (Feinsinger, P., Spears, E.E.,
			Poole, R.W. (1981) <doi:10.2307/1936664>). Niche overlap measurements include 
			Levin's Overlap (Ludwig, J.A. and Reynolds, J.F. (1988, ISBN:0471832359)) and a Jaccard 
			similarity index of Feinsinger's PS values between taxa pairs, as Proportional Overlap.
A collection of functions for computations and visualizations of microbial pan-genomes.
The microplot function writes a set of R graphics files to be used as
  microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word',
  or 'Excel' files.  For 'LaTeX', we provide methods for the
  Hmisc::latex() generic function to construct 'latex' tabular
  environments which include the graphs.  These can be used directly
  with the operating system 'pdflatex' or 'latex' command, or by using
  one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an
  intermediary.  For 'MS Word', the msWord() function uses the
  'flextable' package to construct 'Word' tables which include the
  graphs.  There are several distinct approaches for constructing HTML
  files.  The simplest is to use the msWord() function with argument
  filetype="html".  Alternatively, use either 'Emacs org-mode' or the
  htmlTable::htmlTable() function to construct an 'HTML' file
  containing tables which include the graphs.  See the documentation
  for our as.htmlimg() function.  For 'Excel' use on 'Windows', the
  file examples/irisExcel.xls includes 'VBA' code which brings the
  individual panels into individual cells in the spreadsheet.
  Examples in the examples and demo subdirectories are shown with
  'lattice' graphics, 'ggplot2' graphics, and 'base' graphics.
  Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and
  'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input
  files and their 'pdf' output files.  Examples for 'HTML' include
  'org-mode' and 'Rmd' input files and their webarchive 'HTML' output
  files.  In addition, the as.orgtable() function can display a
  data.frame in an 'org-mode' document.  The examples for 'MS Word'
  (with either filetype="docx" or filetype="html") work with all
  operating systems.  The package does not require the installation of
  'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files.
Modelling interacting microbial populations - example applications
    include human gut microbiota, rumen microbiota and phytoplankton. Solves a
    system of ordinary differential equations to simulate microbial growth and
    resource uptake over time. This version contains network visualisation functions.
Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a  general simulation methodology:
  Algorithm III of Barnett, Helen, Helena Geys, Tom Jacobs, and Thomas Jaki. (2017) "Optimal Designs for Non-Compartmental
  Analysis of Pharmacokinetic Studies. (currently unpublished)"
  This methodology consist of (1) specifying a pharmacokinetic model
  including variability among animals; (2) generating possible sampling times; (3)
  evaluating performance of each time point choice on simulated data; (4)
  generating possible schemes given a time point choice and additional constraints
  and finally (5) evaluating scheme performance on simulated data. The default
  settings differ from the article of Barnett and others, in the default pharmacokinetic model used and
  the parameterization of variability among animals. Details can be found in the package vignette. A 'shiny'
  web application is included, which guides users from model parametrization to
  optimal microsampling scheme.
Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.
'Microservice' architectural style is an approach to
    developing a single application as a suite of small services, each
    running in its own process and communicating with lightweight
    mechanisms, often an 'HTTP' resource 'API'. These services are built
    around business capabilities and independently deployable by fully
    automated deployment machinery. There is a bare minimum of centralized
    management of these services, which may be written in different
    programming languages and use different data storage technologies.
Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.
An interface to the 'Microsoft 365' (formerly known as 'Office 365') suite of cloud services, building on the framework supplied by the 'AzureGraph' package. Enables access from R to data stored in 'Teams', 'SharePoint Online' and 'OneDrive', including the ability to list drive folder contents, upload and download files, send messages, and retrieve data lists. Also provides a full-featured 'Outlook' email client, with the ability to send emails and manage emails and mail folders.
The toolkit 'µSTASIS' has been developed for the stability analysis of microbiota in a temporal framework by leveraging on iterative clustering. Concretely, the core function uses Hartigan-Wong k-means algorithm as many times as possible for stressing out paired samples from the same individuals to test if they remain together for multiple numbers of clusters over a whole data set of individuals. Moreover, the package includes multiple functions to subset samples from paired times, validate the results or visualize the output.
This toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model as described in Zinn (2014) <doi:10.34196/IJM.00105>.
Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) <doi: 10.1214/08-AOAS167>. The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative.
Set of functions designed to simplify transcriptome analysis and identification of marker molecules using microarrays data. The package includes a set of functions that allows performing full pipeline of analysis including data normalization, summarisation, binary classification, FDR (False Discovery Rate) multiple comparison and the definition of potential biological markers.
Contains functions for converting existing HTML/JavaScript
   source into equivalent 'shiny' functions. Bootstraps the process of making
   new 'shiny' functions by allowing us to turn HTML snippets directly into 
   R functions.
Implementation of Bayesian drug-combination platform design. More and more immuno-oncology drug combinations make the traditional two-arm phase II trials inefficient, which stimulate the emerge of platform trials. In the case of multiple trial objectives such as candidates screening and subgroup analysis, we propose an information borrowing drug-combination Bayesian design for platform trials with subgroup exploration. MIDAS-2 consists of one control arm and several experimental agents. We use Bayesian spike and slab prior to identify factors that should be included in regression model and borrow information between combinations in the existence of subgroup interaction. Promising drug combinations are allowed to graduated early to move to next stage and new combination strategies can be added accordingly. Catch-up rule, curtail rule and early stopping rules are also applied to accelerate the trial process.
The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.
Methods and tools for mixed frequency time series data analysis.
    Allows estimation, model selection and forecasting for MIDAS regressions.
Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's  MIDAS.
This is a wrapper around the C++ implementation of 'MIDAS' (Bhatia et al., 2020) <https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf> by Siddharth Bhatia for graph like data.
Implementation of the mid-n algorithms presented in 
             Wellek S (2015) <DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact 
             sample size calculation for superiority trials with binary outcome.
Apply tests of multiple comparisons based
    on studentized 'midrange' and 'range' distributions. 
    The tests are: Tukey Midrange ('TM' test),
    Student-Newman-Keuls Midrange ('SNKM' test), 
    Means Grouping Midrange ('MGM' test) and 
    Means Grouping Range ('MGR' test). The first two tests were published by 
    Batista and Ferreira (2020) <doi:10.1590/1413-7054202044008020>. 
    The last two are being published.
Evolutionary black box optimization algorithms building on the
  'bbotk' package. 'miesmuschel' offers both ready-to-use optimization
  algorithms, as well as their fundamental building blocks that can be used to
  manually construct specialized optimization loops. The Mixed Integer Evolution
  Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be
  implemented, as well as the multi-objective optimization algorithms NSGA-II
  by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.
Impute the covariance matrix of incomplete data so that factor 
  analysis can be performed. Imputations are made using multiple imputation 
  by Multivariate Imputation with Chained Equations (MICE) and combined with 
  Rubin's rules. Parametric Fieller confidence intervals and nonparametric
  bootstrap confidence intervals can be obtained for the variance explained by 
  different numbers of principal components. The method is described in 
  Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.
Tools for estimating, measuring and working with migration data.
A set of tools for analysing multimodal networks.
   All functions operate with matrices, edge lists, 
   and 'igraph', 'network', and 'tidygraph' objects,
   and on one-mode, two-mode (bipartite), and sometimes three-mode networks.
   It includes functions for measuring 
   centrality, centralization, cohesion, closure, and constraint,
   as well as for network block-modelling, regression, and diffusion models.
   The package is released as a complement to 
   'Multimodal Political Networks' (2021, ISBN:9781108985000),
   and includes various datasets used in the book in addition to other network data.
Tools to help convert credit risk data at two time points 
    into traditional credit state migration (aka, "transition") matrices.
    At a higher level, 'migrate' is intended to help an analyst understand 
    how risk moved in their credit portfolio over a time interval. 
    References to this methodology include: 
    1. Schuermann, T. (2008) <doi:10.1002/9780470061596.risk0409>.
    2. Perederiy, V. (2017) <arXiv:1708.00062>.
Calculate various indices, like Crude Migration Rate,
    different Gini indices or the Coefficient of Variation among others, to
    show the (un)equality of migration.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
We report an information-theoretic method which learns a large
    class of causal or non-causal graphical models from purely observational
    data, while including the effects of unobserved latent variables, commonly
    found in many datasets. Starting from a complete graph, the method
    iteratively removes dispensable edges, by uncovering significant information
    contributions from indirect paths, and assesses edge-specific confidences
    from randomization of available data. The remaining edges are then oriented
    based on the signature of causality in observational data. This approach can
    be applied on a wide range of datasets and provide new biological insights
    on regulatory networks from single cell expression data, genomic alterations
    during tumor development and co-evolving residues in protein structures.
    For more information you can refer to:
    Cabeli et al. PLoS Comp. Bio. 2020 <doi:10.1371/journal.pcbi.1007866>,
    Verny et al. PLoS Comp. Bio. 2017 <doi:10.1371/journal.pcbi.1005662>.
Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response.
             Reference for mean score method, inverse probability weighted method is
             Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.
Functions for estimating structural equation models using 
    instrumental variables.
An interface to build machine learning models for
    classification and regression problems. 'mikropml' implements the ML
    pipeline described by Topçuoğlu et al. (2020)
    <doi:10.1128/mBio.00434-20> with reasonable default options for data
    preprocessing, hyperparameter tuning, cross-validation, testing, model
    evaluation, and interpretation steps.  See the website
    <https://www.schlosslab.org/mikropml/> for more information,
    documentation, and examples.
Weakly supervised (WS), multiple instance (MI) data lives in
    numerous interesting applications such as drug discovery, object
    detection, and tumor prediction on whole slide images. The 'mildsvm'
    package provides an easy way to learn from this data by training
    Support Vector Machine (SVM)-based classifiers. It also contains
    helpful functions for building and printing multiple instance data
    frames. The core methods from 'mildsvm' come from the following
    references: Kent and Yu (2022) <arXiv:2206.14704>; Xiao, Liu, and Hao
    (2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012)
    <https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>;
    Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews
    et al. (2003)
    <https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>.
    Many functions use the 'Gurobi' optimization back-end to improve the
    optimization problem speed; the 'gurobi' R package and associated
    software can be downloaded from <https://www.gurobi.com> after
    obtaining a license.
A variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted).
Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Milet et al 2020 <doi:10.1101/2020.01.17.910109>.
The multiple instance data set consists of many independent
    subjects (called bags) and each subject is composed of several components
    (called instances). The outcomes of such data set are binary or categorical responses,
    and, we can only observe the subject-level outcomes. For example, in manufacturing
    processes, a subject is labeled as "defective" if at least one of its own
    components is defective, and otherwise, is labeled as "non-defective". The
    'milr' package focuses on the predictive model for the multiple instance
    data set with binary outcomes and performs the maximum likelihood estimation
    with the Expectation-Maximization algorithm under the framework of logistic
    regression. Moreover, the LASSO penalty is attached to the likelihood function
    for simultaneous parameter estimation and variable selection.
Guesses the MIME type from a filename extension using the data
    derived from /etc/mime.types in UNIX-type systems.
Generalized low-rank models for mixed and incomplete data frames. The main function may be used for dimensionality reduction of imputation of numeric, binary and count data (simultaneously). Main effects such as column means, group effects, or effects of row-column side information (e.g. user/item attributes in recommendation system) may also be modelled in addition to the low-rank model. Geneviève Robin, Olga Klopp, Julie Josse, Éric Moulines, Robert Tibshirani (2018) <arXiv:1806.09734>.
Provides an intuitive framework for ad-hoc statistical analysis of 1H-NMR metabolomics by Nightingale Health. It allows to easily explore new metabolomics measurements assayed by Nightingale Health, comparing the distributions with a large Consortium (BBMRI-nl); project previously published metabolic scores [<doi:10.1016/j.ebiom.2021.103764>, <doi:10.1161/CIRCGEN.119.002610>, <doi:10.1038/s41467-019-11311-9>, <doi:10.7554/eLife.63033>, <doi:10.1161/CIRCULATIONAHA.114.013116>, <doi:10.1007/s00125-019-05001-w>]; and calibrate the metabolic surrogate values to a desired dataset.
The MIMS-unit algorithm is developed to compute Monitor Independent
    Movement Summary Unit, a measurement to summarize raw accelerometer data 
    while ensuring harmonized results across different devices. It also includes
    scripts to reproduce results in the related publication 
    (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).
Calculate dissolved gas concentrations from raw MIMS 
	(Membrane Inlet Mass Spectrometer) signal data. Use mimsy() on 
	a formatted CSV file to return dissolved gas concentrations 
	(mg and microMole) of N2, O2, Ar based on 
	gas solubility at temperature, pressure, and salinity. See references 
	Benson and Krause (1984) <DOI:10.4319/lo.1992.37.6.1307>, Garcia and 
	Gordon (1992) <DOI:10.4319/lo.1984.29.3.0620>, Stull (1947) 
	<DOI:10.1021/ie50448a022>, and Hamme and Emerson (2004) 
	<DOI:10.1016/j.dsr.2004.06.009> for more information. Easily save the 
	output to a nicely-formatted multi-tab 'Excel' workbook with mimsy.save(). 
	Supports dual-temperature standard calibration for dual-bath MIMS setups.
A versatile tool that aims at (1) defining the minimum background extent necessary to fit Species Distribution Models reliable enough to extract ecologically relevant conclusions from them and (2) optimizing the modelling process in terms of computation demands. See Rotllan-Puig, X. & Traveset, A. (2021) <https://www.sciencedirect.com/science/article/pii/S0304380020304191>.
Allows users to produce estimates and MSE for multivariate variables using Linear Mixed Model. The package follows the approach of Datta, Day and Basawa (1999) <doi:10.1016/S0378-3758(98)00147-5>.
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, and directory structures, into mind map widgets or files ('.mm'), and vice versa. "FreeMind" mind map ('.mm') files can be opened by or imported to common mindmap software such as 'FreeMind' (<http://freemind.sourceforge.net/wiki/index.php/Main_Page>).
Citrus is a computational technique developed for the analysis
    of high dimensional cytometry data sets. This package extracts, statistically
    analyzes, and visualizes marker expression from 'citrus' data. This code was used to 
    generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. 
    “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile 
    Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus,
    please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download
    the 'citrus' package, please see <https://github.com/nolanlab/citrus>.
This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <DOI:10.1080/00401706.2018.1552203>. The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642.
The nonparametric two-stage Bayesian adaptive design is a novel phase II clinical trial design for finding the minimum effective dose (MinED). This design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. It is used to design single-agent trials.
Wrapper for 'minepy' implementation of Maximal
        Information-based Nonparametric Exploration statistics (MIC and
        MINE family). Detailed information of the ANSI C implementation of
	'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.
Makes it possible to create an internally consistent
    repository consisting of selected packages from CRAN-like repositories.
    The user specifies a set of desired packages, and 'miniCRAN' recursively
    reads the dependency tree for these packages, then downloads only this
    subset. The user can then install packages from this repository directly,
    rather than from CRAN.  This is useful in production settings, e.g. server
    behind a firewall, or remote locations with slow (or zero) Internet access.
Create minimal, responsive, and style-agnostic HTML documents with
    the lightweight CSS frameworks such as 'sakura', 'Water.css', and 'spcss'.
    Powerful features include table of contents floating as a sidebar,
    folding codes and results, and more.
Quick and simple Tcl/Tk Graphical User Interface 
  to call functions. Also comprises a very simple experimental 
  GUI framework.
Generate central composite designs (CCD)with full as well 
    as fractional factorial points (half replicate) and Box Behnken 
    designs (BBD) with minimally changed run sequence.
Create tile grid maps, which are like choropleth maps except each
  region is represented with equal visual space.
The minimax family of distributions is a two-parameter
        family like the beta family, but computationally a lot more
        tractible.
Provides two main functions, minimax() and miniMaxPro(), for computing minimax 
    and minimax projection designs using the minimax clustering algorithm in Mak and 
    Joseph (2018) <DOI:10.1080/10618600.2017.1302881>. Current design region options 
    include the unit hypercube ("hypercube"), the unit simplex ("simplex"), the unit ball
    ("ball"), as well as user-defined constraints on the unit hypercube ("custom"). Minimax
    designs can also be computed on user-provided images using the function minimax.map(). 
    Design quality can be assessed using the function mMdist(), which computes the minimax
    (fill) distance of a design.
Shiny web application to run meta-analyses. 
    Essentially a graphical front-end to package 'meta' for R. 
    Can be useful as an educational tool, and for quickly 
    analyzing and sharing meta-analyses. 
    Provides output to quickly fill in GRADE (Grading of 
    Recommendations, Assessment, Development and Evaluations)
    Summary-of-Findings tables. 
    Importantly, it allows further processing of the results 
    inside R, in case more specific analyses are needed.
Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.
Provides UI widget and layout functions for writing Shiny apps
    that work well on small screens.
Computes the Minkowski sum of two 3D meshes, resulting in a
    new 3D mesh. The Minkowski addition has applications in mathematical
    morphology and 3D computer graphics. The computations are performed by
    the 'C++' library 'CGAL' (<https://www.cgal.org/>).
Performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) <DOI:10419/161931>.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Derivative-free optimization by quadratic approximation
        based on an interface to Fortran implementations by M. J. D.
        Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
Using this package, one can determine the minimum sample size required so that the absolute deviation of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
Using this package, one can determine the minimum sample size required so that the mean square error of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
This is a R implementation of "Minimum SNPs" software as described in "Price E.P., Inman-Bamber, J., Thiruvenkataswamy, V., Huygens, F and Giffard, P.M." (2007) <doi:10.1186/1471-2105-8-278> "Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants."
It can be used to create/encode molecular "license-plates" from sequences and to also decode the "license-plates" back to sequences.  While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) <doi:10.1093/bioinformatics/btw194>. It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) <doi:10.1038/srep41184>.
An implementation of the iterative proportional fitting (IPFP), 
    maximum likelihood, minimum chi-square and weighted least squares procedures
    for updating a N-dimensional array with respect to given target marginal 
    distributions (which, in turn can be multidimensional). The package also
    provides an application of the IPFP to simulate multivariate Bernoulli
    distributions.
Generic functions to produce area/bar/box/line plots of data following IAMC (Integrated Assessment Modeling Consortium) submission format.
Lightweight parallel code execution and distributed computing.
    Designed for simplicity, a 'mirai' evaluates an R expression asynchronously,
    on local or network resources, resolving automatically upon completion.
    Features efficient task scheduling, scalability beyond R connection limits,
    and transports faster than TCP/IP for inter-process communications, courtesy
    of 'nanonext' and 'NNG' (Nanomsg Next Gen).
Fitting recurrent events survival models for
    left-censored data with multiple imputation of the number of previous episodes.
    See Hernández-Herrera G, Moriña D, Navarro A. (2020) <arXiv:2007.15031>.
Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.
Providing tools for microRNA (miRNA) text mining. 
    miRetrieve summarizes miRNA literature by extracting, counting, and 
    analyzing miRNA names, thus aiming at gaining biological insights into a 
    large amount of text within a short period of time. To do so, miRetrieve 
    uses regular expressions to extract miRNAs and tokenization to 
    identify meaningful miRNA associations. In addition, miRetrieve uses 
    the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020) 
    "miRTarBase 2020: updates to the experimentally validated microRNA–target 
    interaction database" <doi:10.1093/nar/gkz896>) to display field-specific 
    miRNA-mRNA interactions. The most important functions are available as a 
    Shiny web application under <https://miretrieve.shinyapps.io/miRetrieve/>.
Test for overall association between microbiome composition data 
  and phenotypes via phylogenetic kernels. The phenotype can be univariate 
  continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), 
  survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), 
  multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and 
  structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). 
  The package can also use robust regression (unpublished work) and 
  integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). 
  In each case, the microbiome community effect is modeled nonparametrically 
  through a kernel function, which can incorporate phylogenetic tree information.
A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 <doi:10.1186/s12859-015-0715-9>. The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 <doi:10.1016/j.softx.2020.100569>. Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information.
Machine learning method specifically designed for
    pre-miRNA prediction. It takes advantage of unlabeled sequences to improve
    the prediction rates even when there are just a few positive examples, when
    the negative examples are unreliable or are not good representatives of
    its class. Furthermore, the method can automatically search for negative
    examples if the user is unable to provide them. MiRNAss can find a good
    boundary to divide the pre-miRNAs from other groups of sequences; it
    automatically optimizes the threshold that defines the classes boundaries,
    and thus, it is robust to high class imbalance. Each step of the method is
    scalable and can handle large volumes of data.
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of dichotomous and polytomous response data using
    unidimensional and multidimensional latent trait models under the Item
    Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). 
    Exploratory and confirmatory models can be estimated with quadrature (EM) 
    or stochastic (MHRM) methods. Confirmatory
    bi-factor and two-tier analyses are available for modeling item testlets.
    Multiple group analysis and mixed effects designs also are available for
    detecting differential item and test functioning as well as modeling
    item and person covariates. Finally, latent class models such as the DINA,
    DINO, multidimensional latent class, and several other discrete latent
    variable models, including mixture and zero-inflated response models, 
    are supported.
Provides tools to generate an HTML interface for creating adaptive
    and non-adaptive educational and psychological tests using the shiny
    package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). 
    Suitable for applying unidimensional and multidimensional
    computerized adaptive tests (CAT) using item response theory methodology and for
    creating simple questionnaires forms to collect response data directly in R.
    Additionally, optimal test designs (e.g., "shadow testing") are supported
    for tests which contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying the behavior of computerized adaptive test banks.
Package for combined miRNA- and mRNA-testing.
Provides constrained joint maximum likelihood estimation
    algorithms for item factor analysis (IFA) based on multidimensional item response theory
    models. So far, we provide functions for exploratory and confirmatory IFA based on the 
    multidimensional two parameter logistic (M2PL) model for binary response data. Comparing 
    with traditional estimation methods for IFA, the methods implemented in this package scale
    better to data with large numbers of respondents, items, and latent factors. The computation
    is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to:
    1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for 
    High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. 
    <doi:10.1007/s11336-018-9646-5>;
    2. Chen, Y., Li, X., & Zhang, S. (2019). Structured Latent Factor Analysis for Large-scale Data: 
    Identifiability, Estimability, and Their Implications. Journal of the American Statistical 
    Association, <doi: 10.1080/01621459.2019.1635485>.
Provides singular value decomposition based estimation algorithms for exploratory item factor analysis (IFA) 
    based on multidimensional item response theory models. For more information, please refer to:
    Zhang, H., Chen, Y., & Li, X. (2020). A note on exploratory item factor analysis by 
    singular value decomposition. Psychometrika, 1-15, <DOI:10.1007/s11336-020-09704-7>.
Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.
A collection of miscellaneous 3d plots, including
    isosurfaces.
Various functions for random number generation, density 
             estimation, classification, curve fitting, and spatial 
             data analysis.
Implementing various things including functions for LaTeX tables,
    the Kalman filter, web scraping, development tools, relative risk and odds
    ratio.
Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) <doi:10.1007/s11222-016-9705-7>. Misclassification probabilities can either be specified as fixed or estimated. Models with time dependent misclassification may also be fitted.
Estimates models that extend the standard GLM to take
    misclassification into account. The models require side information from a secondary data set
    on the misclassification process, i.e. some sort of misclassification
    probabilities conditional on some common covariates.
    A detailed description of the algorithm can be found in
    Dlugosz, Mammen and Wilke (2015) <http://www.zew.de/PU70410>.
A collection of miscellaneous methods to simplify various tasks,
  including plotting, data.frame and matrix transformations, environment
  functions, regular expression methods, and string and logical operations, as
  well as numerical and statistical tools. Most of the methods are simple but
  useful wrappers of common base R functions, which extend S3 generics or
  provide default values for important parameters.
Miscellaneous small tools and utilities.
   Many of them facilitate the work with matrices,
   e.g. inserting rows or columns, creating symmetric matrices,
   or checking for semidefiniteness.
   Other tools facilitate the work with regression models,
   e.g. extracting the standard errors,
   obtaining the number of (estimated) parameters,
   or calculating R-squared values.
Clears the workspace. Useful for the beginnings of R scripts, to
    avoid potential problems with accidentally using information from variables
    or functions from previous script evaluations, too many figure windows open
    at the same time, packages that you don't need any more, or a cluttered
    console.  Uses code from various StackOverflow users.  See help(mise) for
    pointers to the relevant StackOverflow pages.
Penalized regression methods, such as lasso and elastic net, are used in
    many biomedical applications when simultaneous regression coefficient
    estimation and variable selection is desired. However, missing data
    complicates the implementation of these methods, particularly when
    missingness is handled using multiple imputation. Applying a variable
    selection algorithm on each imputed dataset will likely lead
    to different sets of selected predictors, making it difficult
    to ascertain a final active set without resorting to ad hoc
    combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet)
    and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes,
    developed by Du et al (2020), currently under review. They, by construction,
    force selection of the same variables across multiply imputed data.
    'miselect' also provides cross validated variants of these methods.
Open-source software for computing Likelihood ratios thresholds and error rates in DNA kinship testing. Marsico FL. et al (2021) <doi:10.1016/j.fsigen.2021.102519>.
Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.
Partial Replacement Imputation Estimation (PRIME) can overcome problems caused by missing covariates in additive partially linear model. PRIME conducts imputation and regression simultaneously with known and unknown model structure. More details can be referred to 
    Zishu Zhan, Xiangjie Li and Jingxiao Zhang. (2022) <arXiv:2205.14994>.
There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates.
Provides a simple and trustworthy methodology for the analysis of misreported continuous time series. See Moriña, D, Fernández-Fontelo, A, Cabaña, A, Puig P. (2021) <arXiv:2003.09202v2>.
Enables investigation of the predictors of misreporting on sensitive survey questions through a multivariate list experiment regression method. The method permits researchers to model whether a survey respondent's answer to the sensitive item in a list experiment is different from his or her answer to an analogous direct question.
Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) <doi:10.1186/1471-2105-8-25>).
Offers a convenient pipeline to test and compare various missing data
  imputation algorithms on simulated and real data. These include simpler methods, such as mean and median
  imputation and random replacement, but also include more sophisticated algorithms already implemented in popular 
  R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren 
  and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) 
  <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 
  'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 
  'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables 
  vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation 
  algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and 
  sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns.
  'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm
  in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you 
  assess imputation performance.
Implements two methods: a nonparametric risk adjustment and a
    data imputation method that use general population mortality tables to allow a
    correct analysis of time to disease recurrence. Also includes a powerful set of
    object oriented survival data simulation functions.
Implements the computation of discrepancy statistics summarizing differences between the density of imputed and observed values and the construction of weights to balance covariates that are part of the missing data mechanism as described in Marbach (2021) <arXiv:2107.05427>.
The function 'missForest' in this package is used to
        impute missing values particularly in the case of mixed-type
        data. It uses a random forest trained on the observed values of
        a data matrix to predict the missing values. It can be used to
        impute continuous and/or categorical data including complex
        interactions and non-linear relations. It yields an out-of-bag
        (OOB) imputation error estimate without the need of a test set
        or elaborate cross-validation. It can be run in parallel to 
        save computation time.
Many times, you will not find data for all dates. After first January, 2011 you may have next data on 20th January, 2011 and so on. Also available dates may have zero values. Try to gather all such kinds of data in different excel sheets of a single excel file. Every sheet will contain two columns (1st one is dates and second one is the data). After loading all the sheets into different elements of a list, using this you can fill the gaps for all the sheets and mark all the corresponding values as zeros. Here I am talking about daily data. Finally, it will combine all the filled results into one data frame (first column is date and other columns will be corresponding values of your sheets) and give one csv file. Number of columns in the data frame will be number of sheets plus one. Then imputation will be done. Daily to weekly conversion is also possible.  More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.
Contains a suite of functions for health economic evaluations with missing outcome data. 
  The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). 
  Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models.
  In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. 
  The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.
A system for Analysis of LSD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).
A system for Analysis of RBD when there is one missing observation. Methods for this process is described in A.M.Gun,M.K.Gupta,B.Dasgupta(2019,ISBN:81-87567-81-3).
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.
Supply functions for the creation and handling of missing
    data as well as tools to evaluate missing data methods. Nearly all
    possibilities of generating missing data discussed by Santos et al.
    (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
    implemented.  Functions are supplied to compare parameter estimates
    and imputed values to true values to evaluate missing data methods.
    Evaluations of these types are done, for example, by Cetin-Berber et
    al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
    <doi:10.1093/bioinformatics/bth499>.
Efficient procedures for fitting the conditional graphical
    lasso models linking a set of predictor variables to a set of response
    variables (or tasks), when the response data may contain missing
    values. 'missoNet' simultaneously estimates the predictor coefficients
    for all tasks by leveraging information from one another, in order to
    provide more accurate predictions in comparison to modeling them
    individually. Meanwhile, 'missoNet' is able to estimate the response
    network structure influenced by conditioning predictor variables in a
    L1-regularized conditional Gaussian graphical model. In contrast to
    most penalized multi-task regression (conditional graphical lasso)
    methods, 'missoNet' has the capability of obtaining estimates even
    when the response data is corrupted by missing values. The method
    automatically enjoys the theoretical and computational benefits of
    convexity, and returns solutions that are comparable/close to the
    estimates without any missing values. The package also includes
    auxiliary functions for data simulation, goodness-of-fit evaluation,
    regularization parameter tuning, and visualization of results, as well
    as predictions in new data.
A system for testing differential effects among treatments in case of Randomised Block Design and Latin Square Design when there is one missing observation. Methods for this process are as described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).
Alternative implementation of the beautiful 'MissForest'
    algorithm used to impute mixed-type data sets by chaining random
    forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
    <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
    lightning fast random jungle package 'ranger'. Between the iterative
    model fitting, we offer the option of using predictive mean matching.
    This firstly avoids imputation with values not already present in the
    original data (like a value 0.3334 in 0-1 coded variable).  Secondly,
    predictive mean matching tries to raise the variance in the resulting
    conditional distributions to a realistic level. This would allow e.g.
    to do multiple imputation when repeating the call to missRanger().  A
    formula interface allows to control which variables should be imputed
    by which.
When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 
  due to missing information between node pairs), it is possible to account for the underlying process
  that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
  adjusts the popular stochastic block model from network data sampled under various missing data conditions, 
  as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.
The Self-Organizing Maps with Built-in Missing Data Imputation. Missing values are imputed and regularly updated during the online Kohonen algorithm. Our method can be used for data visualisation, clustering or imputation of missing data. It is an extension of the online algorithm of the 'kohonen' package. The method is described
    in the article "Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values" by S. Rejeb, C. Duveau, T. Rebafka (2022) <arXiv:2202.07963>.
Provide all the data sets and statistical analysis applications used in "Modern Industrial Statistics: with applications in R, MINITAB and JMP" by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2021, which is a third revised and expanded revision of "Modern Industrial Statistics: Design and Control of Quality and Reliability", R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
A flexible computational framework for mixture distributions with the focus on the composite models.
Various reliability analysis methods for rare event inference:1) computing failure probability (probability that the output of a numerical model exceeds a threshold), 2) computing quantile of low or high-order, 3) Wilks formula to compute quantile(s) from a sample or the size of the required i.i.d. sample.
Miscellaneous functions for descriptive statistics (e.g., frequency table, cross tabulation, multilevel descriptive statistics, multilevel R-squared measures, within-group and between-group correlation matrix, various effect size measures), data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and group scores, reading and writing SPSS and Excel files), missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), item analysis (e.g., coefficient alpha and omega, confirmatory factor analysis), and  statistical analysis (e.g., confidence intervals, collinearity diagnostics, analysis of variance, t-test, z-test, sample size determination).
Provides tools for multiple imputation of missing data in multilevel
 modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo',
 and several functions for visualization, data management and the analysis 
 of multiply imputed data sets.
Allows the estimation and downstream statistical analysis of the mitochondrial DNA Heteroplasmy calculated from single-cell datasets <https://github.com/ScialdoneLab/MitoHEAR/tree/master>.
Tools to perform analyses and combine results from
        multiple-imputation datasets.
Extract, transform and load MITRE standards.
    This package gives you an approach to cybersecurity data sets.
    All data sets are build on runtime downloading raw data from MITRE public services.
    MITRE <https://www.mitre.org/> is a government-funded research organization 
    based in Bedford and McLean. Current version includes most used standards as
    data frames. It also provide a list of nodes and edges with all relationships.
Implements the Mittag-Leffler function, distribution,
  random variate generation, and estimation. Based on the Laplace-Inversion
  algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem.  Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>). The imputation models are: bootstrapping imputation (Lubin et.al (2004) <doi:10.1289/ehp.7199>), univariate Bayesian imputation (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian regression imputation.
Estimation/multiple imputation programs for mixed categorical
    and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for model-based clustering and classification.
 It supports various types of data: continuous, categorical and counting
 and can handle mixed data of these types. It can fit Gaussian (with diagonal
 covariance structure), gamma, categorical and Poisson models. The algorithms
 also support missing values. This package can be used as an independent
 alternative to the (not free) 'mixtcomp' software available at
 <https://massiccc.lille.inria.fr/>.
Model time series using mixture autoregressive (MAR)
             models.  Implemented are frequentist (EM) and Bayesian
             methods for estimation, prediction and model
             evaluation. See Wong and Li (2002)
             <doi:10.1111/1467-9868.00222>, Boshnakov (2009)
             <doi:10.1016/j.spl.2009.04.009>), and the extensive
             references in the documentation.
Developed for the following tasks. 1- simulating realizations from the canonical, 
              restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for
              density function of the finite mixture models. 3- Monte Carlo approximation for the 
              observed Fisher information matrix, asymptotic standard error, and the corresponding  
              confidence intervals for parameters of the mixture models sing the method proposed by
              Basford et al. (1997) <https://espace.library.uq.edu.au/view/UQ:57525>.
Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects.
Deconvolution of thermal decay curves allows you to quantify proportions 
    of biomass components in plant litter. Thermal decay curves derived from 
    thermogravimetric analysis (TGA) are imported, modified, and then modelled in a 
    three- or four- part  mixture model using the Fraser-Suzuki function. The output 
    is estimates for weights of pseudo-components corresponding to hemicellulose, 
    cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, 
    H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. 
    (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and 
    Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.
Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.
Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet 
    (Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
    to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
    infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
    paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
    applicable to large datasets. It is described and tested in the accompanying paper by 
    Ahlmann-Eltze and Yau (2018) <doi:10.1109/DSAA.2018.00068>.
Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.
Semi-parametric approach for sparse canonical correlation analysis 
    which can handle mixed data types: continuous, binary and truncated continuous.
    Bridge functions are provided to connect Kendall's tau to latent correlation
    under the Gaussian copula model. The methods are described in 
    Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and 
    Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.
Implementation of the co-clustering method for mixed type data proposed in M. Selosse, J. Jacques, C. Biernacki (2018) <https://hal.archives-ouvertes.fr/hal-01893457>. It consists in clustering simultaneously the rows (observations) and the columns (features) of a heterogeneous data set.
Functions for testing randomness for a univariate time series with arbitrary distribution  (discrete, continuous, mixture of both types) and for testing  independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in  Genest, Nešlehová, Rémillard & Murphy (2019).
Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when 
  the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization 
  with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically 
  performing parameter selection to identify low-rank substructures in the coefficient matrix.
Fits mixed membership models with discrete multivariate data (with
    or without repeated measures) following the general framework of Erosheva et al
    (2004). This package uses a Variational EM approach by approximating the
    posterior distribution of latent memberships and selecting hyperparameters
    through a pseudo-MLE procedure. Currently supported data types are
    Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with 
    fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al 
    (2014) for other examples of mixed membership models.
The estimation of the parameters in mixed Poisson models.
Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) 
    and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). 
    Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets.
    For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).
Inference on stochastic differential models Ornstein-Uhlenbeck or
    Cox-Ingersoll-Ross, with one or two random effects in the drift function.
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.
Utilizing model-based clustering (unsupervised)
        for functional magnetic resonance imaging (fMRI) data.
        The developed methods (Chen and Maitra (2021) <arXiv:2102.03639>)
        include 2D and 3D clustering analyses
        (for p-values with voxel locations) and
        segmentation analyses (for p-values alone) for fMRI data where p-values
        indicate significant level of activation responding to stimulate
        of interesting. The analyses are mainly identifying active
        voxel/signal associated with normal brain behaviors.
        Analysis pipelines (R scripts) utilizing this package
        (see examples in 'inst/workflow/') is also implemented with high
        performance techniques.
Multiple imputation using 'XGBoost', subsampling, and predictive mean 
    matching as described in Deng and Lumley (2023) <arXiv:2106.01574>. Our
    method utilizes the capabilities of XGBoost, a highly efficient implementation
    of gradient boosted trees, to capture interactions and non-linear relations
    automatically. Moreover, we have integrated subsampling and predictive mean
    matching to minimize bias and reflect appropriate imputation variability. This
    package supports various types of variables and offers flexible settings for
    subsampling and predictive mean matching. Additionally, it includes diagnostic
    tools for evaluating the quality of the imputed values.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of  multiple scaled generalized hyperbolic distributions, the 'cMSGHD'  is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.
Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>.
Developed to deal with multi-locus genotype data, this package is especially designed for those panel which include different type of markers. Basic genetic parameters like allele frequency, genotype frequency, heterozygosity and Hardy-Weinberg test of mixed genetic data can be obtained.     In addition, a new test for mutual independence which is compatible for mixed genetic data is developed in this package.
Kernel-based methods are powerful methods for integrating 
    heterogeneous types of data. mixKernel aims at providing methods to combine
    kernel for unsupervised exploratory analysis. Different solutions are 
    provided to compute a meta-kernel, in a consensus way or in a way that 
    best preserves the original topology of the data. mixKernel also integrates
    kernel PCA to visualize similarities between samples in a non linear space
    and from the multiple source point of view. Functions to assess and display
    important variables are also provided in the package. Ref: Jerome Mariette 
    and Nathalie Villa-Vialaneix (2018) <doi:10.1093/bioinformatics/btx682>.
Specification and estimation of multinomial logit
    models.  Large datasets and complex models are supported, with an
    intuitive syntax.  Multinomial Logit Models, Mixed models, random
    coefficients and Hybrid Choice are all supported.  For more
    information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.
The main functions perform mixed models analysis by least squares
    or REML by adding the function r() to formulas of lm() and glm(). A collection of
    text-book statistics for higher education is also included, e.g. modifications
    of the functions lm(), glm() and associated summaries from the package 'stats'.
Provides sampling and density functions for matrix
    variate normal, t, and inverted t distributions;  ML estimation for matrix
    variate normal and t distributions using the EM algorithm,
    including some restrictions on the parameters; and classification by linear and
    quadratic discriminant analysis for matrix variate normal and t
    distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>.
    Performs clustering with matrix variate normal and t mixture models.
A collection of functions to perform various meta-analytical models
  through a unified mixed-effects framework, including standard univariate
  fixed and random-effects meta-analysis and meta-regression, and non-standard
  extensions such as multivariate, multilevel, longitudinal, and dose-response
  models.
Mixed variable optimization for non-linear functions.
    Can optimize function whose inputs are a combination of continuous,
    ordered, and unordered variables.
Simple tools to perform mixture optimization based on the 'desirability' package by Max Kuhn.
    It also provides a plot routine using 'ggplot2' and 'patchwork'.
Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
Provides supplemental functions for the 'mixRasch'
    package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a 
    plotting function to compare item parameters for multiple
    class models and a function that provides average theta values for each class in
    a mixture model.
Fits mixtures of (possibly multivariate) regressions
	(which has been described as doing ANCOVA when you don't
	know the levels). Turner (2000) <doi:10.1111/1467-9876.00198>.
It offers random-forest-based functions to impute clustered
    incomplete data. The package is tailored for but not limited to imputing
    multitissue expression data, in which a gene's expression is measured on the
    collected tissues of an individual but missing on the uncollected tissues.
The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).
Creates and runs Bayesian mixing models to analyze
    biological tracer data (i.e. stable isotopes, fatty acids), which estimate the
    proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR'
    is not one model, but a framework that allows a user to create a mixing model
    based on their data structure and research questions, via options for fixed/
    random effects, source data types, priors, and error terms. 'MixSIAR' incorporates
    several years of advances since 'MixSIR' and 'SIAR'.
The utility of this package is in simulating mixtures of Gaussian
        distributions with different levels of overlap between mixture
        components.  Pairwise overlap, defined as a sum of two
        misclassification probabilities, measures the degree of
        interaction between components and can be readily employed to
        control the clustering complexity of datasets simulated from
        mixtures. These datasets can then be used for systematic
        performance investigation of clustering and finite mixture
        modeling algorithms. Among other capabilities of 'MixSim', there
        are computing the exact overlap for Gaussian mixtures,
        simulating Gaussian and non-Gaussian data, simulating outliers
        and noise variables, calculating various measures of agreement
        between two partitionings, and constructing parallel
        distribution plots for the graphical display of finite mixture
        models.
Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.
Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew 
    power exponential and power exponential distributions, respectively. A generalized expectation-maximization 
    framework is used for parameter estimation. Methodology for mixtures of power exponential distributions is 
    from Dang et al. (2015) <doi: 10.1111/biom.12351>.
Provides an optimization method based on sequential
    quadratic programming (SQP) for maximum likelihood estimation of
    the mixture proportions in a finite mixture model where the
    component densities are known. The algorithm is expected to obtain
    solutions that are at least as accurate as the state-of-the-art
    MOSEK interior-point solver (called by function "KWDual" in the
    'REBayes' package), and they are expected to arrive at solutions
    more quickly when the number of samples is large and the number of
    mixture components is not too large. This implements the "mix-SQP"
    algorithm, with some improvements, described in Y. Kim,
    P. Carbonetto, M. Stephens & M. Anitescu (2020)
    <DOI:10.1080/10618600.2019.1689985>.
Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian
              stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating
              parameters of the symmetric stable distribution within the Bayesian framework.
Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).
Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) 
 dose-response data. Predicting mixture toxicity based on reference 
 models such as 'concentration addition', 'independent action', and 'generalized 
 concentration addition'.
A set of utility functions for analysing and modelling data from 
    continuous report short-term memory experiments using either the 2-component
    mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the 
    3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users 
    are also able to simulate from these models.
An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
Functions for computing the penalized maximum likelihood estimate (PMLE) or maximum likelihood estimate (MLE), testing the order of a finite mixture model using EM-test, drawing histogram of observations and the fitted density or probability mass function of the mixture model.
Implementation of robust model-based cluster analysis for data sets with missing values at random. 
    The models used are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>), 
    Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>), 
    Multivariate Skew's t Mixture (MStM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>),
    Multivariate t  Mixture (MtM, Wang et al., 2004, <doi:10.1016/j.patrec.2004.01.010>), and 
    Multivariate Normal Mixture (MNM, Ghahramani and Jordan, 1994, <doi:10.21236/ADA295618>).
Fit mixture regression models with nonsusceptibility/cure for left-truncated and interval-censored (LTIC) data (see Chen et al. (2013) <doi:10.1002/sim.5845>). This package also provides the nonparametric maximum likelihood estimator (NPMLE) for the survival/event curves with LTIC data.
Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.
Tool for exploring DNA and amino acid variation and inferring the presence of target lineages from microbial high-throughput genomic DNA samples that potentially contain mixtures of variants/lineages. MixviR was originally created to help analyze environmental SARS-CoV-2/Covid-19 samples from environmental sources such as wastewater or dust, but can be applied to any microbial group. Inputs include reference genome information in commonly-used file formats (fasta, bed) and one or more variant call format (VCF) files, which can be generated with programs such as Illumina's DRAGEN, the Genome Analysis Toolkit, or bcftools. See DePristo et al (2011) <doi:10.1038/ng.806> and Danecek et al (2021) <doi:10.1093/gigascience/giab008> for these tools, respectively. Available outputs include a table of mutations observed in the sample(s), estimates of proportions of target lineages in the sample(s), and an R Shiny dashboard to interactively explore the data.
Optimization algorithms implemented in R, including
    conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the
    limited memory BFGS (L-BFGS) methods. Most internal parameters can be set 
    through the call interface. The solvers hold up quite well for 
    higher-dimensional problems.
A set of classes and methods to set up and run multi-species, trait
    based and community size spectrum ecological models, focused on the marine
    environment.
Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), <doi:10.1016/B978-1-55860-247-2.50035-8>) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), <doi:10.1093/oxfordjournals.aje.a113284>; Hosmer et al (1997), <doi:10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O>). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), <doi:10.1093%2Facref%2F9780199976720.001.0001>).
Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), <doi:10.2307/1268758>), robust coefficient of variation (CV) (Arachchige et al. (2019), <arXiv:1907.01110>), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for 'ggplot2' (Wickham (2016), ISBN:978-3-319-24277-4).
Calculation routines based on the FOCUS Kinetics Report (2006,
  2014).  Includes a function for conveniently defining differential equation
  models, model solution based on eigenvalues if possible or using numerical
  solvers.  If a C compiler (on windows: 'Rtools') is installed, differential
  equation models are solved using automatically generated C functions.
  Heteroscedasticity can be taken into account using variance by variable or
  two-component error models as described by Ranke and Meinecke (2018)
  <doi:10.3390/environments6120124>.  Hierarchical degradation models can
  be fitted using nonlinear mixed-effects model packages as a back end as
  described by Ranke et al. (2021) <doi:10.3390/environments8080071>.  Please
  note that no warranty is implied for correctness of results or fitness for a
  particular purpose.
Computation of various confidence intervals (Altman et al. (2000), ISBN:978-0-727-91375-3; Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) including bootstrapped versions (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) as well as Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2), permutation (Janssen (1997), <doi:10.1016/S0167-7152(97)00043-6>), bootstrap (Davison and Hinkley (1997), ISBN:978-0-511-80284-3) and multiple imputation (Barnard and Rubin (1999), <doi:10.1093/biomet/86.4.948>) t-test and Wilcoxon tests. Graphical visualization by volcano and Bland-Altman plots (Bland and Altman (1986), <doi:10.1016/S0140-6736(86)90837-8>; Shieh (2018), <doi:10.1186/s12874-018-0505-y>).
Package for fast computation of the maximum kernel likelihood estimator (mkle).
It's a Modern K-Means clustering algorithm allowing data of any number of dimensions, any initial center, and any number of clusters to expect.
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.
Package solves multiple knapsack optimisation problem. 
    Given a set of items, each with volume and value, 
    it will allocate them to knapsacks of a given size in a way that
    value of top N knapsacks is as large as possible.
Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015), <doi:10.1093/nar/gkv007> ) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).
Power analysis and sample size calculation for Welch and Hsu (Hedderich and Sachs (2018), ISBN:978-3-662-56657-2) t-tests including Monte-Carlo simulations of empirical power and type-I-error. Power and sample size calculation for Wilcoxon rank sum and signed rank tests via Monte-Carlo simulations. Power and sample size required for the evaluation of a diagnostic test(-system) (Flahault et al. (2005), <doi:10.1016/j.jclinepi.2004.12.009>; Dobbin and Simon (2007), <doi:10.1093/biostatistics/kxj036>) as well as for a single proportion (Fleiss et al. (2003), ISBN:978-0-471-52629-2; Piegorsch (2004), <doi:10.1016/j.csda.2003.10.002>; Thulin (2014), <doi:10.1214/14-ejs909>), comparing two negative binomial rates (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>), and ANCOVA (Shieh (2020), <doi:10.1007/s11336-019-09692-3>).
Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs.
Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.
Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.
Provides 'R6' abstract classes for building machine learning models 
    with 'scikit-learn' like API. <https://scikit-learn.org/> is a popular module 
    for 'Python' programming language which design became de facto a standard 
    in industry for machine learning tasks.
A collection of artificial and real-world machine learning
        benchmark problems, including, e.g., several
        data sets from the UCI repository.
Tools to help visualize Major League Baseball analysis in 'ggplot2' 
  and 'gt'. You provide team/player information and 'mlbplotR' will transform 
  that information into team colors, logos, or player headshots for graphics.
Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.
Contains a collection of datasets for working with machine learning tasks.
    It will contain datasets for supervised machine learning Jiang (2020)<doi:10.1016/j.beth.2020.05.002> and will include datasets for classification and regression.
    The aim of this package is to use data generated around health and other domains.
Exploratory data analysis and manipulation functions for multi-
    label data sets along with an interactive Shiny application to ease their use.
Large collection of multilabel datasets along with the functions
    needed to export them to several formats, to make partitions, and to obtain
    bibliographic information.
Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.
Difference scaling is a method for scaling perceived 
  supra-threshold differences.  The package contains functions that
  allow the user to design and run a difference scaling experiment, 
  to fit the resulting data by maximum likelihood and test the
  internal validity of the estimated scale.
Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.
A unified interface is provided to various machine learning
  algorithms like LDA, QDA, k-nearest neighbour, LVQ, random forest, SVM, ... It
  allows to train, test, and apply cross-validation using similar functions and
  function arguments with a minimalist and clean, formula-based interface.
  Missing data are threated the same way as base and stats R functions for all
  algorithms, both in training and testing. Confusion matrices are also provided
  with a rich set of metrics calculated and a few specific plots.
Estimate asymptotic efficient closed-form estimators and provide goodness of fit, estimates, plot and etc.
    Yue, S. (2001) <doi:10.1002/hyp.259>.
    Mosimann, James E. (1962) <doi:10.1093/biomet/49.1-2.65>.
We provide functions to compute the nonparametric 
  maximum likelihood estimator (MLE) for 
  the bivariate distribution of (X,Y), when 
  realizations of (X,Y) cannot be observed directly. 
  To be more precise, we consider the situation 
  where we observe a set of rectangles in R^2 that are known 
  to contain the unobservable realizations of (X,Y). We
  compute the MLE based on such a set of rectangles. 
  The methods can also be used for univariate censored data (see data set
  'cosmesis'), and for 
  censored data with competing risks (see data set 'menopause'). 
  We also provide functions to visualize the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
        univariate and multi-dimensional outputs with diagnostic plots 
        following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>.
        Contact the maintainer for a package version that includes 
        sensitivity analysis.
Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.
Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.
Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.
The Iterative Proportional Fitting (IPF) algorithm operates on count data. 
    This package offers implementations for several algorithms that extend this to 
    nested structures: 'parent' and 'child' items for both of which constraints can be provided.
    The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>,
    Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>,
    and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods
    is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.
R interface to 'MLflow', open source platform for
    the complete machine learning life cycle, see <https://mlflow.org/>.
    This package supports installing 'MLflow', tracking experiments,
    creating and running projects, and saving and serving models.
Climate-sensitive forest simulator based on the principles of 
  machine learning. It stimulates all key processes in the forest: radial growth, 
  height growth, mortality, crown recession, regeneration and harvesting. The 
  method for predicting tree heights was described by Skudnik and Jevšenak
  (2022) <doi:10.1016/j.foreco.2022.120017>, while the method for predicting
  basal area increments (BAI) was described by Jevšenak and Skudnik (2021) 
  <doi:10.1016/j.foreco.2020.118601>.
Contains the datasets for use with the book Salvan, Sartori and Pace (2020, ISBN:978-88-470-4002-1) "Modelli Lineari Generalizzati".
It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2023) <doi:10.18637/jss.v106.i03>).
Tools and functions to fit a multilevel index of dissimilarity.
Machine learning algorithms have been used for performing 
    single missing data imputation and most recently, multiple imputations. 
    However, this is the first attempt for using automated machine learning algorithms 
    for performing both 
    single and multiple imputation. Automated machine learning is a procedure for 
    fine-tuning the model automatic, performing a random search for a model that 
    results in less error, without overfitting the data. The main idea is 
    to allow the model to set its own parameters for imputing each variable separately 
    instead of setting fixed predefined parameters to impute all variables 
    of the dataset.
    Using automated machine learning, the package fine-tunes an Elastic 
    Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting,
    or Stacked Ensemble machine learning model (from one or a combination of other 
    supported algorithms) for imputing the missing 
    observations. This procedure has been implemented for the 
    first time by this package and is expected to outperform other packages for 
    imputing missing data that do not fine-tune their models. The multiple imputation 
    is implemented via bootstrapping without letting the duplicated observations to 
    harm the cross-validation procedure, which is the way imputed variables are evaluated.
    Most notably, the package implements automated procedure for handling imputing imbalanced 
    data (class rarity problem), which happens when a factor variable has a level that is far more 
    prevalent than the other(s). This is known to result in biased predictions, hence, biased 
    imputation of missing data. However, the autobalancing procedure ensures that instead of 
    focusing on maximizing accuracy (classification error) in imputing factor variables, 
    a fairer procedure and imputation method is practiced.
Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.
An implementation of Multi-level Monte Carlo for R.  This package
    builds on the original 'Matlab' and C++ implementations by Mike Giles to provide
    a full MLMC driver and example level samplers.  Multi-core parallel sampling
    of levels is provided built-in.
A collection of evaluation metrics, including loss, score and
    utility functions, that measure regression, classification and ranking performance.
A collection of miscellaneous helper function for running multilevel/mixed models in 'lme4'. This package aims to provide functions to compute common tasks when estimating multilevel models such as computing the intraclass correlation and design effect, centering variables, estimating the proportion of variance explained at each level, pseudo-R squared, random intercept and slope reliabilities, tests for homogeneity of variance at level-1, and cluster robust and bootstrap standard errors. The tests and statistics reported in the package are from Raudenbush & Bryk (2002, ISBN:9780761919049), Hox et al. (2018, ISBN:9781138121362), and Snijders & Bosker (2012, ISBN:9781849202015).
Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.
Maximum likelihood estimates (MLE) of the proportions
    of 5-mC and 5-hmC in the DNA using information from BS-conversion,
    TAB-conversion, and oxBS-conversion methods. One can use information from all three methods 
    or any combination of two of them. Estimates are based on Binomial model by
    Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and
    Kiihl et al. (2019) <doi:10.1515/sagmb-2018-0031>.
Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) <doi:10.1038/ng.2314>. The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions.
Computational strategies for multivariate linear
        mixed-effects models with missing values, Schafer and Yucel
        (2002), Journal of Computational and Graphical Statistics, 11,
        421-442.
An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection.
The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.
A declarative language for specifying multilevel models,
    solving for population parameters based on specified variance-explained effect
    size measures, generating data, and conducting power analyses to determine
    sample size recommendations. The specification allows for any number of
    within-cluster effects, between-cluster effects, covariate effects
    at either level, and random coefficients. Moreover, the models do not
    assume orthogonal effects, and predictors can correlate at either level
    and accommodate models with multiple interaction effects.
Data and examples from a multilevel modelling software review
  as well as other well-known data sets from the multilevel modelling
  literature.
Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, 'mlmtools', is a suite of pre- and post-estimation tools for multilevel models in 'R'. Package implements post-estimation tools designed to work with models estimated using 'lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.
An implementation of several machine learning algorithms for 
    multivariate time series. The package includes functions allowing the
    execution of clustering, classification or outlier detection methods,
    among others. It also incorporates a collection of multivariate time
    series datasets which can be used to analyse the performance of new
    proposed algorithms. Some of these datasets are stored in GitHub data
    packages 'ueadata1' to 'ueadata8'. To access these data packages, run
    'install.packages(c('ueadata1', 'ueadata2', 'ueadata3', 'ueadata4', 'ueadata5', 'ueadata6', 'ueadata7', 'ueadata8'), repos='<https://anloor7.github.io/drat/>')'.
    The installation takes a couple of minutes but we strongly encourage the
    users to do it if they want to have available all datasets of mlmts.
    Practitioners from a broad variety of fields could
    benefit from the general framework provided by 'mlmts'.
Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and 'McCaffrey' (2002) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ> is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X).
Maximum likelihood estimation of random utility discrete
             choice models. The software is described in Croissant (2020)
              <doi:10.18637/jss.v095.i11> and the underlying methods in
              Train (2009) <doi:10.1017/CBO9780511805271>.
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
A fast, flexible machine learning library, written in C++, that
             aims to provide fast, extensible implementations of cutting-edge
             machine learning algorithms.  See also Curtin et al. (2018)
             <doi:10.21105/joss.00726>.
Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.
An implementation of classifier chains (CC's) for multi-label
    prediction. Users can employ an external package (e.g. 'randomForest',
    'C50'), or supply their own. The package can train a single set of CC's or
    train an ensemble of CC's -- in parallel if running in a multi-core
    environment. New observations are classified using a Gibbs sampler since
    each unobserved label is conditioned on the others. The package includes
    methods for evaluating the predictions for accuracy and aggregating across
    iterations and models to produce binary or probabilistic classifications.
We implement a surrogate modeling algorithm to guide simulation-based
    sample size planning. The method is described in detail in a recent preprint
    (Zimmer & Debelak (2022) <doi:10.31234/osf.io/tnhb2>).
    It supports multiple study design parameters and optimization with respect to a
    cost function. It can find optimal designs that correspond to a desired 
    statistical power or that fulfill a cost constraint.
Quantification is a prominent machine learning task that has received an 
    increasing amount of attention in the last years. The objective is to predict the 
    class distribution of a data sample. This package is a collection of machine learning 
    algorithms for class distribution estimation. This package include algorithms from
    different paradigms of quantification. These methods are described in the paper: 
    A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set 
    size in quantification assessment. In Proceedings of the Twenty-Ninth International 
    Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020.
    <doi:10.24963/ijcai.2020/366>.
Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There
    is also an experimental extension for survival analysis, clustering
    and general, example-specific cost-sensitive learning. Generic
    resampling, including cross-validation, bootstrapping and subsampling.
    Hyperparameter tuning with modern optimization techniques, for single-
    and multi-objective problems.  Filter and wrapper methods for feature
    selection. Extension of basic learners with additional operations
    common in machine learning, also allowing for easy nested resampling.
    Most operations can be parallelized.
Efficient, object-oriented programming on the
    building blocks of machine learning. Provides 'R6' objects for tasks,
    learners, resamplings, and measures. The package is geared towards
    scalability and larger datasets by supporting parallelization and
    out-of-memory data-backends like databases. While 'mlr3' focuses on
    the core computational operations, add-on packages provide additional
    functionality.
Extends the 'mlr3' package with a connector to the package
    'batchtools'. This allows to run large-scale benchmark experiments on
    scheduled high-performance computing clusters.
Implements methods for post-hoc analysis and
    visualisation of benchmark experiments, for 'mlr3' and beyond.
Extends the 'mlr3' package with cluster analysis.
A small collection of interesting and educational machine
    learning data sets which are used as examples in the 'mlr3' book
    (<https://mlr3book.mlr-org.com>), the use case gallery
    (<https://mlr3gallery.mlr-org.com>), or in other examples. All data
    sets are properly preprocessed and ready to be analyzed by most
    machine learning algorithms.  Data sets are automatically added to the
    dictionary of tasks if 'mlr3' is loaded.
Extends the 'mlr3' package with a backend to
    transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL',
    'MariaDB', or 'PostgreSQL'. The package provides two additional backends:
    'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to
    interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases
    and also on Apache Parquet files.
Integrates fairness auditing and bias mitigation methods for the 'mlr3' ecosystem.
    This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as
    "Reweighing" described in 'Kamiran, Calders' (2012) <doi:10.1007/s10115-011-0463-8>  and
    "Equalized Odds" described in 'Hardt et al.' (2016) <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
    Integration with 'mlr3' allows for auditing of ML models as well as convenient joint tuning of
    machine learning algorithms and debiasing methods.
Extends 'mlr3' with filter methods for feature selection.
    Besides standalone filter methods built-in methods of any
    machine-learning algorithm are supported.  Partial scoring of
    multivariate filter methods is supported.
Feature selection package of the 'mlr3' ecosystem. It selects
    the optimal feature set for any 'mlr3' learner. The package works with
    several optimization algorithms e.g. Random Search, Recursive Feature
    Elimination, and Genetic Search. Moreover, it can automatically
    optimize learners and estimate the performance of optimized feature
    sets with nested resampling.
Successive Halving (Jamieson and Talwalkar (2016)
    <arXiv:1502.07943>) and Hyperband (Li et al. 2018 <arXiv:1603.06560>)
    optimization algorithm for the mlr3 ecosystem. The implementation in
    mlr3hyperband features improved scheduling and parallelizes the evaluation
    of configurations. The package includes tuners for hyperparameter
    optimization in mlr3tuning and optimizers for black-box optimization in
    bbotk.
Recommended Learners for 'mlr3'. Extends 'mlr3'
    with interfaces to essential machine learning packages on
    CRAN.  This includes, but is not limited to: (penalized) linear and
    logistic regression, linear and quadratic discriminant analysis,
    k-nearest neighbors, naive Bayes, support vector machines, and
    gradient boosting.
A modern and flexible approach to Bayesian Optimization / Model
    Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox
    providing both ready-to-use optimization algorithms as well as their fundamental
    building blocks allowing for straightforward implementation of custom
    algorithms. Single- and multi-objective optimization is supported as well as
    mixed continuous, categorical and conditional search spaces. Moreover, using
    'mlr3mbo' for hyperparameter optimization of machine learning models within the
    'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use
    optimization algorithms include Efficient Global Optimization by Jones et al.
    (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006)
    <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008)
    <doi:10.1007/978-3-540-87700-4_78>.
Implements multiple performance measures for
    supervised learning.  Includes over 40 measures for regression and
    classification. Additionally, meta information about the performance
    measures can be queried, e.g. what the best and worst possible
    performances scores are.
Frequently used helper functions and assertions used in
    'mlr3' and its companion packages. Comes with helper functions for
    functional programming, for printing, to work with 'data.table', as
    well as some generally useful 'R6' classes. This package also
    supersedes the package 'BBmisc'.
Provides an interface to 'OpenML.org' to list and download
    machine learning data, tasks and experiments. The 'OpenML' objects can
    be automatically converted to 'mlr3' objects.  For a more
    sophisticated interface which also allows uploading to 'OpenML', see
    the 'OpenML' package.
Dataflow programming toolkit that enriches 'mlr3' with a diverse
  set of pipelining operators ('PipeOps') that can be composed into graphs.
  Operations exist for data preprocessing, model fitting, and ensemble
  learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can
  therefore be resampled, benchmarked, and tuned.
A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.
Extends the 'mlr3' ML framework with methods for spatial
    objects. Data storage and prediction are supported for packages
    'terra', 'raster' and 'stars'.
Extends the mlr3 ML framework with spatio-temporal resampling
    methods to account for the presence of spatiotemporal autocorrelation
    (STAC) in predictor variables. STAC may cause highly biased
    performance estimates in cross-validation if ignored.
Hyperparameter optimization package of the 'mlr3' ecosystem. It
    features highly configurable search spaces via the 'paradox' package and
    finds optimal hyperparameter configurations for any 'mlr3' learner.
    'mlr3tuning' works with several optimization algorithms e.g. Random
    Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and
    Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize
    learners and estimate the performance of optimized models with nested
    resampling.
Collection of search spaces for hyperparameter optimization in the
  'mlr3' ecosystem. It features ready-to-use search spaces for many popular
  machine learning algorithms. The search spaces are from scientific articles
  and work for a wide range of data sets.
The 'mlr3' package family is a set of packages for
    machine-learning purposes built in a modular fashion. This wrapper
    package is aimed to simplify the installation and loading of the core
    'mlr3' packages. Get more information about the 'mlr3' project at
    <https://mlr3book.mlr-org.com/>.
Visualization package of the 'mlr3' ecosystem. It features plots
    for mlr3 objects such as tasks, learners, predictions, benchmark results,
    tuning instances and filters via the 'autoplot()' generic of 'ggplot2'.
    The package draws plots with the 'viridis' color palette and applies the
    minimal theme. Visualizations include barplots, boxplots, histograms, ROC
    curves, and Precision-Recall curves.
Toolset that enriches 'mlr' with a diverse set of preprocessing
    operators. Composable Preprocessing Operators ("CPO"s) are first-class
    R objects that can be applied to data.frames and 'mlr' "Task"s to modify
    data, can be attached to 'mlr' "Learner"s to add preprocessing to machine
    learning algorithms, and can be composed to form preprocessing pipelines.
The 'mlrMBO' package can ordinarily not be used for optimization within 'mlr3', because of
  incompatibilities of their respective class systems. 'mlrintermbo' offers a compatibility
  interface that provides 'mlrMBO' as an 'mlr3tuning' 'Tuner' object, for tuning of machine
  learning algorithms within 'mlr3', as well as a 'bbotk' 'Optimizer' object for optimization
  of general objective functions using the 'bbotk' black box optimization framework. The
  control parameters of 'mlrMBO' are faithfully reproduced as a 'paradox' 'ParamSet'.
Flexible and comprehensive R toolbox for model-based optimization
    ('MBO'), also known as Bayesian optimization. It implements the Efficient
    Global Optimization Algorithm and is designed for both single- and multi-
    objective optimization with mixed continuous, categorical and conditional
    parameters. The machine learning toolbox 'mlr' provide dozens of regression
    learners to model the performance of the target algorithm with respect to
    the parameter settings. It provides many different infill criteria to guide
    the search process. Additional features include multi-point batch proposal,
    parallel execution as well as visualization and sophisticated logging
    mechanisms, which is especially useful for teaching and understanding of
    algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that
    single components can be easily replaced or adapted by the user for specific
    use cases.
The stepwise regression with assumptions checking and the possible Box-Cox transformation.
Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs).
Generate a stream of pseudo-random numbers generated using the MLS 
    Junk Generator algorithm. Functions exist to generate single pseudo-random 
    numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Likelihood-based estimation of conditional transformation
  models via the most likely transformation approach described in
  Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020)
  <DOI:10.18637/jss.v092.i01>.
Additional documentation, a package vignette and 
  regression tests for package mlt.
A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.
A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
Estimates the multi-level vector autoregression model on time-series data.
             Three network structures are obtained: temporal networks, contemporaneous
             networks and between-subjects networks.
Simulation, inference and clustering of multilevel networks using a
    Stochastic Block Model framework as described in Chabert-Liddell, Barbillon,
    Donnet and Lazega (2021) <doi:10.1016/j.csda.2021.107179>. 
    A multilevel network is defined as the junction of two interaction networks, 
    the upper level or inter-organizational level and the lower level or 
    inter-individual level. The inter-level represents an affiliation 
    relationship.
Simulation and visualization of complex
    models for longitudinal data. The models are encoded using the model coding
    language 'Mlxtran' and automatically converted into C++ codes. 
    That allows one to implement very easily complex ODE-based models and complex 
    statistical models, including mixed effects models, for continuous, count, 
    categorical, and time-to-event data.
Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.
Various utilities for the Multiplicative Multinomial distribution.
A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human Medulloblastoma (MB) patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA (Principal Component Analysis) plots and heatmaps. Deena Gendoo and Benjamin Haibe-Kains (2016) <doi:10.1186/s13029-016-0053-y>.
Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. (2022) <doi:10.1371/journal.pcbi.1009659>).
Used for general multiple mediation analysis. 
	The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.
Used for general multiple mediation analysis with big data sets.
Contains the data sets for the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The book will be published by Oxford University Press in 2018 with ISBN-13: 978-019882472.
Provides tools for performing mathematical morphology operations,
    such as erosion and dilation, on data of arbitrary dimensionality. Can also
    be used for finding connected components, resampling, filtering, smoothing
    and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile.
Uses memory-mapping to enable the random access of elements of
  a text file of characters separated by characters as if it were a simple
  R(cpp) matrix.
Mobile-monitoring or "sensors on a mobile platform", is an increasingly 
    popular approach to measure high-resolution pollution data at the street level. 
    Coupled with location data, spatial visualisation of air-quality parameters 
    helps detect localized areas of high air-pollution, also called hotspots. 
    In this approach, portable sensors are mounted on a vehicle and driven on 
    predetermined routes to collect high frequency data (1 Hz). 
    'mmaqshiny' is for analysing, visualising and spatial mapping of 
    high-resolution air-quality data collected by specific devices installed on 
    a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate  
    matter with size less than 2.5 microns), Black carbon mass concentrations 
    (BC), ultra-fine particle number concentrations, carbon dioxide along with 
    GPS coordinates and relative humidity (RH) data collected by popular 
    portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, 
    LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It 
    incorporates device specific cleaning and correction algorithms. RH correction 
    is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) 
    <doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear 
    regression coefficients for correcting the PM2.5 data (if required). BC data
    will be cleaned for the vibration generated noise, by adopting the statistical 
    procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>, 
    followed by a loading correction as suggested by Ban-Weiss et al., (2009)  
    <doi:10.1021/es8021039>. For the number concentration data, provision is 
    given for dilution correction factor (if a diluter is used with CPC3007; 
    default value is 1). The package joins the raw, cleaned and corrected data 
    from the above said instruments and outputs as a downloadable csv file.
Supports Bayesian models with full and partial (hence
    arbitrary) dependencies between random variables. Discrete and continuous
    variables are supported, and conditional joint probabilities and probability
    densities are estimated using Kernel Density Estimation (KDE). The full
    general form, which implements an extension to Bayes' theorem, as well as
    the simple form, which is just a Bayesian network, both support regression
    through segmentation and KDE and estimation of probability or relative
    likelihood of discrete or continuous target random variables. This package
    also provides true statistical distance measures based on Bayesian models.
    Furthermore, these measures can be facilitated on neighborhood searches,
    and to estimate the similarity and distance between data points.
    Related work is by Bayes (1763) <doi:10.1098/rstl.1763.0053>
    and by Scutari (2010) <doi:10.18637/jss.v035.i03>.
Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
Fits the mixed cumulative incidence functions model suggested by 
    <doi:10.1093/biostatistics/kxx072> which decomposes within cluster 
    dependence of risk and timing. The estimation method supports computation in 
    parallel using a shared memory C++ implementation. A sandwich estimator of the 
    covariance matrix is available. Natural cubic splines are used to provide a 
    flexible model for the cumulative incidence functions.
An implementation of modified maximum contrast methods (Sato et al. (2009) <doi:10.1038/tpj.2008.17>;
  Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997)
  <doi:10.1177/009286159703100213>): Functions mmcm.mvt() and mcm.mvt() give P-value by using randomized quasi-Monte
  Carlo method with pmvt() function of package 'mvtnorm', and mmcm.resamp() gives P-value by using a permutation method.
Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) <doi:10.1534/genetics.109.105486>.
Matching longitudinal methodology models with complex sampling design. It fits fixed
    and random effects models and covariance structured models so far. It also
    provides tools to perform statistical tests considering these specifications as described in : Pacheco, P. H. (2021). "Modeling complex longitudinal data in R: development of a statistical package." <https://repositorio.ufjf.br/jspui/bitstream/ufjf/13437/1/pedrohenriquedemesquitapacheco.pdf>.
The aim of the package is two-fold: (i) To implement 
    the MMD method for attribution of individuals to sources using 
    the Hamming distance between multilocus genotypes. (ii) To select 
    informative genetic markers based on information theory concepts 
    (entropy, mutual information and redundancy). The package implements the 
    functions introduced by Perez-Reche, F. J., Rotariu, O., Lopes, B. S., 
    Forbes, K. J. and Strachan, N. J. C. Mining whole genome sequence data to 
    efficiently attribute individuals to source populations. 
    Scientific Reports 10, 12124 (2020) <doi:10.1038/s41598-020-68740-6>.
    See more details and examples in the README file.
A method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.
Provides functions for the robust estimation of 
	parametric families of copulas using minimization of 
	the Maximum Mean Discrepancy, following the article
	Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022)
	<doi:10.1080/01621459.2021.2024836>.
Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one
    random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing
    independent time effect; Model 3, introducing correlated time effect.
    mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.
Fit multivariate mixture of normal distribution using
        covariance structure.
Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. 
  See Meyer (1985) <doi:10.2307/2530651> and Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.
Multiple 2 by 2 tables often arise in meta-analysis which combines statistical evidence from multiple studies. Two risks within the same study are possibly correlated because they share some common factors such as environment and population structure. This package implements a set of novel Bayesian approaches for multivariate meta analysis when the risks within the same study are independent or correlated. The exact posterior inference of odds ratio, relative risk, and risk difference given either a single 2 by 2 table or multiple 2 by 2 tables is provided. Luo, Chen, Su, Chu, (2014) <doi:10.18637/jss.v056.i11>, Chen, Luo, (2011) <doi:10.1002/sim.4248>, Chen, Chu, Luo, Nie, Chen, (2015) <doi:10.1177/0962280211430889>, Chen, Luo, Chu, Su, Nie, (2014) <doi:10.1080/03610926.2012.700379>, Chen, Luo, Chu, Wei, (2013) <doi:10.1080/19466315.2013.791483>.
Implements a computational framework to predict microbial community-based 
     metabolic profiles with 'O2PLS' model. It provides procedures of model 
     training and prediction. Paired microbiome and metabolome data are needed 
     for modeling, and the trained model can be applied to predict metabolites 
     of analogous environments using new microbial feature abundances.
A set of tools for fitting Markov-modulated linear regression, where responses Y(t) are time-additive, and model operates in the external environment, which is described as a continuous time Markov chain with finite state space.    
    Model is proposed by Alexander Andronov (2012) <arXiv:1901.09600v1> and algorithm of parameters estimation is based on eigenvalues and eigenvectors decomposition.
    Markov-switching regression models have the same idea of varying the regression parameters randomly in accordance with external environment. The difference is that for Markov-modulated linear regression model the external environment is described as a continuous-time homogeneous irreducible Markov chain with known parameters while switching models consider Markov chain as unobserved and estimation procedure involves estimation of transition matrix.
    These models have significant differences in terms of the analytical approach.
    Also, package provides a set of data simulation tools for Markov-modulated linear regression (for academical/research purposes).
    Research project No. 1.1.1.2/VIAA/1/16/075.
Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression
	coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
Provides functions for measuring
    population divergence from genotypic data.
Perform multivariate modeling of evolved traits, with special attention to
 understanding the interplay of the multi-factorial determinants of their origins
 in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>).
 This software primarily concentrates on phylogenetic regression analysis, enabling
 implementation of tree transformation averaging and visualization functionality.
 Functions additionally support information theoretic approaches
 (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>;
 Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>)
 such as model averaging and selection of phylogenetic models.
 There are other numerous functions for visualizing confounded variables,
 plotting phylogenetic trees, as well as reporting and exporting modeling results.
 Lastly, as challenges to ecology are inherently multifarious, and therefore often
 multi-dataset, this package features several functions to support the identification,
 interpolation, merging, and updating of missing data and outdated nomenclature.
To determine the number of quantitative assays needed for a sample 
    of data using pooled testing methods, which include mini-pooling (MP), MP 
    with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number 
    of assays needed, the package also provides a tool to conduct Monte Carlo (MC) 
    to simulate different orders in which the sample would be collected to form pools. 
    Using MC avoids the dependence of the estimated number of assays on any specific 
    ordering of the samples to form pools.
A generalization of principal component analysis for integrative
  analysis. The method finds principal components that describe single matrices
  or that are common to several matrices. The solutions are sparse. Rank of
  solutions is automatically selected using cross validation. The method is
  described in Kallus et al. (2019) <arXiv:1911.04927>.
Marginalizes prediction functions using Monte-Carlo integration and computes permutation importance.
Compute similarities and distances between marked point processes.
Simple helpers for matrix multiplication on data.frames. These allow for more
    concise code during low level mathematical operations, and help ensure code is more
    easily read, understood, and serviced.
Researchers doing a mixed-methods analysis (nested analysis as
    developed by Lieberman (2005) <doi:10.1017/S0003055405051762>) can
    use the package for the classification of cases and case selection using
    results of a linear regression. One can designate cases 
    as typical, deviant, extreme and pathway case and use different case 
    selection strategies for the choice of a case belonging to one of
    these types.
Mixed models for repeated measures (MMRM) are a popular
    choice for analyzing longitudinal continuous outcomes in randomized
    clinical trials and beyond; see Cnaan, Laird and Slasor (1997)
    <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>
    for a tutorial and Mallinckrodt, Lane and Schnell (2008)
    <doi:10.1177/009286150804200402> for a review. This package implements
    MMRM based on the marginal linear model without random effects using
    Template Model Builder ('TMB') which enables fast and robust model
    fitting. Users can specify a variety of covariance matrices, weight
    observations, fit models with restricted or standard maximum
    likelihood inference, perform hypothesis testing with Satterthwaite or
    Kenward-Roger adjustment, and extract least square means
    estimates by using 'emmeans'.
Subset a control group to match an intervention group on a set of features using multivariate matching and propensity score calipers. Based on methods in Rosenbaum and Rubin (1985).
Teaching materials, e.g. R programs, Shiny apps, data and PDF/HTML documents, 
    which are stored on the Internet in a ZIP file (e.g. as a GitHub repository) can be downloaded. 
    They can be displayed or run locally. The content of the ZIP file is temporarily
    (or permanently) stored. 
    The GitHub repository 'sigbertklinke/mmstat4.data' is used as the default repository. 
    In addition, some auxiliary functions are implemented.
An implementation of multiple maps t-distributed stochastic
    neighbor embedding (t-SNE). Multiple maps t-SNE is a method for
    projecting high-dimensional data into several low-dimensional maps such that
    non-metric space properties are better preserved than they would be by a single
    map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE.
    When projecting onto more than one map, multiple maps t-SNE estimates a set
    of latent weights that allow each point to contribute to one or more maps
    depending on similarity relationships in the original data. This
    implementation is a port of the original 'Matlab' library by Laurens van der
    Maaten. 
    See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>.
    This material is based upon work supported by the United States Air Force 
    and Defense Advanced Research Project Agency (DARPA) under Contract No. 
    FA8750-17-C-0020.
    Any opinions, findings and conclusions or recommendations expressed in this
    material are those of the author(s) and do not necessarily reflect the views 
    of the United States Air Force and Defense Advanced Research Projects Agency.
    Distribution Statement A: Approved for Public Release; Distribution Unlimited.
A variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model.
    Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients). 
    Users can also visualize the heatmap of the posterior mean of covariance matrix. 
    Kim, T. Nicolae, D. (2019) <https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>.
    Guan, Y. Stephens, M. (2011) <doi:10.1214/11-AOAS455>.
The first day of any MMWR week is Sunday.
    MMWR week numbering is sequential beginning with 1
    and incrementing with each week to a maximum of 52
    or 53. MMWR week #1 of an MMWR year is the first week
    of the year that has at least four days in the calendar
    year. This package provides functionality to convert
    Dates to MMWR day, week, and year and the reverse.
Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <arXiv:2009.07662>.
Diagnostic tools as residual analysis, global, 
    local and total-local influence for the multivariate model 
    from the random intercept Poisson generalized log gamma model 
    are available in this package. Including also, the estimation 
    process by maximum likelihood method, for details see 
    Fabio, L. F; Villegas, C. L.; Carrasco, J.M.F and de Castro, M. (2021) 
    <doi:10.1080/03610926.2021.1939380>.
Interactions between different biological entities are crucial for the function of biological systems. 
    In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted.
    The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. 
    However, such variations often occur locally and do not concern the whole network. 
    To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). 
    MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation.
    Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.
A toolbox for modeling manifest and latent group differences and moderation effects in various statistical network models.
An API package for the Members' Name Information Service operated 
    by the UK parliament. Documentation for the API itself can be found 
    here: <http://data.parliament.uk/membersdataplatform/default.aspx>.
Density evaluation and random number generation for the Matrix-Normal Inverse-Wishart (MNIW) distribution, as well as the the Matrix-Normal, Matrix-T, Wishart, and Inverse-Wishart distributions.  Core calculations are implemented in a portable (header-only) C++ library, with matrix manipulations using the 'Eigen' library for linear algebra.  Also provided is a Gibbs sampler for Bayesian inference on a random-effects model with multivariate normal observations.
Perform FlexBoost in R. FlexBoost is a newly suggested algorithm based on AdaBoost by adjusting adaptive loss functions. Not only FlexBoost but also other machine learning algorithms (e.g. Support Vector Machines) will be added. For more details on FlexBoost see Jeon, Y. S., Yang, D. H., & Lim, D. J. (2019) <doi:10.1109/access.2019.2938356>.
Conducts moderated nonlinear factor analysis (e.g., Curran et al., 2014,
    <doi:10.1080/00273171.2014.889594>). 
    Regularization methods are implemented for assessing non-invariant items. 
    Currently, the package includes dichotomous items and unidimensional
    item response models. Extensions will be included in future package
    versions.
Functions to easily return simulated predicted probabilities and
    first differences for multinomial logit models. It takes a specified 
    scenario and a multinomial model to predict probabilities with a set of
    coefficients, drawn from a simulated sampling distribution. The simulated 
    predictions allow for meaningful plots with means and confidence intervals.
    The methodological approach is based on the principles laid out by King,
    Tomz, and Wittenberg (2000) <doi:10.2307/2669316> and Hanmer and Ozan Kalkan 
    (2016) <doi:10.1111/j.1540-5907.2012.00602.x>.
An interactive presentation on  the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/MultinomPresentation>.
Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.
A data generator of multivariate non-normal data in R. It combines two different methods to generate non-normal data, one with user-specified multivariate skewness and kurtosis (more details can be found in the paper: Qu, Liu, & Zhang, 2019 <doi:10.3758/s13428-019-01291-5>), and the other with the given marginal skewness and kurtosis. The latter one is the widely-used Vale and Maurelli's method. It also contains a function to calculate univariate and multivariate (Mardia's Test) skew and kurtosis.
Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution and Gaussian copula (with various marginal distributions) using methods described in A. Genz (2004) <doi:10.1023/B:STCO.0000035304.20635.31>, A. Genz, F. Bretz (2009) <doi:10.1007/978-3-642-01689-9> and E. Kossova, B. Potanin (2018) <https:ideas.repec.org/a/ris/apltrx/0346.html>.
Functions are provided for computing the density and the
  distribution function of d-dimensional normal and "t" random variables,
  possibly truncated (on one side or two sides),  and for generating random 
  vectors sampled from these distributions, except sampling from the truncated
  "t". Moments of arbitrary order of a multivariate truncated normal are
  computed, and converted to cumulants up to order 4. 
  Probabilities are computed via non-Monte Carlo methods; different routines 
  are used in the case d=1, d=2, d=3, d>3, if d denotes the dimensionality.
Fits the Bayesian multinomial probit model via Markov chain
 Monte Carlo.  The multinomial probit model is often used to analyze 
 the discrete choices made by individuals recorded in survey data. 
 Examples where the multinomial probit model may be useful include the 
 analysis of product choice by consumers in market research and the 
 analysis of candidate or party choice by voters in electoral studies.  
 The MNP package can also fit the model with different choice sets for 
 each individual, and complete or partial individual choice orderings 
 of the available alternatives from the choice set. The estimation is
 based on the efficient marginal data augmentation algorithm that is 
 developed by Imai and van Dyk (2005). "A Bayesian Analysis of the 
 Multinomial Probit Model Using the Data Augmentation." Journal of 
 Econometrics, Vol. 124, No. 2 (February), pp. 311-334. 
 <doi:10.1016/j.jeconom.2004.02.002>  Detailed examples are given in 
 Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial 
 Probit Model."  Journal of Statistical Software, Vol. 14, No. 3 (May), 
 pp. 1-32. <doi:10.18637/jss.v014.i03>.
Allows to analyze the reading data obtained with the MNREAD Acuity 
    Chart, a continuous-text reading acuity chart for normal and low vision. 
    Provides the necessary functions to plot the MNREAD curve and estimate 
    automatically the four MNREAD parameters: Maximum Reading Speed, 
    Critical Print Size, Reading Acuity and Reading Accessibility Index.
    Parameters can be estimated either with the standard method 
    or with a nonlinear mixed-effects (NLME) modeling. 
    See Calabrese et al. 2018 for more details <doi:10.1167/18.1.8>.
An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
Various affine invariant multivariate normality tests are provided. It is designed to accompany the survey article Ebner, B. and Henze, N. (2020) <arXiv:2004.07332> titled "Tests for multivariate normality -- a critical review with emphasis on weighted L^2-statistics". We implement new and time honoured L^2-type tests of multivariate normality, such as the Baringhaus-Henze-Epps-Pulley (BHEP) test, the Henze-Zirkler test, the test of Henze-Jiménes-Gamero, the test of Henze-Jiménes-Gamero-Meintanis, the test of Henze-Visage, the Dörr-Ebner-Henze test based on harmonic oscillator and the Dörr-Ebner-Henze test based on a double estimation in a PDE. Secondly, we include the measures of multivariate skewness and kurtosis by Mardia, Koziol, Malkovich and Afifi and Móri, Rohatgi and Székely, as well as the associated tests. Thirdly, we include the tests of multivariate normality by Cox and Small, the 'energy' test of Székely and Rizzo, the tests based on spherical harmonics by Manzotti and Quiroz and the test of Pudelko. All the functions and tests need the data to be a n x d matrix where n is the samplesize (number of rows) and d is the dimension (number of columns).
Generate the monotonic binning and
    perform the woe (weight of evidence) transformation for the logistic regression
    used in the consumer credit scorecard development. The woe transformation is a piecewise
    transformation that is linear to the log odds. For a numeric variable, all of its monotonic 
    functional transformations will converge to the same woe transformation.
A framework for interacting with
  R modules such as Reports, Models, and Scripts from a mobile device. 
  The framework allows you to list available modules and select a
  module of interest using a basic e-mail interface. After 
  selecting a specific module, you can either run it as is or 
  provide input via the e-mail interface. After parsing your
  request, R will send the results back to your mobile device.
Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included.
Models the dependence between two variables in the extremes, identifies most 
    relevant models among six models: the conditional extremes model, the Jt-KDE model 
    and four copulae (Gumbel, Galambos, Normal, FGM). Bivariate return periods for 
    the six models and bivariate level curves can be created.
    Methods used in the package are described in the following reference:
    Tilloy, Malamud, Winter and Joly-Laugel (2020) <doi:10.5194/nhess-20-2091-2020>
    Supporting references for the conditional extremes model,
    Jt-KDE model and for copula modelling are the following: 
    Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>
    Cooley, Thibaud, Castillo and Wehner (2019) <doi:10.1007/s10687-019-00348-0>
    Nelsen (2006) <doi:10.1007/0-387-28678-0>.
Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.
Functions for calculating metrics for the measurement biodiversity
    and its changes across scales, treatments, and gradients. The methods 
    implemented in this package are described in:
    Chase, J.M., et al. (2018) <doi:10.1111/ele.13151>, 
    McGlinn, D.J., et al. (2019) <doi:10.1111/2041-210X.13102>, and
    McGlinn, D.J., et al. (2021) <doi:10.1002/ecy.3233>.
Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.
A statistical framework and analysis tool for open chromatin 
  analysis designed specifically for single cell ATAC-seq (Assay for 
  Transposase-Accessible Chromatin) data, after cell type/cluster 
  identification. These novel modules remove unwanted technical variation, 
  identify open chromatin, robustly models repeated measures in single cell 
  data, implement advanced statistical frameworks to model zero-inflation for 
  differential and co-accessibility analyses, and integrate with existing 
  databases and modules for downstream analyses to reveal biological insights. 
  MOCHA provides a statistical foundation for complex downstream analysis to 
  help advance the potential of single cell ATAC-seq for applied studies. 
  Methods for zero-inflated statistics are as described in: 
  Ghazanfar, S., Lin, Y., Su, X. et al. (2020) <doi:10.1038/s41592-020-0885-x>.
  Pimentel, Ronald Silva, "Kendall's Tau and Spearman's Rho for Zero-Inflated 
  Data" (2009) <https://scholarworks.wmich.edu/dissertations/721/>.
The two main functionalities of this package are creating
    mock objects (functions) and selectively intercepting calls to a given
    function that originate in some other function. It can be used with
    any testing framework available for R. Mock objects can be injected
    with either this package's own stub() function or a similar
    with_mock() facility present in the 'testthat' package.
Provides a means to mock a package function, i.e.,
    temporarily substitute it for testing. Designed as a drop-in
    replacement for the now deprecated 'testthat::with_mock()' and
    'testthat::local_mock()'.
With the deprecation of mocking capabilities shipped with
    'testthat' as of 'edition 3' it is left to third-party packages to replace
    this functionality, which in some test-scenarios is essential in order to
    run unit tests in limited environments (such as no Internet connection).
    Mocking in this setting means temporarily substituting a function with a
    stub that acts in some sense like the original function (for example by
    serving a HTTP response that has been cached as a file). The only exported
    function 'with_mock()' is modeled after the eponymous 'testthat' function
    with the intention of providing a drop-in replacement.
Creates modules inline or from a file. Modules can contain any R object and be nested. Each module have their own scope and package "search path" that does not interfere with one another or the user's working environment.
Package for processing downloaded MODIS Surface reflectance
    Product HDF files. Specifically, MOD09 surface reflectance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" <doi:10.3758/s13428-018-1088-6> .
Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
This is a 'shiny' module that encapsulates various charting options available in 'htmlwidgets', and provides options for each type of chart, a 'crosstalk' like interface for aggregate reports between 'DT' and other chart types.
Provides estimators of the mode of univariate
    data or univariate distributions.
Given independent and identically distributed observations X(1), ..., X(n) from a density f,
 provides five methods to perform a multiscale analysis about f as well as the necessary critical
 values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
 for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
 any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
 I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
 correction term Gamma in either method offers another two approaches which are more powerful on small scales and
 less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
 compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
 equivalent to the first and was introduced in Rufibach and Walther (2010).
Model-based trees for subgroup analyses in clinical trials and
  model-based forests for the estimation and prediction of personalised
  treatment effects (personalised models). Currently partitioning of linear
  models, lm(), generalised linear models, glm(), and Weibull models,
  survreg(), is supported.  Advanced plotting functionality is supported for
  the trees and a test for parameter heterogeneity is provided for the
  personalised models. For details on model-based trees for subgroup analyses
  see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for
  details on model-based forests for estimation of individual treatment effects
  see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.
Implements a general interface for model-based estimations
    for a wide variety of models (see list of supported models using the
    function 'insight::supported_models()'), used in the computation of
    marginal means, contrast analysis and predictions.
This is a cross-platform linear model to 'SQL' compiler. It generates 'SQL' from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a 'SQL' character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant 'SQL' can be included in a 'SELECT' statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.
Provides two important functions for producing Gain chart and Lift chart for any classification model.
Data sets used for demonstrating or testing model-related
    packages are contained in this package.
Uses 'dplyr' and 'tidyeval' to fit statistical models inside
    the database. It currently supports KMeans and linear regression
    models.
Website generator with HTML summaries for predictive models.
    This package uses 'DALEX' explainers to describe global model behavior. 
    We can see how well models behave (tabs: Model Performance, Auditor),
    how much each variable contributes to predictions (tabs: Variable Response) 
    and which variables are the most important for a given model (tabs: Variable Importance).
    We can also compare Concept Drift for pairs of models (tabs: Drifter).
    Additionally, data available on the website can be easily recreated in current R session.
    Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307 
    at Warsaw University of Technology, Faculty of Mathematics and Information Science.
An developer focused, low dependency package in 'tidymodels' that
    provides functions to register how models are to be used. Functions to
    register models are complimented with accessor functions to retrieve
    registered model information to aid in model fitting and error handling.
Defines classes and methods to learn models and use them
  to predict binary outcomes.  These are generic tools, but we also
  include specific examples for many common classifiers.
Calculate the financial impact of using a churn model in terms of cost, revenue, profit and return on investment.
An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) <doi:10.1007/s11135-013-9884-7> (see also Harden and Desmarais, 2011 <doi:10.1177/1532440011408929>) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) <doi:10.1093/pan/mpr042>. These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) <doi:10.1177/0010414009341714> and Joshi & Mason (2008) <doi:10.1177/0022343308096155> that are included to facilitate examples from real-world analysis.
Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
The model.matrix() function in R is  convenient for transforming training dataset for modeling. 
    But it does not save any parameter used in transformation, so it is hard to apply the same transformation 
    to test dataset or new dataset. This package is created to solve the problem.
Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.
Plots to assess the quality of predictive models from a business perspective. 
    Using these plots, it can be shown how implementation of the model will impact business 
    targets like response on a campaign or return on investment. Different scopes can be selected: 
    compare models, compare datasets or compare target class values and various plot customization 
    and highlighting options are available.
    targets like response on a campaign. Different scopes can be selected: compare models, compare 
    datasets or compare target class values and various plot customization and highlighting options 
    are available.
Functions for modelling that help you seamlessly integrate
    modelling into a pipeline of data manipulation and visualisation.
Automate the explanatory analysis of machine learning predictive 
    models. Generate advanced interactive model explanations in the form of 
    a serverless HTML site with only one line of code. This tool is 
    model-agnostic, therefore compatible with most of the black-box predictive
    models and frameworks. The main function computes various (instance and 
    model-level) explanations and produces a customisable dashboard, which 
    consists of multiple panels for plots with their short descriptions. It is 
    possible to easily save the dashboard and share it with others. modelStudio 
    facilitates the process of Interactive Explanatory Model Analysis introduced 
    in Baniecki et al. (2023) <doi:10.1007/s10618-023-00924-w>.
Create beautiful and customizable tables to summarize several
    statistical models side-by-side. Draw coefficient plots, multi-level
    cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and
    correlation matrices. This package supports dozens of statistical models, and
    it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel,
    RTF, JPG, or PNG. Tables can easily be embedded in 'Rmarkdown' or 'knitr'
    dynamic documents. Details can be found in Arel-Bundock (2022)
    <doi:10.18637/jss.v103.i01>.
Provides a number of testthat tests that can be
    used to verify that tidy(), glance() and augment() methods meet
    consistent specifications. This allows methods for the same generic to
    be spread across multiple packages, since all of those packages can
    make the same guarantees to users about returned objects.
The time series forecasting framework for use with the 'tidymodels' ecosystem. 
    Models include ARIMA, Exponential Smoothing, and additional time series models
    from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" 
    (<https://otexts.com/fpp2/>).
    Refer to "Prophet: forecasting at scale" 
    (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).
A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, 
    weighted averaging, and stacking. These techniques are popular methods 
    to improve forecast accuracy and stability. Refer to papers such as 
    "Machine-Learning Models for Sales Time Series Forecasting" Pavlyshenko, B.M. (2019) <doi:10.3390>.
A 'modeltime' extension that implements forecast resampling tools
    that assess time-based model performance and stability for a single time series, 
    panel data, and cross-sectional time series analysis.
A collection of tools to deal with statistical models. 
  The functionality is experimental and the user interface is likely to
  change in the future. The documentation is rather terse, but packages `coin'
  and `party' have some working examples. However, if you find the
  implemented ideas interesting we would be very interested in a discussion
  of this proposal. Contributions are more than welcome!
Makes a word cloud of text, sized by the frequency of the word, and colored either by user-specified colors or colored by the strength of the coefficient of that text derived from a regression model.
Causal moderated mediation analysis using the methods proposed by Qin and Wang (2022). Causal moderated mediation analysis is crucial for investigating how, for whom, and where a treatment is effective by assessing the heterogeneity of mediation mechanism across individuals and contexts. This package enables researchers to estimate and test the conditional and moderated mediation effects, assess their sensitivity to unmeasured pre-treatment confounding, and visualize the results. The package is built based on the quasi-Bayesian Monte Carlo method, because it has relatively better performance at small sample sizes, and its running speed is the fastest. The package is applicable to a treatment of any scale, a binary or continuous mediator, a binary or continuous outcome, and one or more moderators of any scale.
Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.
Provides functions that fit two modern education-based value-added models.  
                 One of these models is the quantile value-added model.  This model 
                 permits estimating a school's value-added based on specific quantiles of 
                 the post-test distribution.  Estimating value-added based on quantiles 
                 of the post-test distribution provides a more complete picture of an 
                 education institution's contribution to learning for students of all 
                 abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017)
                 <doi:10.1111/rssa.12195> for more details.  The second model is a temporally 
                 dependent value-added model. This model takes into account the temporal 
                 dependence that may exist in school performance  between two cohorts in one 
                 of two ways.  The first is by modeling school  random effects with a 
                 non-stationary AR(1) process.  The second is by  modeling school effects 
                 based on previous cohort's post-test performance. In addition to more 
                 efficiently estimating value-added, this model permits making statements 
                 about the persistence of a schools effectiveness. The standard  value-added 
                 model is also an option.
User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.
Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.
Compute important quantities when we consider stochastic systems that are observed continuously. 
             Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. 
             The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.
Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.
This is a shiny module that presents a file picker user interface to get an Excel file name, and reads the Excel sheets using 'readxl' package and returns the resulting sheet(s) as a vector and data in dataframe(s).
Algorithms for multivariate outlier detection when missing values
    occur. Algorithms are based on Mahalanobis distance or data depth.
    Imputation is based on the multivariate normal model or uses nearest
    neighbour donors. The algorithms take sample designs, in particular
    weighting, into account. The methods are described in Bill and Hulliger
    (2016) <doi:10.17713/ajs.v45i1.86>.
Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by  Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size.
    Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>.
    Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. 
    sen, P. K. (1968).<doi:10.2307/2285891>.
    Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>.
    Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>.
    Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>.
    Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>.
    Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>.
    Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.
Programmatic interface to the Oak Ridge National Laboratories
    'MODIS Land Products Subsets' web services 
    (<https://modis.ornl.gov/data/modis_webservice.html>). Allows for easy
    downloads of 'MODIS' time series directly to your R workspace or
    your computer.
Allows automating the creation of time series of rasters derived
    from MODIS satellite land products data. It performs several typical
    preprocessing steps such as download, mosaicking, reprojecting and resizing
    data acquired on a specified time period. All processing parameters
    can be set using a user-friendly GUI. Users can select which layers of
    the original MODIS HDF files they want to process, which additional
    quality indicators should be extracted from aggregated MODIS quality
    assurance layers and, in the case of surface reflectance products,
    which spectral indexes should be computed from the original reflectance
    bands. For each output layer, outputs are saved as single-band raster
    files corresponding to each available acquisition date. Virtual files
    allowing access to the entire time series as a single file are also created.
    Command-line execution exploiting a previously saved processing options
    file is also possible, allowing users to automatically update time series
    related to a MODIS product whenever a new image is available.
    For additional documentation refer to the following article: 
    Busetto and Ranghetti (2016) <doi:10.1016/j.cageo.2016.08.020>.
Calculate predicted levels and marginal effects,
    using the delta method to calculate standard errors. This is an R-based
    version of the 'margins' command from Stata.
The algorithms implemented here are used to detect the community structure of a network. 
            These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Methods for modeling moderator variables in cross-sectional, temporal, and multi-level networks. Includes model selection techniques and a variety of plotting functions. Implements the methods described by Swanson (2020) <https://www.proquest.com/openview/d151ab6b93ad47e3f0d5e59d7b6fd3d3>.
'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent.
Contains basic tools for performing 
  multiple-output quantile regression and computing
  regression quantile contours by means of directional
  regression quantiles. In the location case, one can thus
  obtain halfspace depth contours in two to six dimensions.
  Hallin, M., Paindaveine, D. and Šiman, M. (2010)
  Multivariate quantiles and multiple-output regression quantiles:
  from L1 optimization to halfspace depth. Annals of Statistics 38, 635-669
  For more references about the method, see Help pages.
Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.
Collection of tools for regression and classification tasks. The package implements a consistent user interface to the most popular regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, and complements it with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453> to 
    estimate 50 percent effective dose (ED50) in the study of drug sensitivity. The method has 
    its own advantages for that it can provide robust ED50 estimation. This package contains 
    the modified function of Choi's turning point method.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
Provides modules as an organizational unit for source code. Modules
    enforce to be more rigorous when defining dependencies and have
    a local search path. They can be used as a sub unit within packages
    or in scripts.
RMSD and Internal Energy analysis of NAMD and YASARA Molecular Dynamics output files. Allows to comparison of different dynamics per different complexes. Input files have to be ASCII files tab separated.
Modular implementation of Multiobjective Evolutionary Algorithms 
              based on Decomposition (MOEA/D) [Zhang and Li (2007), 
              <DOI:10.1109/TEVC.2007.892759>] for quick assembling and 
              testing of new algorithmic components, as well as easy 
              replication of published MOEA/D proposals. The full framework is
              documented in a paper published in the Journal of Statistical 
              Software [<doi:10.18637/jss.v092.i06>].
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
This is a thin wrapper around the 'MOEX' 'ISS' REST interface, see
    <https://iss.moex.com>.  It allows to quickly fetch price candles for a 
    particular security, obtain its profile information and so on.
Identifying important factors from a large number of potentially important factors of a highly nonlinear and computationally expensive black box model is a difficult problem. Xiao, Joseph, and Ray (2022) <doi:10.1080/00401706.2022.2141897> proposed Maximum One-Factor-at-a-Time (MOFAT) designs for doing this. A MOFAT design can be viewed as an improvement to the random one-factor-at-a-time (OFAT) design proposed by Morris (1991) <doi:10.1080/00401706.1991.10484804>. The improvement is achieved by exploiting the connection between Morris screening designs and Monte Carlo-based Sobol' designs, and optimizing the design using a space-filling criterion. This work is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921646>.
Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for data sets with large number of variables. For more information, see Sinha, Malo & Kuosmanen (2015) <doi:10.1080/10618600.2014.899236>.
Support JSON flattening in a long data frame way, where the nesting keys will be stored in the absolute path. It also
   provides an easy way to summarize the basic description of a JSON list. The idea of 'mojson' is to transform a JSON object in an 
   absolute serialization way, which means the early key-value pairs will appear in the heading rows of the resultant data frame. 
   'mojson' also provides an alternative way of comparing two different JSON lists, returning the left/inner/right-join style results.
Contains functions for performing Mokken
   scale analysis on test and questionnaire data.
   It includes an automated item selection algorithm, and various checks of model assumptions.
Multi-Objective optimization based on the Kriging metamodel.
  Important functions: mkm() (builder for the multiobjective models), MVPF() (sequential minimizator using variance reduction), 
  MEGO() (generalization of ParEgo) and HEGO() (minimizator using the expected hypervolume improvement).
  References are Passos and Luersen (2018) <doi:10.1590/1679-78254324>.
Surface topography calculations of Dirichlet's normal energy,
    relief index, surface slope, and orientation patch count for teeth using scans of
    enamel caps.
    Importantly, for the relief index and orientation patch count calculations to
    work, the scanned tooth files must be oriented with the occlusal plane parallel
    to the x and y axes, and perpendicular to the z axis. The files should also be
    simplified, and smoothed in some other software prior to uploading into R.
Model for simulating language evolution in terms of cultural evolution (Smith & Kirby (2008) <DOI:10.1098/rstb.2008.0145>; Deacon 1997). The focus is on the emergence of argument-marking systems (Dowty (1991) <DOI:10.1353/lan.1991.0021>, Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) <DOI:10.1075/sl.21.2.04ari>, Lestrade (2010) <DOI:10.7282/T3ZG6R4S>), person indexing (Ariel 1999, Dahl (2000) <DOI:10.1075/fol.7.1.03dah>, Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) <DOI:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015) <DOI:10.1002/9781118346136.ch27>) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) <DOI:10.3765/bls.v13i0.1834>, Givon (1995) <DOI:10.1075/z.74>, Croft (2000), Saffran (2001) <DOI:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) <DOI:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). In Lestrade (2015b) <DOI:10.15496/publikation-8640>, Lestrade (2015c) <DOI:10.1075/avt.32.08les>, and Lestrade (2016) <DOI:10.17617/2.2248195>), which reported on the results of preliminary versions, this package was announced as WDWTW (for who does what to whom), but for reasons of pronunciation and generalization the title was changed.
A set of functions to manage data shared on a 
  'MOLGENIS Armadillo' server.
Discover 'OpenID Connect' endpoints and authenticate
    using device flow. Used by 'MOLGENIS' packages.
Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.
Networks provide a means to incorporate molecular interactions into
    reasoning, but on the omics-level, they are currently mainly used to combine
    genomic and proteomic information. We here present a novel network analysis
    pipeline that enables integrative analysis of multi-omics data including
    metabolomics. It allows for comparative conclusions between two different
    conditions, such as tumor subgroups, healthy vs. disease, or generally control
    vs. perturbed.
    Our approach focuses on interactions and their strength instead of on node
    properties and includes molecules with low abundance and unknown function. We
    use correlation-induced networks that are reduced and combined to form 
    heterogeneous, multi-omics molecular networks. Prior information such as
    metabolite-protein interactions are incorporated. A semi-local, path-based
    integration step denoises the network and ensures integrative conclusions. As
    case studies, we investigate differential drug response in breast cancer tumor
    datasets providing proteomics, transcriptomics, phospho-proteomics and
    metabolomics data and contrasting patients with different estrogen receptor
    status.
    Our proposed pipeline leverages multi-omics data for differential predictions,
    e.g. on drug response, and includes prior information on interactions.
    The case study presented in the vignette uses data published by
    Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only
    to the software and explicitly not to the included data.
Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and  the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Functions to calculate: moments, Pearson's kurtosis,
        Geary's kurtosis and skewness; tests related to them
        (Anscombe-Glynn, D'Agostino, Bonett-Seier).
Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
The goal of 'Momocs' is to provide a complete, convenient, 
       reproducible and open-source toolkit for 2D morphometrics.
       It includes most common 2D morphometrics approaches on outlines, open outlines, 
       configurations of landmarks, traditional morphometrics, and facilities for data preparation, 
       manipulation and visualization with a consistent grammar throughout.
       It allows reproducible, complex morphometrics analyses and other morphometrics approaches 
       should be easy to plug in, or develop from, on top of this canvas.
Cluster any count data matrix with a fixed number of variables, such as document/term matrices. It integrates the dimension reduction aspect of topic models in the mixture models framework. Inference is done by means of a greedy Classification Variational Expectation Maximisation (C-VEM) algorithm. An Integrated Classication Likelihood (ICL) model selection is designed for selecting the latent dimension (number of topics) and the number of clusters. For more details, see the article of Jouvin et. al. (2020) <arxiv:1909.00721>.
'MetaOMineR' suite is a set of R packages that offers many functions and modules needed for the analyses 
    of quantitative metagenomics data. 'momr' is the core package and contains routines for biomarker identification and exploration.
    Developed since the beginning of field, 'momr' has evolved and is structured around the different modules 
    such as preprocessing, analysis, vizualisation, etc. See package help for more information.
It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.
A HTML widget rendering the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the built-in features of the 'Monaco' editor, the widget allows to prettify multiple languages, to view the 'HTML' rendering of 'Markdown' code, and to view and resize 'SVG' images.
An API wrapper for the 'Monash University Probabilistic Footy 
    Tipping Competition' <https://probabilistic-footy.monash.edu/~footy/index.shtml>. 
    Allows users to submit tips directly to the competition from R.
Keep track of dates in terms of fractional calendar months 
  per Damien Laker "Time Calculations for Annualizing Returns: the Need for Standardization", 
  The Journal of Performance Measurement, 2008.
  Model dates as of close of business.
  Perform date arithmetic in units of "months" and "years".
  Allow "infinite" dates to model "ultimate" time.
The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample. 
  As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). 
  We named the package after the famous painter as the graphical output recalls Mondrian’s paintings.
Allows to pull data from MonetDB into R.
High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'.
    Includes support for aggregation, indexing, map-reduce, streaming, encryption,
    enterprise authentication, and GridFS. The online user manual provides an overview 
    of the available methods in the package: <https://jeroen.github.io/mongolite/>.
Translate R code into MongoDB aggregation pipelines.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
Train and make predictions from a multi-layer perceptron neural
        network with optional partial monotonicity constraints.
Performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) 
	development. All functions handle both binary and continuous target variable. 
	Functions that use isotonic regression in the first stage of binning process have an additional 
	feature for correction of minimum percentage of observations and minimum target rate per bin. 	
	Additionally, monotonic trend can be identified based on raw data or, if known in advance,
	forced by functions' argument. Missing values and other possible special values are treated 
	separately from so-called complete cases.
This is an add-on package to the 'monobin' package that simplifies its use. It provides shiny-based user interface (UI) 
	     that is especially handy for less experienced 'R' users as well as for those who intend to perform quick scanning 
	     of numeric risk factors when building credit rating models. The additional functions implemented in 
	     'monobinShiny' that do no exist in 'monobin' package are: descriptive statistics, special case and outliers imputation. 
	     The function descriptive statistics is exported and can be used in 'R' sessions independently from the user interface, 
	     while special case and outlier imputation functions are written to be used with shiny UI.
Generate a monochrome palette from a starting colour 
    for a specified number of colours. The package can also be used to display 
    colour palettes in the plot window, with or without hex colour code labels.
Implementation of the Monothetic Clustering
    algorithm (Chavent, 1998 <doi:10.1016/S0167-8655(98)00087-7>) on
    continuous data sets. A lot of extensions are included in the package,
    including applying Monothetic clustering on data sets with circular
    variables, visualizations with the results, and permutation and
    cross-validation based tests to support the decision on the number of
    clusters.
Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology charts and distributional and diversity maps are also available.
Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range.
Estimation of multivariate normal (MVN) and student-t data of 
 arbitrary dimension where the pattern of missing data is monotone.
 See Pantaleo and Gramacy (2010) <arXiv:0907.2135>.
 Through the use of parsimonious/shrinkage regressions 
 (plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
 the package can handle a nearly arbitrary amount of missing data. 
 The current version supports maximum likelihood inference and 
 a full Bayesian approach employing scale-mixtures for Gibbs sampling.
 Monotone data augmentation extends this Bayesian approach to arbitrary 
 missingness patterns.  A fully functional standalone interface to the 
 Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown),
 Horseshoe (from Carvalho, Polson, & Scott), and ridge regression 
 with model selection via Reversible Jump, and student-t errors 
 (from Geweke) is also provided.
Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. 
 "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) <doi:10.7717/peerj-cs.56>.
Functions for fitting monotone polynomials to data.
             Detailed discussion of the methodologies used can be
             found in Murray, Mueller and Turlach (2013)
             <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and
             Turlach (2016) <doi:10.1080/00949655.2016.1139582>.
An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>. The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas <arXiv:2007.01390>.
The monotone package contains a fast up-and-down-blocks implementation for the pool-adjacent-violators algorithm 
  for simple linear ordered monotone regression, including two spin-off functions
  for unimodal and bivariate monotone regression (see <doi:10.18637/jss.v102.c01>).
Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) <doi:10.1016/j.jfineco.2010.06.006> and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.
Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) <doi:10.3150/bj/1151525131>.
Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.
Simplifies Monte Carlo simulation studies by automatically 
             setting up loops to run over parameter grids and parallelising
             the Monte Carlo repetitions. It also generates LaTeX tables.
Monte Carlo simulation allows to test different conditions given to the correct structural equation models. This package runs Monte Carlo simulations under different conditions (such as sample size or normality of data). Within the package data sets can be simulated and run based on the given model.
  First, continuous and normal data sets are generated based on the given model. Later Fleishman's power method (1978) <DOI:10.1007/BF02293811> is used to add non-normality if exists. 
  When data generation is completed (or when generated data sets are given) model test can also be run.
  Please cite as "Orçan, F. (2021). MonteCarloSEM: An R Package to Simulate Data for SEM. International Journal of Assessment Tools in Education, 8 (3), 704-713."
A collection of functions to connect to a 'Moodle' database, cache relevant tables locally and generate learning analytics. 
    'Moodle' is an open source Learning Management System (LMS) developed by MoodleHQ. For more information about Moodle, visit <https://moodle.org>.
Several analysis-related functions for the book entitled "R
    statistics and graph for medical articles" (written in Korean), version 1,
    by Keon-Woong Moon with Korean demographic data with several plot
    functions.
Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>.  It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.
Provides real & simulated datasets containing time-series traffic observations
    and additional information pertaining to Loop 1 "Mopac" located in Austin, Texas.
With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'MOQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.
Supports morphological analysis for Japanese by using 'MeCab'. 
    Can input data.frame and obtain all results of 'MeCab' and row number of 
    original data.frame as a text id.
Additions to 'party' and 'partykit' packages : tools for the interpretation of forests (surrogate trees, prototypes, etc.), feature selection (see Gregorutti et al (2017) <arXiv:1310.5726>, Hapfelmeier and Ulm (2013) <doi:10.1016/j.csda.2012.09.020>, Altmann et al (2010) <doi:10.1093/bioinformatics/btq134>) and parallelized versions of conditional forest and variable importance functions. Also modules and a shiny app for conditional inference trees.
Automatically segments a 3D array of voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been accepted for publication in the journal Landscape Ecology. Detailed references will be updated here once those are known.
Tokenize text into morphemes. The morphemepiece algorithm uses a 
  lookup table to determine the morpheme breakdown of words, and falls back on a 
  modified wordpiece tokenization algorithm for words not found in the lookup 
  table.
Provides data about morphemes, the smallest units of meaning in a 
    language.
Mixture of logistic regressions parameters (H)estimation with
    (U)spectral methods. The main methods take d-dimensional inputs and a vector
    of binary outputs, and return parameters according to the GLMs mixture model
    (General Linear Model). For more details see chapter 3 in the PhD thesis of
		Mor-Absa Loum: <https://www.theses.fr/s156435>, available here
		<https://theses.hal.science/tel-01877796/document>.
A toolset for Geometric Morphometrics and mesh processing. This
    includes (among other stuff) mesh deformations based on reference points,
    permutation tests, detection of outliers, processing of sliding
    semi-landmarks and semi-automated surface landmark placement.
Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section.
Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022.
Advanced methods for a valuable quantitative environmental risk 
   assessment using Bayesian inference of survival and reproduction Data. Among
   others, it facilitates Bayesian inference of the general unified
   threshold model of survival (GUTS). See our companion paper 
   Baudrot and Charles (2021) <doi:10.21105/joss.03200>,
   as well as complementary details in Baudrot et al. (2018)
   <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al.
   (2017) <doi:10.1021/acs.est.6b05326>.
A collection of functions for the analysis of archaeological mortality
    data (on the topic see e.g. Chamberlain 2006 
    <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). 
    It takes demographic data in different formats and displays the result in a standard life table
    as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population).
Life expectancy is highly correlated over time among countries and 
  between males and females. These associations can be used to improve forecasts. 
  Here we have implemented a method for forecasting female life expectancy based on 
  analysis of the gap between female life expectancy in a country compared with
  the record level of female life expectancy in the world. Second, to forecast 
  male life expectancy, the gap between male life expectancy and female life 
  expectancy in a country is analysed. We named this method the Double-Gap model.
  For a detailed description of the method see Pascariu et al. (2017). 
  <doi:10.1016/j.insmatheco.2017.09.011>.
Classes to implement and plot cohort life tables
    for actuarial calculations. In particular, birth-year dependent mortality
    tables using a yearly trend to extrapolate from a base year are implemented,
    as well as period life table, cohort life tables using an age shift, and
    merged life tables.
Age-specific mortality rates are estimated and projected using 
    the Kannisto, Lee-Carter and related methods as described in 
    Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.
Returns information about characters, locations, and episodes from
    'The Rick and Morty' API: <https://rickandmortyapi.com>.
Data sets and utilities from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Software to support the introductory *MOSAIC Calculus* 
    textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>),
    one of many data- and modeling-oriented educational resources developed by 
    Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and
    numerical differentiation and integration, as well as support for 
    applied linear algebra (for data science), and differential equations/dynamics.
    Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc.
    The software is suitable for general use, but intended mainly for teaching calculus.
Common utilities used in other MOSAIC-family packages are 
    collected here.
Data sets from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.
Provides functions for evaluating, displaying, and interpreting statistical models. The goal is to abstract the operations on models from the particular architecture of the model. For instance, calculating effect sizes rather than looking at coefficients. The package includes interfaces to both regression and classification architectures, including lm(), glm(), rlm() in 'MASS', random forests and recursive partitioning, k-nearest neighbors, linear and quadratic discriminant analysis, and models produced by the 'caret' package's train(). It's straightforward to add in other other model architectures.
This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.
High dimensionality, noise and heterogeneity among
    samples and features challenge the omic integration task. Here we
    present an omic integration method based on sparse singular value
    decomposition (SVD) to deal with these limitations, by: a. obtaining
    the main axes of variation of the combined omics, b. imposing sparsity
    constraints at both subjects (rows) and features (columns) levels
    using Elastic Net type of shrinkage, and c. allowing both linear and
    non-linear projections (via t-Stochastic Neighbor Embedding) of the
    omic data to detect clusters in very convoluted data
    (Gonzalez-Reymundez et. al, 2022) <doi:10.1093/bioinformatics/btac179>.
Provides functions similar to the 'SAS' macros previously provided
              to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826>
              and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers
              which outline practical benefits and challenges of factorial
              and fractional factorial experiments for scientists interested
              in developing biological and/or behavioral interventions, especially
              in the context of the multiphase optimization strategy
              (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
              currently contains three functions. First, RelativeCosts1() draws a graph
              of the relative cost of complete and reduced factorial designs versus
              other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
              which contains a list of random numbers that can be used to conveniently
              assign participants to conditions in an experiment with
              many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
              size, or required sample size of a factorial or fractional factorial
              experiment, for main effects or interactions, given several possible choices
              of effect size metric, and allowing pretests and clustering.
Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.
Learning, manipulation and  evaluation of mixtures of  truncated basis  functions 
  (MoTBFs),  which include mixtures of  polynomials (MOPs) and  mixtures of truncated 
  exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
  networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The  package provides  functionality for learning  univariate, multivariate and
  conditional  densities, with the  possibility of incorporating prior  knowledge. Structural
  learning of hybrid Bayesian  networks is also provided. A set of useful tools is provided,
  including  plotting, printing  and likelihood  evaluation. This  package  makes use of  S3 
  objects, with two new classes called 'motbf' and 'jointmotbf'.
Measure of the Effect ('MOTE') is an effect size calculator, including a 
    wide variety of effect sizes in the mean differences family (all versions of d) and 
    the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central 
    confidence intervals for each effect size, relevant test statistics, and output 
    for reporting in APA Style (American Psychological Association, 2010, 
    <ISBN:1433805618>) with 'LaTeX'. In research, an over-reliance on p-values 
    may conceal the fact that a study is under-powered (Halsey, Curran-Everett, 
    Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>). A test may be statistically 
    significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 
    <doi:10.1177/0959354312436870>). Although the American Psychological Association 
    has long advocated for the inclusion of effect sizes (Wilkinson & American 
    Psychological Association Task Force on Statistical Inference, 1999 
    <doi:10.1037/0003-066X.54.8.594>), the vast majority of peer-reviewed, 
    published academic studies stop short of reporting effect sizes and confidence 
    intervals (Cumming, 2013, <doi:10.1177/0956797613504966>). 'MOTE' simplifies 
    the use and interpretation of effect sizes and confidence intervals. For more 
    information, visit <https://www.aggieerin.com/shiny-server>.
Describes spatial patterns of categorical raster data for 
    any defined regular and irregular areas. 
    Patterns are described quantitatively using built-in signatures 
    based on co-occurrence matrices but also allows for 
    any user-defined functions. 
    It enables spatial analysis such as search, change detection,
    and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).
Tools for spectral clustering of weighted directed networks using motif
    adjacency matrices. Methods perform well on large and sparse networks, and
    random sampling methods for generating weighted directed networks are also
    provided. Based on methodology detailed in Underwood, Elliott and Cucuringu
    (2020) <arXiv:2004.01293>.
Tools for motif analysis in multi-level networks.
    Multi-level networks combine multiple networks in one, e.g.
    social-ecological networks. Motifs are small configurations of nodes
    and edges (subgraphs) occurring in networks. 'motifr' can visualize
    multi-level networks, count multi-level network motifs and compare
    motif occurrences to baseline models. It also identifies contributions
    of existing or potential edges to motifs to find critical or missing
    edges. The package is in many parts an R wrapper for the excellent
    'SESMotifAnalyser' 'Python' package written by Tim Seppelt.
Functions for fitting models of trait evolution
        on phylogenies for continuous traits. The majority of functions
        described in Thomas and Freckleton (2012) <doi:10.1111/j.2041-210X.2011.00132.x> 	and include functions that allow for tests of variation in the rates of trait evolution.
The temporal relationship between motor neurons can offer 
    explanations for neural strategies. We combined functions to reduce neuron 
    action potential discharge data and analyze it for short-term, time-domain 
    synchronization. Even more so, motoRneuron combines most available methods 
    for the determining cross correlation histogram peaks and most available 
    indices for calculating synchronization into simple functions. See 
    Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> 
    for a more thorough introduction.
Functions to estimate start and duration of moult from moult 
             data, based on models developed in Underhill 
             and Zucchini (1988, 1990).
Lattice functions for drawing folded empirical cumulative
    distribution plots, or mountain plots. A mountain plot is similar
    to an empirical CDF plot, except that the curve increases from
    0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
    the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.
Mouse-tracking, the analysis of mouse movements in computerized
    experiments, is a method that is becoming increasingly popular in the
    cognitive sciences. The mousetrap package offers functions for importing,
    preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
    An introduction into mouse-tracking analyses using mousetrap can be found
    in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2021)
    <doi:10.31234/osf.io/v685r> (preprint: <https://psyarxiv.com/v685r>).
Contains functions to access movement data stored in 'movebank.org'
    as well as tools to visualize and statistically analyze animal movement data,
    among others functions to calculate dynamic Brownian Bridge Movement Models.
    Move helps addressing movement ecology questions.
Provides the facility to calculate non-isotropic accumulated cost surface, least-cost paths, least-cost corridors, least-cost networks using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) <doi:10.1016/j.softx.2019.100331>.
Provides tools for animal movement modelling using hidden Markov
    models. These include processing of tracking data, fitting hidden Markov models
    to movement data, visualization of data and fitted model, decoding of the state
    process...
Tools to visualize movement data (e.g. from GPS tracking) and temporal changes of environmental data (e.g. from remote sensing) by creating video animations.
Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.
Fit and simulate mixtures of von Mises-Fisher distributions.
Multidimensional projection techniques are used to create two
    dimensional representations of multidimensional data sets.
Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>,
      Wang et al. (2015) <doi:10.1002/bimj.201400143>,
      Wang et al. (2016) <doi:10.1177/0962280214530608>,
      Wang (2021) <doi:10.1007/s11749-021-00770-2>,
      Wang (2020) <arXiv:2010.02848>.
Overcomes one of the major challenges in mobile (passive)
    sensing, namely being able to pre-process the raw data that comes from
    a mobile sensing app, specifically "m-Path Sense" <https://m-path.io>.
    The main task of 'mpathsenser' is therefore to read "m-Path Sense"
    JSON files into a database and provide several convenience functions
    to aid in data processing.
Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.
It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
Interaction between a genetic variant (e.g., a single nucleotide polymorphism) and an environmental variable (e.g., physical activity) can have a shared effect on multiple phenotypes (e.g., blood lipids). We implement a two-step method to test for an overall interaction effect on multiple phenotypes. In first step, the method tests for an overall marginal genetic association between the genetic variant and the multivariate phenotype. The genetic variants which show an evidence of marginal overall genetic effect in the first step are prioritized while testing for an overall gene-environment interaction effect in the second step. Methodology is available from: A Majumdar, KS Burch, S Sankararaman, B Pasaniuc, WJ Gauderman, JS Witte (2020) <doi:10.1101/2020.07.06.190256>.
Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold.  The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator.  Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.
Computing modified profile likelihood estimates for Weibull Shape and Regression Parameters. Modified likelihood estimates are provided.
Model stability and variable inclusion plots [Mueller and Welsh
    (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
    (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
    (2008, <doi:10.1214/07-AOS517>); Jiang et al. 
    (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
Leverages the R language to automate latent variable model estimation
	and interpretation using 'Mplus', a powerful latent variable modeling program
	developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package
    provides routines for creating related groups of models, running batches of
    models, and extracting and tabulating model parameters and fit statistics.
Uses recursive partitioning to create homogeneous subgroups based on structural equation models 
             fit in 'Mplus', a stand-alone program developed by Muthen and Muthen.
Exploratory graphical analysis of multivariate data,
    specifically gene expression data with different projection
    methods: principal component analysis, correspondence analysis,
    spectral map analysis.
Functions to compute and plot multivariate (partial) Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
    for comparisons between all types of variables including continuous vs
    continuous, continuous vs discrete and discrete vs discrete. Uses a
    nonparametric bias correction giving Bias Corrected Mutual Information
    (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
    large genomic datasets.
Allows users to simulate matrix population models with
    particular characteristics based on aspects of life history such as
    mortality trajectories and fertility trajectories. Also allows the
    exploration of sampling error due to small sample size.
Calculates the Most Probable Number (MPN) to quantify the
    concentration (density) of microbes in serial dilutions of a laboratory
    sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>).
    Also calculates the Aerobic Plate Count (APC) for similar microbial
    enumeration experiments.
Symbolic computing with multivariate polynomials in R.
A flexible framework for power analysis using Monte
    Carlo simulation for settings in which considerations of the correlations
    between predictors are important. Users can set up a data generative model
    that preserves dependence structures among predictors given existing data
    (continuous, binary, or ordinal). Users can also generate power curves to
    assess the trade-offs between sample size, effect size, and power of a design.
    This package includes several statistical models common in environmental
    mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.
Analysis of experimental multi-parent populations to detect regions
             of the genome (called quantitative trait loci, QTLs) influencing
             phenotypic traits measured in unique and multiple environments.
             The population must be composed of crosses between a set of at
             least three parents (e.g. factorial design, 'diallel', or nested
             association mapping). The functions cover data processing,
             QTL detection, and results visualization. The implemented methodology
             is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017)
             <doi:10.1007/s00122-017-2923-3> as well as in Garin, Malosetti
             and van Eeuwijk (2020) <doi: 10.1007/s00122-020-03621-0>.
Fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards. See Burke & MacKenzie (2016) <doi:10.1111/biom.12625> and Burke et al (2020) <doi:10.1111/rssc.12398>.
Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
A low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>,
  which applies a mass-preserving spline to soil attributes. Splining soil data 
  is a safe way to make continuous down-profile estimates of attributes measured
  over discrete, often discontinuous depth intervals.
Supplementary materials and datasets for the book "Modern Psychometrics With R" (Mair, 2018, Springer useR! series).
Fitting and testing multinomial processing tree (MPT) models, a
  class of nonlinear models for categorical data.  The parameters are the
  link probabilities of a tree-like graph and represent the latent cognitive
  processing steps executed to arrive at observable response categories
  (Batchelder & Riefer, 1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009
  <doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988
  <doi:10.1037/0033-295x.95.3.318>).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
Statistical or cognitive modeling usually requires a number of more or less 
    arbitrary choices creating one specific path through a 'garden of forking paths'. 
    The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, 
    <doi:10.1177/1745691616658637>) offers a principled alternative in which results 
    for all possible combinations of reasonable modeling choices are reported. 
    MPTmultiverse performs a multiverse analysis for multinomial processing tree 
    (MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models combining 
    maximum-likelihood/frequentist and Bayesian estimation approaches with 
    different levels of pooling (i.e., data aggregation). For the 
    frequentist approaches, no pooling (with and without parametric or nonparametric 
    bootstrap) and complete pooling are implemented using 
    MPTinR <https://cran.r-project.org/package=MPTinR>. 
    For the Bayesian approaches, no pooling, complete pooling, and three different 
    variants of partial pooling are implemented using 
    TreeBUGS <https://cran.r-project.org/package=TreeBUGS>. The main function is 
    fit_mpt() who performs the multiverse analysis in one call.
Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis (3rd ed), by Montgomery, Peck
             and Vining.
             Some additional data sets and functions are also included.
Useful functions to analyze proteomic workflows including
    number of identifications, data completeness, missed cleavages,
    quantitative and retention time precision etc. Various software
    outputs are supported such as 'ProteomeDiscoverer', 'Spectronaut', 'DIA-NN'
    and 'MaxQuant'.
Complements the book "Using R for Modelling and  
    Quantitative Methods in Fisheries" ISBN: 9780367469894, being 
    published in September 2020 by Chapman & Hall in their "Using R series". 
    There are numerous functions and data-sets that are used in the 
    book's many practical examples.
Parametric modeling of M-quantile regression coefficient functions.
The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.
Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.
Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) <doi:10.2307/2556151>; Jolly (1965) <doi:10.2307/2333826>; Seber (1965) <doi:10.2307/2333827>) and Huggin's (1989) <doi:10.2307/2336377> closed population. 
	Link functions include logit, sine, and hazard.  Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) <doi:10.2307/2280784> estimator.
Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.
Bayesian estimation of inverse variance weighted (IVW), Burgess 
    et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et 
    al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian 
    randomization analyses.
A collection of functions for processing and analyzing metabolite data. 
    The namesake function mrbin() converts 1D 
    or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and
    performs basic processing steps in a reproducible way. Negative values, a
    common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used
    parameters are stored in a readable text file and can be restored from that
    file to enable exact reproduction of the data at a later time. The function fia() ranks features according
    to their impact on classifier models, especially artificial neural network models.
A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
    mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
    To infer which of the captured features are credible, Bayesian analysis is used.
    The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
    (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
    (3) Posterior credibility analysis of the differences of smooths created.
    The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011>.
    Matlab code is available under <http://cc.oulu.fi/~lpasanen/MRBSiZer/>.
Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>.  This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix.
Animal abundance estimation via conventional, multiple covariate
    and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
    fitting is performed via maximum likelihood. Also included are diagnostics
    and plotting for fitted detection functions. Abundance estimation is via a
    Horvitz-Thompson-like estimator.
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.
Tools to get marine regions data from
    <https://www.marineregions.org/>. Includes tools to get region metadata,
    as well as data in 'GeoJSON' format, as well as Shape files. Use cases
    include using data downstream to visualize 'geospatial' data by marine
    region, mapping variation among different regions, and more.
Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. <doi:10.3390/pr9101697>.
Model fitting, sampling and visualization
    for the (Hidden) Markov Random Field model with pairwise interactions and 
    general interaction structure from 
    Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>,
    which has many popular models used in 2-dimensional lattices
    as particular cases, like the Ising Model and Potts Model.
    A complete manuscript describing the package is available in
    Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.
Performs the MRFA approach proposed by Sung et al. (2019+) <arXiv:1709.07064> to fit
    and predict nonlinear regression problems, particularly for large-scale and
    high-dimensional problems. The application includes deterministic or stochastic
    computer experiments, spatial datasets, and so on.
Approximate node interaction parameters of Markov Random Fields 
    graphical networks. Models can incorporate additional covariates, allowing users to estimate
    how interactions between nodes in the graph are predicted to change across
    covariate gradients. The general methods implemented in this package are described 
    in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Tools to compute depth measures and implementations of related 
             tasks such as outlier detection, data exploration and 
            classification of multivariate, regression and functional data.
Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).
Simulation from an 'mrgsolve' 
    <https://cran.r-project.org/package=mrgsolve> model using a parallel backend.  
    Input data sets are split (chunked) and simulated in parallel using 
    mclapply() or future_lapply() 
    <https://cran.r-project.org/package=future.apply>.
Perform sensitivity analysis on ordinary differential equation 
    based models, including ad-hoc graphical analyses based on structured 
    sequences of parameters as well as local sensitivity analysis. Functions 
    are provided for creating inputs, simulating scenarios and plotting outputs.
Fast simulation from ordinary differential equation
    (ODE) based models typically employed in quantitative pharmacology and
    systems biology.
Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) 
    self-exciting process, with given immigrant hazard rate functions and 
    offspring density function. Calculate the likelihood of a MRHawkes process 
    with given hazard rate functions and offspring density function for an 
    (increasing) sequence of event times. Calculate the Rosenblatt residuals of the 
    event times. Predict future event times based on observed event times up to a 
    given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.
It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices.  The indices are proposed in Cugmas and Ferligoj (2018) <http://ibmi.mf.uni-lj.si/mz/2018/no-1/Cugmas2018.pdf>.
Implements various methods for tissue classification in magnetic
        resonance (MR) images of the brain, including normal mixture models
        and hidden Markov normal mixture models, as outlined in Feng &
        Tierney (2011) <doi:10.18637/jss.v044.i07>. These methods allow a
        structural MR image to be classified into gray matter, white matter
        and cerebrospinal fluid tissue types.
Estimation and comparison of the performances of diagnostic tests
    in multi-reader multi-case studies where true case statuses (or ground
    truths) are known and one or more readers provide test ratings for multiple
    cases.  Reader performance metrics are provided for area under and expected
    utility of ROC curves, likelihood ratio of positive or negative tests, and
    sensitivity and specificity.  ROC curves can be estimated empirically or
    with binormal or binormal likelihood-ratio models.  Statistical comparisons
    of diagnostic tests are based on the ANOVA model of Obuchowski-Rockette and
    the unified framework of Hillis (2005) <doi:10.1002/sim.2024>.  The ANOVA
    can be conducted with data from a full factorial, nested, or partially
    paired study design; with random or fixed readers or cases; and covariances
    estimated with the DeLong method, jackknifing, or an unbiased method.  Smith
    and Hillis (2020) <doi:10.1117/12.2549075>.
A causal mediation approach under the counterfactual framework 
    to test the significance of total, direct and indirect effects. In this approach, 
    a group of methylated sites from a predefined region are utilized as the mediator, 
    and the functional transformation is used to reduce the possible high dimension 
    in the region-based methylated sites and account for their location information.
Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, <doi:10.1093/bib/bbw145>), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, <doi:10.1016/j.molp.2022.02.012>).
Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.
Computes mutual information matrices from continuous, categorical 
  and survival variables, as well as feature selection with minimum redundancy, 
  maximum relevance (mRMR) and a new ensemble mRMR technique. Published in
  De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.
Computes multiple correlation coefficient when the data matrix is given and tests its significance.
A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC 
            (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also 
            contains functions to simulate data under a certain topology, to visualize a graph in different 
            ways, and to compare graphs and quantify the differences. 
            See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.
An easy-to-use IMAP client that provides tools for message searching,
    selective fetching of message attributes, mailbox management, attachment extraction, 
    and several other IMAP features, paving the way for e-mail data analysis in R.
We can calculate directly used this package the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.
We provide the framework to analyze multiresolution partitions (e.g. country, provinces, subdistrict) where each individual data point belongs to only one partition in each layer (e.g. i belongs to subdistrict A, province P, and country Q).   We assume that a partition in a higher layer subsumes lower-layer partitions (e.g. a nation is at the 1st layer subsumes all provinces at the 2nd layer). Given N individuals that have a pair of real values (x,y) that generated from  independent variable X and dependent variable Y. Each individual i belongs to one partition per layer. Our goal is to find which partitions at which highest level that all individuals  in the these partitions share the same linear model Y=f(X) where f is a linear function. The framework deploys the Minimum Description Length principle (MDL) to infer solutions. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2021) <doi:10.1145/3424670>.
Frequentist and Bayesian linear regression for large data sets. Useful 
    when the data does not fit into memory (for both frequentist and Bayesian regression), 
    to make running time manageable (mainly for Bayesian regression), and to reduce 
    the total running time because of reduced or less severe memory-spillover into 
    the virtual memory. This is an implementation of Merge & Reduce for linear regression
    as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020).
    'Streaming statistical models via Merge & Reduce'. International Journal of
    Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.
An implementation of the MRS algorithm for comparison across distributions,
  as described in Jacopo Soriano, Li Ma (2016) <doi:10.1111/rssb.12180>. 
  The model is based on a nonparametric process taking the form of a Markov model 
  that transitions between a "null" and an "alternative" state 
  on a multi-resolution partition tree of the sample space. 
  MRS effectively detects and characterizes a variety of underlying differences. 
  These differences can be visualized using several plotting functions.
Estimates marginal causal excursion effects and moderated causal excursion effects for micro-randomized trial (MRT). Applicable to MRT with binary treatment options and continuous or binary outcomes. The method for MRT with continuous outcomes is the weighted centered least squares (WCLS) by Boruvka et al. (2018) <doi:10.1080/01621459.2017.1305274>. The method for MRT with binary outcomes is the estimator for marginal excursion effect (EMEE) by Qian et al. (2021) <doi:10.1093/biomet/asaa070>.
Provide a sample size calculator for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.
Provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on methodology developed in Qian et al. (2020) <doi:10.1093/biomet/asaa070>. Also provides a power calculator when the sample size is input by the user.
Calculates ARMSS (age related multiple sclerosis severity), and both local and global MSSS (multiple sclerosis severity score).
Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.
Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among response variables and borrowing the strength from related areas and auxiliary variables to increase the effectiveness of sample size, the multivariate model in this package is based on multivariate model 1 proposed by Roberto Benavent and Domingo Morales (2016) <doi:10.1016/j.csda.2015.07.013>. Benchmarking in Small Area Estimation is a modification of Small Area Estimation model to guarantee that the aggregate weighted mean of the county predictors equals the corresponding weighted mean of survey estimates. Difference Benchmarking is the simplest benchmarking method but widely used by multiplying empirical best linear unbiased prediction (EBLUP) estimator by the common adjustment factors (J.N.K Rao and Isabel Molina, 2015).
Implements area level of multivariate small area estimation using Hierarchical Bayesian method under Normal and T distribution. The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Multi-step adaptive elastic-net (MSAENet) algorithm for
    feature selection in high-dimensional regressions proposed in
    Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
    with support for multi-step adaptive MCP-net (MSAMNet) and
    multi-step adaptive SCAD-net (MSASNet) methods.
The new methodology "moving subset analysis" provides functions to investigate the effect of environmental conditions on the CO2 fertilization effect within longterm free air carbon enrichment (FACE)  experiments. In general, the functionality is applicable to derive the influence of a third variable (forcing experiment-support variable) on the relation between a dependent and an independent variable.
Estimates the sample size needed to detect microbial contamination 
 in a lot with a user-specified detection probability and user-specified analytical sensitivity.
 Various patterns of microbial contamination are accounted for: homogeneous (Poisson), 
 heterogeneous (Poisson-Gamma) or localized(Zero-inflated Poisson).
  Ida Jongenburger et al. (2010) <doi:10.1016/j.foodcont.2012.02.004> 
  "Impact of microbial distributions on food safety".
  Leroy Simon (1963) <doi:10.1017/S0515036100001975>
  "Casualty Actuarial Society - The Negative Binomial and Poisson Distributions Compared".
Visualizes multiple sequence alignments dynamically within the
    Shiny web application framework.
Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element
 isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks,
 examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI)
 ion sources.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).
Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters. Numerical solutions are provided for its extensions.
Create native charts for 'Microsoft PowerPoint' and 'Microsoft Word' documents. 
 These can then be edited and annotated. Functions are provided to let users create charts, modify 
 and format their content. The chart's underlying data is automatically saved within the 
 'Word' document or 'PowerPoint' presentation. It extends package 'officer' that does 
 not contain any feature for 'Microsoft' native charts production.
Functions to classify mass spectra in known categories, and to determine discriminant mass-over-charge values. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-over-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-over-charge values. Three vignettes illustrating how to use the functions of this package from real data sets are also available online to help users: <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Ecrobiav3.html>, <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Klebsiellav3.html> and <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_DAv3.html>.
Model based clustering using 
    the multivariate multiple Scaled t (MST) and multivariate multiple 
    scaled contaminated normal (MSCN) distributions. The MST is an 
    extension of the multivariate Student-t distribution to include 
    flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a  heavy-tailed
    generalization of the multivariate normal (MN) distribution to
    model elliptical contoured scatters in the presence of mild outliers
    (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.
Three generalizations of the synthetic control method (which has 
    already an implementation in package 'Synth') are implemented: first, 
    'MSCMT' allows for using multiple outcome variables, second, time series 
    can be supplied as economic predictors, and third, a well-defined 
    cross-validation approach can be used.
    Much effort has been taken to make the implementation as stable as possible 
    (including edge cases) without losing computational efficiency.
    A detailed description of the main algorithms is given in 
    Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
Find common entities detected in both positive and negative
    ionization mode, delete this entity in the less sensible mode and combine both
    matrices.
Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.
Methods for analyzing and using quartets displayed on a collection of gene trees, 
      primarily to make inferences about the species tree or network under the multi-species 
      coalescent model. These include quartet hypothesis tests for the model, as developed by 
      Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, simplex plots of quartet concordance factors
      as presented by Allman et al. (2020) <doi:10.1101/2020.02.13.948083>, species tree inference methods 
      based on quartet distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and 
      Yourdkhani and Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, and the NANUQ algorithm for inference of level-1 
      species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>. Software announcement by 
      Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.
Statistical tests for validating multispecies coalescent gene tree simulators, using pairwise distances and rooted triple counts. Background is given by Allman, Banos, and Rhodes (2019) <arXiv:1908.01424>.
R Client for the Microsoft Cognitive Services Text Analytics
    REST API, including Sentiment Analysis, Topic Detection, Language Detection,
    and Key Phrase Extraction. An account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
R Client for the Microsoft Cognitive Services 
  'Text-to-Speech' REST API, including voice synthesis. A valid account 
  must be registered at the Microsoft Cognitive Services website 
  <https://azure.microsoft.com/services/cognitive-services/> in order to 
  obtain a (free) API key. Without an API key, this package will not 
  work properly.
R Client for the Microsoft Cognitive Services Web Language Model
    REST API, including Break Into Words, Calculate Conditional
    Probability, Calculate Joint Probability, Generate Next Words, and List
    Available Models. A valid account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
Implements the method of successive dichotomizations by
    Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, 
    which estimates item measures, person measures and ordered rating
    category thresholds given ordinal rating scale data.
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) <doi:10.5705/ss.202016.0117>.
Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components.  The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
Development, simulation testing, and implementation of management procedures for fisheries 
    (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).
A companion to the Chinese book ``Modern Statistical Graphics''.
Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2019) <doi:10.18637/jss.v091.i04>.
Implements the three parallel forecast combinations of Markov Switching GARCH and extreme learning machine model along with the selection of appropriate model for volatility forecasting. For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>.
Multinomial logistic regression with sparse group lasso
        penalty. Simultaneous feature selection and parameter
        estimation for classification. Suitable for high dimensional
        multiclass classification with many classes. The algorithm
        computes the sparse group lasso penalized maximum likelihood
        estimate. Use of parallel computing for cross validation and
        subsampling is supported through the 'foreach' and 'doParallel'
        packages. Development version is on GitHub, please report
        package issues on GitHub.
This is the library that can serialize or unserialize MessagePack format data.
Computes the degrees of freedom of the lasso,
        elastic net, generalized elastic net and adaptive lasso based
        on the generalized path seeking algorithm.  The optimal model
        can be selected by model selection criteria including Mallows'
        Cp, bias-corrected AIC (AICc), generalized cross validation
        (GCV) and BIC.
Provides new functions info(), warn() and error(), similar to message(),
    warning() and stop() respectively. However, the new functions can have a 'level'
    associated with them, so that when executed the global level option determines whether
    they are shown or not. This allows debug modes, outputting more information. The can also
    output all messages to a log file.
Allows for the computation of mSHAP values on
    two-part models as proposed  by Matthews, S. and Hartman, B. 
    (2021) <arXiv:2106.08990>.
    Also contains functions for simple plotting of
    the results (or any SHAP values). For information about the
    TreeSHAP algorithm that mSHAP builds on, see Lundberg, S.M.,
    Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz,
    R., Himmelfarb, J., Bansal, N., Lee, S.I. (2020)
    <doi:10.1038/s42256-019-0138-9>.
The Molecular Signatures Database ('MSigDB') is one of the most widely 
    used and comprehensive databases of gene sets for performing gene set 
    enrichment analysis <doi:10.1016/j.cels.2015.12.004>. The 'msig' package provides 
    you with powerful, easy-to-use and flexible query functions for the 'MsigDB' 
    database.
        There are 2 query modes in the 'msig' package: online query and local query. 
    Both queries contain 2 steps: gene set name and gene.
        The online search is divided into 2 modes: registered search and 
    non-registered browse. For registered search, email that you registered should 
    be provided.
        Local queries can be made from local database, which can be updated by msig_update() function.
Provides the 'Molecular Signatures Database' (MSigDB) gene
    sets typically used with the 'Gene Set Enrichment Analysis' (GSEA)
    software (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>,
    Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>) in a standard R
    data frame with key-value pairs. The package includes the human genes
    as listed in MSigDB as well as the corresponding symbols and IDs for
    frequently studied model organisms such as mouse, rat, pig, fly, and
    yeast.
Utility functions for mutational signature analysis
             as described in Alexandrov, L. B. (2020)
             <doi:10.1038/s41586-020-1943-3>.
             This package provides two groups of functions. One is
             for dealing with mutational signature "exposures"
             (i.e. the counts of mutations in a sample that are
             due to each mutational signature). The other group of
             functions is for matching or comparing sets of 
             mutational signatures. 'mSigTools' stands for 
             mutational Signature analysis Tools.
Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.
Performs a multiscale analysis of a nonparametric
  regression or nonparametric regressions with time series errors. In case
  of one regression, with the help of this package it is possible to detect
  the regions where the trend function is increasing or decreasing.
  In case of multiple regressions, the test identifies regions where
  the trend functions are different from each other. See
  Khismatullina and Vogt (2020) <doi:10.1111/rssb.12347>,
  Khismatullina and Vogt (2022) <doi:10.48550/arXiv.2209.10841> and
  Khismatullina and Vogt (2023) <doi:10.1016/j.jeconom.2021.04.010>
  for more details on theory and applications.
The 'MSiP' is a computational approach to predict protein-protein interactions from large-scale affinity purification mass 'spectrometry' (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.
An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
Programmatic access to 'OncoTree' API
    <http://oncotree.mskcc.org/>. Get access to tumor main types, identifiers
    and utility routines to map across to other tumor classification systems.
Analysis of annual average ocean water level time series
    from long (minimum length 80 years) individual records, providing improved
    estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on Singular Spectrum Analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also contains a forecasting module to consider the
    implication of user defined quantum of sea level rise between the end of the
    available historical record and the year 2100. A wide range of screen and pdf
    plotting options are available in the package.
Functions for fitting continuous-time Markov and hidden
    Markov multi-state models to longitudinal data.  Designed for
    processes observed at arbitrary times in continuous time (panel data)
    but some other observation schemes are supported. Both Markov
    transition rates and the hidden Markov output process can be modelled
    in terms of covariates, which may be constant or piecewise-constant
    in time.
Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.
A fast and general method for restructuring classical longitudinal data into
    augmented ones. The reason for this is to facilitate the modeling of longitudinal data under
    a multi-state framework using the 'msm' package.
Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). 
             The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".
Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This also serves as a companion package for the 
    STAT 571: Multivariate Analysis course offered by the Department of Statistics
    at the University of Illinois at Urbana-Champaign ('UIUC').
Given the maximum available sample size (N) for an experiment, and the target levels of Type I 
             and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT 
             the package can also obtain its operating characteristics and implement the test for a given 
             sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. 
             The proposed test has shown evidence of reducing the average sample size required to perform 
             statistical hypothesis tests at specified levels of significance and power. Currently, the package
             implements one-sample proportion tests, one and two-sample z tests, and one and two-sample 
             t tests. A brief user guidance for this package is provided below. One can also refer to the 
             supplemental information for the same.
This is a toolkit for multivariate process monitoring. It computes several multivariate control charts e.g. Hotelling, Chi-squared, MEWMA, MCUSUM and Generalized Variance. Ten didactic datasets are included. It also contains some tools for assessing multivariate normality e.g. Mardia's, Royston's and Henze-Zirkler's tests. Please, see the NEWS file for the latest changes in the package.
This is a implementation of design methods for multi-state reliability demonstration tests (MSRDT) with failure count data, 
    which is associated with the work from the published paper "Multi-state Reliability Demonstration Tests" by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>.
    It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). 
    For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively.
    It also provides the implementation of conventional design method, namely binomial tests for failure count data.
Provides methods to perform parameter estimation and 
  make analysis of multivariate observed outcomes through time which depends 
  on a latent state variable. All methods scale well in the dimension 
  of the observed outcomes at each time point. The package contains an 
  implementation of a Laplace approximation, particle filters like 
  suggested by Lin, Zhang, Cheng, & Chen (2005)
  <doi:10.1198/016214505000000349>, and the gradient and observed information
  matrix approximation suggested by Poyiadjis, Doucet, & Singh (2011) 
  <doi:10.1093/biomet/asq062>.
A collection of helper functions for analyzing Second Primary Cancer data, 
    including functions to reshape data, to calculate patient states and analyze cancer incidence.
Tools that extend the functionality of the 'RODBC' package to work
  with Microsoft SQL Server databases. Makes it easier to browse the database
  and examine individual tables and views.
Constructs trees for multivariate survival data using marginal and frailty models.
    Grows, prunes, and selects the best-sized tree.
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.
This is a tool for epidemiologist, medical data analyst, 
    medical or public health professionals. It contains three domains of functions:
    1) data management, 2) statistical analysis and 3) calculating 
    epidemiological measures.
Implements a minimum-spanning-tree-based heuristic for k-means clustering using a union-find disjoint set and the algorithm in Kruskal (1956) <doi:10.1090/S0002-9939-1956-0078686-7>.
A collection of statistical tests for the detection of differential
    item functioning (DIF) in multistage tests. Methods entail logistic regression,
    an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests.
    The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform 
    DIF effects are available depending on which method is used.
A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.
Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2022) <https://grodriguezrondon.com/research/> that can be used to identify the number of regimes in Markov switching models.
Software to aid in modeling and analyzing mass-spectrometry-based
    proteome melting data. Quantitative data is imported and normalized and
    thermal behavior is modeled at the protein level. Methods exist for
    normalization, modeling, visualization, and export of results. For a
    general introduction to MS-based thermal profiling, see Savitski et al.
    (2014) <doi:10.1126/science.1255784>.
Implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.
Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. It holds various item response theory (IRT) and score-based methods to select the next module and estimate ability levels (Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).
Interface for creating data sets and extracting data through the
    'MicroStrategy' REST API. Access the demo API at
    <https://demo.microstrategy.com/MicroStrategyLibrary/api-docs/index.html>.
Estimators for multivariate symmetrical uncertainty based
    on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>,
    total correlation, information gain and symmetrical uncertainty of
    categorical variables.
Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).
Functions for metabolomics data analysis: data preprocessing, 
  orthogonal signal correction, PCA analysis, PCA-DA analysis, 
	PLS-DA analysis, classification, feature selection, correlation 
	analysis, data visualisation and re-sampling strategies.
Build multiscalar territorial analysis based on various contexts.
Perform multi-trait rare-variant association tests using the summary statistics and adjust for possible sample overlap. Package is based on "Multi-Trait Analysis of Rare-Variant Association Summary Statistics using MTAR" by Luo, L., Shen, J., Zhang, H., Chhibber, A. Mehrotra, D.V., Tang, Z., 2019 (submitted).
The purpose of this package is to share a collection of functions the author wrote during weekends for managing
    kitchen and garden tasks, e.g. making plant growth charts or Thanksgiving kitchen schedule charts, etc. 
    Functions might include but not limited to:
    (1) aiding summarizing time related data; 
    (2) generating axis transformation from data; and
    (3) aiding Markdown (with html output) and Shiny file editing.
Implements Mander & Thompson's (2010) 
    <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs
    optimal under the alternative hypothesis for phase II [cancer] trials.  Also
    provides an implementation of Simon's (1989) 
    <doi:10.1016/0197-2456(89)90015-9> original methodology and 
    allows exploration of the operating characteristics of sub-optimal designs.
The Mass Transportation Distance rank histogram was developed to assess the reliability of scenarios with equal or different probabilities of occurrence <doi:10.1002/we.1872>.
Several robust estimators for linear regression and variable selection are provided. 
              Included are Maximum tangent likelihood estimator by Qin, et al., (2017) <arxiv:1708.05439>, 
              least absolute deviance estimator and Huber regression. The penalized version of each of these 
              estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to 
              produce consistent estimates for both fixed and high-dimensional settings.
Calculate Sample Size and Power for
    Association Studies Involving Mitochondrial DNA Haplogroups.
    Based on formulae by Samuels et al. AJHG, 2006. 78(4):713-720. <DOI:10.1086/502682>.
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>.
An implementation of Multi-Task Logistic Regression (MTLR) for R. 
  This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves
  by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.
Simultaneous multiple outcomes prediction based on revised stacking algorithms, which enables the integration of information from predictions of individual models. An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019)  Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" <doi:10.1093/bioinformatics/btz531>.
Use morphological image processing and edge detection algorithms to automatically measure tree ring widths on digital images. Users can also manually mark tree rings on species with complex anatomical structures. The arcs of inner-rings and angles of successive inclined ring boundaries are used to correct ring-width series. The package provides a Shiny-based application, allowing R beginners to easily analyze tree ring images and export ring-width series in standard file formats.
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
Mahalanobis-Taguchi (MT) system is a collection of multivariate
    analysis methods developed for the field of quality engineering. MT system
    consists of two families depending on their purpose. One is a family of
    Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see
    Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and
    Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a
    family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y.
    (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods
    for the family of MT methods and one basic method for the family of T
    methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi
    Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the
    MT method and the two-sided Taguchi (T1) method is for the family of T
    methods. In addition, the Ta and Tb methods, which are the improved versions
    of the T1 method, are included.
Generates mid upper arm circumference (MUAC) and body mass index (BMI) 
            for age z-scores and percentiles based on LMS method for children and 
            adolescents up to 19 years that can be used to assess nutritional and health status and
            define risk of adverse health events.
Nonparametric approach to estimate the location of block boundaries (change-points) of 
    non-overlapping blocks in a random symmetric matrix which consists of random variables whose 
    distribution changes from block to block.
    BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) <doi:10.1016/j.jmva.2017.12.005>.
A data package containing public domain information on requests made by the
             'MuckRock' (https://www.muckrock.com/) project under the United States
             Freedom of Information Act.
Formatting and structuring multi-parameter spatiotemporal data
  is often a time-consuming task. This package offers functions and data structures 
  designed to easily organize and visualize these data for applications in geology, 
  paleolimnology, dendrochronology, and paleoclimate. See Dunnington and Spooner (2018)
  <doi:10.1139/facets-2017-0026>.
Nonparametric unfolding item response theory (IRT) model for dichotomous data (see W.H. Van Schuur (1984). Structure in Political Beliefs: A New Model for Stochastic Unfolding with Application to European Party Activists, and W.J.Post (1992). Nonparametric Unfolding Models: A Latent Structure Approach). The package implements MUDFOLD (Multiple UniDimensional unFOLDing), an iterative item selection algorithm that constructs unfolding scales from dichotomous preferential-choice data without explicitly assuming a parametric form of the item response functions. Scale diagnostics from Post(1992) and estimates for the person locations proposed by Johnson(2006) and Van Schuur(1984) are also available. This model can be seen as the unfolding variant of Mokken(1971) scaling method.
Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.
Produces a smooth estimate of the hazard
  function for censored data.
The muHVT package is a collection of R functions to facilitate building topology preserving maps for rich multivariate data.See <https://en.wikipedia.org/wiki/Voronoi_diagram> for more information. Credits to Mu Sigma for their continuous support throughout the development of the package.
A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.
Generates Muller plot from parental/genealogy/phylogeny information and population/abundance/frequency dynamics data.
    Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist).
    A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process.
    To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time.
    MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs.
Allows the user to create graphs with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized.
    Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>.
    More about multilayered graphs and their usage can be found in our review paper:
    Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.
Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, 'mulset' can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.
Integer Programming Formulations Applied to Univariate and Multivariate Allocation Problems.
Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.
Contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.
Simultaneous tests and confidence intervals
  for general linear hypotheses in parametric models, including 
  linear, generalized linear, linear mixed effects, and survival models.
  The package includes demos reproducing analyzes presented
  in the book "Multiple Comparisons Using R" (Bretz, Hothorn, 
  Westfall, 2010, CRC Press).
Convert a logical vector or a vector of
    p-values or a correlation, difference, or distance
    matrix into a display identifying the pairs for
    which the differences were not significantly
    different.  Designed for use in conjunction with
    the output of functions like TukeyHSD, dist{stats},
    simint, simtest, csimint, csimtest{multcomp},
    friedmanmc, kruskalmc{pgirmess}.
Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.
Equivalence tests and related confidence intervals for the
 comparison of two treatments, simultaneously for one or many normally
 distributed, primary response variables (endpoints). The step-up
 procedure of Quan et al. (2001) is both applied for differences and
 extended to ratios of means. A related single-step procedure is also
 available.
Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints.
    The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative
    hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate
    permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated 
    that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal
    significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved
    by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple
    testing procedures.
GEE solver for correlated nominal or ordinal multinomial responses
    using a local odds ratios parameterization.
Provides a multi action button for usage in 'shiny'
    applications.
Covers several approaches to ANOVA (Analysis of Variance), specifically studying a balanced two-factor fixed-fixed ANOVA design. It consists of four sections. The first section uses a dynamic scheme to indicate which possible alternatives to follow depending on the fulfillment of the assumptions of the model. It also presents an analysis on the fulfillment of the assumptions of linearity, homoscedasticity, normality, and independence in the residuals of the model, as well as dynamic statistical graphs on the residuals of the model. The second section presents an analysis with a non-parametric approach of Kruskal Wallis. After Kruskal Wallis, a Post-Hoc analysis of multiple comparisons on the medians of the treatments is carried out. The third section presents a classical parametric ANOVA. Following classical ANOVA, a post-hoc analysis of multiple comparisons on the medians of the treatments, factor levels by Dunn's test, and statistical graphs for the treatments and factor levels are shown. Additionally, a post-hoc analysis of multiple comparisons on the means of the treatments is done. The fourth section presents an analysis of variance under a Bayesian approach. In this section, interactive statistical graphs are presented on the posterior distributions of treatments, factor levels, and a convergence analysis of the estimated parameters, using MCMC (Markov Chain Monte Carlo). These results are displayed in an interactive glossy panel which allows modification of the test arguments, contains interactive statistical plots, and presents automatic conclusions depending on the fulfillment of the assumptions of the balanced two-factor fixed ANOVA model.
The base apply function and its variants, as well as the related
    functions in the 'plyr' package, typically apply user-defined functions to a
    single argument (or a list of vectorized arguments in the case of mapply). The
    'multiApply' package extends this paradigm with its only function, Apply, which
    efficiently applies functions taking one or a list of multiple unidimensional
    or multidimensional arrays (or combinations thereof) as input. The input
    arrays can have different numbers of dimensions as well as different dimension
    lengths, and the applied function can return one or a list of unidimensional or
    multidimensional arrays as output. This saves development time by preventing the
    R user from writing often error-prone and memory-inefficient loops dealing with
    multiple complex arrays. Also, a remarkable feature of Apply is the transparent
    use of multi-core through its parameter 'ncores'. In contrast to the base apply
    function, this package suggests the use of 'target dimensions' as opposite
    to the 'margins' for specifying the dimensions relevant to the function to be
    applied.
Efficient finite difference method for valuing European and American multi-asset options.
Estimation routines for several classes of affine term structure of interest rates models. All the models are based on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014) <doi:10.1111/jofi.12131>. Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015) <doi:10.1016/j.jfineco.2014.09.004> and Candelon and Moura (2021) <http://hdl.handle.net/2078.1/249985> are also available.
Computationally efficient functions to provide direct likelihood-based
    inference for partially-observed multivariate birth-death processes.  Such processes
    range from a simple Yule model to the complex susceptible-infectious-removed model
    in disease dynamics.  Efficient likelihood evaluation facilitates maximum likelihood
    estimation and Bayesian inference.
Meta-analyses can be compromised by studies' internal biases (e.g.,
  confounding in nonrandomized studies) as well as by publication bias. This
  package conducts sensitivity analyses for the joint effects of these biases
  (per Mathur (2022) <doi:10.31219/osf.io/u7vcb>). These sensitivity analyses 
  address two questions: (1) For a given severity of internal bias across
  studies and of publication bias, how much could the results change?; and
  (2) For a given severity of publication bias, how severe would internal bias
  have to be, hypothetically, to attenuate the results to the null or by a given
  amount?
Provides a GUI with which users can construct and interact
        with Multibiplot Analysis.
Functions and datasets to support Smilde, Næs and Liland (2021, ISBN: 978-1-119-60096-1) 
   "Multiblock Data Fusion in Statistics and Machine Learning - Applications in the Natural and Life Sciences". 
   This implements and imports a large collection of methods for multiblock data analysis with common interfaces, result- and plotting 
   functions, several real data sets and six vignettes covering a range different applications.
Evaluate hypotheses concerning the distribution of multinomial
  proportions using bridge sampling. The bridge sampling routine is able to
  compute Bayes factors for hypotheses that entail inequality constraints,
  equality constraints, free parameters, and mixtures of all three. These
  hypotheses are tested against the encompassing hypothesis, that all parameters
  vary freely or against the null hypothesis that all category proportions are equal.
  For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.
Implements a generalization of the Cochran-Armitage trend test to
    multinomial data. In addition to an overall test, multiple testing adjusted
    p-values for trend in individual outcomes and power calculation is
    available.
Provides a basic interface for accessing annotation data from
   the Multi-CAST collection, a database of spoken natural language texts
   edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
   diverse set of languages and has been annotated across multiple levels.
   Annotation data is downloaded on request from the servers of the
   University of Bamberg. See the Multi-CAST website
   <https://multicast.aspra.uni-bamberg.de/> for more information and a list
   of related publications.
Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) <doi:10.1348/000711005X64817>, determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values.
A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret.  For complex problems RF performs better.  Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...).  Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model.  We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) <doi:10.1093/bioinformatics/btab088>.
A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.
The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from <http://tamivox.org/dave/multinomial/index.html> and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here <https://jeromekelleher.net/category/combinatorics.html>. The C++ code and Python code are distributed without conditions.
Estimation of multivariate differences between two groups (e.g., multivariate sex differences) with regularized regression methods and predictive approach. See Lönnqvist & Ilmarinen (2021) <doi:10.1007/s11109-021-09681-2> and Ilmarinen et al. (2022) <doi:10.1177/08902070221088155>.
    Includes tools that help in understanding difference score reliability, predictions of difference score variables, conditional intra-class correlations, and heterogeneity of variance estimates. Package development was supported by the Academy of Finland research grant 338891.
Code to support a systems biology research program from inception through publication.  The methods focus on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.
Partition a data frame across multiple worker processes to
    provide simple multicore parallelism.
An implementation for multivariate functional additive mixed
    models (multiFAMM), see Volkmann et al. (2021, <arXiv:2103.06606>). It builds on developed methods for univariate sparse 
    functional regression models and multivariate functional principal component
    analysis. This package contains the function to run a multiFAMM and some
    convenience functions useful when working with large models. An additional 
    package on GitHub contains more convenience functions to reproduce the 
    analyses of the corresponding paper 
    (<https://github.com/alexvolkmann/multifammPaper>).
A suite of functions for performing analyses, based on a multiverse approach, for conditioning data. Specifically, given the appropriate data, the functions are able to perform t-tests, analyses of variance, and mixed models for the provided data and return summary statistics and plots. The function is also able to return for all those tests p-values, confidence intervals, and Bayes factors. The methods are described in Lonsdorf, Gerlicher, Klingelhofer-Jens, & Krypotos <doi:10.31234/osf.io/2z6pd>.
Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022.
Methods for the analysis of how ecological drivers affect the
    multifunctionality of an ecosystem based on methods of Byrnes et al. 
    2016 <doi:10.1111/2041-210X.12143> and Byrnes et al. 
    2022 <doi:10.1101/2022.03.17.484802>. Most standard
    methods in the literature are implemented (see vignettes) in a tidy
    format.
Read a table of fixed width formatted data of different types into
    a data.frame for each type.
Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2022), <arXiv:2208.14721>.
Functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options.
Methods and models for analysing multigraphs as introduced by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit tests.
Multivariate analysis methods including principal component analysis,
    partial least square regression, and multiblock analysis to describe,
    summarize, and visualize data with a group structure.
Two method new of multigroup and simulation of data.
  The first technique called multigroup PCA (mgPCA) this multivariate exploration approach
  that has the idea of considering the structure of groups and / or different types
  of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality
  Reduction (MDR) it is another multivariate exploration method that is based on
  projections. In addition, a method called Single Dimension Exploration (SDE) was
  incorporated for to analyze the exploration of the data. It could help us in a better
  way to observe the behavior of the multigroup data with certain variables of interest.
Wrapper around the Unix join facility which is more efficient than the built-in R routine merge().
 The package enables the joining of multiple files on disk at once. 
 The files can be compressed and various filters can be deployed before joining.
 Compiles only under Unix.
Estimation and inference for multiple kink quantile regression. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, "Wei Zhong, Chuang Wan and Wenyang Zhang (2020). Estimation and inference for multi-kink quantile regression, submitted".
A flexible, efficient implementation of multilateral price index calculations.
             Includes common methods focused on time product dummy regression and GEKS variations.
             Allows for extension of the methods through automatic window splicing.
             See Krsinich (2016) <doi: 10.1515/jos-2016-0021>.
Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations.
Implement Bayesian Multilevel Modelling for compositional data 
             in a multilevel framework. Compute multilevel compositional data and 
             Isometric log ratio (ILR) at between and within-person levels, 
             fit Bayesian multilevel models for compositional predictors and outcomes, 
             and run post-hoc analyses such as isotemporal substitution models.
Bindings for hierarchical regression models for use with the
    'parsnip' package. Models include longitudinal generalized linear
    models (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and
    mixed-effect models (Pinheiro and Bates)
    <doi:10.1007/978-1-4419-0318-1_1>.
Conducts and visualizes propensity score analysis for multilevel, 
  or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.
Effect sizes, diagnostics and performance metrics for
  multilevel and mixed effects models.
  Includes marginal and conditional 'R2' estimates for linear mixed effects models
  based on Johnson (2014) <doi:10.1111/2041-210X.12225>.
Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications.
Provides install functions of other languages 
             such as 'java', 'python'.
Traditional and spatial capture-mark-recapture analysis with
    multiple non-invasive marks. The models implemented in 'multimark' combine
    encounter history data arising from two different non-invasive "marks",
    such as images of left-sided and right-sided pelage patterns of bilaterally
    asymmetrical species, to estimate abundance and related demographic
    parameters while accounting for imperfect detection. Bayesian models are
    specified using simple formulae and fitted using Markov chain Monte Carlo.
    Addressing deficiencies in currently available software, 'multimark' also
    provides a user-friendly interface for performing Bayesian multimodel
    inference using non-spatial or spatial capture-recapture data consisting of a single
    conventional mark or multiple non-invasive marks. See McClintock (2015) <doi:10.1002/ece3.1676> and Maronde et al. (2020) <doi:10.1002/ece3.6990>.
A latent variable model based on factor analytic and mixture of experts models, designed to infer food intake from multiple biomarkers data. The model is framed within a Bayesian hierarchical framework, which provides flexibility to adapt to different biomarker distributions and facilitates inference on food intake from biomarker data alone, along with the associated uncertainty. Details are in D'Angelo, et al. (2020) <arXiv:2006.02995>.
A set of functions which use the Expectation Maximisation (EM) 
    algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) 
    <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from 
    incomplete data via the EM algorithm, Journal of the Royal Statistical 
    Society, 39(1), 1--22) to take a finite mixture model approach to 
    clustering. The package is designed to cluster multivariate data that have 
    categorical and continuous variables and that possibly contain missing 
    values. The method is described in Hunt, L. and Jorgensen, M. (1999) 
    <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 
    41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) 
    <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data 
    with missing information, Computational Statistics & Data Analysis, 41(3-4), 
    429--440.
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
Identifying comorbidities, frailty, and multimorbidity in claims 
    and administrative data is often a duplicative process.
    The functions contained in this package are meant to first prepare the data to a format
    acceptable by all other packages, then provide a uniform and simple approach to
    generate comorbidity and multimorbidity metrics based on these claims data. The package
    is ever evolving to include new metrics, and is always looking for new measures to include.
    The citations used in this package include the following publications: 
    Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>,
    Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>,
    Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>,
    Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>,
    Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>,
    Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>,
    Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>,
    Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>,
    Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.
Model fitting and simulation for Gaussian and logistic inner 
    product MultiNeSS models for multiplex networks. The package implements 
    a convex fitting algorithm with fully adaptive parameter tuning, 
    including options for edge cross-validation. For more details see 
    MacDonald et al. (2020).
Functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.
Analyze multilevel networks as described in Lazega et al (2008)
    <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders 
    (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an 
    extension to 'igraph'.
Network meta-analysis and network meta-regression models for 
    aggregate data, individual patient data, and mixtures of both individual 
    and aggregate data using multilevel network meta-regression as described by
    Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a
    Bayesian framework using 'Stan'.
An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial distribution given a set of observations, proposed by Sison and Glaz in their paper:
        Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association, 90:366-369 (1995). 
        The method is an R translation of the SAS code implemented by May and Johnson in their paper:
        May, W.L. and W.D. Johnson. Constructing two-sided simultaneous confidence intervals for multinomial proportions for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000). 
        Paper and code available at <DOI:10.18637/jss.v005.i06>.
Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2022) <DOI:10.48550/arXiv.2207.13984>. These models are estimated under  a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>. In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>.
Implements Gibbs sampling and Bayes factors for multinomial models with
    linear inequality constraints on the vector of probability parameters. As
    special cases, the model class includes models that predict a linear order 
    of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models 
    assuming that the parameter vector p must be inside the convex hull of a 
    finite number of predicted patterns (i.e., vertices). A formal definition of 
    inequality-constrained multinomial models and the implemented computational
    methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). 
    Multinomial models with linear inequality constraints: Overview and improvements 
    of computational methods for Bayesian inference. Journal of Mathematical 
    Psychology, 91, 70-87. <doi:10.1016/j.jmp.2019.03.004>.
    Inequality-constrained multinomial models have applications in the area of 
    judgment and decision making to fit and test random utility models  
    (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of 
    preferences. Psychological Review, 118, 42–56, <doi:10.1037/a0021150>) or to 
    perform outcome-based strategy classification to select the decision strategy 
    that provides the best account for a vector of observed choice frequencies 
    (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information 
    processing to decisions: Formalizing and comparing probabilistic choice models. 
    Cognitive Psychology, 96, 26–40. <doi:10.1016/j.cogpsych.2017.05.003>).
Provides implementations of functions that can be used to
    test multivariate integration routines. The package covers six
    different integration domains (unit hypercube, unit ball, unit sphere,
    standard simplex, non-negative real numbers and R^n). For each domain
    several functions with different properties (smooth,
    non-differentiable, ...) are available. The functions are available in
    all dimensions n >= 1. For each function the exact value of the
    integral is known and implemented to allow testing the accuracy of
    multivariate integration routines. Details on the available test
    functions can be found at on the development website.
Matching algorithm based on network-flow structure. 
  Users are able to modify the emphasis on three different 
  optimization goals: two different distance measures and 
  the number of treated units left unmatched. The method is proposed by Pimentel
  and Kelz (2019) <doi:10.1080/01621459.2020.1720693>.
  The 'rrelaxiv' package, which provides an alternative solver for
	the underlying network flow problems, carries an
	academic license and is not available on CRAN, but
	may be downloaded from Github at 
	<https://github.com/josherrickson/rrelaxiv/>.
Spatio-temporal multivariate occupancy models can handle multiple species in occupancy models.  This method for fitting such models is described in Hepler and Erhardt (2021) "A spatiotemporal model for multivariate occupancy data" <https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2657>.
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.
Tools to create a layout for figures made of multiple panels, and
    to fill the panels with base, 'lattice', 'ggplot2' and 'ComplexHeatmap'
    plots, grobs, as well as content from all image formats supported by
    'ImageMagick' (accessed through 'magick').
Performs genetic association tests between SNPs
        (one-at-a-time) and multiple phenotypes (separately or in joint
        model).
The iterative procedure estimates structural changes in the success probability of Bernoulli variables. It estimates the number and location of the breakpoints as well as the success probability of the different sequences between the breakpoints. In addition, it provides a graphical illustration of the result.
Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.
Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints.
Tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.
An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) 
    and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for 
    multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes 
    are able to be calculated for either null model, and subsequently classified into one of 
    four different interaction classifications (e.g., antagonistic or synergistic interactions). 
    Analyses can be conducted on data for single experiments through to large meta-analytical datasets. 
    Minimal input (or statistical knowledge) is required, with any output easily understood. 
    Summary figures are also able to be easily generated.
Algebraic procedures for the analysis of multiple social networks are delivered with this 
	    package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. Among other things, it 
	    makes it possible to create and manipulate multiplex, multimode, and multilevel network data 
	    with different formats. There are effective ways available to treat multiple networks with 
	    routines that combine algebraic systems like the partially ordered semigroup or the semiring 
	    structure with the relational bundles occurring in different types of multivariate network 
	    data sets. It also provides an algebraic approach for affiliation networks through Galois 
	    derivations between families of the pairs of subsets in the two domains.
Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.
Various utilities to manipulate multivariate polynomials.
Fits the Multiple Random Dot Product Graph Model and performs a test
	 for whether two networks come from the same distribution. Both methods are 
	 proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph
	 Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational
	 and Graphical Statistics).
Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap. See Keyel and Wiegand
    (2016) <doi:10.1111/2041-210X.12558> for more details.
Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.
Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.
Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.
Tools to solve real-world problems with multiple classes classifications by computing the areas under ROC and PR curve via micro-averaging and macro-averaging. The vignettes of this package can be found via <https://github.com/WandeRum/multiROC>. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Tools necessary to reconstruct clonal affiliations from
    temporally and/or spatially separated measurements of viral
    integration sites. For this means it utilizes correlations present
    in the relative readouts of the integration sites. Furthermore,
    facilities for filtering of the data and visualization of different
    steps in the pipeline are provided with the package.
Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Misiuk et al. (2021) <doi:10.1080/01490419.2021.1925789>; Wilson et al. (2007) <doi:10.1080/01490410701295962>; Wood (1996) <https://hdl.handle.net/2381/34503>).
Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.)
Functions to perform sensitivity analysis on a model with multivariate output.
The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and applying the model in the 'test set' (Solanes et al., Neuroimage 2023, 265:119800). This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes.
Multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879>, Qin, Hong, Deutsch, and Bein (2019) <doi:10.1111/rssa.12446>, and Qin, Deutsch, and Hong (2021) <doi:10.1002/pam.22268>. It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects transmitted through one single mediator or two concurrent (conditionally independent) mediators. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. The package also provides a weighting-based balance checking function for assessing the remaining overt bias.
Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.
Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
Algorithms to build set partitions and commutator matrices and their use in the 
    construction of multivariate d-Hermite polynomials;  estimation and derivation of theoretical  vector moments and vector
    cumulants  of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to
    estimation and derivation of multivariate measures of skewness and kurtosis;  estimation and derivation of asymptotic 
    covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis.
    The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates. This package is an implementation of the method described in D.J. Thomson (1982) "Spectrum estimation and harmonic analysis" <doi:10.1109/PROC.1982.12433>.
Provides utility functions for multivariate analysis (factor analysis, discriminant analysis, and others). The package is primary written for the course Multivariate analysis and for the course Computer intensive methods at the masters program of Applied Statistics at University of Ljubljana.
Functions for simulating, estimating and forecasting stationary Vector Autoregressive (VAR) models for multiple subject data using the penalized multi-VAR framework in Fisher, Kim and Pipiras (2020) <arXiv:2007.05052>.
Distance multivariance is a measure of dependence which can be used to detect 
    and quantify dependence of arbitrarily many random vectors. The necessary functions are
    implemented in this packages and examples are given. It includes: distance multivariance, 
    distance multicorrelation, dependence structure detection, tests of independence and
    copula versions of distance multivariance based on the Monte Carlo empirical transform.
    Detailed references are given in the package description, as starting point for the 
    theoretic background we refer to:
    B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using 
    the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), 
    <doi:10.1515/stat-2020-0001>.
Package with multivariate analysis methodologies for experiment evaluation.
	 The package estimates dissimilarity measures, builds dendrograms, obtains MANOVA,
	 principal components, canonical variables, etc. (Pacote com metodologias de analise
	 multivariada para avaliação de experimentos. O pacote estima medidas de dissimilaridade,
	 construi de dendogramas, obtem a MANOVA,  componentes principais, variáveis canônicas, etc.)
Models and predicts multiple output features in single random forest considering the 
    linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.
Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.
It performs variable selection in a multivariate linear model by estimating the covariance matrix 
 of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion.
 The method is described in the paper Perrot-Dockès et al. (2017) <arXiv:1704.00076>.
A multivariate generalization of the emulator package.
Implement 'multiverse' style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016)
    <doi:10.1177/1745691616658637>, (Dragicevic P., Jansen Y., Sarma A., Kay M., Chevalier F., 2019) <doi:10.1145/3290605.3300295> 
    to show the robustness of statistical inference. 'Multiverse analysis' is a philosophy of 
    statistical reporting where paper authors report the outcomes of many different statistical 
    analyses in order to show how fragile or robust their findings are. 
    The 'multiverse' package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>
    allows users to concisely and flexibly implement 'multiverse-style' 
    analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.
Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).
Computation of an estimation of the long-memory parameters and
     the long-run covariance matrix using a multivariate model
	 (Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
	 implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
	 approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>).
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>].  Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.
Exports two functions implementing
    multi-way clustering using the method suggested by Cameron, Gelbach, &
    Miller (2011) and cluster (or block)
    bootstrapping for estimating variance-covariance matrices. Normal one and
    two-way clustering matches the results of other common statistical
    packages.  Missing values are handled transparently and rudimentary
    parallelization support is provided.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics.  Supported by grant NSF DMS 1712839.  See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.
In the case of multivariate ordinal responses, parameter estimates can be severely biased if personal response styles are ignored. This packages provides methods to account for personal response styles and to explain the effects of covariates on the response style, as proposed by Schauberger and Tutz 2021 <doi:10.1177/1471082X20978034>. The method is implemented both for the multivariate cumulative model and the multivariate adjacent categories model.
Multivariate version of the two-sample Gehan and logrank tests, as described in L.J Wei & J.M Lachin (1984) and Persson et al. (2019).
Implementation of commonly used p-value-based and
        parametric multiple testing procedures (computation of adjusted
        p-values and simultaneous confidence intervals) and parallel
        gatekeeping procedures based on the methodology presented in
        the book "Multiple Testing Problems in Pharmaceutical
        Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
        Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.
Tools for performing model selection and model averaging. Automated
             model selection through subsetting the maximum model, with optional
             constraints for model inclusion. Model parameter and prediction
             averaging based on model weights derived from information criteria
             (AICc and alike) or custom model weighting schemes.
Fit multiplicative mixed models using maximum likelihood estimation via the Template
  Model Builder (TMB), Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM (2016) <doi:10.18637/jss.v070.i05>.
  One version of the multiplicative mixed model is applied in Piepho (1999) <doi:10.1111/j.0006-341X.1999.01120.x>.
  The package provides functions for calculating confidence intervals for the model parameters
  and for performing likelihood ratio tests.
Multidimensional unfolding using Schoenemann's algorithm for metric
   and Procrustes rotation of unfolding results.
Provides easy access to, and manipulation of, the Munsell 
    colours. Provides a mapping between Munsell's 
    original notation (e.g. "5R 5/10") and hexadecimal strings suitable 
    for use directly in R graphics. Also provides utilities 
    to explore slices through the Munsell colour tree, to transform 
    Munsell colours and display colour palettes.
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB.  Includes ISCC-NBS color block lookup.  Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
Data and code for the paper by Ehm, Gneiting, Jordan and
    Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet
    Representations, and Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).
Calculates k-best solutions and costs for an assignment problem following the method outlined in Murty (1968) <doi:10.1287/opre.16.3.682>.
Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.
Provides a framework to factorise electromyography (EMG) data.
    Tools are provided for raw data pre-processing, non negative matrix factorisation,
    classification of factorised data and plotting of obtained outcomes.
    In particular, reading from ASCII files is supported, along with wide-used
    filtering approaches to process EMG data. All steps include one or more sensible
    defaults that aim at simplifying the workflow. Yet, all functions are largely
    tunable at need. Example data sets are included.
An aid for learning and using music theory. You can build chords, scales, and chord progressions using 12-note equal temperament tuning (12-ET) or user-defined tuning. Includes functions to visualize notes on a piano using ASCII plots in the console and to plot waveforms using base graphics. It allows simple playback of notes and chords using the 'audio' package.
Provides functions allowing for (1) easy aggregation of multivariate time series into custom time scales, (2) comparison of statistical summaries between different data sets at multiple time scales (e.g. observed and bias-corrected data), (3) comparison of relations between variables and/or different data sets at multiple time scales (e.g. correlation of precipitation and temperature in control and scenario simulation) and (4) transformation of time series at custom time scales.
A simple system for saving and loading objects in
    R. Long running computations can be stashed after the first run and
    then reloaded the next time. Dependencies can be added to ensure that
    a computation is re-run if any of its dependencies or inputs have
    changed.
Designed to ease the application and comparison of multiple
    hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are 
    standardized and usable by the accompanying 'mutossGUI'.
The mutossGUI package provides a graphical user interface for the MuToss Project.
Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The framework includes modules that support raw data import and processing, mutational signature extraction, and results interpretation and visualization. The framework accepts widely used file formats storing information about DNA variants, such as Variant Call Format files. The framework performs Non-Negative Matrix Factorization to extract mutational signatures explaining the observed set of DNA mutations. Bootstrapping is performed as part of the analysis. The framework supports parallelization and is optimized for use on multi-core systems. The software was described by Fantini D et al (2020) <doi:10.1038/s41598-020-75062-0> and is based on a custom R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <doi:10.1016/j.celrep.2012.12.008>.
The Mutual Information Index (M) introduced to social science literature by
    Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup
    segregation measure that is highly decomposable and that according to Frankel
    and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo
    (2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit
    Decomposability and Strong Group Decomposability properties. This package allows
    computing and decomposing the total index value into its "between" and
    "within" terms. These last terms can also be decomposed into their
    contributions, either by group or unit characteristics. The factors that produce
    each "within" term can also be displayed at the user's request. The results can
    be computed considering a variable or sets of variables that define separate
    clusters.
An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.
Functions, data sets, analyses and examples from the book 
  `An Introduction to Applied Multivariate Analysis with R' 
  (Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (<http://www.gnu.org/software/gsl/>) and 'Rcpp'
        (<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.
Package for multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.
Pacote para analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada.
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by debug package.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
Extends 'multiverse' package
    (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021)
    <doi:10.31219/osf.io/yfbwm>, which allows users perform to create explorable
    multiverse analysis in R. This extension provides an additional level of
    abstraction to the 'multiverse' package with the aim of creating
    user friendly syntax to researchers, educators, and students in statistics.
    The 'mverse' syntax is designed to allow piping and takes hints from
    the 'tidyverse' grammar. The package allows users to define and inspect
    multiverse analysis using familiar syntax in R.
Generates multivariate subgaussian stable probabilities using the QRSVN
    algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394>
    but by sampling positive stable variates not chi/sqrt(nu).
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>,
    Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by 
	Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.
Methods for estimating and utilizing the multivariate generalized
    propensity score (mvGPS) for multiple continuous exposures described in
    Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow
    estimation of a dose-response surface relating the joint distribution of multiple
    continuous exposure variables to an outcome. Weights are constructed assuming a
    multivariate normal density for the marginal and conditional distribution of
    exposures given a set of confounders. Confounders can be different for different
    exposure variables. The weights are designed to achieve balance across all
    exposure dimensions and can be used to estimate dose-response surfaces.
Computes regression deletion diagnostics for multivariate linear models and provides some associated
	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
	provided.
Pearson and Spearman correlation coefficients are commonly used to quantify the strength
	of bivariate associations of genomic variables.  For example, correlations of gene-level 
	DNA copy number and gene expression measurements may be used to assess the impact of 
	DNA copy number changes on gene expression in tumor tissue.  'MVisAGe' enables users to 
	quickly compute and visualize the correlations in order to assess the effect of regional 
	genomic events such as changes in DNA copy number or DNA methylation level.  Please see
	Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research 
	<doi:10.1158/0008-5472.CAN-17-3464>.
Allows a user to conduct multivariate multiple regression using analytic p-values rather than classic approximate F-tests.
Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.
Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.
Epistasis, commonly defined as the interaction between genetic
  loci, is known to play an important role in the phenotypic variation of
  complex traits. As a result, many statistical methods have been developed to
  identify genetic variants that are involved in epistasis, and nearly all of
  these approaches carry out this task by focusing on analyzing one trait at a
  time. Previous studies have shown that jointly modeling multiple phenotypes
  can often dramatically increase statistical power for association mapping. In
  this  package, we present the 'multivariate MArginal ePIstasis Test'
  ('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic
  detection method which seeks to detect marginal epistasis or the combined
  pairwise interaction effects between a given variant and all other variants.
  By searching for marginal epistatic effects, one can identify genetic variants
  that are involved in epistasis without the need to identify the exact
  partners with which the variants interact – thus, potentially alleviating
  much of the statistical and computational burden associated with conventional
  explicit search based methods. Our proposed 'mvMAPIT' builds upon this
  strategy by taking advantage of correlation structure between traits to
  improve the identification of variants involved in epistasis.
  We formulate 'mvMAPIT' as a  multivariate linear mixed model and develop a
  multi-trait variance component estimation algorithm for efficient parameter
  inference and P-value computation. Together with reasonable model
  approximations, our proposed approach is scalable to moderately sized
  genome-wide association studies.
  Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>.
  Stamp et al. (2022) <doi:10.1101/2022.11.30.518547>.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
 Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Offers a general framework of multivariate mixed-effects
        models for the joint analysis of multiple correlated outcomes with clustered 
        data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may 
        depend on the values themselves (missing not at random and non-ignorable), 
        or may depend on only the covariates (missing at random and ignorable), or both.
        This package provides functions for two models: 1) mvMISE_b() 
        allows correlated outcome-specific random intercepts with a factor-analytic 
        structure, and 2) mvMISE_e() allows the correlated outcome-specific 
        error terms with a graphical lasso penalty on the error precision matrix. Both functions 
        are motivated by the multivariate data analysis on data with clustered structures 
        from labelling-based quantitative proteomic studies. These models and functions 
        can also be applied to univariate and multivariate analyses of clustered data 
        with balanced or unbalanced design and no missingness.
Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to
    data generated from a continuous-time multivariate industrial or natural
    process. Employ PCA-based dimension reduction to extract linear combinations
    of relevant features, reducing computational burdens. For a description of 
    ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.
    The multi-state application of ADPCA is from a manuscript under current
    revision entitled "Multi-State Multivariate Statistical Process Control" by
    Odom, Newhart, Cath, and Hering, and is  expected to appear in Q1 of 2018.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood.  See
    Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.
Performs multivariate normality tests and graphical approaches and
    implements multivariate outlier detection and univariate normality of marginal
    distributions through plots and tests, and performs multivariate Box-Cox transformation 
    (Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.
Tools of Bayesian analysis framework using the method
  suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for
  multivariate normal (MVN) distribution and multivariate normal
  mixture (MixMVN) distribution:
  a) calculating Bayesian posteriori of (Mix)MVN distribution;
  b) generating random vectors of (Mix)MVN distribution;
  c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.
Provides computationally efficient tools related to the
    multivariate normal and Student's t distributions. The main functionalities
    are: simulating multivariate random vectors, evaluating multivariate normal or
    Student's t densities and Mahalanobis distances. These tools are very efficient
    thanks to the use of C++ code and of the OpenMP API.
Package for  moving grid adjustment 
	      in plant breeding field trials.
Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.
Finds the Maximum Likelihood (ML) Estimate of the mean vector
        and variance-covariance matrix for multivariate normal data
        with missing values.
A simple informative powerful test (mvnTest()) for multivariate normality proposed by 
    Zhou and Shao (2014) <doi:10.1080/02664763.2013.839637>, which combines kurtosis 
    with Shapiro-Wilk test that is easy for biomedical researchers to understand and 
    easy to implement in all dimensions. This package also contains some other multivariate 
    normality tests including Fattorini's FA test (faTest()), Mardia's skewness and kurtosis 
    test (mardia()), Henze-Zirkler's test (mhz()), Bowman and Shenton's test (msk()), 
    Royston’s H test (msw()), and Villasenor-Alva and Gonzalez-Estrada's test (msw()). Empirical 
    power calculation functions for these tests are also provided. In addition, this package 
    includes some functions to generate several types of multivariate distributions mentioned in 
    Zhou and Shao (2014).
Generalization of shapiro-wilk test for multivariate
        variables.
Given a vector of multivariate normal data, a matrix of
    covariates and the data covariance matrix, generate new multivariate normal
    samples that have the same covariance matrix based on permutations of
    the transformed data residuals.
Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test,  Royston test and Henze-Zirkler test.
Provides a test of multivariate normality of an unknown sample 
    that does not require estimation of the nuisance parameters, the mean and covariance 
    matrix.  Rather, a sequence of transformations removes these nuisance parameters and
    results in a set of sample matrices that are positive definite.  These matrices are 
    uniformly distributed on the space of positive definite matrices in the unit 
    hyper-rectangle if and only if the original data is multivariate normal (Fairweather,
    1973, Doctoral dissertation, University of Washington). The package performs a 
    goodness of fit test of this hypothesis. In addition to the test, functions in the 
    package give visualizations of the support region of positive definite matrices for 
    bivariate samples.
A flexible framework for fitting multivariate
    ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.
Various methods for multivariate outlier detection: arw, a Mahalanobis-type method with an adaptive outlier cutoff value; locout, a method incorporating local neighborhood; pcout, a method for high-dimensional data; mvoutlier.CoDa, a method for compositional data. References are provided in the corresponding help files.
Fast manipulation of symbolic multivariate polynomials
  using the 'Map' class of the Standard Template Library.  The package
  uses print and coercion methods from the 'mpoly' package (Kahle
  2013, "Multivariate polynomials in R", The R Journal, 5(1):162), but
  offers speed improvements.  It is comparable in speed to the 'spray'
  package for sparse arrays, but retains the symbolic benefits of
  'mpoly'.  To cite the package in publications, use Hankin 2022
  <doi:10.48550/ARXIV.2210.15991>.  Uses 'disordR' discipline.
Estimates multivariate subgaussian stable densities 
    and probabilities as well as generates random variates using product 
    distribution theory.  A function for estimating the parameters from 
    data to fit a distribution to data is also provided, using the 
    method from Nolan (2013) <DOI:10.1007/s00180-013-0396-7>.
Tools for high-dimensional peaks-over-threshold inference and simulation
  of spatial extremal processes. Key references include de Fondeville and Davison (2018) <doi:10.1093/biomet/asy026>, Thibaud and Opitz (2015) <doi:10.1093/biomet/asv045>, Wadsworth and Tawn <doi:10.1093/biomet/ast042>.
Tools for estimating multivariate probit models,
   calculating conditional and unconditional expectations,
   and calculating marginal effects on conditional and unconditional
   expectations.
Provides methods to construct multivariate grids, which can be used
    for multivariate quadrature. This grids can be based on different quadrature
    rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
    quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
    multidimensional grid the product-rule or the combination- technique can be
    applied.
Functions used for graphing in multivariate contexts. These 
    functions are designed to support produce reasonable graphs with minimal 
    input of graphing parameters. The motivation for these functions was to 
    support students learning multivariate concepts and R - there may be 
    other functions and packages better-suited to practical data analysis. For
    details about the ellipse methods see Johnson and Wichern (2007, 
    ISBN:9780131877153).
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
            (i) Normalization and/or variance stabilization of the data,
            (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
            (iii) Generation of diverse diagnostic plots,
            (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Compute the coefficient of determination for outcomes in n-dimensions. 
  May be useful for multidimensional predictions (such as a multinomial model) or
  calculating goodness of fit from latent variable models such as probabilistic
  topic models like latent Dirichlet allocation or deterministic topic models 
  like latent semantic analysis. Based on Jones (2019) <arXiv:1911.11061>.
This package implements the generalization of the Shapiro-Wilk test
        for multivariate normality proposed by Villasenor-Alva and
        Gonzalez-Estrada (2009).
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005>. The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from <https://github.com/venelin/PCMBaseCpp/>.
Routines to perform estimation and inference under the multivariate
  t-distribution <doi:10.1007/s10182-022-00468-2>. Currently, the following methodologies 
  are implemented: multivariate mean and covariance estimation, hypothesis testing 
  about equicorrelation and homogeneity of variances, the Wilson-Hilferty transformation, 
  QQ-plots with envelopes and random variate generation.
Multivariate hypothesis tests and the confidence intervals. It can be used to test the hypothesizes about mean vector or vectors (one-sample, two independent samples, paired samples), covariance matrix (one or more matrices), and the correlation matrix. Moreover, it can be used for robust Hotelling T^2 test at one sample case in high dimensional data. For this package, we have benefited from the studies Rencher (2003), Nel and Merwe (1986) <DOI: 10.1080/03610928608829342>, Tatlidil (1996), Tsagris (2014), Villasenor Alva and Estrada (2009) <DOI: 10.1080/03610920802474465>.
Functions to run fixed effects or random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles,
  random deviates and densities.
A function for plotting multivariate time series data.
Implementation of Matched Wake Analysis (mwa) for studying causal relationships in spatiotemporal event data, introduced by Schutte and Donnay (2014) <doi:10.1016/j.polgeo.2014.03.001>.
Algorithms for solving various Maximum
    Weight Connected Subgraph Problems, including variants with
    budget constraints, cardinality constraints, weighted edges and signals.
    The package represents an R interface to high-efficient solvers based on
    relax-and-cut approach (Álvarez-Miranda E., Sinnl M. (2017) <doi:10.1016/j.cor.2017.05.015>)
    mixed-integer programming (Loboda A., Artyomov M., and Sergushichev A. (2016) <doi:10.1007/978-3-319-43681-4_17>)
    and simulated annealing.
The Moving-Window Lasso (MWLasso) method for genome-wide association studies. A window scans the design matrix. For predictors in the same window, their coefficients estimates are smoothed.
A two stage moving-window Ridge method for coefficients estimation and model prediction. In the first stage, moving-window penalty and L1 penalty are applied. In the second stage, ridge regression is applied.
Implements random  number generation, plotting, and estimation algorithms for the two-parameter one-sided and two-sided M-Wright (Mainardi-Wright) family.
             The M-Wright  distributions naturally generalize the widely used one-sided (Airy and half-normal or half-Gaussian) and symmetric (Airy and Gaussian or normal) models. 
             These are widely studied in time-fractional differential equations. References: Cahoy and Minkabo (2017) <doi:10.3233/MAS-170388>; Cahoy (2012) <doi:10.1007/s00180-011-0269-x>; Cahoy (2012) <doi:10.1080/03610926.2010.543299>; Cahoy (2011); Mainardi, Mura, and Pagnini (2010) <doi:10.1155/2010/104505>.
A simple function, mwsApp(), that runs a 'shiny' app spanning multiple,
  connected windows. This uses all standard 'shiny' conventions, and depends only on
  the 'shiny' package.
For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.
Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, <doi:10.1080/15598608.2011.10483735>.
Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>. b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>. c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>. d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. <doi:10.1101/431734>. e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>. g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. <doi:10.1109/TCBB.2020.3029952>.
Provides a convenient interface in 'OpenMx' for building
    Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of
    Derivatives (MMOD).
Implements methods to normalize multiplexed imaging data, including
    statistical metrics and visualizations to quantify technical variation in 
    this data type. Reference for methods listed here: Harris, C., Wrobel, J., 
    & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging 
    Data. Journal of Open Source Software, 7(71), 4180, 
    <doi:10.21105/joss.04180>.
The stepwise variable selection procedure (with iterations
 between the 'forward' and 'backward' steps) can be used to obtain
 the best candidate final regression model in regression analysis.
 All the relevant covariates are put on the 'variable list' to be
 selected. The significance levels for entry (SLE) and for stay
 (SLS) are usually set to 0.15 (or larger) for being conservative.
 Then, with the aid of substantive knowledge, the best candidate
 final regression model is identified manually by dropping the
 covariates with p value > 0.05 one at a time until all regression
 coefficients are significantly different from 0 at the chosen alpha
 level of 0.05.
The chi-squared test for goodness of fit 
    and independence test.
The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
Perform correlation and linear regression test
    among the numeric fields in a data.frame automatically
    and make plots using pairs or lattice::parallelplot.
Plot the daily and cumulative number of downloads of your packages.
    It is designed to be slightly more convenient than the several similar programs. If you want to run this each morning,
    you do not need to keep typing in the names of your packages. Also, this combines the daily and cumulative counts in one
    run, you do not need to run separate programs to get both types of information.
Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes (Drost et al. (2017) <doi:10.1093/bioinformatics/btx835>). The aim of this tool is to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest (Drost et al. (2016) <doi:10.1101/051565>).
We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.
Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS.  The n1qn1 routine is useful since it allows prespecification of a Hessian.
       If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the
       'Scilab' optimization documentation located at 
       <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.
Provides some functions to get Korean text sample from news articles in
             Naver which is popular news portal service <https://news.naver.com/> in Korea.
Implements methods to perform fast approximate K-nearest neighbor search on input matrix. Algorithm based on the 'N2' implementation of an approximate nearest neighbor search using hierarchical  Navigable Small World (NSW) graphs. The original algorithm is described in "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs", Y. Malkov and D. Yashunin, <doi:10.1109/TPAMI.2018.2889473>, <arXiv:1603.09320>.
This comprehensive toolkit provide a consistent and 
    extensible framework for working with missing values in vectors. The 
    companion package 'tidyimpute' provides similar functionality for list-like 
    and table-like structures).
    Functions exist for detection, removal, replacement, imputation, 
    recollection, etc. of 'NAs'.
Functions for reading cancer record files which follow a format
    defined by the North American Association of Central Cancer Registries
    (NAACCR).
An R wrapper for 'libnabo', an exact or approximate k nearest
    neighbour library which is optimised for low dimensional spaces (e.g. 3D).
    'libnabo' has speed and space advantages over the 'ANN' library wrapped by
    package 'RANN'. 'nabor' includes a knn function that is designed as a 
    drop-in replacement for 'RANN' function nn2. In addition, objects which 
    include the k-d tree search structure can be returned to speed up repeated 
    queries of the same set of target points.
NanoString nCounter data are gene expression assays
    where there is no need for the use of enzymes or amplification
    protocols and work with fluorescent barcodes (Geiss et al. (2018)
    <doi:10.1038/nbt1385>). Each barcode is assigned a
    messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its
    target can be counted. As a result each count of a specific barcode
    represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString
    quality Control dasHbOard) is able to analyse the exported NanoString
    nCounter data and facilitates the user in performing a quality
    control. 'NACHO' does this by visualising quality control metrics,
    expression of control genes, principal components and sample specific
    size factors in an interactive web application.
Contains methods described by Dennis Helsel in 
             his book "Nondetects And Data Analysis: Statistics 
             for Censored Environmental Data".
Contains methods described by Dennis Helsel in 
             his book "Statistics for Censored Environmental Data
             using Minitab and R" (2011) and courses and videos at 
             <https://practicalstats.com>. This package adds new functions to 
             the `NADA` Package.
Creates a uniform interface for several advanced imputations missing data methods. Every available method can be used as a part of 'mlr3' pipelines which allows easy tuning and performance evaluation. Most of the used functions work separately on the training and test sets (imputation is trained on the training set and impute training data. After that imputation is again trained on the test set and impute test data).
Constructs (non)additive genetic relationship matrices, and their
    inverses, from a pedigree to be used in linear mixed effect models (A.K.A.
    the 'animal model'). Also includes other functions to facilitate the use of
    animal models. Some functions have been created to be used in conjunction
    with the R package 'asreml' for the 'ASReml' software, which can be
    obtained upon purchase from 'VSN' international 
    (<https://vsni.co.uk/software/asreml>).
This data package contains the Item Response Theory (IRT) parameters for the National Center for Education Statistics (NCES) items used on the National Assessment of Education Progress (NAEP) from 1990 to 2015. The values in these tables are used along with NAEP data to turn student item responses into scores and include information about item difficulty, discrimination, and guessing parameter for 3 parameter logit (3PL) items. Parameters for Generalized Partial Credit Model (GPCM) items are also included. The adjustments table contains the information regarding the treatment of items (e.g., deletion of an item or a collapsing of response categories), when these items did not appear to fit the item response models used to describe the NAEP data. Transformation constants change the score estimates that are obtained from the IRT scaling program to the NAEP reporting metric. Values from the years 2000 - 2013 were taken from the NCES website <https://nces.ed.gov/nationsreportcard/> and values from 1990 - 1998 and 2015 were extracted from their NAEP data files. All subtest names were reduced and homogenized to one word (e.g. "Reading to gain information" became "information"). The various subtest names for univariate transformation constants were all homogenized to "univariate".
Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
For use in summary functions to omit missing values 
    conditionally using specified checks.
A set of convenience functions as well as geographical/political
  data about Nigeria, aimed at simplifying work with data and information that
  are specific to the country.
An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences.
In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli, Categorical, Gaussian, Poisson and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.
In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. The naivereg is nonparametric version of 'ivregress' in 'Stata' with IV selection and high dimensional features. The package is based on the paper by Q. Fan and W. Zhong, "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2018), Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991> as well as a series of working papers led by the same authors.
Density, distribution function, quantile function and random 
    generation for the Nakagami distribution of Nakagami (1960) 
    <doi:10.1016/B978-0-08-009306-2.50005-4>.
Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
A system for organizing column names in data. Aimed at supporting a 
  prefix-based and suffix-based column naming scheme. Extends 'dplyr' functionality
  to add ordering by function and more explicit renaming.
User-friendly wrappers for 
 named capture regular expressions.
 Introduction and comparison in research paper
 by Hocking (2019), R Journal.
 <doi:10.32614/RJ-2019-050>
 RE2 engine ('re2r' package)
 <https://github.com/qinwf/re2r>
 was removed from CRAN in Mar 2020
 so must be installed from github.
Provides 'visual citations' containing the metadata of a
    scientific paper and a 'QR' code.  A 'visual citation' is a banner
    containing title, authors, journal and year of a publication.  This
    package can create such banners based on 'BibTeX' and 'BibLaTeX'
    references or call the reference metadata from 'Crossref'-API. The
    banners include a QR code pointing to the 'DOI'.  The resulting HTML
    object or PNG image can be included in a presentation to point the
    audience to good resources for further reading.  Styling is possible
    via predefined designs or via custom 'CSS'.  This package is not
    intended as replacement for proper reference manager packages, but a
    tool to enrich scientific presentation slides and conference posters.
The Needleman-Wunsch global alignment algorithm can be
  used to find approximate matches between sample names in different
  data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.
It names the 'R Markdown' chunks of files based on
    the filename.
This package provides user-level functions to manage
        namespaces not (yet) available in base R: 'registerNamespace',
        'unregisterNamespace', 'makeNamespace', and
        'getRegisteredNamespace'

  ('makeNamespaces' is extracted from the R 'base' package source code:
        src/library/base/R/namespace.R)
Wraps the 'nametag' library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models.
    Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.
Calculation of molecular number and brightness from
    fluorescence microscopy image series. The software was published in a
    2016 paper <doi:10.1093/bioinformatics/btx434>. The seminal paper for
    the technique is Digman et al. 2008 <doi:10.1529/biophysj.107.114645>.
    A review of the technique was published in 2017
    <doi:10.1016/j.ymeth.2017.12.001>.
Missing values are ubiquitous in data and need to be explored and
    handled in the initial stages of analysis. 'naniar' provides data 
    structures and functions that facilitate the plotting of missing values and 
    examination of imputations. This allows missing data dependencies to be 
    explored with minimal deviation from the common work patterns of 'ggplot2' 
    and tidy data. The work is fully discussed at Tierney & Cook (2023) 
    <doi:10.18637/jss.v105.i07>.
Provides an 'R' interface to the 'nanoarrow' 'C' library and the
  'Apache Arrow' application binary interface. Functions to import and
  export 'ArrowArray', 'ArrowSchema', and 'ArrowArrayStream' 'C' structures
  to and from 'R' objects are provided alongside helpers to facilitate zero-copy
  data transfer among 'R' bindings to libraries implementing the 'Arrow' 'C'
  data interface.
R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. NNG is 
    a socket library providing high-performance scalability protocols, a
    cross-platform standard for messaging and communications. Serves as a
    concurrency framework for building distributed applications, utilising 'aio'
    objects which resolve automatically upon completion of asynchronous
    operations. Implements synchronisation primitives, allowing R to wait upon
    events being signalled by concurrent messaging threads.
Provides quality control (QC), normalization, and
    batch effect correction operations for 'NanoString nCounter' data,
    Talhouk et al. (2016) <doi:10.1371/journal.pone.0153844>.  Various
    metrics are used to determine which samples passed or failed QC.  Gene
    expression should first be normalized to housekeeping genes, before a
    reference-based approach is used to adjust for batch effects.  Raw
    NanoString data can be imported in the form of Reporter Code Count
    (RCC) files.
Full 64-bit resolution date and time functionality with
 nanosecond granularity is provided, with easy transition to and from
 the standard 'POSIXct' type. Three additional classes offer interval,
 period and duration functionality for nanosecond-resolution timestamps.
Conducts Bayesian Hypothesis tests of a point null hypothesis against a two-sided alternative
             using Non-local Alternative Prior (NAP) for one- and two-sample z- and t-tests 
             (Pramanik and Johnson, 2022). Under the alternative, the NAP is assumed on the standardized 
             effects size in one-sample tests and on their differences in two-sample tests. The package 
             considers two types of NAP densities: (1) the normal moment prior, and (2) the composite alternative. 
             In fixed design tests, the functions calculate the Bayes factors and the expected weight of evidence
             for varied effect size and sample size. The package also provides a sequential testing framework using the
             Sequential Bayes Factor (SBF) design. The functions calculate the operating characteristics (OC)
             and the average sample number (ASN), and also conducts sequential tests for a sequentially observed data.
An R wrapper for pulling data from the National Public Transport Access Nodes ('NaPTAN') API (<https://www.api.gov.uk/dft/national-public-transport-access-nodes-naptan-api/#national-public-transport-access-nodes-naptan-api>). This allows users to download 'NaPTAN' transport information, for the full dataset, by ATCO region code, or by name of region.
Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input
    to produce delays in the execution of code and can silence/prevent typical sources of error.
Computes the nonlinear cointegrating autoregressive distributed lag model with automatic bases aic and bic lags selection of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).
Stacking arrays according to dimension names, subset-aware
    splitting and mapping of functions, intersecting along arbitrary
    dimensions, converting to and from data.frames, and many other helper
    functions.
A toolkit for medical records data analysis. The 'naryn'
    package implements an efficient data structure for storing medical
    records, and provides a set of functions for data extraction,
    manipulation and analysis.
Provides functions to access NASA's Earth Imagery and Assets API
    and the Earth Observatory Natural Event Tracker (EONET) webservice.
Client for 'NASA' 'POWER' global meteorology, surface solar
    energy and climatology data 'API'.  'POWER' (Prediction Of Worldwide Energy 
    Resource) data are freely available for download with varying spatial
    resolutions dependent on the original data and with several temporal
    resolutions depending on the POWER parameter and community.  This work is
    funded through the 'NASA' Earth Science Directorate Applied Science Program.
    For more on the data themselves, the methodologies used in creating, a web-
    based data viewer and web access, please see <https://power.larc.nasa.gov/>.
This package contains tidied data from the ASA 2006 data expo,
    as well as a number of useful other related data sets.
Functions for interacting directly with the Nasdaq Data Link API to offer data in a number of formats usable in R, downloading a zip with all data from a Nasdaq Data Link database, and the ability to search. This R package uses the Nasdaq Data Link API. For more information go to <https://docs.data.nasdaq.com/>. For more help on the package itself go to <https://data.nasdaq.com/tools/r>.
Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
    biological image data, especially traced neurons. Reads and writes 3D images
    in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
    format. Traced neurons can be imported from and written to SWC and 'Amira'
    LineSet and SkeletonGraph formats. These data can then be visualised in 3D
    via 'rgl', manipulated including applying calculated registrations, e.g.
    using the 'CMTK' registration suite, and analysed. There is also a simple
    representation for neurons that have been subjected to 3D skeletonisation
    but not formally traced; this allows morphological comparison between
    neurons including searches and clustering (via the 'nat.nblast' extension
    package).
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a
    collection of NBLAST-related functions for neuronal morphology comparison (Costa et al. (2016) <doi: 10.1016/j.neuron.2016.06.012>).
Extends package 'nat' (NeuroAnatomy Toolbox) by
    providing objects and functions for handling template brains.
Utility functions that may be of general interest but are 
    specifically required by the 'NeuroAnatomy Toolbox' ('nat'). Includes
    functions to provide a basic make style system to update files based on
    timestamp information, file locking and 'touch' utility. Convenience 
    functions for working with file paths include 'abs2rel', 'split_path' 
    and 'common_path'. Finally there are utility functions for working with 
    'zip' and 'gzip' files including integrity tests.
Fast functions implemented in C++ via 'Rcpp' to support the
    'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large
    speed-ups for basic manipulation of neuronal skeletons over pure R
    functions found in the 'nat' package. The expectation is that end
    users will not use this package directly, but instead the 'nat'
    package will automatically use routines from this package when it is
    available to enable large performance gains.
Provides streamlined installation for packages from the 'natverse',
    a suite of R packages for computational neuroanatomy built on top of the
    'nat' 'NeuroAnatomy Toolbox' package. Installation of the complete
    'natverse' suite requires a 'GitHub' user account and personal access token
    'GITHUB_PAT'. 'natmanager' will help the end user set this up if necessary.
Color palettes for data visualization inspired by National Parks. Currently contains 15 color schemes and checks for colorblind-friendliness of palettes.
Interface to 'NatureServe' (<https://www.natureserve.org/>).
    Includes methods to get data, image metadata, search taxonomic names,
    and make maps.
Natural strata can be used in observational studies to balance
    the distributions of many covariates across any number of treatment
    groups and any number of comparisons. These strata have proportional 
    amounts of units within each stratum across the treatments, allowing 
    for simple interpretation and aggregation across strata. Within each 
    stratum, the units are chosen using randomized rounding of a linear 
    program that balances many covariates.
    To solve the linear program, the 'Gurobi' commercial optimization software 
    is recommended, but not required. The 'gurobi' R package can be installed following the instructions 
    at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.
Classify occurrence records based on confidence
    levels of species identification. In addition, implement tools to filter
    occurrences inside grid cells and to manually check for possibles errors with 
	an interactive shiny application.
Provides functions related to human natural ordering.
    It handles adjacent digits in a character sequence as a number so that
    natural sort function arranges a character vector by their numbers, not digit
    characters. It is typically seen when operating systems lists file names. For
    example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered because 1
    < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms
    arrange it into a-1.png, a-10.png, a-2.png owing to their third and fourth
    characters.
Collection of example animal sounds for bioacoustic analysis.
Provides a navigation menu to enable pipe-friendly data processing for hierarchical data structures.
    By activating the menu items, you can perform operations on each item while maintaining the overall structure in attributes.
Implements the navigated weighting (NAWT) proposed by Katsumata (2020)
    <arXiv:2005.10998>, which improves the inverse probability weighting by 
    utilizing estimating equations suitable for a specific pre-specified parameter 
    of interest (e.g., the average treatment effects or the average treatment 
    effects on the treated) in propensity score estimation. It includes the 
    covariate balancing propensity score proposed by Imai and Ratkovic (2014) 
    <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in 
    propensity score estimation. The point estimate of the parameter of interest
    as well as coefficients for propensity score estimation and their 
    uncertainty are produced using the M-estimation. The same functions can be 
    used to estimate average outcomes in missing outcome cases.
Model-based clustering of high-dimensional non-negative
             data that follow Generalized Negative Binomial distribution. All functions 
             in this package applies to either continuous or integer data. Correlation
            between variables are allowed, while samples are assumed to be independent.
Provides interface to the online basketball data resources such as
      Basketball reference API <https://www.basketball-reference.com/> and helps
      R users analyze basketball data.
Palettes generated from NBA jersey colorways.
Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.
We constructed 'NBBttest' for identifying genes or RNA isoforms differentially expressed between two conditions on RNA-seq count data. Package 'NBBttest' can  perform data quality check, data normalization, differential analysis, annotation and graphic analysis. In differential analysis, 'NBBttest' can identify differentially expressed genes and differential RNA isoforms in alternative splicing sites and alternative polyadenylation sites, differential sgRNA, and differential CRISPR (clustered regularly interspaced short palindromic repeats) screening genes. In graphic analysis, 'NBBttest' provides two types of heatmaps to visualize differential expression at gene or isoform level using z-score and n-score and creates pathway heatmap. 'NBBttest' can plot differentially expressed exons within a specified gene. In addition, 'NBBttest' provides a tool for annotation of genes and exons. The methods used in 'NBBttest' were new statistical methods developed from Tan and others (2015) <doi:10.1371/journal.pone.0123658>. The statistical methods are robust and very powerful in identifying genes or RNA isoforms differentially expressed between two conditions in small samples.
An implementation of the Naive Bayes Classifier (NBC) algorithm
    used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) 
    <DOI:10.1186/s12916-015-0521-2>.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Calls the 'Jupyter' script 'nbconvert' to create vignettes from
  notebooks. Those notebooks ('.ipynb' files) are files containing rich text,
  code, and its output. Code cells can be edited and evaluated interactively.
  See <https://jupyter.org/> for more information.
The Dirichlet (aka NBD-Dirichlet) model describes the purchase incidence and brand choice of consumer products.  We estimate the model and summarize various theoretical quantities of interest to marketing researchers. Also provides functions for making tables that compare observed and theoretical statistics.
Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint
    with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including 
    (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario.
Catalogue of NBER working papers published between June 1973 and December 2021.
We developed a negative binomial factor regression model to estimate structured (sparse) associations between a feature matrix X and overdispersed count data Y.  With 'nbfar', microbiome count data Y can be used, for example, to associate host or environmental covariates with microbial abundances. Currently, two models are available: a) Negative Binomial reduced rank regression (NB-RRR), b) Negative Binomial co-sparse factor regression (NB-FAR). Please refer the manuscript 'Mishra, A. K., & Müller, C. L. (2021). Negative Binomial factor regression with application to microbiome data analysis. bioRxiv.' for more details.
We proposed a package for the classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is an extension of the 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA, respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et al. is not sparse. Therefore, it uses all variables in the classifier. Here, we extend Dong et al.'s algorithm to the sparse case by shrinking overdispersion towards 0 (Yu et al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version.
Allow users to obtain basketball statistics for 
    the Australian basketball league 'NBL'<https://nbl.com.au/>. 
    Stats include play-by-play, shooting locations, results and 
    box scores for teams and players.
Tools to run simple direct gravitational N-body simulations. It can access different external N-body simulators, but also has a simple built-in default simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles.
Perform non-bipartite matching and matched randomization. A
    "bipartite" matching utilizes two separate groups, e.g. smokers being
    matched to nonsmokers or cases being matched to controls. A "non-bipartite"
    matching creates mates from one big group, e.g. 100 hospitals being
    randomized for a two-arm cluster randomized trial or 5000 children who
    have been exposed to various levels of secondhand smoke and are being
    paired to form a greater exposure vs. lesser exposure comparison. At the
    core of a non-bipartite matching is a N x N distance matrix for N potential
    mates. The distance between two units expresses a measure of similarity or
    quality as mates (the lower the better). The 'gendistance()' and
    'distancematrix()' functions assist in creating this. The 'nonbimatch()'
    function creates the matching that minimizes the total sum of distances
    between mates; hence, it is referred to as an "optimal" matching. The
    'assign.grp()' function aids in performing a matched randomization. Note
    bipartite matching can be performed using the prevent option in
    'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
    regression inferences from RNA-Sequencing Data.
An implementation of network-based statistics in R using mixed effects models.
    Theoretical background for Network-Based Statistics can be found in Zalesky et al. (2010)
    <doi:10.1016/j.neuroimage.2010.06.041>. For Mixed Effects Models check the
    R package <https://CRAN.R-project.org/package=nlme>.
An interactive document on  the topic of naive Bayes classification  analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/NBShiny/>.
Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (2020) <doi:10.1093/ije/dyaa031>.
User-friendly functions for extracting a data
 table (row for each match, column for each group)
 from non-tabular text data using regular expressions,
 and for melting columns that match a regular expression.
 Patterns are defined using a readable syntax
 that makes it easy to build complex patterns
 in terms of simpler, re-usable sub-patterns.
 Named R arguments are translated to column names
 in the output; capture groups without names are used
 internally in order to provide a standard interface
 to three regular expression C libraries (PCRE, RE2, ICU).
 Output can also include numeric columns via
 user-specified type conversion functions.
 RE2 engine (re2r package) was removed from CRAN in Mar 2020
 so must be installed from github.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52,
  <https://journals.sagepub.com/doi/full/10.1177/1094428115584005>).
  NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
  (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output). A quick start guide for using this package can be found here: <https://repub.eur.nl/pub/78323/> or <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2624981>.
A flexible tool that can perform
    (i) traditional non-compartmental analysis (NCA) and
    (ii) Simulation-based posterior predictive checks for population
    pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics.
Conduct a noncompartmental analysis with industrial strength.
             Some features are
             1) CDISC SDTM terms
             2) Automatic or manual slope selection
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             5) Produce pdf, rtf, text report files.
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
Makes NCBI taxonomic data locally available and
        searchable as an R object.
Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. 
    The implemented analysis methods are described in: 
    Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, 
    Saville et al. (2022) <doi:10.1177/17407745221112013> and 
    Schmidli et al. (2014) <doi:10.1111/biom.12242>.
Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets.  Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily.  It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files.  This package replaces the former ncdf package, which only worked with netcdf version 3 files.  For various reasons the names of the functions have had to be changed from the names in the ncdf package.  The old ncdf package is still available at the URL given below, if you need to have backward compatibility.  It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem.  However, the ncdf package does not provide an interface for netcdf version 4 files.
Contains a collection of helper functions for dealing with 
    'NetCDF' files <https://www.unidata.ucar.edu/software/netcdf/> 
    opened using 'ncdf4', particularly 'NetCDF' files that conform to the
    Climate and Forecast (CF) Metadata Conventions 
    <http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html>.
Tools to create time series and geometry 'NetCDF' files.
Tools for handling 'NetCDF' metadata in data frames. The metadata is provided
    as relations in tabular form, to avoid having to scan printed header output or to navigate 
    nested lists of raw metadata.
Computes the noncentral Dunnett's test distribution (pdf, cdf and quantile) and generates random numbers.
Spatial (cross-)covariance and related geostatistical tools: the
        nonparametric (cross-)covariance function , the spline correlogram, the
        nonparametric phase coherence function, local indicators of spatial 
        association (LISA), (Mantel) correlogram, (Partial) Mantel test.
Extract metadata from 'NetCDF' data sources, these can be files, file handles or
 servers. This package leverages and extends the lower level functions of the 'RNetCDF' package 
 providing a consistent set of functions that all return data frames. We introduce named concepts 
 of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 
 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library 
 itself with only the named concepts of 'variables', 'dimensions' and 'attributes'.
A set of handy functions. Includes a versatile one line progress bar, one 
 line function timer with detailed output, time delay function, text histogram, object 
 preview, CRAN package search, simpler package installer, Linux command install check, 
 a flexible Mode function, top function, simulation of correlated data, and more.
A set of techniques that can be used to develop, validate, and implement automated classifiers. A powerful tool for transforming raw data into meaningful information, 'ncodeR' (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is designed specifically for working with big data: large document collections, logfiles, and other text data.
Construct and manipulate hierarchical Archimedean copulas with multivariate compound distributions. The model used is the one of Cossette et al. (2017) <doi:10.1016/j.insmatheco.2017.06.001>.
An efficient unified nonconvex penalized estimation algorithm for
    Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit,
    and Cox proportional hazard regression models.
    The unified algorithm is implemented based on the convex concave procedure and
    the algorithm can be applied to most of the existing nonconvex penalties.
    The algorithm also supports convex penalty:
    least absolute shrinkage and selection operator (LASSO).
    Supported nonconvex penalties include
    smoothly clipped absolute deviation (SCAD),
    minimax concave penalty (MCP), truncated LASSO penalty (TLP),
    clipped LASSO (CLASSO), sparse ridge (SRIDGE),
    modified bridge (MBRIDGE) and modified log (MLOG).
    For high-dimensional data (data set with many variables),
    the algorithm selects relevant variables producing a parsimonious regression model.
    Kim, D., Lee, S. and Kwon, S. (2018) <arXiv:1811.05061>,
    Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>,
    Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>.
    (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine
    Graziadio Business School and the National Research Foundation of Korea.)
Provides functionality for performing Nearest Centroid (NC) Sampling. The NC sampling procedure was developed for forestry applications and selects plots for ground measurement so as to maximize the efficiency of imputation estimates. It uses multiple auxiliary variables and multivariate clustering to search for an optimal sample. Further details are given in Melville G. & Stone C. (2016) <doi:10.1080/00049158.2016.1218265>.
Inference and dependence measure for the non-central squared Gaussian, Student, Clayton, Gumbel, and Frank copula models.The description of the methodology is taken from Section 3 of Nasri, Remillard and Bouezmarni (2019) <doi:10.1016/j.jmva.2019.03.007>.
Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more.
    'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering),
    while incorporating the dependencies across multiple types of Omics data.
    (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).
Fits regularization paths for linear regression, GLM, and Cox
  regression models using lasso or nonconvex penalties, in particular the
  minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)
  penalty, with options for additional L2 penalties (the "elastic net" idea).
  Utilities for carrying out cross-validation as well as post-fitting
  visualization, summarization, inference, and prediction are also provided.
  For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388>
  or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.
Non-parametric dimensionality reduction function. Reduction with and without feature selection. Plot functions. Kosztyan et. al. (2022) <doi:10.1016/j.knosys.2022.109180>.
Streaming 'JSON' ('ndjson') has one 'JSON' record per-line
        and many modern 'ndjson' files contain large numbers of records.
        These constructs may not be columnar in nature, but it is often
        useful to read in these files and "flatten" the structure out to
        enable working with the data in an R 'data.frame'-like context.
        Functions are provided that make it possible to read in plain
        'ndjson' files or compressed ('gz') 'ndjson' files and either
        validate the format of the records or create "flat" 'data.table'
        structures from them.
Naive discriminative learning implements learning and
    classification models based on the Rescorla-Wagner equations and their
    equilibrium equations.
An interactive presentation on  the topic of normal distribution using 'rmarkdown' and 'shiny' packages. It is helpful to those who want to learn normal distribution quickly and get a hands on experience. The presentation has a template for solving problems on normal distribution. Runtime examples are provided in the package function as well as at  <https://kartikeyastat.shinyapps.io/NormalDistribution/>.
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.
Implements a path algorithm for Near-Isotonic
        Regression. For more details see the help files.
Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016, <DOI:10.1186/s12859-016-1203-6>).
Simplify the exploratory data analysis process for multiple network
             data sets with the help of hierarchical clustering, consensus 
             clustering and heatmaps. Multiple network data consists of multiple
             disjoint networks that have common variables (e.g. ego networks). 
             This package contains the necessary tools for exploring such data,
             from the data pre-processing stage to the creation of dynamic
             visualizations.
Utilities for unambiguous, neat and legible
    representation of data (date, time stamp, numbers, percentages and strings) 
    for presentation of analysis , aiming for elegance and consistency.
    The purpose of this package is to format data, that is better 
    for presentation and any automation jobs that reports numbers.
Collapse, partition, combine, fill gaps in and expand date/time ranges.
User-friendly, clear and simple statistics, primarily for
  publication in psychological science. The main functions are wrappers for
  other packages, but there are various additions as well. Every relevant step
  from data aggregation to reportable printed statistics is covered for basic
  experimental designs.
A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.
Tools to generate Necklaces, Bracelets, Lyndon words and de Bruijn sequences. The generation relies on integer partitions and uses the 'KStatistics' package. Methods used in the package refers to E. Di Nardo and G. Guarino (2022) <arXiv:2208.06855>.
Showcasing needmining (the semi-automatic extraction of customer needs from
	social media data) with Twitter data. It uses the handling of the Twitter API provided by
	the package 'rtweet' and the textmining algorithms provided by the package 'tm'.
	Niklas Kuehl (2016) <doi:10.1007/978-3-319-32689-4_14> wrote an introduction to the topic of needmining.
A simple function for easier package loading and auto-installation.
Methods to reduce confounding bias from unmeasured confounders in observational studies of vaccine efficacy using negative control outcomes.
The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.
Estimating the number of essential genes in a genome on
    the basis of data from a random transposon mutagenesis experiment,
    through the use of a Gibbs sampler.
    Lamichhane et al. (2003) <doi:10.1073/pnas.1231432100>.
Researchers often want to evaluate whether there is a negligible
    relationship among variables. The 'negligible' package provides functions that 
    are useful for conducting negligible effect testing (also called
    equivalence testing). For example, there are functions for evaluating the 
    equivalence of means or the presence of a negligible association (correlation/
    regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>.
    Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>.
    Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>.
    Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>.
    Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>.
    van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>.
    Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>.
    Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.
A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty.
Neighbourhood functions are key components of
  local-search algorithms such as Simulated Annealing or
  Threshold Accepting.  These functions take a solution and
  return a slightly-modified copy of it, i.e. a neighbour.
  The package provides a function neighbourfun() that
  constructs such neighbourhood functions, based on
  parameters such as admissible ranges for elements in a
  solution.  Supported are numeric and logical solutions.
  The algorithms were originally created for
  portfolio-optimisation applications, but can be used for
  other models as well.  Several recipes for neighbour
  computations are taken from "Numerical Methods and
  Optimization in Finance" by M. Gilli, D. Maringer and
  E. Schumann (2019, ISBN:978-0128150658).
Classification, regression, and clustering with k nearest neighbors
    algorithm. Implements several distance and similarity measures, covering
    continuous and logical features. Outputs ranked neighbors. Most features of
    this package are directly based on the PMML specification for KNN.
Provides several direct search optimization algorithms based on the
  simplex method. The provided algorithms are direct search algorithms, i.e.
  algorithms which do not use the derivative of the cost function. They are
  based on the update of a simplex. The following algorithms are available: the
  fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained
  optimization with a fixed shape simplex, 1962) <doi:10.1080/00401706.1962.10490033>, 
  the variable shape simplex method of Nelder and Mead (unconstrained optimization
  with a variable shape simplex made, 1965) <doi:10.1093/comjnl/7.4.308>, and 
  Box's complex method (constrained optimization with a variable shape simplex,
  1965) <doi: 10.1093/comjnl/8.1.42>.
To study network evolution models and different blockmodeling approaches. Various functions enable generating (temporal) networks with a selected blockmodel type, taking into account selected local network mechanisms. The development of this package is financially supported the Slovenian Research Agency (www.arrs.gov.si) within the research program P5<96>0168 and the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).
Calculates a cumulative summation nonparametric extended 
    median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. 
    It then generates a control chart to assess processes and determine if any streams are out of control.
The aim of neo2R is to provide simple and low level connectors
   for querying neo4j graph databases (<https://neo4j.com/>).
   The objects returned by the query functions are either lists or data.frames
   with very few post-processing.
   It allows fast processing of queries returning many records.
   And it let the user handle post-processing according to the data model
   and his needs.
Sends queries to a specified 'Neo4J' graph database, capturing results in a dataframe where appropriate.  
    Other useful functions for the importing and management of data on the 'Neo4J' server and basic local server admin.
A Modern and Flexible 'Neo4J' Driver, allowing you to query 
    data on a 'Neo4J' server and handle the results in R. It's modern in 
    the sense it provides a driver 
    that can be easily integrated in a data analysis workflow, especially by 
    providing an API working smoothly with other data analysis and graph 
    packages. It's flexible in the  way it returns the results, by 
    trying to stay as close as 
    possible to the way 'Neo4J' returns data. That way, you have the control 
    over the way you will compute the results. At the same time, the result 
    is not too complex, so that the "heavy lifting" of data wrangling is not 
    left to the user.
Functions for downloading,
    calibrating, and analyzing atmospheric isotope data bundled
    into the eddy covariance data products of the National Ecological
    Observatory Network (NEON) <https://www.neonscience.org>. 
    Calibration tools are provided for carbon and water isotope products.
    Carbon isotope calibration details are found in Fiorella et al. (2021)
    <doi:10.1029/2020JG005862>, and the readme 
    file at <https://github.com/lanl/NEONiso>. Tools for calibrating water 
    isotope products have been added as of 0.6.0, but have known deficiencies
    and should be considered very experimental currently.
NEON observational data are provided via the NEON Data Portal <https://www.neonscience.org> 
  and NEON API, and can be downloaded and reformatted by the 'neonUtilities' package. NEON observational data 
  (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and 
  algal chemistry) are published in a format consisting of one or more tabular data files. This package provides 
  tools for performing common operations on NEON observational data, including checking for duplicates and 
  joining tables.
The National Ecological Observatory Network (NEON) provides access
  to its numerous data products through its REST API, 
  <https://data.neonscience.org/data-api/>. This package provides a
  high-level user interface for downloading and storing NEON data products.
  Unlike 'neonUtilities', this package will avoid repeated downloading,
  provides persistent storage, and improves performance.  'neonstore' can also
  construct a local 'duckdb' database of stacked tables, making it possible
  to work with tables that are far to big to fit into memory.
NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org>
    or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from
    the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files
    can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting 
    data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and 
    converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.
Access and manipulation of data using the Neotoma Paleoecology Database.
        <https://api.neotomadb.org/api-docs/>.
Set of functions to estimate kidney function and other phenotypes of interest in nephrology based on different biomechimal traits. MDRD, CKD-EPI, and Virga equations are compared in Pattaro (2013) <doi:10.1159/000351043>, where the respective references are given. In addition, the software includes Stevens (2008) <doi:10.1053/j.ajkd.2007.11.018> and Cockroft (1976) <doi:10.1159/000180580> formulas. The race-free CKD-EPI equations (2021) <doi:10.1056/NEJMoa2102953> are also implemented from ver.1.3.
Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.
An interface to Neptune. A metadata store for MLOps, built for teams that run a lot of experiments.
    It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata.
    Neptune is used for:
    • Experiment tracking: Log, display, organize, and compare ML experiments in a single place.
    • Model registry: Version, store, manage, and query trained models, and model building metadata.
    • Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live
    For more information see <https://neptune.ai/>.
Serves data from the United States Environmental Protection Agency (USEPA) National Eutrophication Survey <https://www.epa.gov/national-aquatic-resource-surveys>.
This tool set provides a set of functions to fit the nested Dirichlet process mixture of products of multinomial distributions (NDPMPM) model for nested categorical household data in the presence of impossible combinations. It has direct applications in imputing missing values for and generating synthetic versions of nested household data.
Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
Provides a nested menu widget for usage in 'Shiny'
    applications. This is useful for hierarchical choices (e.g. continent,
    country, city).
A modelling framework for nested data using the 'tidymodels' 
    ecosystem. Specify how to nest data using the 'recipes' package, create 
    testing and training splits using 'rsample', and fit models to this data
    using the 'parsnip' and 'workflows' packages. Allows any model to be fit
    to nested data.
Library to plot performance profiles (Dolan and More (2002) <doi:10.1007/s101070100263>) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) <doi:10.19139/soic-2310-5070-679>) for a given data frame.
Implementation of forward selection based on cross-validated
             linear and logistic regression.
This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem.  Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.
Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax.
Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.
In networks, treatments may spill over from the treated individual to his or her social contacts and outcomes may be contagious over time. Under this setting, causal inference on the collective outcome observed over all network is often of interest. We use chain graph models approximating the projection of the full longitudinal data onto the observed data to identify the causal effect of the intervention on the whole outcome. Justification of such approximation is demonstrated in Ogburn et al. (2018) <arXiv:1812.04990>.
Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of 'netClust'.
Facilitates network clustering and evaluation of cluster
        configurations.
Implements a class of univariate and multivariate spatio-network generalised linear mixed models for areal unit and network data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) <doi:10.1177/1471082X0100100202>).
Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package.
Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <arXiv:1912.12551>.
Implementations of various control theory methods for use 
            in brain and psychological networks. Contains controllability
            statistics from Pasqualetti, Zampieri & Bullo (2014) <doi:10.1109/TCNS.2014.2310254>,
            optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities.
The 'NetCoupler' algorithm identifies potential direct effects of 
    correlated, high-dimensional variables formed as a network with an external 
    variable. The external variable may act as the dependent/response variable 
    or as an independent/predictor variable to the network.
Efficient procedures for fitting and cross-validating the overlapping group Lasso (implemented in C++) for Cox models with time-dependent covariates. The penalty term is a weighted sum of infinity norms of (overlapping) groups of coefficients, which can select variables structurally with a specific grouping structure.
Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.
Empirical statistical analysis, visualization and simulation of
    diffusion and contagion processes on networks. The package implements algorithms
    for calculating network diffusion statistics such as transmission rate, hazard
    rates, exposure models, network threshold levels, infectiousness (contagion),
    and susceptibility. The package is inspired by work published in Valente,
    et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN:
    9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011)
    <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.
Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface;  2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.
Functions to analyze and visualize communication data, 
      based on network theory and resampling methods. 
      Farine, D. R. (2017) <doi:10.1111/2041-210X.12772>;
      Carsey, T., & Harden, J. (2014) <doi:10.4135/9781483319605>.
      Primarily targeted at datasets of facial expressions coded with the Facial Action Coding System. 
      Ekman, P., Friesen, W. V., & Hager, J. C. (2002). "Facial action coding system - investigator's guide" <https://www.paulekman.com/facial-action-coding-system/>.
Methods for the generation of a wide range of network geographies,
    e.g., grid networks or clustered networks. Useful for the generation of
    benchmarking instances for the investigation of, e.g., Vehicle-Routing-Problems
    or Travelling Salesperson Problems.
Carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.
A multi-core R package that contains a set of tools based on copula graphical
             models for accomplishing the three interrelated goals in genetics and genomics in an
			 unified way: (1) linkage map construction, (2) constructing linkage disequilibrium
			 networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-
			 phenotype-environment interactions networks. 
			 The 'netgwas' package can deal with biparental inbreeding and outbreeding species with
			 any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes),
			 tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where 
			 number of variables p is considerably larger than number of sample sizes (p >> n). 
			 The computations is memory-optimized using the sparse matrix output. The 'netgwas' 
			 implements the methodological developments in Behrouzi and Wit (2017)
             <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <doi:10.1093/bioinformatics/bty777>.
The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.
Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
Implementation of network integration approaches
    comprising unweighted and weighted integration methods. Unweighted integration
    is performed considering the average, per-edge average, maximum and minimum of
    networks edges. Weighted integration takes into account a weight for each 
    network during the fusion process, where the weights express
    the ''predictiveness strength'' of each network considering a specific predictive
    task. Weights can be learned using a machine learning algorithm able to associate
    the weights to the assessment of the accuracy of the learning algorithm 
    trained on the network itself. The implemented methods can be applied to 
    effectively integrate different biological networks modelling a wide range
    of problems in bioinformatics (e.g. disease gene prioritization, protein
    function prediction, drug repurposing, clinical outcome prediction).
Tools for managing large sets of network data and performing whole network analysis.
    This package is focused on the network based statistic jackknife method, and 
    implements a framework that can be extended to other network manipulations and analyses.
Represent 'network' or 'igraph' objects whose vertices can be represented by features in an 'sf' object as a network graph surmising a 'sf' plot. Fits into 'ggplot2' grammar.
A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis":
 - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>;
 - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>;
 - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>;
 - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>;
 - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>;
 - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>;
 - league table with network meta-analysis results;
 - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>;
 - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>;
 - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>;
 - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>;
 - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>;
 - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>.
The field of immunology benefits from software that can
    predict which peptide sequences trigger an immune response.
    'NetMHCIIpan' is a such a tool: it predicts the
    binding strength of a short peptide to a Major Histocompatibility
    Complex class II (MHC-II) molecule.
    'NetMHCIIpan' can be used from a web server at 
    <https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-3.2/>
    or from the command-line, using a local installation. This package
    allows to call 'NetMHCIIpan' from R.
Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks,
             incorporating node-level predictors of mixed-membership vectors, as well as 
             dyad-level predictors. For networks observed over time, the model defines a hidden
             Markov process that allows the effects of node-level predictors to evolve in discrete,
             historical periods. In addition, the package offers a variety of utilities for 
             exploring results of estimation, including tools for conducting posterior 
             predictive checks of goodness-of-fit and several plotting functions. The package 
             implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic
             Blockmodel Regression for Social Networks: Application to International Conflicts',
             available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.
Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.
A graph visualization engine that puts an emphasis on 
  aesthetics at the same time of providing default parameters that yield
  out-of-the-box-nice visualizations. The package is built on top of
  'The Grid Graphics Package' and seamlessly work with 'igraph' and 
  'network' objects.
Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.
Implements methods for centrality related analyses of networks. 
    While the package includes the possibility to build more than 20 indices, 
    its main focus lies on index-free assessment of centrality via partial 
    rankings obtained by neighborhood-inclusion or positional dominance. These 
    partial rankings can be analyzed with different methods, including 
    probabilistic methods like computing expected node ranks and relative 
    rank probabilities (how likely is it that a node is more central than another?).
    The methodology is described in depth in the vignettes and in
    Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.
Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and  generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.
Functions for assessing the replication/preservation of a network 
  module's topology across datasets through permutation testing; Ritchie et al. 
  (2015) <doi: 10.1016/j.cels.2016.06.012>.
Statistical entropy analysis of network data as introduced by Frank and Shafie (2016) <doi:10.1177/0759106315615511>, and in a forthcoming book by Nowicki, Shafie and Frank (2022).
Calculates network measures such as Largest Connected Component (LCC), Proximity, Separation, Jaccard Index, 
    along with permutation, when needed.
Segregation is a network-level property such that edges between 
  predefined groups of vertices are relatively less likely. Network homophily 
  is a individual-level tendency to form relations with people who are similar 
  on some attribute (e.g. gender, music taste, social status, etc.). In general
  homophily leads to segregation, but segregation might arise without 
  homophily. This package implements descriptive indices measuring 
  homophily/segregation. It is a computational companion 
  to Bojanowski & Corten (2014) <doi:10.1016/j.socnet.2014.04.001>.
The network structural equation modeling conducts a network 
    statistical analysis on a data frame of coincident observations of 
    multiple continuous variables [1]. 
    It builds a pathway model by exploring a pool of domain knowledge guided 
    candidate statistical relationships between each of the variable pairs, 
    selecting the 'best fit' on the basis of a specific criteria such as 
    adjusted r-squared value.
    This material is based upon work supported by the U.S. National Science 
    Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, 
    under the NSF Solicitation: 
    NSF 20-570 Industry-University Cooperative Research Centers Program 
    [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, 
    Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) 
    <doi:10.1109/ACCESS.2013.2267611>.
We developed a comprehensive tool that helps with visualization and analysis of networks with the same variables across multiple factor levels. The 'netShiny' contains most of the popular network features such as centrality measures, modularity, and other summary statistics (e.g. clustering coefficient). It also contains known tools to look at the (dis)similarities between two networks, such as pairwise distance measures between networks, set operations on the nodes of the networks, distribution of the weights of the edges and a network representing the difference between two correlation matrices. The package 'netShiny' also contains tools to perform bootstrapping and find clusters in networks. See the 'netShiny' manual for more information, documentation and examples.
Assists actuaries and other insurance modellers in pricing,
    reserving and capital modelling for non-life insurance and
    reinsurance modelling. Provides functions that help model
    excess levels, capping and pure Incurred but not reported
    claims (pure IBNR).
    Includes capped mean, exposure curves and increased limit
    factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced
    LogNormal-Pareto and Sliced Gamma-Pareto distributions.
    Includes mean, probability density function (pdf), cumulative
    probability function (cdf) and inverse cumulative probability
    function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto
    distributions.
    Includes calculating pure IBNR exposure with LogNormal and
    Gamma distribution for reporting delay.
    Includes a shiny tool to simulate insurance claims.
R interface for the 'netstat' command line utility used to retrieve and parse 
    commonly used network statistics, including available and in-use 
    transmission control protocol (TCP) ports. Primers offering technical background information 
    on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" 
    by Michael Kerrisk (2014) 
    <https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) 
    <https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.
A set of functions for studying network robustness, resilience and vulnerability.
Features tools for the network data analysis and community detection. 
    Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. 
    Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models.
    Implements the following: 
    Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138>
    Bickel and Sarkar (2015) <doi:10.1111/rssb.12117>
    Lei (2016) <doi:10.1214/15-AOS1370>
    Wang and Bickel (2017) <doi:10.1214/16-AOS1457>
    Zhang and Amini (2020) <arXiv:2012.15047>
    Le and Levina (2022) <doi:10.1214/21-EJS1971>.
Enables users to retrieve data, meta-data, and codebooks 
    from <https://nettskjema.no/>. The data from the API is richer than from the
    online data portal. Mowinckel (2021) <doi:10.5281/zenodo.4745481>.
Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon 'igraph'.
Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
Tools to create and modify network objects.  The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.
Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
This permutation based hypothesis test, suited for Gaussian and binary data, 
    assesses the difference between two networks based on several invariance measures 
    (e.g., network structure invariance, global strength invariance, edge invariance). 
    Network structures are estimated with l1-regularized partial correlations (Gaussian data) 
    or with l1-regularized logistic regression (eLasso, binary data). Suited for comparison 
    of independent and dependent samples. For dependent samples, only supported for data of 
    one group which is measured twice. See van Borkulo et al. (2017) 
    <doi:10.13140/RG.2.2.29455.38569>.
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
    graphs from 'R'.
Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) <doi:10.3233/978-1-60750-692-8-227> for an overview on spectral class of inter-graph distance measures.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
Simulates the extinction of species in ecological networks and it analyzes
             its cascading effects, described in Dunne et al. (2002) <doi:10.1073/pnas.192407699>.
A network Maze generator that creates different types of network mazes.
This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.
An implementation of some of the core 'network' package functionality based on a 
    simplified data structure that is faster in many research applications. This package is designed 
    for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for
    binary and weighted, directed and undirected, bipartite and unipartite networks; no current 
    support for multigraphs, hypergraphs, or loops.
Collection of functions for fast manipulation, handling, and analysis of large-scale
    networks based on family and social data. Functions are utility functions used to manipulate data
    in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data.
    When possible, the functions should be able to handle millions of data points quickly for use in combination
    with data from large public national registers and databases.
    Kenneth Lange (2003, ISBN:978-8181281135).
Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.
Provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, mostly through the use of Stan. In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2021) <arXiv:2109.10204>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Includes assorted tools for network analysis. Bridge centrality; goldbricker; MDS, PCA, & eigenmodel network plotting.
Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.
Training of neural networks using backpropagation,
    resilient backpropagation with (Riedmiller, 1994) or without
    weight backtracking (Riedmiller and Braun, 1993) or the
    modified globally convergent version by Anastasiadis et al.
    (2005). The package allows flexible settings through
    custom-choice of error and activation function. Furthermore,
    the calculation of generalized weights (Intrator O & Intrator
    N, 1993) is implemented.
Visualization and analysis tools to aid in the interpretation of
    neural network models.  Functions are available for plotting,
    quantifying variable importance, conducting a sensitivity analysis, and
    obtaining a simple list of model weights.
Analysis functions to quantify inputs importance in neural network models.
  Functions are available for calculating and plotting the inputs importance and obtaining
  the activation function of each neuron layer and its derivatives. The importance of a given
  input is defined as the distribution of the derivatives of the output with respect to that
  input in each training data point <doi:10.18637/jss.v102.i07>.
Base package for 'Neuroconductor', which includes many helper 
    functions that interact with objects of class 'nifti', implemented by
    package 'oro.nifti', for reading/writing and also other manipulation 
    functions.
Annotated neuroblastoma copy number profiles,
	     a benchmark data set for change-point detection algorithms.
Installs 'Neuroconductor' packages from the release repository <https://neuroconductor.org/releases/> or from 'GitHub'.
Neural decoding is method of analyzing neural data that  
    uses a pattern classifiers to predict experimental conditions based 
    on neural activity. 'NeuroDecodeR' is a system of objects that 
    makes it easy to run neural decoding analyses. For more information
    on neural decoding see Meyers & Kreiman (2004)
    <doi:10.7551/mitpress/8404.003.0024>.
Downloads and reads data from Human 'Connectome' Project 
    <https://db.humanconnectome.org> using Amazon Web Services ('AWS') 
    'S3' buckets.
A collection of data structures that represent
    volumetric brain imaging data. The focus is on basic data handling for 3D
    and 4D neuroimaging data. In addition, there are function to read and write
    NIFTI files and limited support for reading AFNI files.
Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.
Preprocessing pipeline for normalizing and cleaning T1-weighted, T2-weighted and FLAIR MRI images coming from different sources, diseases, patients, scanners and sites.
Generates functional Magnetic Resonance Imaging (fMRI) 
             time series or 4D data. Some high-level 
             functions are created for fast data generation with only 
             a few arguments and a diversity of functions to define 
             activation and noise. For more advanced users it is possible 
             to use the low-level functions and manipulate the arguments.
             See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.
Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.
In the working paper titled "Why You Should Never Use the Hodrick-Prescott 
   Filter", James D. Hamilton proposes a new alternative to economic time series 
   filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.
Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.
Extends the classical Newman studentized range statistic
  in various ways that can be applied to genome-scale transcriptomic
  or other expression data.
Interface to gather news from the 'News API', based on a multilevel query <https://newsapi.org/>. A personal API key is required.
Programmatically collect normalized news from
    (almost) any website. An 'R' clone of the
    <https://github.com/kotartemiy/newscatcher> 'Python' module.
Semissupervised model for geographical document classification (Watanabe 2018) <doi:10.1080/21670811.2017.1293487>. 
    This package currently contains seed dictionaries in English, German, French, Spanish, Italian, Russian, Hebrew, Arabic Japanese and Chinese (Simplified and Traditional).
Adding updates (version or bullet points) to the NEWS.md
    file.
Implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a  model for recurrent event, when the data are correlated or not  correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be  estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.
Responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts and style sheets are based on 'jQuery' <https://jquery.com/> and 'Bootstrap' <https://getbootstrap.com/>.
Indices, heuristics and strategies to help determine the number of factors/components to retain:
                1. Acceleration factor (af with or without Parallel Analysis);
                2. Optimal Coordinates (noc with or without Parallel Analysis);
                3. Parallel analysis (components, factors and bootstrap);
                4. lambda > mean(lambda) (Kaiser, CFA and related);
                5. Cattell-Nelson-Gorsuch (CNG);
                6. Zoski and Jurs multiple regression (b, t and p);
                7. Zoski and Jurs standard error of the regression coeffcient (sescree);
                8. Nelson R2;
                9. Bartlett khi-2;
               10. Anderson khi-2;
               11. Lawley khi-2 and
               12. Bentler-Yuan khi-2.
Commodity pricing models are (systems of) stochastic differential equations that are utilized for the valuation and hedging of commodity contingent claims (i.e. derivative products on the commodity) and other commodity related investments. Commodity pricing models that capture market dynamics are of great importance to commodity market participants in order to exercise sound investment and risk-management strategies. Parameters of commodity pricing models are estimated through maximum likelihood estimation, using available term structure futures data of a commodity. 'NFCP' (n-factor commodity pricing) provides a framework for the modeling, parameter estimation, probabilistic forecasting, option valuation and simulation of commodity prices through state space and Monte Carlo methods, risk-neutral valuation and Kalman filtering. 'NFCP' allows the commodity pricing model to consist of n correlated factors, with both random walk and mean-reverting elements. The n-factor commodity pricing model framework was first presented in the work of Cortazar and Naranjo (2006) <doi:10.1002/fut.20198>. Examples presented in 'NFCP' replicate the two-factor crude oil commodity pricing model presented in the prolific work of Schwartz and Smith (2000) <doi:10.1287/mnsc.46.7.893.12034> with the approximate term structure futures data applied within this study provided in the 'NFCP' package.
This is the R API for the 'nfer' formalism (<http://nfer.io/>).
    'nfer' was developed to specify event stream abstractions for spacecraft 
    telemetry such as the Mars Science Laboratory.  Users write rules using 
    a syntax that borrows heavily from Allen's Temporal Logic that, when 
    applied to an event stream, construct a hierarchy of temporal intervals 
    with data.  The R API supports loading rules from a file or mining them 
    from historical data.  Traces of events or pools of intervals are 
    provided as data frames.
A set of functions to estimate outcomes of fourth down
    plays in the National Football League and obtain fourth down plays
    from <https://www.nfl.com/> and <https://www.espn.com/>.
A set of functions to access National Football
    League play-by-play data from <https://www.nfl.com/>.
A set of functions to visualize National Football League
    analysis in 'ggplot2'.
A minimal package for downloading data from 'GitHub'
    repositories of the 'nflverse' project.
A set of functions to simulate National Football League
    seasons including the sophisticated tie-breaking procedures.
The intent here is to enable the simulation of plays/drives and
    evaluate game-play strategies in the National Football League (NFL).
    Built-in strategies include going for it on fourth down and varying the 
    proportion of passing/rushing plays during a drive. The user should be
    familiar with nflscrapR data before trying to write his/her own 
    strategies. This work is inspired by a blog post by Mike Lopez, 
    currently the  Director of Data and Analytics at the NFL, Lopez (2019) <https://statsbylopez.netlify.app/post/resampling-nfl-drives/>.
The 'nflverse' is a set of packages dedicated to data of the
    National Football League. This package is designed to make it easy to
    install and load multiple 'nflverse' packages in a single step. Learn
    more about the 'nflverse' at <https://nflverse.nflverse.com/>.
Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a technical description of the model <https://www.mcw.edu/-/media/MCW/Departments/Biostatistics/tr72.pdf?la=en>.
Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.
Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.
Posterior distribution of case-control fine-mapping. Specifically, Bayesian variable selection for single-nucleotide polymorphism (SNP) data using the normal-gamma prior. Alenazi A.A., Cox A., Juarez M,. Lin W-Y. and Walters, K. (2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. <doi:10.1002/gepi.22213>.
Provides an 'htmlwidgets' <https://www.htmlwidgets.org/> interface to 'NGL.js' <http://nglviewer.org/ngl/api/>.
    'NGLvieweR' can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications.
    It includes a set of API functions to manipulate the viewer after creation in Shiny.
An n-gram is a sequence of n "words" taken, in order, from a
    body of text.  This is a collection of utilities for creating,
    displaying, summarizing, and "babbling" n-grams.  The
    'tokenization' and "babbling" are handled by very efficient C
    code, which can even be built as its own standalone library.
    The babbler is a simple Markov chain.  The package also offers
    a vignette with complete example 'workflows' and information about
    the utilities offered in the package.
Retrieve and plot word frequencies through time from the "Google
    Ngram Viewer" <https://books.google.com/ngrams>.
A simple n-gram (contiguous sequences of n items from a
    given sequence of text) tokenizer to be used with the 'tm' package with no
    'rJava'/'RWeka' dependency.
Provides tools for analyzing spatial data, especially non-
    Gaussian areal data. The current version supports the sparse restricted
    spatial regression model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x>,
	the centered autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>,
	and the Bayesian spatial filtering model of Hughes (2017) <arXiv:1706.04651>.
Body Shape and related measurements from the US National Health
    and Nutrition Examination Survey (NHANES, 1999-2004).  See
    http://www.cdc.gov/nchs/nhanes.htm for details.
Utility to retrieve data from the National Health and Nutrition 
	Examination Survey (NHANES) website <https://www.cdc.gov/nchs/nhanes/index.htm>.
Tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in 'nhdplusTools' are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.
Tools for working with the National Hydrography Dataset, with 
    functions for querying, downloading, and networking both the NHD 
    <https://www.usgs.gov/national-hydrography> 
    and NHDPlus <https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus> datasets.
Retrieves and processes the data exposed by the open 'NHL' API. This includes information on players, teams, games, tournaments, drafts, standings, schedules and other endpoints. A lower-level interface to access the data via URLs directly is also provided.
Each dataset contains scores for every game during a specific season of the NHL.
Add game events to a database file to use for statistical analysis of hockey games. This means we only call the 'NHL' API
    once for each game we want to add. We will have very fast retrieval of data once games have been added since the data is stored locally.
    We use the API located at <https://statsapi.web.nhl.com/api/v1/teams> with supplemental data from <https://www.nhl.com/scores/>.
    Other endpoints can be found at <https://gitlab.com/dword4/nhlapi>.
Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) <doi:10.1111/j.1541-0420.2010.01550.x>. Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.
Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using 'NHS Predict'. 
            The time interval for the estimation can be set up to 15 years, with default at 10.
            Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates.
            An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates
            the correct standardised inputs.
            This work is not affiliated with the development of 'NHS Predict' and its underlying statistical model.
            Details on 'NHS Predict' can be found at: <doi:10.1186/bcr2464>.
            The web version of 'NHS Predict': <https://breast.predict.nhs.uk/>.
            A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions,
            and an additional dataset is provided for running example with the dedicated SCAN function.
Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary <https://datadictionary.nhs.uk/data_elements_overview.html>.
    The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups.
    The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained.
    This package was commissioned by the NHS-R community <https://nhsrcommunity.com/> to provide this consistency of lookups.
    The OpenSafely lookups have now been added <https://www.opencodelists.org/docs/>.
Provides functions for working with NHS number checksums.
    The UK's National Health Service issues NHS numbers to all users of its
    services and this package implements functions for verifying that the
    numbers are valid according to the checksum scheme the NHS use.
    Numbers can be validated and checksums created.
Free United Kingdom National Health Service (NHS) and other healthcare, or population health-related data for education and training purposes. This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data.  This package exists to support skills development in the NHS-R community: <https://nhsrcommunity.com/>.
Provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHSE/I
  programme 'Making Data Count', and allows users to draw XmR charts, use change points and apply rules with summary
  indicators for when rules are breached.
Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings.
The implementation of Markov Model Multiple Imputation
  with the application to COVID-19 scale, NIAID OS.
Color palettes based on nature inspired colours in "Sri Lanka".
Get or set UNIX priority (niceness) of running R process.
Species Identification using DNA Barcodes Integrated with 
             Environmental Niche Models.
Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%).  Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions.  It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region.  The article by Swanson et al. (Ecology, 2015) provides a detailed description of the methodology.  See the package vignette for a worked example using fish stable isotope data.
A collection of tools that allow users to perform critical steps 
    in the process of assessing ecological niche evolution over phylogenies, with
    uncertainty incorporated explicitly in reconstructions. The method proposed
    here for ancestral reconstruction of ecological niches characterizes species'
    niches using a bin-based approach that incorporates uncertainty in estimations.
    Compared to other existing methods, the approaches presented here reduce risk
    of overestimation of amounts and rates of ecological niche evolution. The
    main analyses include: initial exploration of environmental data in occurrence
    records and accessible areas, preparation of data for phylogenetic analyses,
    executing comparative phylogenetic analyses of ecological niches, and plotting
    for interpretations. Details on the theoretical background and methods used 
    can be found in: Owens et al. (2020) <doi:10.1002/ece3.6359>,
    Peterson et al. (1999) <doi:10.1126/science.285.5431.1265>,
    Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4>,
    Peterson (2011) <doi:10.1111/j.1365-2699.2010.02456.x>,
    Barve et al. (2011) <doi:10.1111/ecog.02671>, 
    Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>,
    Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, 
    Saupe et al. (2018) <doi:10.1093/sysbio/syx084>, and 
    Cobos et al. (2021) <doi:10.1111/jav.02868>.
Provides utility functions and objects for Extreme Value
    Analysis. These include probability functions with their exact
    derivatives w.r.t. the parameters that can be used for estimation
    and inference, even with censored observations. The
    transformations exchanging the two parameterizations of Peaks Over
    Threshold (POT) models: Poisson-GP and Point-Process are also
    provided with their derivatives.
Tools for reading and writing NIfTI-1.1 (NII) files, including optimized voxelwise read/write operations and a simplified method to write dataframes to NII.
    Specification of the NIfTI-1.1 format can be found here <https://nifti.nimh.nih.gov/nifti-1>.
    Scientific publication first using these tools
        Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) <doi:10.1016/j.neuroimage.2020.116764> "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764.
Computes and plots the boundary between night and day.
A collection of functions used in remote sensing analysis (e.g., conversion from digital numbers to radiance, reflectance, and temperature). It includes several algorithms to calculate the albedo: Liang (2000) <doi:10.1016/S0034-4257(00)00205-4>, Silva et al. (2016) <doi:10.32614/RJ-2016-051>, Tasumi et al. (2008) <doi:10.1061/(ASCE)1084-0699(2008)13:2(51)>, among others; and include functions to derive several spectral indices. Although the current version implements basic functions, it will be expandable to a more robust tool for water cycle modeling (e.g., to include surface runoff and evapotranspiration calculations) in the near future. 
    This package is under development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo.
Routines for enumerating all existing nonnegative integer solutions of a linear Diophantine equation. The package provides routines for solving 0-1, bounded and unbounded knapsack problems; 0-1, bounded and unbounded subset sum problems; additive partitioning of natural numbers; and one-dimensional bin-packing problem.
Th-U-Pb electron microprobe age dating of monazite, as originally 
             described in <doi:10.1016/0009-2541(96)00024-1>.
Miscellaneous R functions developed as collateral damage over the
    course of work in statistical and scientific computing for research. These
    include, for example, utilities that supplement existing idiosyncrasies of
    the R language, extend existing plotting functionality and aesthetics, help
    prepare data objects for imputation, and extend access to command line tools
    and systems-level information.
Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.
A system for writing hierarchical statistical models largely
    compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models
    and do basic R-style math, and compiling both models and nimbleFunctions via
    custom-generated C++. 'NIMBLE' includes default methods for MCMC, Monte Carlo
    Expectation Maximization, and some other tools. The nimbleFunction system makes
    it easy to do things like implement new MCMC samplers from R, customize the
    assignment of samplers to different parts of a model from R, and compile the
    new samplers automatically via C++ alongside the samplers 'NIMBLE' provides.
    'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New
    distributions and functions can be added, including as calls to external
    compiled code. Although most people think of MCMC as the main goal of the
    'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing
    arbitrary other kinds of model-generic algorithms as well. A full User Manual is
    available at <https://r-nimble.org>.
Functions for adaptive parallel tempering (APT) with NIMBLE models. Adapted from 'Lacki' & 'Miasojedow' (2016) <DOI:10.1007/s11222-015-9579-0> and 'Miasojedow, Moulines and Vihola' (2013) <DOI:10.1080/10618600.2013.778779>.
Provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the 'nimble' modelling framework.  It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.
Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. 
  Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models.
  (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).
A collection of common univariate bounded probability distributions transformed to the unbounded real line, for the purpose of increased MCMC efficiency.
Provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.
Includes five particle filtering algorithms for use with state space
    models in the 'nimble' system: 'Auxiliary', 'Bootstrap', 'Ensemble Kalman filter',
    'Iterated Filtering 2', and 'Liu-West', as described in Michaud et al. (2021),
    <doi:10.18637/jss.v100.i03>. A full User Manual is available at
    <https://r-nimble.org>.
Principal Components Analysis of a matrix using Non-linear Iterative Partial Least Squares or weighted Expectation Maximization PCA with Gram-Schmidt orthogonalization of the scores and loadings. Optimized for speed. See Andrecut (2009) <doi:10.1089/cmb.2008.0221>.
An implementation of the National Information Platforms for 
    Nutrition or NiPN's analytic methods for assessing quality of anthropometric 
    datasets that include measurements of weight, height or length, middle upper 
    arm circumference, sex and age. The focus is on anthropometric status but 
    many of the presented methods could be applied to other variables.
Digital map data of Japan for choropleth mapping, including a circle cartogram.
Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
    Testing.
Provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <doi:10.1177/0962280218786302> for further details.
Datasets for testing nonlinear regression routines.
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for 
    SI (International System of Units) and non-SI units, plus unit conversions
    Based on the data from NIST (National Institute of Standards and Technology, USA)
Contains data, code, and figures from Hill et al. 2018a (Journal of Experimental Marine Biology and Ecology; <DOI: 10.1016/j.jembe.2018.07.006>) and Hill et al. 2018b (Data In Brief <DOI: 10.1016/j.dib.2018.09.133>). Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species.
Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time.
Snow water equivalent is modeled with the process based 'delta.snow' model and empirical regression models using relationships between density and diverse at-site parameters. The methods are described in Winkler et al. (2021) <doi:10.5194/hess-25-1165-2021>, Guyennon et al. (2019) <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016) <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009) <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010) <doi:10.1175/2010JHM1202.1>.
Provides an R interface to free geocoding REST APIs maintained by the New Jersey Office of GIS <https://njgin.nj.gov/njgin/edata/geocoding/index.html#!/> and commonly used shapefiles.
SQL like query interface to fetch data from any 'Jira' installation. The data is fetched using 'Jira' REST API, which can be found at the following URL: <https://developer.atlassian.com/cloud/jira/platform/rest/v2>.
Download and analyze motor vehicle crash data released by the New Jersey Department of Transportation (NJDOT).
  The data in this package is collected through the filing of NJTR-1 form by police officers, which provide a standardized way of documenting a motor vehicle crash that occurred in New Jersey.
  3 different data tables containing data on crashes, vehicles & pedestrians released from 2001 to the present can be downloaded & cleaned using this package.
Client for programmatic access to the 2007 and 2012 National 
  Lakes Assessment database <https://www.epa.gov/national-aquatic-resource-surveys/nla> 
  containing data for hundreds of lakes in the lower 48 states of the contiguous US.
Optimization for nonlinear objective and constraint functions.  Linear or nonlinear equality and inequality constraints are allowed.  It accepts the input parameters as a constrained matrix.
Nested loop cross validation for classification purposes for misclassification error rate estimation.
  The package supports several methodologies for feature selection: random forest, Student t-test, limma, 
  and provides an interface to the following classification methods in the 'MLInterfaces' package: linear, 
  quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized 
  linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
  the classifier are included: plot of the ranks of the features, scores plot for a specific 
  classification algorithm and number of features, misclassification rate 
  for the different number of features and classification algorithms tested and ROC plot.
  For further details about the methodology, please check:
  Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) 
  <doi:10.2202/1544-6115.1078>.
Solve a system of nonlinear equations using a Broyden or a Newton method
             with a choice of global strategies such as line search and trust region.
             There are options for using a numerical or user supplied Jacobian,
             for specifying a banded numerical Jacobian and for allowing
             a singular or ill-conditioned Jacobian.
Six growth models are fitted using non-linear least squares. These are the Richards, the 3, 4 and 5 parameter logistic, the Gompetz and the Weibull growth models. Reference: Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach". BMC medical research methodology, 21(1), 1-11. <doi:10.1186/s12874-020-01165-x>.
Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) <doi:10.1016/0165-1889(80)90069-X>,  and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy <doi:10.1103/PhysRevLett.85.461>, and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors <doi:10.1103/PhysRevE.69.066138>. There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information.
Create and manipulate numeric list ('nlist') objects.  An
    'nlist' is an S3 list of uniquely named numeric objects.  An numeric
    object is an integer or double vector, matrix or array.  An 'nlists'
    object is a S3 class list of 'nlist' objects with the same names,
    dimensionalities and typeofs.  Numeric list objects are of interest
    because they are the raw data inputs for analytic engines such as
    'JAGS', 'STAN' and 'TMB'.  Numeric lists objects, which are useful for
    storing multiple realizations of of simulated data sets, can be
    converted to coda::mcmc and coda::mcmc.list objects.
Estimation of relatively complex nonlinear mixed-effects models, including the Sigmoidal Mixed Model and the Piecewise Linear Mixed Model with abrupt or smooth transition, through a single intuitive line of code and with automated generation of starting values.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Datasets and utility  functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models:
 A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.
Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, <https://www.page-meeting.org/?abstract=1434>).
Fit and compare nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is 
    by compiled C code provided in the 'rxode2' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Datasets for 'nlmixr2' and 'rxode2'. 'nlmixr2' is used for fitting and comparing
    nonlinear mixed-effects models in differential
    equations with flexible dosing information commonly seen in pharmacokinetics
    and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 
    <doi:10.1007/s10928-015-9409-1>). Differential equation solving is 
    by compiled C code provided in the 'rxode2' package
    (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for
    support functions like preconditioned fits
    <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate
    selection.
A model library for 'nlmixr2'.  The models include
  (and plan to include) pharmacokinetic, pharmacodynamic, and
  disease models used in pharmacometrics.  Where applicable,
  references for each model are included in the meta-data for
  each individual model.  The package also includes model
  composition and modification functions to make model updates
  easier.
Fit and compare nonlinear mixed-effects models in
    differential equations with flexible dosing information commonly seen
    in pharmacokinetics and pharmacodynamics (Almquist, Leander, and
    Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation
    solving is by compiled C code provided in the 'rxode2' package (Wang,
    Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for
    'ggplot2' plotting methods for 'nlmixr2' objects.
This allows you to generate reporting workflows around 'nlmixr2' analyses with outputs in Word and PowerPoint. You can specify figures, tables and report structure in a user-definable 'YAML' file. Also you can use the internal functions to access the figures and tables to allow their including in other outputs (e.g. R Markdown).
Provides functions to fit linear mixed models
	based on convolutions of the generalized Laplace (GL) distribution.
	The GL mixed-effects model includes four special cases with normal random
	effects and normal errors (NN), normal random effects and Laplace errors (NL),
	Laplace random effects and normal errors (LN), and Laplace random effects
	and Laplace errors (LL). The methods are described in Geraci and Farcomeni (2020,
	Statistical Methods in Medical Research) <doi:10.1177/0962280220903763>.
Replacement for nls() tools for working with nonlinear least squares problems.
      The calling structure is similar to, but much simpler than, that of the nls()
      function. Moreover, where nls() specifically does NOT deal with small or zero
      residual problems, nlmrt is quite happy to solve them. It also attempts to be
      more robust in finding solutions, thereby avoiding 'singular gradient' messages
      that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach
      in nlmrt generally works more reliably to get a solution, though this may be 
      one of a set of possibilities, and may also be statistically unsatisfactory.
      Added print and summary as of August 28, 2012.
Package to select best model  among several linear and nonlinear models. The main function uses the gnls() function from the 'nlme' package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".
It includes four methods: DCOL-based K-profiles clustering, non-linear network reconstruction, non-linear hierarchical clustering, and variable selection for generalized additive model. References:  Tianwei Yu (2018)<DOI: 10.1002/sam.11381>; Haodong Liu and others (2016)<DOI: 10.1371/journal.pone.0158247>; Kai Wang and others (2015)<DOI: 10.1155/2015/918954>; Tianwei Yu and others (2010)<DOI: 10.1109/TCBB.2010.73>.
Solve optimization problems using an R interface to NLopt. NLopt is a 
    free/open-source library for nonlinear optimization, providing a common
    interface for a number of different free optimization routines available
    online as well as original implementations of various other algorithms.
    See <https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more
    information on the available algorithms. Building from included sources 
    requires 'CMake'. On Linux and 'macOS', if a suitable system build of 
    NLopt (2.7.0 or later) is found, it is used; otherwise, it is built 
    from included sources via 'CMake'. On Windows, NLopt is obtained through 
    'rwinlib' for 'R <= 4.1.x' or grabbed from the 'Rtools42 toolchain' for 
    'R >= 4.2.0'.
Basic classes and methods for Natural Language Processing.
Stanford 'CoreNLP' annotation client.
  Stanford 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/index.html> integrates all 
  NLP tools from the Stanford Natural Language Processing Group, 
  including a part-of-speech (POS) tagger, a named entity recognizer (NER), 
  a parser, and a coreference resolution system, and provides model files 
  for the analysis of English. More information can be found in the README.
Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), <doi:10.1080/01621459.2019.1668794>). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), <doi:10.1214/15-EJS1035>) and other metrics are included.
Utilities for Natural Language Processing.
Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples.
Likelihood inference based on higher order approximations 
  for nonlinear models with possibly non constant variance.
This is a package which can help you search for the root
        of a equation.
Estimate the non-linear odds ratio and plot it against a continuous exposure.
Setup, run and analyze 'NetLogo' (<https://ccl.northwestern.edu/netlogo/>) model simulations in 'R'.
    'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. 
    However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses.
    The user defines all information that is needed in an intuitive framework, using class objects.
    Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user.
    By nesting model calls in future environments, large simulation design with many runs can be executed in parallel.
    This also enables simulating 'NetLogo' experiments on remote high performance computing machines.
    In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.
Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum.
Adds brute force and multiple starting values to nls.
Estimation of structural equation models with nonlinear effects
  and underlying nonnormal distributions.
A few utilities for summarizing, testing, and plotting non-linear
    regression models fit with nls(), nlsList() or nlme().
Non-linear shrinkage estimation of population eigenvalues and covariance
    matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).
We solve non linear least squares problems with optional
    equality and/or inequality constraints. Non linear iterations are
    globalized with back-tracking method. Linear problems are solved by
    dense QR decomposition from 'LAPACK' which can limit the size of
    treated problems. On the other side, we avoid condition number
    degradation which happens in classical quadratic programming approach.
    Inequality constraints treatment on each non
    linear iteration is based on 'NNLS' method (by Lawson and Hanson).
    We provide an original function 'lsi_ln' for solving linear least squares
    problem with inequality constraints in least norm sens. Thus if Jacobian of
    the problem is rank deficient a solution still can be provided.
    However, truncation errors are probable in this case.
    Equality constraints are treated by using a basis of Null-space.
    User defined function calculating residuals must return a list having
    residual vector (not their squared sum) and Jacobian. If Jacobian is
    not in the returned list, package 'numDeriv' is used to calculated
    finite difference version of Jacobian. The 'NLSIC' method was fist
    published in Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.
Data sets and nonlinear regression models dedicated to predictive microbiology.
Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi:10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi:10.1016/j.spl.2011.03.019>.
Provides tools for working with nonlinear least squares problems.
      For the estimation of models reliable and robust tools than nls(), where the
      the Gauss-Newton method frequently stops with 'singular gradient' messages. 
      This is accomplished by using, where possible, analytic derivatives to compute
      the matrix of derivatives and a stabilization of the solution of the estimation
      equations. Tools for approximate or externally supplied derivative matrices
      are included. Bounds and masks on parameters are handled properly.
Set of functions implementing the algorithm described in Fernandez 
    Torvisco et al. (2018) for fitting separable nonlinear regression curves. 
    See Fernandez Torvisco, Rodriguez-Arias Fernandez and Cabello Sanchez (2018)
    <doi:10.2298/FIL1812233T>.
Several tools for assessing the quality of fit of a gaussian nonlinear model are provided.
Uses a modified lifting algorithm on which it builds the
        nondecimated lifting transform. It has applications in wavelet
        shrinkage.
Fits a non-linear transformation model ('nltm') for
        analyzing survival data, see Tsodikov (2003) <doi:10.1111/1467-9868.00414>. The
        class of 'nltm' includes the following currently supported
        models: Cox proportional hazard, proportional hazard cure,
        proportional odds, proportional hazard - proportional hazard
        cure, proportional hazard - proportional odds cure, Gamma
        frailty, and proportional hazard - proportional odds.
R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.
Provides functions to calculate the normalised Lineage-Through-
    Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures
    the difference between two Lineage-Through-Time curves, where each curve is
    normalised both in time and in number of lineages.
Wald Test for nonlinear restrictions on model parameters and confidence
             intervals for nonlinear functions  of parameters using delta-method.  Applicable
             after ANY model, provided parameters estimates and their covariance matrix are
             available.
Set of functions for accessing database of network meta-analyses described in 
  Petropoulou M, et al. Bibliographic study showed improving statistical methodology of network
  meta-analyses published between 1999 and 2015
  <doi:10.1016/j.jclinepi.2016.11.002>. The database is hosted in a REDcap database at the 
  Institute of Social and Preventive Medicine (ISPM) in the University of Bern.
Implements HSROC (hierarchical summary receiver operating characteristic) model developed by Ma, Lian, Chu, Ibrahim, and Chen (2018) <doi:10.1093/biostatistics/kxx025>
  and hierarchical model developed by Lian, Hodges, and Chu (2019) <doi:10.1080/01621459.2018.1476239> for performing meta-analysis for 1-5 diagnostic tests to simultaneously 
    compare multiple tests within a missing data framework. This package evaluates the accuracy of 
    multiple diagnostic tests and also gives graphical representation of the results.
Performs network meta-analysis using integrated nested Laplace approximations ('INLA') 
             which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. 
             Includes methods to assess the heterogeneity and inconsistency in the network. 
             Contains more than ten different network meta-analysis dataset. 
             'INLA' package can be obtained from <https://www.r-inla.org>.
A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis :
               - simple outlier and influential detection measures
               - outlier and influential detection measures by considering study deletion (shift the mean)
               - plots for outlier and influential detection measures
	       - Q-Q plot for network meta-analysis
               - Forward Search algorithm in network meta-analysis. 
               - forward plots to monitor statistics in each step of the forward search algorithm
               - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.
A graphical display of results from network meta-analysis (NMA). 
  It is suitable for outcomes like odds ratio (OR), risk ratio (RR), 
  risk difference (RD) and standardized mean difference (SMD). 
  It also has an option to visually display and compare 
  the surface under the cumulative ranking (SUCRA) of different treatments.
Derives the most frequent hierarchies along with their probability of occurrence. One can also define complex hierarchy criteria and calculate their probability. Methodology based on Papakonstantinou et al. (2021) <DOI:10.21203/rs.3.rs-858140/v1>.
Calculation and presentation of decision-invariant bias adjustment
    thresholds and intervals for Network Meta-Analysis, as described by 
    Phillippo et al. (2018) <doi:10.1111/rssa.12341>. These describe the 
    smallest changes to the data that would result in a change of decision.
Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.
Efficient tools for preparation, checking and post-processing of data in PK/PD (pharmacokinetics/pharmacodynamics) modeling, with focus on use of Nonmem. Attention is paid to ensure consistency, traceability, and Nonmem compatibility of Data. Rigorously checks final Nonmem datasets. Implemented in 'data.table', but easily integrated with 'base' and 'tidyverse'.
Provides a framework to perform Non-negative Matrix
    Factorization (NMF). The package implements a set of already published algorithms
    and seeding methods, and provides a framework to test, develop and plug
    new/custom algorithms. Most of the built-in algorithms have been optimized
    in C++, and the main interface function provides an easy way of performing
    parallel computations on multicore machines.
Calculates the normalized mutual information (NMI) of two community structures in network analysis.
Contains functions useful for debugging, set operations on vectors,
    and 'UTC' date and time functionality. It adds a few vector manipulation 
    verbs to 'purrr' and 'dplyr' packages. It can also generate an R file to 
    install and update packages to simplify deployment into production. The 
    functions were developed at the data science firm 'Numeract LLC' and are 
    used in several packages and projects.
A program for Bayesian analysis of univariate normal mixtures with an unknown number
  of components, following the approach of Richardson and Green (1997) <doi:10.1111/1467-9868.00095>.
  This makes use of reversible jump Markov chain Monte Carlo methods that are capable of jumping
  between the parameter sub-spaces corresponding to different numbers of components in the mixture.
  A sample from the full joint distribution of all unknown variables is thereby generated,
  and this can be used as a basis for a thorough presentation of many aspects of the posterior
  distribution.
Provides residuals and overdispersion metrics to assess the fit of N-mixture models obtained using the package 'unmarked'. 
    Details on the methods are given in Knape et al. (2017) <doi:10.1101/194340>.
We fit inverse probability weighting estimator and the augmented inverse probability weighting for non-monotone missing at random data.
Functions, examples and data from the first and
  the second edition of "Numerical Methods and Optimization
  in Finance" by M. Gilli, D. Maringer and E. Schumann
  (2019, ISBN:978-0128150658).  The package provides
  implementations of optimisation heuristics (Differential
  Evolution, Genetic Algorithms, Particle Swarm
  Optimisation, Simulated Annealing and Threshold
  Accepting), and other optimisation tools, such as grid
  search and greedy search.  There are also functions for
  the valuation of financial instruments such as bonds and
  options, for portfolio selection and functions that help
  with stochastic simulations.
A Non-Metric Space Library ('NMSLIB' <https://github.com/nmslib/nmslib>) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the 'NMSLIB' <https://github.com/nmslib/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the 'NMSLIB' <https://github.com/nmslib/nmslib> 'Python' Library.
Perform an exploration and a preliminary analysis on the dose-
    response relationship of nanomaterial toxicity. Several functions are
    provided for data exploration, including functions for creating a subset of
    dataset, frequency tables and plots. Inference for order restricted dose-
    response data is performed by testing the significance of monotonic
    dose-response relationship, using Williams, Marcus, M, Modified M and 
    Likelihood ratio tests. Several methods of multiplicity adjustment 
    are also provided. Description of the methods can be found in <https://github.com/rahmasarina/dose-response-analysis/blob/main/Methodology.pdf>.
Due to 'Rstudio's' status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with 'R'. The 'NMVANOVA' (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice 'Rstudio' users to perform different model variations one-way analysis of variance without downloading  multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.
This shows how NONMEM(R) <http://www.iconplc.com/innovation/nonmem/> software works. NONMEM's classical estimation methods like 'First Order(FO) approximation', 'First Order Conditional Estimation(FOCE)', and 'Laplacian approximation' are explained.
Calculates spatial pattern analysis using a T-square sample procedure.
  This method is based on two measures "x" and "y".
  "x" - Distance from the random point to the nearest individual.
  "y" - Distance from individual to its nearest neighbor.
  This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species' distribution (random, uniform or clumped patterns).
  Ludwig & Reynolds (1988, ISBN:0471832359).
Datasets and functions to benchmark (convergence, speed, ease of use) R packages dedicated to regression with neural networks (no classification in this version). The templates for the tested packages are available in the R, R Markdown and HTML formats at <https://github.com/pkR-pkR/NNbenchmarkTemplates> and <https://theairbend3r.github.io/NNbenchmarkWeb/index.html>. The submitted article to the R-Journal can be read at <https://www.inmodelia.com/gsoc2020.html>.
Provides nearest-neighbors matching and 
    analysis of case-control data. Cui, Z., Marder, E. P., Click, E. S., 
    Hoekstra, R. M., & Bruce, B. B. (2022) <doi:10.1097/EDE.0000000000001504>.
Generates 'LaTeX' code for drawing well-formatted neural network diagrams with 'TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce 'LaTeX' code for drawing activation functions which are crucial in neural network analysis. To make the code work in a 'LaTeX' editor, users need to install and import some 'TeX' packages including 'TikZ' in the setting of 'TeX' file.
Software for feed-forward neural networks with a single
  hidden layer, and for multinomial log-linear models.
Automatic time series modelling with neural networks. 
    Allows fully automatic, semi-manual or fully manual specification of networks. For details of the
	specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>;
	and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.
Functions to compute the non-negative garrote estimator as 
             proposed by Breiman (1995) <https://www.jstor.org/stable/1269730>
             with the penalized initial estimators extension as proposed by 
             Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.
K-nearest neighbor search for projected and non-projected 'sf' spatial layers. Nearest neighbor search uses (1) C code from 'GeographicLib' for lon-lat point layers, (2) function knn() from package 'nabor' for projected point layers, or (3) function st_distance() from package 'sf' for line or polygon layers. The package also includes several other utility functions for spatial analysis.
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
Contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN.
An R interface to the Lawson-Hanson implementation of an
        algorithm for non-negative least squares (NNLS).  Also allows
        the combination of non-negative and non-positive constraints.
Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.
Nonlinear nonparametric statistics using partial moments.  Partial moments are the elements of variance and asymptotically approximate the area of f(x).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.  NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance.  All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).
Contains the functions for testing the spatial patterns (of segregation, spatial symmetry, 
      association, disease clustering, species correspondence and reflexivity) based on nearest neighbor relations, 
      especially using contingency tables such as 
      nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and 
      Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein),
      nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>),
      species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) <doi:10.2436/20.8080.02.72>)
      for two (or higher) dimensional data. Also contains functions for generating patterns of segregation, association,
      uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>), 
      and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014)
      <doi:10.1002/sim.6053>), and for visualization of all these patterns for the two dimensional data.
      The tests are usually (asymptotic) normal z-tests and chi-square tests.
Estimate the NNT using the proposed method in Yang and Yin's paper (2019)
  <doi:10.1371/journal.pone.0223301>, in which the NNT-RMST (number needed to 
  treat based on the restricted mean survival time) is defined as the RMST (restricted 
  mean survival time) in the control group divided by the difference in RMSTs 
  between the treatment and control groups up to a chosen time t.
Helps a clinical trial team  discuss
    the clinical goals  of a well-defined biomarker with a diagnostic,
    staging, prognostic, or predictive purpose.  From this discussion will
    come a statistical plan for a (non-randomized) validation trial.
    Both prospective and retrospective trials are supported. In a specific
    focused discussion, investigators should determine the range of
    "discomfort" for the NNT, number needed to treat.  The meaning of
    the discomfort range, [NNTlower, NNTupper], is that within this range
    most physicians would feel discomfort either in treating or withholding
    treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg,
    become the targets of the study's design.  If the trial can demonstrate
    that a positive biomarker test yields an NNT less than NNTlower,
    and that a negative biomarker test yields an NNT less than NNTlower,
    then the biomarker may be useful for patients. A highlight of the package
    is visualization of a "contra-Bayes" theorem, which produces criteria for
    retrospective case-controls studies.
Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.
Functions for revealing what happens when effect size estimates 
    from previous studies are taken into account when evaluating each new dataset 
    in a study sequence. The analyses can be conducted for cumulative
    meta-analyses and for Bayesian data analyses. The package contains sample 
    data for a wide selection of research topics. Jointly considering 
    previous findings along with new data is more likely to result in correct 
    conclusions than does the traditional practice of not incorporating previous
    findings, which often results in a back and forth ping-pong of conclusions 
    when evaluating a sequence of studies.
    O'Connor & Ermacora (2021, <doi:10.3758/bf03200807>).
Provides a small set of tools for collecting data from National 
    Oceanic and Atmospheric Administration (NOAA) data sources.  The functions 
    provided in the package are wrappers around NOAA's existing APIs which is
    found at <https://api.tidesandcurrents.noaa.gov/api/prod/>.
Allows users to explore and plot data from the
    National Oceanic and Atmospheric Administration (NOAA) 
    Storm Events database through R for United States counties. 
    Functionality includes matching storm event listings by time and 
    location to hurricane best tracks data. This work was 
    supported by grants from the Colorado Water Center, the National Institute 
    of Environmental Health Sciences (R00ES022631) and the National Science 
    Foundation (1331399).
Generate pseudonymous animal names that are delightful and easy to 
    remember like the Likable Leech and the Proud Chickadee. A unique pseudonym 
    can be created for every unique element in a vector or row in a data frame. 
    Pseudonyms can be customized and tracked over time, so that the same input 
    is always assigned the same pseudonym.
Designed to be compatible with the 'R' package 'DBI' (Database Interface)
    when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>.
    To do this the 'R' 'AWS' Software Development Kit ('SDK') 'paws' 
    <https://github.com/paws-r/paws> is used as a driver.
Simplified document database access and manipulation,
     providing a common API across supported 'NoSQL' databases 
     'Elasticsearch', 'CouchDB', 'MongoDB' as well as 
     'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.
Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.
Simulate DNA sequences for the node substitution model.
    In the node substitution model, substitutions accumulate additionally 
    during a speciation event, providing a potential mechanistic explanation for 
    substitution rate variation. This package provides tools to simulate
    such a process, simulate a reference process with only substitutions along
    the branches, and provides tools to infer phylogenies from alignments. More
    information can be found in Janzen (2021) <doi:10.1093/sysbio/syab085>.
An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. <DOI:10.1111/2041-210X.12283>. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped using the SOS statistic.
Provides a compact variation of the usual syntax of function
  declaration, in order to support tidyverse-style quasiquotation of a
  function's arguments and body.
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).
Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186.  Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.
Implementation of models for the controlled introduction of errors in 
	classification datasets. This package contains the noise models described in 
	Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, 
	attributes and both simultaneously.
Cross-Entropy optimisation of unconstrained deterministic and noisy
    functions illustrated in Rubinstein and Kroese (2004, ISBN:
    978-1-4419-1940-3) through a highly flexible and customisable function which 
    allows user to define custom variable domains, sampling distributions,
    updating and smoothing rules, and stopping criteria. Several built-in
    methods and settings make the package very easy-to-use under standard
    optimisation problems.
Quantifies and removes technical noise from high-throughput 
        sequencing data. Two approaches are used, one based on the count
        matrix, and one using the alignment BAM files directly.
        Contains several options for every step of the process, as well
        as tools to quality check and assess the stability of output.
Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph 
    and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying 
    binary graph. This procedure comes with a control of the false discovery rate. The method is described
    in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, 
    E. Roquain, F. Villers (2020) <arXiv:1907.10176>.
Converts numeric vectors to character vectors of English
    number names. Provides conversion to cardinals, ordinals, numerators,
    and denominators. Supports negative and non-integer numbers.
Similarity measures for hierarchical clustering of objects characterized by
    nominal (categorical) variables. Evaluation criteria for nominal data clustering.
Lite interface for getting data from 'OSM' service
    'Nominatim' <https://nominatim.org/release-docs/latest/>. Extract
    coordinates from addresses, find places near a set of coordinates,
    search for amenities and return spatial objects on 'sf' format.
Access UK official statistics from the 'Nomis' database. 
    'Nomis' includes data from the Census, the Labour Force Survey, DWP benefit 
    statistics and other economic and demographic data from the Office for 
    National Statistics, based around statistical geographies. See 
    <https://www.nomisweb.co.uk/api/v01/help> for full API documentation.
A tool for drawing sassy 'UML' diagrams based on a simple syntax,
  see <https://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams 
  in the PNG format.
A nomogram can not be easily applied,
    because it is difficult to calculate the points or even the survival probability.
  The package, including a function of nomogramEx(),
    is to extract the polynomial equations to calculate the points of each variable,
    and the survival probability corresponding to the total points.
A nomogram, which can be carried out in 'rms' package, provides a 
    graphical explanation of a prediction process. However, it is not very easy
    to draw straight lines, read points and probabilities accurately. Even, it 
    is hard for users to calculate total points and probabilities for all 
    subjects.
  This package provides formula_rd() and formula_lp() functions to fit the 
    formula of total points with raw data and linear predictors respectively by
    polynomial regression. Function points_cal() will help you calculate the 
    total points. prob_cal() can be used to calculate the probabilities after
    lrm(), cph() or psm() regression. 
  For more complex condition, interaction or restricted cubic spine, TotalPoints.rms() 
    can be used.
Implements the nomord_probe() function, which performs a statistical analysis on an input vector (sequence) of nominal (categorical) values.
Conduct a noncompartmental analysis with industrial strength.
             Some features are
             1) Use of CDISC SDTM terms
             2) Automatic or manual slope selection
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
A finite-population significance test of the 'sharp' causal null hypothesis that
    treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers.
    A generalized likelihood ratio test statistic is used, and the resulting p-value is exact.
    Currently, it is assumed that there are only Compliers and Never Takers in the population.
Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.
It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.
Performs 'nonlinear Invariant Causal Prediction' to estimate the 
    causal parents of a given target variable from data collected in
    different experimental or environmental conditions, extending
    'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016), 
    <arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml, 
    J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models', 
    <arXiv:1706.08576>.
Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.
Functions for nonlinear time series analysis. This package permits
    the computation of the  most-used nonlinear statistics/algorithms
    including generalized correlation dimension, information dimension,
    largest Lyapunov exponent, sample entropy and Recurrence
    Quantification Analysis (RQA), among others. Basic routines
    for surrogate data testing are also included. Part of this work
    was based on the  book "Nonlinear time series analysis" by
    Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Loading NONMEM (NONlinear Mixed-Effect Modeling, <https://www.iconplc.com/innovation/nonmem/>) and PSN (Perl-speaks-NONMEM, <https://uupharmacometrics.github.io/PsN/>) output files to extract parameter estimates, provide visual predictive check (VPC) and goodness of fit (GOF) plots, and simulate with parameter uncertainty.
Systematically creates and modifies NONMEM(R) control streams. Harvests
 NONMEM output, builds run logs, creates derivative data, generates diagnostics.
 NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for 
 nonlinear mixed effects modeling. See 'package?nonmemica'.
Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0),
  using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in
  (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).
Testing non-nested models via theory supplied by Vuong (1989) <DOI:10.2307/1912557>.
    Includes tests of model distinguishability and of model fit that can be applied
    to both nested and non-nested models. Also includes functionality to obtain
    confidence intervals associated with AIC and BIC. This material is partially based on
    work supported by the National Science Foundation under Grant Number SES-1061334.
Implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardia’s Pareto of Type I, Logistic, Burr, Cook-Johnson’s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.
Contains the following 5 nonparametric hypothesis tests:
    The Sign Test,
    The 2 Sample Median Test,
    Miller's Jackknife Procedure,
    Cochran's Q Test, &
    The Stuart-Maxwell Test.
Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.
Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.
Plot, process, and analyze NPO files produced by
    'Nonpareil' <http://enve-omics.ce.gatech.edu/nonpareil/>.
Estimates and plots (as a single plot and as a heat map) the rolling window correlation coefficients between two time series and computes their statistical significance, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) <doi:10.1016/j.ecoinf.2021.101379>.
Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA).
    The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitz–Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables.
    Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities.
    The package 'NonProbEst' implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.
A non-parametric test for multi-observer concordance and
    differences between concordances in (un)balanced data.
Estimation of party/candidate ideological positions
             that correspond to a Nash equilibrium along a 
             one-dimensional space.
Onedimensional Normal (i.e. Gaussian) Mixture Models Classes,
   for, e.g., density estimation or clustering algorithms research and teaching;
   providing the widely used Marron-Wand densities.  Efficient random
   number generation and graphics. Fitting to data by efficient ML (Maximum
   Likelihood) or traditional EM estimation.
Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes for use with 'ggplot2' via custom functions.
The Nordklim dataset 1.0 is a unique and useful achievement for climate 
    analysis. It includes observations of twelve different climate elements from 
    more than 100 stations in the Nordic region, in time span over 100 years.
    The project contractors were NORDKLIM/NORDMET on behalf of the National 
    meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), 
    Norway (DNMI) and Sweden (SMHI).
Regional granularity levels in Norway which are depicted by different codes, 
             have undergone several changes over the years. 
             Identifying when codes have changed and how many changes have taken place
             can be troublesome. This package will help to identify these changes and when the changes
             have taken place. One of the limitation of this package is that it is heavily depending 
             on the codes available from SSB website <https://data.ssb.no/api/klass/v1/api-guide.html>.
An integrated set of functions for the analysis of 
  multivariate normal datasets with missing values, including implementation of
  the EM algorithm, data augmentation, and multiple imputation.
Functions for parameter estimation, Bayesian posterior simulation
        and multiple imputation from incomplete multivariate data under a
        normal model.
Builds general noise SVR models using Naive Online R Minimization Algorithm, NORMA, an optimization method based on classical stochastic gradient descent suitable for computing SVR models in an online setting.
Package including an interactive Shiny application for
    testing normality visually.
Normalize a given Hadamard matrix. A Hadamard matrix is said to be normalized when its first row and first column entries are all 1, see Hedayat, A. and Wallis, W. D. (1978) "Hadamard matrices and their applications. The Annals of Statistics, 1184-1238." <doi:10.1214/aos/1176344370>.
Functions for the normal Laplace distribution. The package is
        under development and provides only limited functionality.
	Density, distribution and quantile functions, random number generation,
	and moments are provided.
A collection of utilities referred to Exponential Power distribution, also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4).
The robustness of many of the statistical techniques, such as factor analysis, applied in 
          the social sciences rests upon the assumption of item-level normality. However, when dealing 
          with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964)
          <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for 
          large datasets of continuous variables, its application in current software programs is cumbersome
          with analysts having to take several steps to normalise each variable. We present an R package 
          'normalr' that enables researchers to make convenient optimal transformations of multiple variables
          in datasets. This R package enables users to quickly and accurately: (1) anchor all of their 
          variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, 
          (3) apply each unique exponent to its variable, (4) rescale resultant values to within their 
          original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, 
          kurtosis, and other inferential assessments of normality.
It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets.
             Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) <doi:10.1101/251140>.
Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) <doi:10.1159/000365637> for details.
Five omnibus tests for testing the composite hypothesis of
        normality.
Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.
Despite that several tests for normality in stationary processes have been proposed
   in the literature, consistent implementations of these tests in programming languages are limited. 
   Four normality test are implemented. The Lobato and Velasco's, Epps, Psaradakis and  Vavra, and the 
   random projections tests for stationary process. Some other diagnostics such as, unit root test for 
   stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. 
   The package also offers residual diagnostic for linear time series models developed in several packages.
The nose package consists of a collection of three
        functions for classifying sparseness in typical 2 x 2 data sets
        with at least one cell should have zero count. These functions
        are based on the three widely applied summary measures for 2 x
        2 categorical data viz, Risk Difference (RD), Relative Risk
        (RR), Odds Ratio (OR). This package helps to identify suitable
        continuity correction for zero cells when a multi centre
        analysis or a meta analysis is carried out. Further, it can be
        considered as a tool for sensitivity analysis for adding a
        continuity correction and to identify the presence of Simpson's
        paradox.
The aim of 'nosoi' (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. 'nosoi' is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.
Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) <doi:10.1111/rssb.12322>.
This is a pure dummy interfaces package which mirrors 'MsSparkUtils' APIs <https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/microsoft-spark-utilities?pivots=programming-language-r> of 'Azure Synapse Analytics' <https://learn.microsoft.com/en-us/azure/synapse-analytics/> for R users,
    customer of Azure Synapse can download this package from CRAN for local development.
Functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.
Computes the probability density function, the cumulative density function, quantile function, random numbers and measures of inference for the following families exponentiated generalized gull alpha power family, exponentiated gull alpha powerfamily, gull alpha power family.
The raw dataset and model used in Lai et al. (2021) 
    Decoupled responses of native and exotic tree diversities to 
    distance from old-growth forest and soil phosphorous in 
    novel secondary forests. Applied Vegetation Science, 24, e12548.
The noweb system for source code, implemented in R.
The Nozzle package provides an API to generate HTML
        reports with dynamic user interface elements based on
        JavaScript and CSS (Cascading Style Sheets). Nozzle was
        designed to facilitate summarization and rapid browsing of
        complex results in data analysis pipelines where multiple
        analyses are performed frequently on big data sets. The package
        can be applied to any project where user-friendly reports need
        to be created.
Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.
Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) <doi: 10.1016/j.mex.2016.05.006> and van Someren et al. (1999) <doi: 10.3109/07420529908998724>. Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.
Performs nonparametric
        analysis of longitudinal data in factorial experiments.
        Longitudinal data are those which are collected from the same
        subjects over time, and they frequently arise in biological
        sciences. Nonparametric methods do not require distributional
        assumptions, and are applicable to a variety of data types
        (continuous, discrete, purely ordinal, and dichotomous).  Such
        methods are also robust with respect to outliers and for small
        sample sizes.
Analysis of multivariate data with two-way completely randomized
    factorial design. The analysis is based on fully nonparametric, rank-based
    methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda,
    Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate
    response is allowed to be ordinal, quantitative, binary or a mixture of the
    different variable types. The package offers two functions performing the
    analysis, one for small and the other for large sample sizes. The underlying methodology
    is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in 
    Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8>.
Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.
These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
Nonparametric smoothing methods for density and regression estimation involving circular data, 
				including the estimation of the mean regression function and other conditional characteristics.
Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.
A non parametric test for change points detection in the dependence between the components of multivariate data, with or without (multiple) changes in the marginal distributions. The full details, justification and examples are published in Rohmer (2016) <doi:10.1016/j.spl.2016.06.026>.
An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.
Provides nonparametric CUSUM tests for detecting changes
  in possibly serially dependent univariate or low-dimensional
  multivariate observations. Retrospective tests sensitive to changes
  in the expectation, the variance, the covariance, the
  autocovariance, the distribution function, Spearman's rho, Kendall's
  tau, Gini's mean difference, and the copula are provided, as well as
  a test for detecting changes in the distribution of independent
  block maxima (with environmental studies in mind). The package also
  contains a test sensitive to changes in the autocopula and a
  combined test of stationarity sensitive to changes in the
  distribution function and the autocopula. The latest additions are
  an open-end sequential test based on the retrospective CUSUM
  statistic that can be used for monitoring changes in the mean of
  possibly serially dependent univariate observations, as well as
  closed-end and open-end sequential tests based on empirical
  distribution functions that can be used for monitoring changes in
  the contemporary distribution of possibly serially dependent
  univariate or low-dimensional multivariate observations.
We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021), which will be posted on arXiv soon.
Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.
Provides routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics.
Provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
             modeling oriented towards flow-cytometry data preprocessing applications. Method is 
             detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.
Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and 
	to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two
	hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted
	log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the
	separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) 
	<doi:10.1002/pst.2062>.
	Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference
	between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities,
	complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles,
	restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the
	Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate
	normal approximation to the set of selected parameters.
Provides functions and examples for histogram, kernel (classical, variable bandwidth and transformations based), discrete and semiparametric hazard rate estimators.
An R-package for calculating sample size of a survival trial with or without cure fractions.
Performs combination tests and sample size calculation for 
   fixed design with survival endpoints using combination tests under either
   proportional hazards or non-proportional hazards. The combination tests 
   include maximum weighted log-rank test and projection test. The sample 
   size calculation procedure is very flexible, allowing for user-defined
   hazard ratio function and considering various trial conditions like 
   staggered entry, drop-out etc. Trial simulation function is also provided 
   to facilitate the empirical power calculation. The references for 
   projection test and maximum weighted logrank test include Brendel et al. (2014)
   <doi:10.1111/sjos.12059> and Cheng and He (2021) <arXiv:2110.03833>. The 
   references for sample size calculation under proportional hazard include
   Schoenfeld (1981) <doi:10.1093/biomet/68.1.316> and Freedman (1982) <doi:10.1002/sim.4780010204>.
   The references for calculation under non-proportional hazards include 
   Lakatos (1988) <doi:10.2307/2531910> and Cheng and He (2021) (under review).
Perform a stratified weighted log-rank test in a randomized controlled trial. Tests can be visualized as a difference in average score on the two treatment arms. These methods are described in Magirr and Burman (2018) <arXiv:1807.11097v1>, Magirr (2020) <arXiv:2007.04767v1>, and Magirr and Jimenez (2022) <arXiv:2201.10445v1>.
Access the United States National Provider Identifier
    Registry API <https://npiregistry.cms.hhs.gov/api/>. Obtain and transform
    administrative data linked to a specific individual or organizational
    healthcare provider, or perform advanced searches based on provider name,
    location, type of service, credentials, and other attributes exposed by
    the API.
Returns nonparametric aligned rank tests for the interaction in two-way factorial designs, on R data sets with repeated measures in 'wide' format. Five ANOVAs tables are reported. A PARAMETRIC one on the original data, one for a CHECK upon the interaction alignments, and three aligned rank tests: one on the aligned REGULAR, one on the FRIEDMAN, and one on the KOCH ranks. In these rank tests, only the resulting values for the interaction are relevant.
An implementation of the Nonparametric Predictive Inference approach in R. It provides tools for quantifying uncertainty via lower and upper probabilities. It includes useful functions for pairwise and multiple comparisons: comparing two groups with and without terminated tails, selecting the best group, selecting the subset of best groups, selecting the subset including the best group.
Provides routines for plotting
      linkage and association results along a chromosome,
      with marker names displayed along the top border.
      There are also routines for generating BED and BedGraph
      custom tracks for viewing in the UCSC genome browser.
      The data reformatting program Mega2 uses this
      package to plot output from a variety of
      programs.
Performing drug response analyses and IC50 estimations using n-Parameter logistic regression. Can also be applied to proliferation analyses.
Provides functions for manipulating nested data frames in a 
    list-column using 'dplyr' <https://dplyr.tidyverse.org/> syntax. Rather than 
    unnesting, then manipulating a data frame, 'nplyr' allows users to 
    manipulate each nested data frame directly. 'nplyr' is a wrapper for 'dplyr'
    functions that provide tools for common data manipulation steps: filtering 
    rows, selecting columns, summarising grouped data, among others.
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. 
             Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods
             for the conditional-mean and conditional-distribution based models with longitudinal Data.
Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).
Nonparametric maximum likelihood estimation or Gaussian
        quadrature for overdispersed generalized linear models and
        variance component models.
Fit multinomial logistic regression with a penalty on the nuclear
    norm of the estimated regression coefficient matrix, using proximal
    gradient descent. Reference: Powers S, Hastie T and Tibshirani R
    (2018) <doi:10.1177/1471082X18777669>.
Performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks' Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable.  The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.
Performs nonparametric tests for equality of location against ordered alternatives.
Posterior sampling in several commonly used distributions using
    normalized power prior as described in 
    Duan, Ye and Smith (2006) <doi:10.1002/env.752> and 
    Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. 
    Sampling of the power parameter is achieved via 
    either independence Metropolis-Hastings or random walk Metropolis-Hastings 
    based on transformation.
Implements a nonparametric statistical test for rank or score data
 from partially-balanced incomplete block-design experiments.
Calculates phenological cycle and anomalies using a non-parametric
    approach applied to time series of vegetation indices derived from remote sensing data 
    or field measurements. The package implements basic and high-level functions for 
    manipulating vector data (numerical series) and raster data (satellite derived products).
    Processing of very large raster files is supported. For more information, please check 
    the following paper:
    Estay, S., Chávez, R.O. (2018) <doi:10.1101/301143>.
Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.
Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors, interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for prediction and inference.
Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.
A method for obtaining nonparametric estimates of regression models
    with or without factor-by-curve interactions using local polynomial kernel
    smoothers or splines. Additionally, a parametric model (allometric model) can be
    estimated.
Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>): lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).
In many binary classification applications, such as disease
    diagnosis and spam detection, practitioners commonly face the need to limit
    type I error (i.e., the conditional probability of misclassifying a class 0
    observation as class 1) so that it remains below a desired threshold. To address
    this need, the Neyman-Pearson (NP) classification paradigm is a natural choice;
    it minimizes type II error (i.e., the conditional probability of misclassifying
    a class 1 observation as class 0) while enforcing an upper bound, alpha, on the
    type I error. Although the NP paradigm has a century-long history in hypothesis
    testing, it has not been well recognized and implemented in classification
    schemes. Common practices that directly limit the empirical type I error to
    no more than alpha do not satisfy the type I error control objective because
    the resulting classifiers are still likely to have type I errors much larger
    than alpha. As a result, the NP paradigm has not been properly implemented
    for many classification scenarios in practice. In this work, we develop the
    first umbrella algorithm that implements the NP paradigm for all scoring-type
    classification methods, including popular methods such as logistic regression,
    support vector machines and random forests. Powered by this umbrella algorithm,
    we propose a novel graphical tool for NP classification methods: NP receiver
    operating characteristic (NP-ROC) bands, motivated by the popular receiver
    operating characteristic (ROC) curves. NP-ROC bands will help choose in a data
    adaptive way and compare different NP classifiers.
Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.
Fits sphere-sphere regression models by estimating locally weighted
    rotations. Simulation of sphere-sphere data according to non-rigid rotation
    models. Provides methods for bias reduction applying iterative procedures
    within a Newton-Raphson learning scheme. Cross-validation is exploited to select
    smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor
    (2018) <doi:10.1080/01621459.2017.1421542>.
Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.
An R implementation of the Necessary and Probably Sufficient (NPS) test for finding valid instrumental variables, as suggested by Amit Sharma (2016, Working Paper) <http://amitsharma.in/pubs/necessary_probably_sufficient_iv_test.pdf>. The NPS test, compares the likelihood that a given set of observational data of the three variables Z, X and Y is generated by a valid instrumental variable model (Z -> X -> Y) to the likelihood that the data is generated by an invalid IV model.
Non-parametric survival analysis of exact and
	     interval-censored observations. The methods implemented
	     are developed by Wang (2007)
	     <doi:10.1111/j.1467-9868.2007.00583.x>, Wang (2008)
	     <doi:10.1016/j.csda.2007.10.018>, Wang and Taylor (2013)
	     <doi:10.1007/s11222-012-9341-9> and Wang and Fani (2018)
	     <doi:10.1007/s11222-017-9724-z>.
A number of statistical tests have been proposed to compare two 
  survival curves, including the difference in (or ratio of) t-year 
  survival, difference in (or ratio of) p-th percentile survival, difference in
  (or ratio of) restricted mean survival time, and the weighted log-rank test. 
  Despite the multitude of options, the convention in survival studies is to assume
  proportional hazards and to use the unweighted log-rank test for design and 
  analysis. This package provides sample size and power 
  calculation for all of the above statistical tests with allowance for 
  flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, 
  mixture cure). It is the companion R package to the paper by Yung and Liu (2019)
  <doi:10.1111/biom.13196>. Specific to the weighted log-rank test, users may 
  specify which approximations they wish to use to estimate the large-sample mean 
  and variance. The default option has been shown to provide substantial
  improvement over the conventional sample size and power equations based on Schoenfeld 
  (1981) <doi:10.1093/biomet/68.1.316>.
Robust nonparametric bootstrap and permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the 'parallel' package.
Implements the procedure from G. J. Ross (2021) - "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <arxiv:2107.01742>. This uses a version of Wild Binary Segmentation to detect multiple location-scale (i.e. mean and/or variance) change points in a sequence of univariate observations, with a strict control on the probability of incorrectly detecting a change point in a sequence which does not contain any.
Facilitates nonresponse bias analysis (NRBA) 
    for survey data.  Such data may arise from a complex
    sampling design with features such as stratification, clustering, or
    unequal probabilities of selection. Multiple types of analyses may be
    conducted: comparisons of response rates across subgroups; comparisons
    of estimates before and after weighting adjustments; comparisons of
    sample-based estimates to external population totals; tests of
    systematic differences in covariate means between respondents
    and full samples; tests of independence between response status
    and covariates; and modeling of outcomes and response status as a 
    function of covariates. Extensive documentation and references are
    provided for each type of analysis. Krenzke, Van de Kerckhove,
    and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>
    and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>
    provide an overview of the methods implemented in this package.
Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence  of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) <doi:10.1093/jssam/smu026> and nonstationary generalized linear mixed model was developed by Hukum  Chandra, Nicola Salvati and Ray Chambers (2017) <doi:10.1016/j.spasta.2017.01.004>. This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress.
Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.
Two implementations of canonical correlation analysis
        (CCA) that are based on iterated regression. By choosing the
        appropriate regression algorithm for each data domain, it is
        possible to enforce sparsity, non-negativity or other kinds of
        constraints on the projection vectors. Multiple canonical
        variables are computed sequentially using a generalized
        deflation scheme, where the additional correlation not
        explained by previous variables is maximized. nscancor() is
        used to analyze paired data from two domains, and has the same
        interface as cancor() from the 'stats' package (plus some extra
        parameters). mcancor() is appropriate for analyzing data from
        three or more domains. See
        <https://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/>
        and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more
        details.
Simulation and estimation for Neyman-Scott spatial cluster point
 process models and their extensions, based on the methodology in Tanaka, Ogata,
 and Stoyan (2008) <doi:10.1002/bimj.200610339>. To estimate parameters by the
 simplex method, parallel computation using 'OpenMP' application programming
 interface is available. For more details see Tanaka, Saga and Nakano
 <doi:10.18637/jss.v098.i06>.
Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Fetch data related to stocks, index, futures & options from the 
    'NSE (National Stock Exchange, India)'. This package is community maintained 
    and is not officially supported by 'NSE'. The accuracy of data is only as 
    correct as provided on <https://www.nseindia.com>.
Functions to capture, inspect, manipulate, and create
             lazy values (promises), "..." lists, and active calls.
Box-constrained multiobjective optimization using the 
        elitist non-dominated sorting genetic algorithm - NSGA-II.
        Fast non-dominated sorting, crowding distance, tournament 
        selection, simulated binary crossover, and polynomial 
        mutation are called in the main program. The methods are 
        described in Deb et al. (2002) <doi:10.1109/4235.996017>.
An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective 
    feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm
    that solves multiple optimization problems simultaneously by applying a non-dominated sorting 
    technique. It uses a reference points based selection operator to explore 
    solution space and preserve diversity. See the original paper by K. Deb and 
    H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.
Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods.  Exact procedures are performed when computationally possible.  Monte Carlo and Asymptotic procedures are performed otherwise.  For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition.
National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access to official data via its API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.
Implementation of Narrowest Significance Pursuit, a general and
    flexible methodology for automatically detecting localised regions in data sequences
    which each must contain a change-point (understood as an abrupt change in the
    parameters of an underlying linear model), at a prescribed global significance level.
    Narrowest Significance Pursuit works with a wide range of distributional assumptions
    on the errors, and yields exact desired finite-sample coverage probabilities,
    regardless of the form or number of the covariates. For details, see P. Fryzlewicz
    (2021) <https://stats.lse.ac.uk/fryzlewicz/nsp/nsp.pdf>.
Mainly for maximum likelihood estimation of nonparametric
             and semiparametric mixture models, but can also be used
             for fitting finite mixtures. The algorithms are
             developed in Wang (2007)
             <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010)
             <doi:10.1007/s11222-009-9117-z>.
Two methods for performing a constrained principal
        component analysis (PCA), where non-negativity and/or sparsity
        constraints are enforced on the principal axes (PAs). The
        function 'nsprcomp' computes one principal component (PC) after
        the other. Each PA is optimized such that the corresponding PC
        has maximum additional variance not explained by the previous
        components. In contrast, the function 'nscumcomp' jointly
        computes all PCs such that the cumulative variance is maximal.
        Both functions have the same interface as the 'prcomp' function
        from the 'stats' package (plus some extra parameters), and both
        return the result of the analysis as an object of class
        'nsprcomp', which inherits from 'prcomp'. See
        <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/>
        and Sigg et al. (2008) <doi:10.1145/1390156.1390277> for more
        details.
A collection of statistical tools for objective (non-supervised) applications 
             of the Regional Frequency Analysis methods in hydrology. 
             The package refers to the index-value method and, more precisely, helps the
             hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions 
             with similar growth curves; (3) fit distribution functions to the 
             empirical regional growth curves.
             Most of the methods are those described in the Flood Estimation Handbook 
            (Centre for Ecology & Hydrology, 1999, ISBN:9781906698003).
             Homogeneity tests from Hosking and Wallis (1993) <doi:10.1029/92WR01980> 
             and Viglione et al. (2007) <doi:10.1029/2006WR005095> are available.
Tools for estimating Receiver Operating Characteristic (ROC) curves,
        building confidence bands, comparing several curves both for dependent and 
        independent data, estimating the cumulative-dynamic ROC curve in presence of
        censored data, and performing meta-analysis studies, among others.
To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) <doi:10.1073/pnas.1324044111>. NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) <doi:10.1073/pnas.1904623116>. A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) <doi:10.1016/j.soilbio.2020.108023>. SES is calculated as described in Kraft et al (2011) <doi:10.1126/science.1208584>. RC is calculated as reported by Chase et al (2011) <doi:10.1890/ES10-00117.1> and Stegen et al (2013) <doi:10.1038/ismej.2013.93>. Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) <doi:10.1038/s41467-020-18560-z>.
Counts syllables in character vectors for English words.  Imputes syllables as the number of vowel sequences for words not found.
Nonparametric test of independence between a pair of spatial objects
    (random fields, point processes) based on random shifts with torus or variance correction. See
    Mrkvička et al. (2021) <doi:10.1016/j.spasta.2020.100430>,
    Dvořák et al. (2022) <doi:10.1111/insr.12503>,
    Dvořák and Mrkvička (2022) <arxiv:2210.05424>.
Tools for 4D nucleome imaging. 
    Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. 
    See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>.
Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The executable of the model can downloaded from <https://github.com/jeroenpullens/NUCOMBog>.
Tools for visual inference. Generate null data sets
    and null plots using permutation and simulation. Calculate distance metrics
    for a lineup, and examine the distributions of metrics.
A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.
The number of distinct alleles observed in a DNA mixture is informative of the number of contributors to the mixture. The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree.
Provides number-theoretic functions for factorization, prime 
    numbers, twin primes, primitive roots, modular logarithm and
    inverses, extended GCD, Farey series and continued fractions.
    Includes Legendre and Jacobi symbols, some divisor functions,
    Euler's Phi function, etc.
Validate, format and compare identification numbers used in Brazil.
  These numbers are used to identify individuals (CPF), vehicles (RENAVAN),
  companies (CNPJ) and etc.
  Functions to format, validate and compare these numbers have been 
  implemented in a vectorized way in order to speed up
  validations and comparisons in big datasets.
Methods for calculating (usually) accurate
	numerical first and second order derivatives. Accurate calculations 
	are done using 'Richardson''s' extrapolation or, when applicable, a
	complex step derivative is available. A simple difference 
	method is also provided. Simple difference is (usually) less accurate
	but is much quicker than 'Richardson''s' extrapolation and provides a 
	useful cross-check. 
	Methods are provided for real scalar and vector valued functions.
High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Format numbers and plots for publication; includes the removal of leading zeros,
           standardization of number of digits, addition of affixes, and a p-value formatter. These
           tools combine the functionality of several 'base' functions such as 'paste()',
           'format()', and 'sprintf()' into specific use case functions that are named in a way
           that is consistent with usage, making their names easy to remember and easy to deploy.
A number series generator that creates number series items based on cognitive models.
To add the table of numbers at risk below the Kaplan-Meier plot.
Package for optimizing regular numeric problems in optically stimulated luminescence 
        dating, such as: equivalent dose calculation, dose rate determination, growth curve fitting,
        decay curve decomposition, statistical age model optimization, and statistical plot visualization.
Provides a tool set for food information and dietary assessment. It 
    uses food composition data from several reference databases, including: 'USDA' (United States), 
    'CIQUAL' (France), 'BEDCA' (Spain) and 'CNF' (Canada). 'NutrienTrackeR' calculates the intake 
    levels for both macronutrient and micronutrients, and compares them with the recommended 
    dietary allowances (RDA). It includes a number of visualization tools, such as time series 
    plots of nutrient intake, and pie-charts showing the main foods contributing to the intake 
    level of a given nutrient. A shiny app exposing the main functionalities of the package is also 
    provided.
Statistical tool for learning the structure of direct associations among variables for 
			 continuous data, discrete data and mixed discrete-continuous data. The package is based
			 on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.
Contains a collection of functions for performing different kinds
    of calculation that are of interest to someone following a diet plan.
    Calculators for the Basal Metabolic Rate are based on Mifflin et al. (1990)
    <doi:10.1093/ajcn/51.2.241> and  McArdle, W. D., Katch, F. I., &
    Katch, V. L. (2010, ISBN:9780812109917).
EM algorithm for fitting Bayesian varying coefficient models with the nonparametric varying coefficient spike-and-slab lasso of Bai et al. (2020) <arXiv:1907.06477>. Also fits penalized frequentist varying coefficient models with the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty.
The n-vector framework uses the normal vector to
    the Earth ellipsoid (called n-vector) as a non-singular position
    representation that turns out to be very convenient for practical
    position calculations.  The n-vector is simple to use and gives exact
    answers for all global positions, and all distances, for both
    ellipsoidal and spherical Earth models.  This package is a translation
    of the 'Matlab' library from FFI, the Norwegian Defence Research
    Establishment, as described in Gade (2010)
    <doi:10.1017/S0373463309990415>.
Functions for working with (grouped) multivariate normal variance mixture
  distributions (evaluation of distribution functions and densities,
  random number generation and parameter estimation), including
  Student's t distribution for non-integer degrees-of-freedom as well as the grouped t
  distribution and copula with multiple degrees-of-freedom parameters.
  See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.
Airline on-time data for all flights departing NYC in 2013.
    Also includes useful 'metadata' on airlines, airports, weather, and
    planes.
Convenient access to New Zealand election
    results by voting place.  Voting places have been matched to Regional Council,
    Territorial Authority, and Area Unit, to facilitate matching with additional data.
    Opinion polls since 2002 and some convenience analytical function are also supplied.
Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.
'LaBB-CAT' is a web-based language corpus management
 system developed by the New Zealand Institute of Language, Brain
 and Behaviour (NZILBB) - see <https://labbcat.canterbury.ac.nz>.
 This package defines functions for accessing corpus data in a 'LaBB-CAT'
 instance. You must have at least version 20230202.1600 of 'LaBB-CAT'
 to use this package.
 For more information about 'LaBB-CAT', see
 Robert Fromont and Jennifer Hay (2008) <doi:10.3366/E1749503208000142>
 or 
 Robert Fromont (2017) <doi:10.1016/j.csl.2017.01.004>.
Datasets of driving offences and fines in New Zealand between 2009 and 2017.
    Originally published by the New Zealand Police at
    <http://www.police.govt.nz/about-us/publication/road-policing-driver-offence-data-january-2009-december-2017>.
Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. 2020 <doi:10.1098/rsif.2020.0084>).
Provides functions to do 'O2PLS-DA' analysis for multiple omics data integration.
            The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" 
            which published by Johan Trygg and Svante Wold at 2003 <doi:10.1002/cem.775>. 
            'O2PLS' is a bidirectional multivariate regression method that aims to separate the covariance between
            two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 <doi:10.1002/cem.1388>; Löfstedt et al., 2012 <doi:10.1016/j.aca.2013.06.026>) 
            from the systematic sources of variance being specific for each data set separately.
Provides carefully chosen color palettes as used a.o. at OpenAnalytics <http://www.openanalytics.eu>.
A general purpose client to work with any 'OAI-PMH'
    (Open Archives Initiative Protocol for 'Metadata' Harvesting) service.
    The 'OAI-PMH' protocol is described at
    <http://www.openarchives.org/OAI/openarchivesprotocol.html>.
    Functions are provided to work with the 'OAI-PMH' verbs: 'GetRecord',
    'Identify', 'ListIdentifiers', 'ListMetadataFormats', 'ListRecords', and
    'ListSets'.
Harvest metadata using the Open Archives Initiative Protocol for Metadata
  Harvesting (OAI-PMH) version 2.0 (for more information, see
  <https://www.openarchives.org/OAI/openarchivesprotocol.html>).
Offers a suite of functions for enhancing R plots.
Implements the efficient algorithm by Ortmann and Brandes (2017)
  <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., 'igraph'), the orbit-aware counts are computed respective each of the edges and nodes.
Generalise the starting point of the array index.
A set of indexes and tests for the analysis of social segregation.
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Data processing, visualisation and analysis of Limit Order Book
    event data.
It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <arXiv:2004.04341>.
This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun().
When people make decisions, they may do so using a wide variety of decision rules. The package allows users to easily create obfuscation games to test the obfuscation hypothesis. It provides an easy to use interface and multiple options designed to vary the difficulty of the game and tailor it to the user's needs. For more detail: Chorus et al., 2021, Obfuscation maximization-based decision-making: Theory, methodology and first empirical evidence, Mathematical Social Sciences, 109, 28-44.
The Open Bodem Index (OBI) is a method to evaluate the quality of soils of agricultural fields in The Netherlands and the sustainability of the current agricultural practices.
    The OBI score is based on four main criteria: chemical, physical, biological and management, which consist of more than 21 indicators. 
    By providing results of a soil analysis and management info the 'OBIC' package can be use to calculate he scores, indicators and derivatives that are used by the OBI.
    More information about the Open Bodem Index can be found at <https://www.openbodemindex.nl/>.
Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.
An 'RStudio' addin to assist with removing objects from the global environment. Features include removing objects according to name patterns and object type. During the course of an analysis, temporary objects are often created and this tool assists with removing them quickly. This can be useful when memory management within 'R' is important.
A mutable Signal object can report changes to its state,
    clients could register functions so that they are called whenever
    the signal is emitted. The signal could be emitted, disconnected,
    blocked, unblocked, and buffered.
Obtain optimum block from Non-overlapping Block Bootstrap method.
Three-dimensional rendering for 'grid' and 'ggplot2' graphics using cubes and cuboids drawn with an oblique projection.  As a special case also supports primary view orthographic projections.  Can be viewed as an extension to the 'isocubes' package <https://github.com/coolbutuseless/isocubes>.
Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.
Applies an objective Bayesian method to the Mb capture-recapture model to estimate the population size N.  The Mb model is a class of capture-recapture methods used to account for variations in capture probability due to animal behavior.  Under the Mb formulation,  the initial capture of an animal may effect the probability of subsequent captures due to their becoming "trap happy" or "trap shy."
Two-part system for first collecting then managing direct
    observation data, as described by Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
Checks that a given dataset passes user-specified 
    rules. The main functions are observe_if() and inspect().
Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.
Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor.  If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions).  Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change.  This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.
Estimates optimal classification (Poole 2000) <doi:10.1093/oxfordjournals.pan.a029814>
		scores from roll call votes supplied though a 'rollcall'
		object from package 'pscl'.
Computes optimal capital allocations based on some standard principles such as Haircut, Overbeck type II and the Covariance Allocation Principle. It also provides some shortcuts for obtaining the Value at Risk and the Expectation Shortfall, using both the normal and the t-student distribution, see Urbina and Guillén (2014)<doi:10.1016/j.eswa.2014.05.017> and Urbina (2013)<http://hdl.handle.net/2099.1/19443>.
Generic function for estimating positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fittings methods include the simple 'reference region' and 'ordinary least squares' (sometimes known as occupancy plot) methods, as well as the more efficient 'restricted maximum likelihood estimation'.
Facilitates the gathering of biodiversity occurrence data 
  from disparate sources. Metadata is managed throughout the process to facilitate 
  reporting and enhanced ability to repeat analyses.
Repeatable processing of species occurrence datasets that makes it easier to propagate 
    georeferencing imprecisions and data input mistakes to downstream analyses, allowing analysts to assess 
    the impacts of these imprecisions in quantifying range of occurrence (EOO) or  area of occupancy (AOO) . Users can use the software to: 
    (a) change each coordinate record's uncertainty from meters to decimal degrees,
    The formula for converting from meters to decimal degrees is in part based on information from the ESRI ArcUser magazine "Measuring in Arc-Seconds" at this site<https://www.esri.com/news/arcuser/0400/wdside.html>
    (b) deal with records that don't have uncertainty values in multiple ways, 
    (c) create a new random location for each occurrence using a uniform distribution with a defined interval 
    within the occurrence location uncertainty, and 
    (d) use repetitions to quantify EOO and AOO with attribute uncertainty.
The classical and extended occupancy distributions occur in cases where balls are randomly allocated 
    to bins. The PDF, CDF, quantile functions, generation of random variates, and calculating 
    the first four central moments of the distributions are implemented as described in 
    O’Neill (2019) <doi:10.1080/00031305.2019.1699445>.
Perform interactive occupation coding during interviews as
    described in Peycheva, D., Sakshaug, J., Calderwood, L. (2021) <doi:10.2478/jos-2021-0042>
    and Schierholz, M., Gensicke, M., Tschersich, N., Kreuter, F. (2018) <doi:10.1111/rssa.12297>.
    Generate suggestions for occupational categories based on free text
    input, with pre-trained machine learning models in German and a ready-to-use
    shiny application provided for quick and easy data collection.
Implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.
Supports the analysis of Oceanographic data, including 'ADCP'
    measurements, measurements made with 'argo' floats, 'CTD' measurements,
    sectional data, sea-level time series, coastline and topographic data, etc.
    Provides specialized functions for calculating seawater properties such as
    potential temperature in either the 'UNESCO' or 'TEOS-10' equation of state.
    Produces graphical displays that conform to the conventions of the
    Oceanographic literature. This package is discussed extensively by
    Kelley (2018) "Oceanographic Analysis with R" <doi:10.1007/978-1-4939-8844-0>.
Provides tools for easy exploration of the world ocean atlas of 
  the US agency National Oceanic and Atmospheric Administration (NOAA). It 
  includes functions to extract NetCDF data from the repository and code to 
  visualize several physical and chemical parameters of the ocean. A Shiny app 
  further allows interactive exploration of the data. The methods for data 
  collecting and quality checks are described in several papers, which can be
  found here: <https://www.ncei.noaa.gov/products/world-ocean-atlas>.
Determine the sea area where the fishing boat operates. 
             The latitude and longitude of geographic coordinates are used to match oceanic areas and economic sea areas. 
	    	     You can plot the distribution map with dotplot() function.
		         Please refer to Flanders Marine Institute (2020) <doi:10.14284/403>.
Creating maps for statistical analysis such as proportional circles, choropleth, typology and flows. Some functions use 'shiny' or 'leaflet' technologies for dynamism and interactivity.
	The great features are :
	- Create maps in a web environment where the parameters are modifiable on the fly ('shiny' and 'leaflet' technologies).
	- Create interactive maps through zoom and pop-up ('leaflet' technology).
	- Create frozen maps with the possibility to add labels.
Plotting toolbox for 2D oceanographic data (satellite data, sea surface temperature, chlorophyll, ocean fronts & bathymetry). Recognized classes and formats include netcdf, Raster, '.nc' and '.gz' files.
Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.
Calculate ocean wave height summary statistics and process data 
    from bottom-mounted pressure sensor data loggers. Derived primarily from 
    MATLAB functions provided by U. Neumeier at 
    <http://neumeier.perso.ch/matlab/waves.html>. Wave number 
    calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) 
    "Direct Solution of Wave Dispersion Equation", American Society of Civil 
    Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, 
    Vol 105, pp 457-459.
Estimates win ratio or Mann-Whitney parameter for two group
 comparisons using ordered composite endpoints with right censoring
 as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.
Several Oceanographic data sets are provided for use
    by the 'oce' package and for other purposes.
Extends 'flexclust' with an R implementation of order constrained
  solutions in k-means clustering (Steinley and Hubert, 2008, <doi:10.1007/s11336-008-9058-z>).
Provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2022) <arXiv:1907.01136>.
Generate and analyze Optimal Channel Networks (OCNs): 
	oriented spanning trees reproducing all scaling features characteristic 
	of real, natural river networks. As such, they can be used in a variety 
	of numerical experiments in the fields of hydrology, ecology and 
	epidemiology. See Carraro et al. (2020) <doi:10.1002/ece3.6479> 
	for a presentation of the package; Rinaldo et al. (2014) 
	<doi:10.1073/pnas.1322700111> for a theoretical overview on the OCN 
	concept; Furrer and Sain (2010) <doi:10.18637/jss.v036.i10> for the 
	construct used.
Regression model where the response variable is a rank-indexed compositional vector (non-negative values that sum up to one and are ordered from the largest to the smallest). Parameters are estimated in the Bayesian framework using MCMC methods.
Implements the Bayesian online changepoint
     detection method by Adams and MacKay (2007) 
     <arXiv:0710.3742> for univariate or multivariate data.
     Gaussian and Poisson probability models are implemented.
      Provides post-processing functions with 
      alternative ways to extract changepoints.
Provides an Interface to Open Collaboration Services 'OCS' (<https://www.open-collaboration-services.org/>) REST API.
Provides functions to access and download data from the 'Open Case Studies' <https://www.opencasestudies.org/> 
    repositories on 'GitHub' <https://github.com/opencasestudies>. Different functions enable 
    users to grab the data they need at different sections in the case study, as well as 
    download the whole case study repository. All the user needs to do is input the name of 
    the case study being worked on. The package relies on the httr::GET() function to access
    files through the 'GitHub' API. The functions usethis::use_zip() and usethis::create_from_github() 
    are used to clone and/or download the case study repositories. To cite an individual case study,
    please see the respective 'README' file at <https://github.com/opencasestudies/>.
    <https://github.com/opencasestudies/ocs-bp-rural-and-urban-obesity> 
    <https://github.com/opencasestudies/ocs-bp-air-pollution>
    <https://github.com/opencasestudies/ocs-bp-vaping-case-study>
    <https://github.com/opencasestudies/ocs-bp-opioid-rural-urban>
    <https://github.com/opencasestudies/ocs-bp-RTC-wrangling>
    <https://github.com/opencasestudies/ocs-bp-RTC-analysis>
    <https://github.com/opencasestudies/ocs-bp-youth-disconnection>
    <https://github.com/opencasestudies/ocs-bp-youth-mental-health>
    <https://github.com/opencasestudies/ocs-bp-school-shootings-dashboard>
    <https://github.com/opencasestudies/ocs-bp-co2-emissions>
    <https://github.com/opencasestudies/ocs-bp-diet>.
A database management tool built as a 'shiny' application. Connect to various 
    databases to send queries, upload files, preview tables, and more.
This package is a simple wrapper for the 'Octopus Energy' API
    <https://developer.octopus.energy/docs/api/>. It handles
    authentication, by storing a provided API key and meter details.
    Implemented endpoints include 'products' for viewing tariff details
    and 'consumption' for viewing meter consumption data.
The aim of 'od' is to provide tools and example datasets for working with
  origin-destination ('OD') datasets of the type used to describe aggregate
  urban mobility patterns (Carey et al. 1981) <doi:10.1287/trsc.15.1.32>.
  The package builds on functions for working with 'OD' data in the package 'stplanr',
  (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053> with a focus on computational
  efficiency and support for  the 'sf' class system (Pebesma 2018) <doi:10.32614/RJ-2018-009>.
  With few dependencies and a simple class system based on data frames,
  the package is intended to facilitate efficient analysis of 'OD' datasets
  and to provide a place for developing new functions.
  The package enables the creation and analysis of geographic entities
  representing large scale mobility patterns,
  from daily travel between zones in cities to migration between countries.
Make querying on 'OData' easier. It exposes an 'ODataQuery' object
    that can be manipulated and provides features such as selection, filtering
    and ordering.
Functions to create, connect, update and query 'HSQL' databases embedded in Open Document Databases files, as 'OpenOffice' and 'LibreOffice' do.
A DBI-compatible interface to ODBC databases.
Anomaly detection in dynamic, temporal networks. The package 
    'oddnet' uses a feature-based method to identify anomalies. First, it computes 
    many features for each network. Then it models the features using time series 
    methods. Using time series residuals it detects anomalies. This way, the 
    temporal dependencies are accounted for when identifying anomalies 
    (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.
Conversion between the most common odds types for sports betting.
    Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw
    Probability are covered in this package.
Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.
A utility to quickly obtain clean and tidy sports
    odds from The Odds API <https://the-odds-api.com>.
Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot.
    This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.
Simplified odds ratio calculation of GAM(M)s &
    GLM(M)s. Provides structured output (data frame) of all predictors and
    their corresponding odds ratios and confident intervals for further
    analyses.  It helps to avoid false references of predictors and
    increments by specifying these parameters in a list instead of using
    'exp(coef(model))' (standard approach of odds ratio calculation for
    GLMs) which just returns a plain numeric output.  For GAM(M)s, odds
    ratio calculation is highly simplified with this package since it
    takes care of the multiple 'predict()' calls of the chosen predictor
    while holding other predictors constant. Also, this package allows
    odds ratio calculation of percentage steps across the whole predictor
    distribution range for GAM(M)s.  In both cases, confident intervals
    are returned additionally. Calculated odds ratio of GAM(M)s can be
    inserted into the smooth function plot.
We proposes a framework that provides real time support for early detection of
    anomalous series within a large collection of streaming time series data. By definition, anomalies
    are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that
    is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the
    system's typical behaviour using a representative sample of the typical behaviour of the system. An
    approach based on extreme value theory is used for this boundary prediction process. Then a sliding
    window is used to test for anomalous series within the newly arrived collection of series. Feature
    based representation of time series is used as the input to the model. To cope with concept drift,
    the forecast boundary for the system's typical behaviour is updated periodically.  More details
    regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al.
    (2019) <doi:10.1080/10618600.2019.1617160>.
Allows performing forwards prediction for the General Unified 
          Threshold model of Survival using compiled ode code. This package 
          was created to avoid dependency with the 'morse' package that requires 
          the installation of 'JAGS'. This package is based on functions from 
          the 'morse' package v3.3.1: Virgile Baudrot, Sandrine Charles, 
          Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, 
          Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, 
          Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.
Wraps the Boost odeint library for integration of differential
    equations.
Simulates a network of ordinary differential equations of order
    two. The package provides an easy interface to construct networks. In addition
    you are able to define different external triggers to manipulate the trajectory.
    The method is described by Surmann, Ligges, and Weihs (2014) <doi:10.1109/ENERGYCON.2014.6850482>.
Performs sensitivity analysis in ordinary differential equation (ode) models.
    The package utilize the ode interface from 'deSolve' and connects it with the 
    sensitivity analysis from 'sensitivity'. Additionally we add a method to
    run the sensitivity analysis on variables with class 'ODEnetwork'. A detailed
    plotting function provides outputs on the calculations.
    The method is described by Weber, Theers, Surmann, Ligges, and Weihs (2018) <doi:10.17877/DE290R-18874>.
An object is called "outlier" if it remarkably deviates from the other objects in a data set. Outlier detection is the process to find outliers by using the methods that are based on distance measures, clustering and spatial methods (Ben-Gal, 2005 <ISBN 0-387-24435-2>). It is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for outlier removing in data processing. This package provides the implementations of some novel approaches to detect the outliers based on typicality degrees that are obtained with the soft partitioning clustering algorithms such as Fuzzy C-means and its variants.
Generate systems of ordinary differential equations
    (ODE) and integrate them, using a domain specific language
    (DSL).  The DSL uses R's syntax, but compiles to C in order to
    efficiently solve the system.  A solver is not provided, but
    instead interfaces to the packages 'deSolve' and 'dde' are
    generated.  With these, while solving the differential equations,
    no allocations are done and the calculations remain entirely in
    compiled code.  Alternatively, a model can be transpiled to R for
    use in contexts where a C compiler is not present.  After
    compilation, models can be inspected to return information about
    parameters and outputs, or intermediate values after calculations.
    'odin' is not targeted at any particular domain and is suitable
    for any system that can be expressed primarily as mathematical
    expressions.  Additional support is provided for working with
    delays (delay differential equations, DDE), using interpolated
    functions during interpolation, and for integrating quantities
    that represent arrays.
After develop a 'ODK' <https://opendatakit.org/> frame, we can link the frame to 'Google Sheets' <https://www.google.com/sheets/about/> and collect data through 'Android' <https://www.android.com/>. This data uploaded to a 'Google sheets'. odk2spss() function help to convert the 'odk' frame into 'SPSS' <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded 'Google sheets' data or read data from 'Google sheets' by using 'ODK' frame 'submission_url'.
OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. 
    In the first layer of the hierarchy, the clusters are separated automatically based on the variation 
    of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy 
    corresponds to the sub clustering process of small clusters based on the distance between the origin 
    and destination of each cluster.
Allows potential users of Scottish Health and Social Care Open Data
    (<https://www.opendata.nhs.scot/>) to easily explore and extract the
    available data.
Functions to compute the one-sided dynamic
	principal components ('odpc') introduced in Peña, Smucler and Yohai (2019)
	<DOI:10.1080/01621459.2018.1520117>. 'odpc' is a novel dimension
	reduction technique for multivariate time series, that is useful
	for forecasting. These dynamic principal components are defined as
	the linear combinations of the present and past values of the series
	that minimize the reconstruction mean squared error.
Calculate the optimal sample size allocation that 
    produces the highest statistical power for experimental 
    studies under a budget constraint, and
    perform power analyses with and without accommodating 
    cost structures of sampling. The designs cover single-level 
    and multilevel experiments detecting main, mediation, 
    and moderation effects (and some combinations). 
    The references for the proposed methods include: 
    (1) Shen, Z., & Kelcey, B. (2020). 
    Optimal sample allocation under unequal costs in 
    cluster-randomized trials.
    Journal of Educational and Behavioral Statistics, 45(4): 446-474.
        <doi:10.3102/1076998620912418>.
    (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for
    three-level multisite cluster-randomized trials. 
    Journal of Research on Educational Effectiveness, 15 (1), 130-150.
    <doi:10.1080/19345747.2021.1953200>.
    (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in
    multisite randomized trials. The Journal of Experimental Education.
    <doi:10.1080/00220973.2020.1830361>.
    (4) Champely, S. (2020). pwr: Basic functions for power analysis 
    (Version 1.3-0) [Software]. Available from 
    <https://CRAN.R-project.org/package=pwr>.
The oblique decision tree (ODT) uses linear combinations of
    predictors as partitioning variables in a decision tree. Oblique
    Decision Random Forest (ODRF) is an ensemble of multiple ODTs
    generated by feature bagging. Both can be used for classification and
    regression as supplements to the classical CART of Breiman (1984)
    <https://imsarchives.nus.edu.sg/oldwww/Programs/014swclass/files/mark.pdf>
    and Random Forest of Breiman (2001) <DOI:10.1023/A:1010933404324>
    respectively.
Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency. 
    In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome 
    variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event 
    outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>. 
    Because ODS data has biased sampling nature, standard statistical analysis such as linear regression 
    will lead to biases estimates of the population parameters. This package implements four statistical 
    methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous 
    outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial 
    linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011) 
    <doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design 
    (Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary 
    outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.
Search and extract data from the Organization for Economic 
    Cooperation and Development (OECD).
Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series.  Method is based on  Kubáček (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G.
             and Witkovský, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.
Solves penalized least squares problems for big tall data
    using the orthogonalizing EM algorithm of Xiong et al. (2016) 
    <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the
    functions cv.oem() and xval.oem() are for cross validation, the latter being an
    accelerated cross validation function for linear models. The big.oem() function
    allows for out of memory fitting. A description of the underlying methods and 
    code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.
Tools to access data from the data web service of the Oesterreichische Nationalbank (OeNB), <https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>.
Developed to help researchers who need to model the kinetics of carbon dioxide (CO2) production in alcoholic fermentation of wines, beers and other fermented products. The following models are available for modeling the carbon dioxide production curve as a function of time: 5PL, Gompertz and 4PL. This package has different functions, which applied can: perform the modeling of the data obtained in the fermentation and return the coefficients, analyze the model fit and return different statistical metrics, and calculate the kinetic parameters: Maximum production of carbon dioxide; Maximum rate of production of carbon dioxide; Moment in which maximum fermentation rate occurs; Duration of the latency phase for carbon dioxide production; Carbon dioxide produced until maximum fermentation rate occurs. In addition, a function that generates graphs with the observed and predicted data from the models, isolated and combined, is available. Gava, A., Borsato, D., & Ficagna, E. (2020)."Effect of mixture of fining agents on the fermentation kinetics of base wine for sparkling wine production: Use of methodology for modeling". <doi:10.1016/j.lwt.2020.109660>.
Methods for statistical analysis and reporting preferred by the US Office of Evaluation Sciences (OES). This package prepares data from standard model output objects (such as from \code{lm()} and \code{estimatr::lm_robust()}) and creates visualizations of treatment effects from the prepared quantities, according to the standards of the US Office of Evaluation Sciences.
Contains data from the May 2020 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.
Contains data from the May 2021 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.
Allows production of 'Microsoft' corporate documents from 'R Markdown' by 
 reusing formatting defined in 'Microsoft Word' documents. You can reuse table styles, 
 list styles but also add column sections, landscape oriented pages. Table and image 
 captions as well as cross-references are transformed into 'Microsoft Word' fields, 
 allowing documents edition and merging without issue with references; the syntax 
 conforms to the 'bookdown' cross-reference definition. Objects generated by 
 the 'officer' package are also supported in the 'knitr' chunks. 
 'Microsoft PowerPoint' presentations also benefit from this as well as the 
 ability to produce editable vector graphics in 'PowerPoint' and also to 
 define placeholder where content is to be added.
Access and manipulate 'Microsoft Word', 'RTF' and 'Microsoft PowerPoint' documents from R. 
  The package focuses on tabular and graphical reporting from R; it also provides two functions
  that let users get document content into data objects. A set of functions 
  lets add and remove images, tables and paragraphs of text in new or existing documents. 
  The package does not require any installation of Microsoft products to be able to write Microsoft 
  files.
Detect the number and locations of change points. The locations can be either exact or in terms of ranges, 
            depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2017) <doi:10.1109/TSP.2017.2711558>.
Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.
Consider a data matrix of n individuals with p variates. The objective general index (OGI)
    is a general index that combines the p variates into a univariate index in order to rank the n
    individuals. The OGI is always positively correlated with each of the variates.
    More details can be found in Sei (2016) <doi:10.1016/j.jmva.2016.02.005>.
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.
Multiple tools are now available for inferring the personalised 
    germ line set from an adaptive immune receptor repertoire. 
    Output from these tools is converted to 
    a single format and supplemented with rich data such as usage and 
    characterisation of 'novel' germ line alleles. This data can be 
    particularly useful when considering the validity of novel inferences. Use
    of the analysis provided is described in <doi:10.3389/fimmu.2019.00435>.
Supports the modeling of ordinal random variables, 
    like the outcomes of races, via Softmax regression,
    under the Harville <doi:10.1080/01621459.1973.10482425> and
    Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.
Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.
Perform a Bayesian estimation of the ordinal exploratory 
    Higher-order General Diagnostic Model (OHOEGDM) for Polytomous Data 
    described by Culpepper, S. A. and Balamuta, J. J. (In Press) <doi:10.1080/00273171.2021.1985949>.
Ordered homogeneity pursuit lasso (OHPL)
    algorithm for group variable selection proposed in Lin et al. (2017)
    <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
    homogeneity structure in high-dimensional data and enjoys the
    grouping effect to select groups of important variables
    automatically. This feature makes it particularly useful for
    high-dimensional datasets with strongly correlated variables,
    such as spectroscopic data.
A client that grants access to the power of the 'ohsome API'
    from R. It lets you analyze the rich data source of the 
    'OpenStreetMap (OSM)' history. You can retrieve the geometry of 'OSM' 
    data at specific points in time, and you can get aggregated statistics 
    on the evolution of 'OSM' elements and specify your own temporal, 
    spatial and/or thematic filters.
Calculate's Tomoka Ohta's partitioning of linkage disequilibrium,
 deemed D-statistics, for pairs of loci. Petrowski et al. (2019) <doi:10.5334/jors.250>.
Facilitates the automatic detection of acoustic signals, 
  providing functions to diagnose and optimize the performance of detection 
  routines. Detections from other software can also be explored and optimized. 
  Reference: Hossin & Sulaiman (2015) <doi:10.5121/ijdkp.2015.5201>.
Provides simple crosstab output with optional statistics (e.g., Goodman-Kruskal Gamma, Somers' d, and Kendall's tau-b) as well as two-way and one-way tables. The package is used within the statistics component of the Masters of Science (MSc) in Social Science of the Internet at the Oxford Internet Institute (OII), University of Oxford, but the functions should be useful for general data analysis and especially for analysis of categorical and ordinal data.
Crawler for 'OJS' pages and scraper for meta-data from articles. 
    You can crawl 'OJS' archives, issues, articles, galleys, and search results. 
    You can scrape articles metadata from their head tag in html, 
    or from Open Archives Initiative ('OAI') records.
    Most of these functions rely on 'OJS' routing conventions 
    (<https://docs.pkp.sfu.ca/dev/documentation/en/architecture-routes>).
Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) <DOI:10.1109/ACCESS.2020.3010099>.
An unofficial wrapper for 'okx exchange v5' API <https://www.okx.com/docs-v5/en/>, including 'REST' API and 'WebSocket' API.
We provide two algorithms for monitoring change points with online matrix-valued time series, under the assumption of a two-way factor structure. The algorithms are based on different calculations of the second moment matrices. One is based on stacking the columns of matrix observations, while another is by a more delicate projected approach. A well-known fact is that, in the presence of a change point, a factor model can be rewritten as a model with a larger number of common factors. In turn, this entails that, in the presence of a change point, the number of spiked eigenvalues in the second moment matrix of the data increases. Based on this, we propose two families of procedures - one based on the fluctuations of partial sums, and one based on extreme value theory - to monitor whether the first non-spiked eigenvalue diverges after a point in time in the monitoring horizon, thereby indicating the presence of a change point. See more details in He et al. (2021)<arXiv:2112.13479>.
'Open Location Codes' <http://openlocationcode.com/>
    are a Google-created standard for identifying geographic locations. 'olctools' provides
    utilities for validating, encoding and decoding entries that follow this
    standard.
Fetch trial data from the Old Bailey Online API <https://www.oldbaileyonline.org/static/DocAPI.jsp>. Data is returned in an analysis-ready data frame with fields for metadata including (but not limited to) the names of the first person speakers, defendants, victims, their recorded genders, verdicts, punishments, crime locations, and dates. Optional parameters allow users to specify the number of results, whether these results contain key terms, and trial dates.
A collection of functions to facilitate analysis of proteomic
    data from Olink, primarily NPX data that has been exported from Olink
    Software. The functions also work on QUANT data from
    Olink by log- transforming the QUANT data. The functions are focused
    on reading data, facilitating data wrangling and quality control
    analysis, performing statistical analysis and generating figures to
    visualize the results of the statistical analysis. The goal of this
    package is to help users extract biological insights from proteomic
    data run on the Olink platform.
Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) <doi:10.31801/cfsuasmas.542988> for example.
Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) <doi:10.1080/00949655.2016.1238088>.
The optimal linear regression olr(), runs all the possible combinations of linear regression
  equations. The olr() returns the equation which has the greatest adjusted R-squared term or the greatest R-squared term based
  on the user's discretion. Essentially, the olr() returns the best fit equation out of all the possible equations. R-squared increases 
  with the addition of an explanatory variable whether it is 'significant' or not, thus this was developed to eliminate that conundrum. 
  Adjusted R-squared is preferred to overcome this phenomenon, but each combination will still produce different results and this will
  return the best one. Complimentary functions are included which list all of the equations, all of the equations in ascending order, 
  a function to give the user a specific model's summary, and the list of adjusted R-squared terms & R-squared terms.
  A 'Python' version is available at: <https://pypi.org/project/olr/>.
Tools designed to make it easier for users, particularly beginner/intermediate R users 
    to build ordinary least squares regression models. Includes comprehensive regression output, 
    heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, 
    model fit assessment and variable selection procedures.
'Shiny' Application to visualize Olympic Data. From 1896 to
    2016. Even Winter Olympics events are included. Data is from Kaggle at
    <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.
It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. 
    The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) <https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. 
    Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC.
It provides functions to generate a correlation matrix
    from a genetic dataset and to use this matrix to predict the phenotype of an
    individual by using the phenotypes of the remaining individuals through
    kriging. Kriging is a geostatistical method for optimal prediction or best
    unbiased linear prediction. It consists of predicting the value of a
    variable at an unobserved location as a weighted sum of the variable at
    observed locations. Intuitively, it works as a reverse linear regression:
    instead of computing correlation (univariate regression coefficients are
    simply scaled correlation) between a dependent variable Y and independent
    variables X, it uses known correlation between X and Y to predict Y.
A tool for interactive exploration of the results from 'omics'
  experiments to facilitate novel discoveries from high-throughput biology. The
  software includes R functions for the 'bioinformatician' to deposit study
  metadata and the outputs from statistical analyses (e.g. differential
  expression, enrichment). These results are then exported to an interactive
  JavaScript dashboard that can be interrogated on the user's local machine or
  deployed online to be explored by collaborators. The dashboard includes
  'sortable' tables, interactive plots including network visualization, and
  fine-grained filtering based on statistical significance.
A collection of functions to analyse '--omics' datasets such as DNA
    methylation and gene expression profiles.
A method for the quantitative prediction using omics data. 
    This package provides functions to construct the quantitative prediction 
    model using omics data.
Performs the O2PLS data integration method for two datasets, yielding joint and data-specific parts for each dataset.
    The algorithm automatically switches to a memory-efficient approach to fit O2PLS to high dimensional data.
    It provides a rigorous and a faster alternative cross-validation method to select the number of components,
    as well as functions to report proportions of explained variation and to construct plots of the results. 
    See the software article by el Bouhaddani et al (2018) <doi:10.1186/s12859-018-2371-3>, 
    and Trygg and Wold (2003) <doi:10.1002/cem.775>.
    It also performs Sparse Group (Penalized) O2PLS, see Gu et al (2020) <doi:10.1186/s12859-021-03958-3> and cross-validation for the degree of sparsity.
In bulk epigenome/transcriptome experiments, molecular expression
    is measured in a tissue, which is a mixture of multiple types of cells.
    This package tests association of a disease/phenotype with a molecular marker
    for each cell type.
    The proportion of cell types in each sample needs to be given as input.
    The package is applicable to epigenome-wide association study (EWAS) and
    differential gene expression analysis.
    Takeuchi and Kato (submitted)
    "omicwas: cell-type-specific epigenome-wide and transcriptome association study".
Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi:10.1037/1082-989X.12.4.414>.
An assortment of helper functions for managing data (e.g.,
	rotating values in matrices by a user-defined angle, switching from
	row- to column-indexing), dates (e.g., intuiting year from messy date
	strings), handling missing values (e.g., removing elements/rows across
	multiple vectors or matrices if any have an NA), and text (e.g.,
	flushing reports to the console in real-time).
The separate p-values of SNPs, RNA expressions and DNA
    methylations are calculated by KM regression. The correlation between
    different omics data are taken into account. This method can be applied to
    either samples with all three types of omics data or samples with two types.
Utility functions for querying electronic health record (EHR) data in
    'OMOP' Common Data Model <https://www.ohdsi.org/data-standardization/the-common-data-model/>
    databases using a 'tidyverse' approach based on 'dbplyr' lazy queries.
    This allows efficient in-database querying and data wrangling without explicit
    writing of 'SQL' queries.
Model mixed integer linear programs in an algebraic way directly in R.
             The model is solver-independent and thus offers the possibility
             to solve a model with different solvers. It currently only supports
             linear constraints and objective functions. See the 'ompr'
             website <https://dirkschumacher.github.io/ompr/> for more information,
             documentation and examples.
A solver for 'ompr' based on the R Optimization Infrastructure ('ROI').
  The package makes all solvers in 'ROI' available to solve 'ompr' models. Please see the
  'ompr' website <https://dirkschumacher.github.io/ompr/> and package docs for more information
  and examples on how to use it.
Build 'SVG' components using element-based functions. With
    an 'svg' object, we can modify its graphical elements with a suite of
    transform functions.
Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.
Data sets for network analysis related to People Analytics.  
  Contains various data sets from the book 'Handbook of Graphs and Networks in People Analytics' 
  by Keith McNulty (2021).
Implementation of a likelihood ratio test of differential
    onset of senescence between two groups. Given two groups with
    measures of age and of an individual trait likely to be subjected to
    senescence (e.g. body mass), 'OnAge' provides an asymptotic p-value
    for the null hypothesis that senescence starts at the same age in
    both groups. The package implements the procedure used in 
    Douhard et al. (2017) <doi:10.1111/oik.04421>.
A database containing the names 
  of the babies born in Ontario between 1917 and 2018. 
  Counts of fewer than 5 names were suppressed for privacy.
Provides a wrapper for the Onboard Data building data API <https://api.onboarddata.io/swagger>. Along with streamlining access to the API, this package simplifies access to sensor time series data, metadata (sensors, equipment, and buildings), and details about the Onboard data model/ontology.
Automated reporting in Word and PowerPoint can require customization for each organizational template. This package works around this by adding standard reporting functions and an abstraction layer to facilitate automated reporting workflows that can be replicated across different organizational templates.
Allows users to discover and retrieve Ocean Networks Canada's oceanographic data in raw, text, image, audio, video or any other format available. Provides a class that wraps web service calls and business logic so that users can download data with a single line of code.
Allows you to easily execute expensive compute operations only once, and save the resulting object to disk.
Bayesian logistic regression model with optional
  EXchangeability-NonEXchangeability parameter modelling for flexible
  borrowing from historical or concurrent data-sources. The safety model
  can guide dose-escalation decisions for adaptive oncology Phase I
  dose-escalation trials which involve an arbitrary number of
  drugs. Please refer to Neuenschwander et al. (2008)
  <doi:10.1002/sim.3230> and Neuenschwander et al. (2016)
  <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Implements methods to fit a parametric Bayesian multi-state model
    to tumor response data.
    The model can be used to sample from the predictive distribution to impute
    missing data and calculate probability of success for custom decision
    criteria in early clinical trials during an ongoing trial.
    The inference is implemented using 'stan'.
Bridges in vitro drug screening with in vivo drug and biomarker discovery. Specifically, predicts in vivo or cancer patient drug response and biomarkers to enrich for response from cell line screening data. Builds model using ridge regression, and enables biomarker discovery by imputing drug response in large cancer molecular datasets. It also enables drug specific biomarker identification by correcting for general level of drug sensitivity shared among the population.
Contains functions to construct and evaluate directed tree structures that
   model the process of occurrence of genetic alterations during carcinogenesis.
Measures different aspects of page content, structure and performance for SEO (Search Engine Optimization).
    Aspects covered include HTML tags used in SEO, duplicate and near-duplicate content, structured data, on-site linking structure and popularity transfer, and many other amazing things. 
    This package can be used to generate a real, full SEO audit report, which serves to detect errors or inefficiencies on a page that can be corrected in order to optimise its performance on search engines.
Single-cell datasets are growing in size, posing challenges as well as opportunities for biology researchers. 'ondisc' (short for "on-disk single cell") enables users to easily and efficiently analyze large-scale single-cell data. 'ondisc' makes computing on large-scale single-cell data FUN: Fast, Universal, and iNtuitive.
The proposed two-stage design can be used for single-arm phase II trial designs with 
    time-to-event endpoints, which is desirable for clinical trials on immunotherapies 
    among cancer patients. There're two advantages of the proposed approach: 1) It provides
    flexible choices of four underlying survival distributions and 2) the power of the design
    is more accurately calculated using exact variance in one-sample log-rank test. The package
    can be used for 1) planning the sample size; 2) conducting the interim and final analyses for
    the Go/No-go decisions. More details about the design method can be found in the paper: 
    Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020). <doi:10.1002/pst.1983>.
Get operating characteristics of one-arm clinical trial designs for time-to-event endpoint through simulation and perform analysis with time-to-event data.
Maximum homogeneity clustering algorithm for one-dimensional data
    described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479>
    via dynamic programming.
Quickly create numeric matrices for machine learning algorithms
    that require them. It converts factor columns into onehot vectors.
The identity provider ['OneLogin']<http://onelogin.com> is used for 
  authentication via Single Sign On (SSO). This package provides an R interface to their API.
Analysis of molecular marker data from model (backcrosses,
    F2 and recombinant inbred lines) and non-model systems (i. e.
    outcrossing species). For the later, it allows statistical
    analysis by simultaneously estimating linkage and linkage
    phases (genetic map construction) according to Wu et al. (2002)
    <doi:10.1006/tpbi.2002.1577>. All analysis are based on multipoint 
    approaches using hidden Markov models.
An R wrapper for the 'OneMap.Sg' API <https://www.onemap.gov.sg/docs/>. 
    Functions help users query data from the API and return raw JSON data
    in "tidy" formats. Support is also available for users to retrieve data from multiple API calls 
    and integrate results into single dataframes, without
    needing to clean and merge the data themselves. This package is best suited for users who would 
    like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.
Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.
Useful functions for one-sample (individual level data)
    Mendelian randomization and instrumental variable analyses. The
    package includes implementations of; the Sanderson and Windmeijer
    (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic,
    the multiplicative structural mean model Hernán and Robins (2006)
    <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor
    substitution and two-stage residual inclusion estimators explained by
    Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.
This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).
Provide principally an eponymic function that numerically computes the Le Cam one-step estimator which is asymptotically efficient (see e.g. L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) and can be computed faster than the maximum likelihood estimator for large observation samples.
Allows code to be run only once on a given computer, using
  lockfiles. Typical use cases include startup messages shown only when a
  package is loaded for the very first time.
Provides a series of functions designed to enable users to easily search and interact with occupational data from the O*NET API <www.onetonline.org>. The package produces parsed and listed XML data for custom interactions, or pre-packaged functions for easy extraction of specific data (e.g., Knowledge, Skills, Abilities, Work Styles, etc.).
We introduce an R function
        one_two_sample() which can deal with one and two (normal)
        samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. 
        For one normal sample x, the function reports
        descriptive statistics, plot, interval estimation and test of
        hypothesis of x. For two normal samples x and y, the function
        reports descriptive statistics, plot, interval estimation and
        test of hypothesis of x and y, respectively. It also reports
        interval estimation and test of hypothesis of mu1-mu2 (the
        difference of the means of x and y) and sigma1^2 / sigma2^2
        (the ratio of the variances of x and y), tests whether x and y
        are from the same population, finds the correlation coefficient
        of x and y if x and y have the same length.
Performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. These are one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test and Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, <https://journal.r-project.org/archive/2018/RJ-2018-022/RJ-2018-022.pdf>).
Quaternions and Octonions are four- and eight- dimensional
  extensions of the complex numbers.  They are normed division
  algebras over the real numbers and find applications in spatial
  rotations (quaternions), and string theory and relativity
  (octonions).  The quaternions are noncommutative and the octonions
  nonassociative.  See the package vignette for more details.
It implements the online Bayesian methods for change point analysis. It can
            also perform missing data imputation with methods from 'VIM'. The reference 
            is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>. 
            The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.
Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.
A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the pre-print paper "onlineforecast: An R package for adaptive and recursive forecasting" <arXiv:2109.12915>.
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Transactions occurring for a UK-based and registered, non-store online 
    retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, <doi:10.1145/1835804.1835882>). 
    This dataset is included in this package with the donor's permission, Dr. Daqing Chen.
Fits two-dimensional data by means of orthogonal nonlinear least-squares using Levenberg-Marquardt minimization and provides functionality for fit diagnostics and plotting.
The Ontario Marginalization Index is a socioeconomic model that is built on Statistics Canada census data.
    The model consists of four dimensions: Residential Instability, Material Deprivation, Dependency and Ethnic Concentration.
    Each of these dimensions are imported for a variety of geographic levels (DA, CD, etc.) for both the 2011 and 2016 administrations of the census (2021 pending).
    These data sets contribute to community analysis of equity with respect to Ontario's Anti-Racism Act.
    The Ontario Marginalization Index data is retrieved from the Public Health Ontario website: <https://www.publichealthontario.ca/en/data-and-analysis/health-equity/ontario-marginalization-index>.
    The shapefile data is retrieved from the Statistics Canada website: <https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-eng.cfm>.
R Interface to 'ONNX' - Open Neural Network Exchange <https://onnx.ai/>. 
             'ONNX' provides an open source format for machine learning models. 
             It defines an extensible computation graph model, as well as definitions
             of built-in operators and standard data types.
Growing collection of helper functions for point pattern analysis. Most functions
  are designed to work with the 'spatstat' (<http://spatstat.org>) package. The focus of 
  most functions are either null models or summary functions for spatial point patterns. 
  For a detailed description of all null models and summary functions, see 
  Wiegand and Moloney (2014, ISBN:9781420082548).
Client for the 'Office of National Statistics'
    ('ONS') API <https://api.beta.ons.gov.uk/v1>.
Tools for annotating characters (character matrices) with anatomical and phenotype ontologies. Includes functions for visualising character annotations and creating simple queries using ontological relationships.
Provides tools to build and work with an ontology of linked (open) 
    data in a tidy workflow. It is inspired by the Food and Agrilculture 
    Organizations (FAO) caliper platform 
    <https://www.fao.org/statistics/caliper/web/> and 
    makes use of the Simple Knowledge Organisation System (SKOS).
Functions for reading ontologies into R as lists and manipulating sets of ontological terms - 'ontologyX: A suite of R packages for working with ontological data', Greene et al 2017 <doi:10.1093/bioinformatics/btw763>.
Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package.
Calculates the Outside Option Index proposed by Caldwell and Danieli
  (2018) <https://drive.google.com/file/d/1j-uwD19S4gqgXIXeYch9jGBCaDhWZlRQ/view>. 
  This index uses the cross- sectional concentration of similar workers 
  across job types to quantify the availability of outside options as a function of 
  workers’ characteristics (e.g. commuting costs, preferences, and skills.)
Intended to create standard human-in-the-loop validity tests for typical automated content analysis such as topic modeling and dictionary-based methods. This package offers a standard workflow with functions to prepare, administer and evaluate a human-in-the-loop validity test. This package provides functions for validating topic models using word intrusion, topic intrusion (Chang et al. 2009,  <https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>) and word set intrusion (Ying et al. 2021) <doi:10.1017/pan.2021.33> tests. This package also provides functions for generating gold-standard data which are useful for validating dictionary-based methods. The default settings of all generated tests match those suggested in Chang et al. (2009) and Song et al. (2020) <doi:10.1080/10584609.2020.1723752>.
Provides the class unions that must be
  preloaded in order for the basic tools in the OOMPA (Object-Oriented 
  Microarray and Proteomics Analysis) project to be defined and loaded.
  It also includes vectorized operations for row-by-row means,
  variances, and t-tests. Finally, it provides new color schemes.
  Details on the packages in the OOMPA project can be found at
  <http://oompa.r-forge.r-project.org/>.
This is a data-only package to provide example data for
  other packages that are part of the "Object-Oriented Microrray and
  Proteomics Analysis" suite of packages. These are described in more
  detail at the package URL.
Helper functions for coding object-oriented programming with
    a focus on R6. Includes functions for assertions and testing, looping,
    and re-usable design patterns including Abstract and Decorator
    classes.
Create simple, hassle-free classes with reference semantics
    similar to 'RefClass' or 'R6' but relying on S3 methods. "oops" class 
    instances tend to be lighter weight and faster to create. Creating a 
    class is as easy creating a list, while generating an instance is a 
    simple function call. Support for inheritance and fixed field classes.
Implementation of optimistic optimization methods for global optimization of deterministic or stochastic functions. The algorithms feature guarantees of the convergence to a global optimum. They require minimal assumptions on the (only local) smoothness, where the smoothness parameter does not need to be known. They are expected to be useful for the most difficult functions when we have no information on smoothness and the gradients are unknown or do not exist. Due to the weak assumptions, however, they can be mostly effective only in small dimensions, for example, for hyperparameter tuning.
A comprehensive and cohesive API for the out-of-sample forecasting workflow: 
             data preparation, forecasting - including both traditional econometric time series models and 
             modern machine learning techniques - forecast combination, model and error analysis, and 
             forecast visualization.
Estimates the out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.
Implements the out-of-treatment testing from Kuelpmann and Kuzmics (2020) <doi:10.2139/ssrn.3441675> based on the Vuong Test introduced in Vuong (1989) <doi:10.2307/1912557>. 
    Out-of treatment testing allows for a direct, pairwise likelihood comparison of theories, calibrated with pre-existing data.
Quantifies hypothesis to data fit for repeated measures 
    and longitudinal data, as described by Thorngate (1987) 
    <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) 
    <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
    pairwise relative orderings which are then compared to determine the
    percentage of orderings in the data that are matched by the hypothesis.
Data integration Web application for biobanks by 'OBiBa'. 'Opal' is
    the core database application for biobanks. Participant data, once
    collected from any data source, must be integrated and stored in a central
    data repository under a uniform model. 'Opal' is such a central repository.
    It can import, process, validate, query, analyze, report, and export data.
    'Opal' is typically used in a research center to analyze the data acquired at
    assessment centres. Its ultimate purpose is to achieve seamless
    data-sharing among biobanks. This 'Opal' client allows to interact with 'Opal'
    web services and to perform operations on the R server side. 'DataSHIELD'
    administration tools are also provided.
The online principal component method can process the online data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.
An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data.
Several function related to Experimental Design are implemented here, see
  "Optimal Experimental Design with R" by Rasch D. et. al (ISBN 9781439816974).
An R wrapper of OpenAI API endpoints (see
    <https://platform.openai.com/docs/introduction> for details). This package
    covers Models, Completions, Chat, Edits, Images, Embeddings, Audio, Files,
    Fine-tunes, Moderations, and legacy Engines endpoints.
Tools to analyse, interpret and understand air pollution
    data. Data are typically regular time series and air quality
    measurement, meteorological data and dispersion model output can be
    analysed. The package is described in Carslaw and Ropkins (2012,
    <doi:10.1016/j.envsoft.2011.09.008>) and subsequent papers.
Combine the air quality data analysis methods of 'openair'
    with the JavaScript 'Leaflet' (<https://leafletjs.com/>) library.
    Functionality includes plotting site maps, "directional analysis"
    figures such as polar plots, and air mass trajectories.
A set of tools to extract bibliographic content from
    'OpenAlex' database using API <https://docs.openalex.org>.
Creates a client with queries for the UK 'Open Banking' ('Open Data') API.
Interface to make HTTP requests to 'OpenBlender' API services. Go to <https://openblender.io> for more information.
Geocode with the OpenCage API, either from place name to longitude 
    and latitude (forward geocoding) or from longitude and latitude to the name 
    and address of a location (reverse geocoding), see 
    <https://opencagedata.com/>.
This package provides an interface to OpenCL, allowing R to leverage computing power of GPUs and other HPC accelerator devices.
A system for embedded scientific computing and reproducible research with R.
    The OpenCPU server exposes a simple but powerful HTTP api for RPC and data interchange
    with R. This provides a reliable and scalable foundation for statistical services or 
    building R web applications. The OpenCPU server runs either as a single-user development
    server within the interactive R session, or as a multi-user Linux stack based on Apache2. 
    The entire system is fully open source and permissively licensed. The OpenCPU website
    has detailed documentation and example apps.
Non-spatial and spatial open-population capture-recapture analysis.
Exposes some of the available 'OpenCV' <https://opencv.org/> algorithms,
    such as edge, body or face detection. These can either be applied to analyze 
    static images, or to filter live video footage from a camera device.
Access data from the "City of Toronto
    Open Data Portal" (<https://open.toronto.ca>) directly from R.
Enables the usage of the OpenDota API from <https://www.opendota.com/>, get game lists, and download JSON's of parsed replays from
    the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file.
An implementation of DuMouchel's (1999) 
  <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the
  market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and
  quantile scores from the posterior distribution using the Gamma-Poisson
  Shrinker (GPS) model to find unusually large cell counts in large, sparse
  contingency tables. Can be used to find unusually high reporting rates of
  adverse events associated with products. In general, can be used to mine any
  database where the co-occurrence of two variables or items is of interest.
  Also calculates relative and proportional reporting ratios. Builds on the work
  of the 'PhViD' package, from which much of the code is derived. Some of the
  added features include stratification to adjust for confounding variables and
  data squashing to improve computational efficiency. Now includes an
  implementation of the EM algorithm for hyperparameter estimation loosely
  derived from the 'mederrRank' package.
Access data and processing functionalities of 'openEO' compliant back-ends in R.
Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
Supplemental functions and data for 'OpenIntro' resources, which 
    includes open-source textbooks and resources for introductory statistics 
    (<https://www.openintro.org/>). The package contains data sets used in our 
    open-source textbooks along with custom plotting functions for reproducing 
    book figures. Note that many functions and examples include color 
    transparency; some plotting elements may not show up properly (or at all) 
    when run in some versions of Windows operating system.
Tools for the analysis of land use and cover (LUC) time series. It 
    includes support for loading spatiotemporal raster data and synthesized 
    spatial plotting. Several LUC change (LUCC) metrics in regular or irregular 
    time intervals can be extracted and visualized through one- and multistep 
    sankey and chord diagrams. A complete intensity analysis according to 
    Aldwaik and Pontius (2012) <doi:10.1016/j.landurbplan.2012.02.010> is 
    implemented, including tools for the generation of standardized multilevel 
    output graphics.
A client for the Open-Meteo API that retrieves Open-Meteo
    weather data in a tidy format. No API key is required. The API specification
    is located at <https://open-meteo.com/en/docs>.
Provides a client for the open-source monitoring and alerting
  toolkit, 'Prometheus', that emits metrics in the 'OpenMetrics' format. Allows
  users to automatically instrument 'Plumber' and 'Shiny' applications, collect
  standard process metrics, as well as define custom counter, gauge, and
  histogram metrics of their own.
We provide an R interface to 'OpenML.org' which is an online machine learning platform where researchers can access open data, download and upload data sets, share their machine learning tasks and experiments and organize them online to work and collaborate with other researchers. 
    The R interface allows to query for data sets with specific properties, and allows the downloading and uploading of data sets, tasks, flows and runs. 
    See <https://www.openml.org/guide/api> for more information.
The 'openMSE' package is designed for building operating models, 
    doing simulation modelling and management strategy evaluation for fisheries.
    'openMSE' is an umbrella package for the 'MSEtool' (Management Strategy Evaluation
    toolkit), 'DLMtool' (Data-Limited Methods toolkit), and 
    SAMtool (Stock Assessment Methods toolkit) packages. By loading and installing
    'openMSE', users have access to the full functionality contained within
    these packages. Learn more about 'openMSE' at <https://openmse.com/>.
Create structural equation models that can be manipulated programmatically.
    Models may be specified with matrices or paths (LISREL or RAM)
    Example models include confirmatory factor, multiple group, mixture
    distribution, categorical threshold, modern test theory, differential
    Fit functions include full information maximum likelihood, maximum likelihood, and weighted least squares.
    equations, state space, and many others.
	Support and advanced package binaries available at <http://openmx.ssri.psu.edu>.
    The software is described in Neale, Hunter, Pritikin, Zahery, Brick,
    Kirkpatrick, Estabrook, Bates, Maes, & Boker (2016) <doi:10.1007/s11336-014-9435-8>.
An interface to the Apache OpenNLP tools (version 1.5.3).
  The Apache OpenNLP library is a machine learning based toolkit for the
  processing of natural language text written in Java.
  It supports the most common NLP tasks, such as tokenization, sentence
  segmentation, part-of-speech tagging, named entity extraction, chunking,
  parsing, and coreference resolution.
  See <https://opennlp.apache.org/> for more information.
Apache OpenNLP jars and basic English language models.
Analyze repertory grids, a qualitative-quantitative 
    data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across 
    various domains ranging from clinical psychology to marketing. The package contains
    functions to quantitatively analyze and visualize repertory grid data 
    (see e.g. Bell, 2005, <doi:10.1002/0470013370.ch9>; 
    Fransella, Bell, & Bannister, 2004, ISBN: 978-0-470-09080-0).
Shiny UI to identify cliques of related constructs in repertory grid data. 
    See Burr, King, & Heckmann (2020) <doi:10.1080/14780887.2020.1794088> for a description 
    of the interpretive clustering (IC) method.
Download environmental measurements and sensor station metadata
    from the API of open data sensor web platform <https://opensensemap.org> for
    analysis in R.
    This platform provides real time data of more than 1500 low-cost sensor
    stations for PM10, PM2.5, temperature, humidity, UV-A intensity and more
    phenomena.
    The package aims to be compatible with 'sf' and the 'Tidyverse', and provides
    several helper functions for data exploration and transformation.
Provides functionalities and data structures to retrieve, analyze and visualize aviation 
    data. It includes a client interface to the 'OpenSky' API <https://opensky-network.org>. It allows 
    retrieval of flight information, as well as aircraft state vectors.
Raman and (FT)IR spectral analysis tool for plastic particles and
    other environmental samples (Cowger et al. 2021,
    <doi:10.1021/acs.analchem.1c00123>). Supported features include reading
    spectral data files (.asp, .csv, .jdx, .spc, .spa, .0), Savitzky-Golay
    smoothing of spectral intensities with smooth_intens(), correcting
    background noise with subtr_bg() in accordance with Zhao et al. (2007)
    <doi:10.1366/000370207782597003>, and identifying spectra using an onboard
    reference library (Cowger et al. 2020, <doi:10.1177/0003702820929064>).
    Analyzed spectra can be shared with the Open Specy community. A Shiny app is
    available via run_app() or online at
    <https://openanalysis.org/openspecy/>.
Bindings to OpenSSL libssl and libcrypto, plus custom SSH key parsers.
    Supports RSA, DSA and EC curves P-256, P-384, P-521, and curve25519. Cryptographic
    signatures can either be created and verified manually or via x509 certificates. 
    AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric
    (public key) encryption or EC for Diffie Hellman. High-level envelope functions 
    combine RSA and AES for encrypting arbitrary sized data. Other utilities include key
    generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random
    number generator, and 'bignum' math methods for manually performing crypto 
    calculations on large multibyte integers.
An open source implementation of the 'STARS' toolbox
    (Peterson & Ver Hoef, 2014, <doi:10.18637/jss.v056.i02>) using 'R' and 'GRASS GIS'.
    It prepares the *.ssn object needed for the 'SSN' package.
    A Digital Elevation Model (DEM) is used to derive stream networks 
    (in contrast to 'STARS' that can clean an existing stream network).
Accesses high resolution raster maps using the OpenStreetMap
    protocol. Dozens of road, satellite, and topographic map servers are directly
    supported, including Apple, Mapnik, Bing, and stamen. Additionally raster maps
    may be constructed using custom tile servers.  Maps can be
    plotted using either base graphics, or ggplot2. This package is not affiliated
    with the OpenStreetMap.org mapping project.
Chronogram database constructed from Open Tree of Life's phylogenetic store.
Setup and connect to 'OpenTripPlanner' (OTP) <http://www.opentripplanner.org/>.
    OTP is an open source platform for multi-modal and multi-agency
    journey planning written in 'Java'. The package allows you to manage a local version or 
    connect to remote OTP server to find walking, cycling, driving, or transit routes.
    This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).
Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include 'InterVA4' by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, 'InterVA5' by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, 'InSilicoVA' by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, 'NBC' by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of 'Tariff' method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The 'NBC' method is implemented by the 'nbc4va' package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the 'Tariff' method.
Simplifies the creation of Excel .xlsx files by providing a
    high level interface to writing, styling and editing worksheets.
    Through the use of 'Rcpp', read/write times are comparable to the
    'xlsx' and 'XLConnect' packages with the added benefit of removing the
    dependency on Java.
Simplifies the creation of 'xlsx' files by
    providing a high level interface to writing, styling and editing
    worksheets.
Misc methods to form online predictions, for regression-oriented 
    time-series, by combining a finite set of forecasts provided by the user. See 
             Cesa-Bianchi and Lugosi (2006) <doi:10.1017/CBO9780511546921> for an overview.
Provides a collection of utilities that allow programming with 
    R's operators. Routines allow classifying operators, 
    translating to and from an operator and its underlying function, and inverting 
    some operators (e.g. comparison operators), etc. All methods can be extended
    to custom infix operators.
A set of binary operators for common tasks such as regex
        manipulation.
Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.
Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters) first started as an open source project with support of Haag-Streit in 2010. It specifies basic functions that allow many visual field tests to be constructed. As of February 2022 it is fully implemented on the Haag-Streit Octopus 900 with partial implementations on the Centervue Compass, Kowa AP 7000, Android phones and the CrewT IMO. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data.
Designed for performing impact analysis of
  opinions in a digital text document (DTD). The 
  package allows a user to assess the extent to which a theme
  or subject within a document impacts the overall opinion 
  expressed in the document. The package can be applied to a wide 
  range of opinion-based DTD, including commentaries on social media
  platforms (such as 'Facebook', 'Twitter' and 'Youtube'), 
  online products reviews, and so on. 
  The utility of 'opitools' was originally demonstrated 
  in Adepeju and Jimoh (2021) <doi:10.31235/osf.io/c32qh> in the 
  assessment of COVID-19 impacts on neighbourhood policing using 
  Twitter data. Further examples can be found in the vignette of 
  the package.
Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.
A decision support tool for prioritizing conservation projects.
    Prioritizations can be developed by maximizing expected feature richness,
    expected phylogenetic diversity, the number of features that meet
    persistence targets, or identifying a set of projects that meet persistence
    targets for minimal cost. Constraints (e.g. lock in specific actions) and
    feature weights can also be specified to further customize prioritizations.
    After defining a project prioritization problem, solutions can be obtained
    using exact algorithms, heuristic algorithms, or random processes. In
    particular, it is recommended to install the 'Gurobi' optimizer (available
    from <https://www.gurobi.com>) because it can identify optimal solutions
    very quickly. Finally, methods are provided for comparing different
    prioritizations and evaluating their benefits. For more information, see
    Hanson et al. (2019) <doi:10.1111/2041-210X.13264>.
Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) <doi:10.18637/jss.v083.i05>.
Given a certain coverage level, obtains simultaneous confidence
    bands for the survival and cumulative hazard functions such that the area
    between is minimized. Produces an approximate solution based on local time
    arguments.
Computes A-, MV-, D- and E-optimal or near-optimal block designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The algorithms used in this package are based on the treatment exchange and array exchange algorithms of Debusho, Gemechu and Haines (2016, unpublished). The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.
Defines thresholds for breaking data into a number of
  discrete levels, minimizing the (mean) squared error within all bins.
Estimates optimal number of biomarkers for two-group
        classification based on microarray data.
Fast, optimal, and reproducible clustering algorithms for
 circular, periodic, or framed data. The algorithms introduced here
 are based on a core algorithm for optimal framed clustering the authors
 have developed (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573>.
 The runtime of these algorithms is O(K N log^2 N), where K is the number
 of clusters and N is the number of circular data points. On a desktop
 computer using a single processor core, millions of data points can be
 grouped into a few clusters within seconds. One can apply the algorithms
 to characterize events along circular DNA molecules, circular RNA
 molecules, and circular genomes of bacteria, chloroplast, and
 mitochondria. One can also cluster climate data along any given
 longitude or latitude. Periodic data clustering can be formulated as
 circular clustering. The algorithms offer a general high-performance
 solution to circular, periodic, or framed data clustering.
Calculates D-, Ds-, A- and I-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Compares designs via their efficiency, and D-augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs.
Tools to assist in safely applying user generated objective and 
   derivative function to optimization programs. These are primarily function 
   minimization methods with at most bounds and masks on the parameters.
   Provides a way to check the basic computation of objective functions that 
   the user provides, along with proposed gradient and Hessian functions, 
   as well as to wrap such functions to avoid failures when inadmissible parameters 
   are provided. Check bounds and masks. Check scaling or optimality conditions. 
   Perform an axial search to seek lower points on the objective function surface. 
   Includes forward, central and backward gradient approximation codes.
Functions to find near-optimal multi-stage designs for continuous outcomes.
Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).
Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <arXiv:2202.06374> for details of methods.
Provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters.
Likelihood based optimal partitioning and indicator
  species analysis. Finding the best binary partition for each species
  based on model selection, with the possibility to take into account
  modifying/confounding variables as described
  in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>.
  The package implements binary and multi-level response models,
  various measures of uncertainty, Lorenz-curve based thresholding,
  with native support for parallel computations.
A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded.
Parse options from the command-line using a simple, clean syntax. 
    It requires little or no specification and supports short and long options,
    GNU-, Java- or Microsoft- style syntaxes, verb commands and more.
A set of standard benchmark optimization functions for R and
    a common interface to sample them.
Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.
Algorithms for D-, A-, I-, and c-optimal designs. Some of the functions in this package require the 'gurobi' software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.
Methods to calculate optimal design parameters
    for one- and two-stage three-arm group-sequential gold-standard
    non-inferiority trial designs
    with or without binding or nonbinding futility boundaries,
    as described in Meis et al. (2023) <doi:10.1002/sim.9630>.
Functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits.
This is a tool to find the optimal rerandomization threshold in non-sequential experiments. We offer three procedures based 
	on assumptions made on the residuals distribution: (1) normality assumed (2) excess kurtosis assumed (3) entire distribution assumed.
	Illustrations are included. Also included is a routine to unbiasedly estimate Frobenius norms of variance-covariance matrices.
	Details of the method can be found in "Optimal Rerandomization via a Criterion that Provides Insurance Against Failed Experiments"
	Adam Kapelner, Abba M. Krieger, Michael Sklar and David Azriel (2020) <arXiv:1905.03337>.
Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) <doi:10.1093/biomet/asz065>.
Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.
Identify the optimal timing for new treatment initiation during multiple state disease transition, including  multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) <doi:10.18637/jss.v038.i07>.
Computes confidence regions on the location of response surface optima. Response surface models can be up to cubic polynomial models in up to 5 controllable factors, or Thin Plate Spline models in 2 controllable factors.
Provides a set of commands to manage an abstract
        optimization method. The goal is to provide a building block
        for a large class of specialized optimization methods. This
        package manages: the number of variables, the minimum and
        maximum bounds, the number of non linear inequality
        constraints, the cost function, the logging system, various
        termination criteria, etc...
Tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function.  This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed.  The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines.
Provides general purpose tools for helping users to implement steepest
             gradient descent methods for function optimization; for details see
             Ruder (2016) <arXiv:1609.04747v2>. Currently, the Steepest 2-Groups
             Gradient Descent and the Adaptive Moment Estimation (Adam) are the
             methods implemented. Other methods will be implemented in the future.
Flexible optimizer with numerous input specifications for detailed
  parameterisation. Designed for complex loss functions with state and 
  parameter space constraints. Visualization tools for validation and analysis
  of the convergence are included.
Provides a unified framework for numerical optimizers in R, 
    particularly for their inputs and outputs.
Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.
Calculates autoecological data (optima and tolerance ranges) of a biological species
    given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the 
    tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa 
    have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a 
    case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.
Provides a parallel version of the L-BFGS-B method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.
Provides a test of replacement and extension of the optim()
    function to unify and streamline optimization capabilities in R
    for smooth, possibly box constrained functions of several or
    many parameters. This version has a reduced set of methods and is
    intended to be on CRAN.
Provides a building block for optimization algorithms
        based on a simplex. The 'optimsimplex' package may be used in the
        following optimization methods: the simplex method of Spendley
        et al. (1962) <doi:10.1080/00401706.1962.10490033>, the method of 
        Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, Box's algorithm for
        constrained optimization (1965) <doi:10.1093/comjnl/8.1.42>, the 
        multi-dimensional search by Torczon (1989) 
        <https://www.cs.wm.edu/~va/research/thesis.pdf>, etc...
Intended to assist in the choice of the sampling strategy to implement in a survey.
Assessment and diagnostics for comparing competing
    clustering solutions, using predictive models. The main intended
    use is for comparing clustering/classification solutions of
    ecological data (e.g. presence/absence, counts, ordinal scores) to
    1) find an optimal partitioning solution, 2) identify
    characteristic species and 3) refine a classification by merging
    clusters that increase predictive performance. However, in a more
    general sense, this package can do the above for any set of
    clustering solutions for i observations of j variables.
Provides a replacement and extension of the optim()
    function to call to several function minimization codes in R in a single
    statement. These methods handle smooth, possibly box constrained functions 
    of several or many parameters. Note that function 'optimr()' was prepared to
    simplify the incorporation of minimization codes going forward. Also implements some
    utility codes and some extra solvers, including safeguarded Newton methods.
    Many methods previously separate are now included here.
    This is the version for CRAN.
Provides both parametric and non-parametric estimates of the 
  correlates of some desired
  outcome (e.g. test scores, income) using a new method proposed by 
  Danieli, Devi and Fryer (2019). This method relaxes the assumption
  that one can alter individual characteristics in any way the data 
  suggest is optimal, and so can be used anytime one wants to use observational
  data to better optimize social experiments designed to increase some desired
  outcome.
Introduces optional types with some() and none, as well as match_with() from functional languages.
Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.
Simple mechanisms for defining and interpreting package options. Provides
    helpers for interpreting environment variables, global options, defining
    default values and more.
Utilizes the Black-Scholes-Merton option pricing model to 
    calculate key option analytics and perform graphical analysis of various option strategies.
    Provides functions to calculate the option premium and option
    greeks of European-style options.
Optimal scaling of a data vector, relative to a set of targets, is
    obtained through a least-squares transformation subject to appropriate measurement
    constraints. The targets are usually predicted values from a statistical
    model. If the data are nominal level, then the transformation must be
    identity-preserving. If the data are ordinal level, then the
    transformation must be monotonic. If the data are discrete, then tied data
    values must remain tied in the optimal transformation. If the data are
    continuous, then tied data values can be untied in the optimal
    transformation.
A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees.
The real-life data is complex in nature. No single model can capture all aspect of complex time series data. In this package, 14 models, namely Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM, Deep LSTM, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbour (KNN), XGBoost (XGB), Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonality (ETS) and TBATS models, have been implemented and their accuracy have been checked. An PCA based error index has been proposed to select a group of best models using MCS algorithms. After selecting the models, the forecasts from these models have been ensembled using optimization techniques. This package allows to implement 20 optimization techniques, namely, Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), Bat Algorithm (BA), Black Hole Optimization Algorithm (BHO), Clonal Selection Algorithm (CLONALG), Cuckoo Search (CS), Cat Swarm Optimization (CSO), Dragonfly Algorithm (DA), Differential Evolution (DE), Firefly Algorithm (FFA), Genetic Algorithm (GA), Gravitational Based Search Algorithm (GBS), Grasshopper Optimisation Algorithm (GOA), Grey Wolf Optimizer (GWO), Harmony Search Algorithm (HS), Krill-Herd Algorithm (KH), Moth Flame Optimizer (MFO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Shuffled Frog Leaping (SFL) and Whale Optimization Algorithm (WOA). This package has been developed using concept of Wang et al. (2022) <doi:10.1016/j.apm.2022.09.004>, Qu et al. (2022) <doi:10.1016/j.eswa.2022.118746> and Kriz (2019) <doi:10.1007/978-3-030-18195-6_21 >.
Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package 'alabama', or by sequential quadratic programming using solver 'slsqp'.  Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package 'cccp'.
A collection of functions that aid in calculating the optimum
  time to stock hatchery reared fish into a body of water given the growth, 
  mortality and cost of raising a particular number of individuals to a certain 
  length.
The popular population genetic software 'Treemix' by
    'Pickrell and Pritchard' (2012) <DOI:10.1371/journal.pgen.1002967>
    estimates the number of migration edges on a population tree.
    However, it can be difficult to determine the number of migration
    edges to include. Previously, it was customary to stop adding migration
    edges when 99.8% of variation in the data was explained, but 'OptM'
    automates this process using an ad hoc statistic based on the
    second-order rate of change in the log likelihood.  'OptM' also
    has added functionality for various threshold modeling
    to compare with the ad hoc statistic.
Distance based bipartite matching using minimum cost flow, oriented
    to matching of treatment and control groups in observational studies ('Hansen'
    and 'Klopfer' 2006 <doi:10.1198/106186006X137047>). Routines are provided to
    generate distances from generalised linear models (propensity score
    matching), formulas giving variables on which to limit matched distances,
    stratified or exact matching directives, or calipers, alone or in
    combination.
A command line parser inspired by Python's 'optparse' library to
    be used with Rscript to write "#!" shebang scripts that accept short and
    long flag/options.
Contains a set of algorithms for creating
        partitions and coverings of objects largely based on operations
        on (dis)similarity relations (or matrices). There are several
        iterative re-assignment algorithms optimizing different
        goodness-of-clustering criteria.  In addition, there are
        covering algorithms 'clique' which derives maximal cliques, and
        'maxpact' which creates a covering of maximally compact sets.
        Graphical analyses and conversion routines are also included.
Solves linear systems of form Ax=b via Gauss elimination, 
  LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods.
Computes A-, MV-, D- and E-optimal or near-optimal row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all pairwise treatment contrasts. The algorithms used in this package are based on the array exchange and treatment exchange algorithms adopted from Debusho, Gemechu and Haines (2016, unpublished) algorithms after adjusting for the row-column designs setup. The package also provides an optional method of using the graphical user interface (GUI) R package tcltk to ensure that it is user friendly.
Splits initial strata into refined strata that optimize covariate balance. For more information, please email the author for a copy of the accompanying manuscript. To solve the linear program, the 'Gurobi' commercial optimization software is recommended, but not required. The 'gurobi' R package can be installed following the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.
The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. 
             The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.
Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.
Use optimization to estimate weights that balance covariates for binary, multinomial, and continuous treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.
Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data.  The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift.  See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.
Functions for computing the value-at-risk in compound Poisson models. 
	The implementation comprises functions for modeling loss frequencies and loss severities with plain, mixed (Frigessi et al. (2012) <doi:10.1023/A:1024072610684>) or spliced distributions using Maximum Likelihood estimation and Bayesian approaches (Ergashev et al. (2013) <doi:10.21314/JOP.2013.131>). 
	In particular, the parametrization of tail distributions includes the fitting of Tukey-type distributions (Kuo and Headrick (2014) <doi:10.1155/2014/645823>). Furthermore, the package contains the modeling of bivariate dependencies between loss severities and frequencies, Monte Carlo simulation for total loss estimation as well as a closed-form approximation based on Degen (2010) <doi:10.21314/JOP.2010.084> to determine the value-at-risk.
Easy-to-use functions to explore Oracle databases and import data
  into R. User interface for the ROracle package.
Provides details such as Morphine Equivalent Dose (MED), 
    brand name and opioid content which are calculated of all oral opioids 
    authorized for sale by Health Canada based on their Drug Identification Number (DIN). 
    MEDs are calculated based on recommendations by Canadian Institute for Health Information (CIHI) 
    and information obtained from Health Canada's Drug Product Database's monthly data dump. 
    Please note in no way should output from this package be a substitute for medical advise.  
    All medications should only be consumed on prescription from a licensed healthcare provider.
Implements orbit counting using a fast combinatorial approach.
	Counts orbits of nodes and edges from edge matrix or data frame, or a
	graph object from the graph package.
Functions to perform subspace clustering and classification.
Provides clustering of genes with similar 
  dose response (or time course) profiles. It implements the method 
  described by Lin et al. (2012).
I tend to repeat the same code chunks over and over again. At
    first, this was fine for me and I paid little attention to such redundancies.
    A little later, when I got tired of manually replacing Linux filepaths with
    the referring Windows versions, and vice versa, I started to stuff some very
    frequently used work-steps into functions and, even later, into a proper R
    package. And that's what this package is - a hodgepodge of various R functions
    meant to simplify (my) everyday-life coding work without, at the same time,
    being devoted to a particular scope of application.
Solve first order autocorrelation problems using an iterative method. This procedure estimates both autocorrelation and beta coefficients recursively until we reach the convergence (8th decimal as default). The residuals are computed after estimating Beta using EGLS approach and Rho is estimated using the previous residuals.
Implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as
   survey sliders, dose-response relationships and indexes. For more information, see
   Kubinec (2022) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package 'brms', which 
   facilitates a range of regression specifications, including hierarchical, dynamic and
   multivariate modeling.
Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540".
Provides the setup and calculations needed
        to run a likelihood-based continual reassessment method (CRM)
        dose finding trial and performs simulations to assess design
        performance under various scenarios. 3 dose finding designs
        are included in this package: ordinal proportional odds model
        (POM) CRM, ordinal continuation ratio (CR) model CRM, and the
        binary 2-parameter logistic model CRM.
        These functions allow customization of design characteristics
        to vary sample size, cohort sizes, target dose-limiting
        toxicity (DLT) rates, discrete or continuous dose levels,
        combining ordinal grades 0 and 1 into one category, and
        incorporate safety and/or stopping rules.
        For POM and CR model designs, ordinal toxicity grades are
        specified by common terminology criteria for adverse events
        (CTCAE) version 4.0.
        Function 'pseudodata' creates the necessary starting models
        for these 3 designs, and function 'nextdose' estimates the
        next dose to test in a cohort of patients for a target DLT
        rate.
        We also provide the function 'crmsimulations' to assess the
        performance of these 3 dose finding designs under various
        scenarios.
Estimate location-shift models or rating-scale models accounting for response styles (RSRS) for the regression analysis of ordinal responses.
Functions to test/check/verify/investigate the ordering of vectors. 
    The 'is_[strictly_]*' family of functions test vectors for 
    'sorted', 'monotonic', 'increasing', 'decreasing' order; 'is_constant' 
    and 'is_incremental' test for the degree of ordering. `ordering` 
    provides a numeric indication of ordering -2 (strictly decreasing) to 
    2 (strictly increasing).
Order, create and store reports from R.  By defining a
    lightweight interface around the inputs and outputs of an
    analysis, a lot of the repetitive work for reproducible research
    can be automated.  We define a simple format for organising and
    describing work that facilitates collaborative reproducible
    research and acknowledges that all analyses are run multiple
    times over their lifespans.
Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions. 
    The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; 
    Naradajah, S. and Rocha, R. (2016), <DOI:10.18637/jss.v069.i10> and Stasinopoulos, M. and Rigby, R. (2015), <DOI:10.1111/j.1467-9876.2005.00510.x>.
    The families of distributions are: Burr distributions, Feller-Pareto distributions, Generalized Pareto Distributions, The Inverse Paralogistic Distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions, 
    gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, 
    beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, 
    exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, 
    exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distribution, Skew normal type 1 distributions, Skew student t distribution, Sinh-Arcsinh distribution, Sichel distribution, Zero inflated Poisson.
All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
Calculates ordinated diet breadth with some plotting functions.
Implementation of cumulative link (mixed) models also known
    as ordered regression models, proportional odds models, proportional
    hazards models for grouped survival times and ordered logit/probit/...
    models. Estimation is via maximum likelihood and mixed models are fitted
    with the Laplace approximation and adaptive Gauss-Hermite quadrature.
    Multiple random effect terms are allowed and they may be nested, crossed or
    partially nested/crossed. Restrictions of symmetry and equidistance can be
    imposed on the thresholds (cut-points/intercepts). Standard model
    methods are available (summary, anova, drop-methods, step,
    confint, predict etc.) in addition to profile methods and slice
    methods for visualizing the likelihood function and checking
    convergence.
Provides a function for fitting various penalized Bayesian
    cumulative link ordinal response models when the number of parameters
    exceeds the sample size. These models have been described in 
    Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.
Ordinal data classification, clustering and co-clustering using model-based approach with the BOS distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) <doi:10.1007/s11222-015-9585-2>).
A regression framework for response variables which are continuous
    self-rating scales such as the Visual Analog Scale (VAS) used in pain
    assessment, or the Linear Analog Self-Assessment (LASA) scales in quality
    of life studies. These scales measure subjects' perception of an intangible
    quantity, and cannot be handled as ratio variables because of their inherent
    non-linearity. We treat them as ordinal variables, measured on a continuous
    scale. A function (the g function) connects the scale with an underlying
    continuous latent variable. The link function is the inverse of the CDF of the
    assumed underlying distribution of the latent variable. A variety of
    link functions are currently implemented. Such models are described in Manuguerra et al (2020) <doi:10.18637/jss.v096.i08>.
The ordinal forest (OF) method allows ordinal regression with high-dimensional
  and low-dimensional data. After having constructed an OF prediction rule using a training dataset, 
  it can be used to predict the values of the ordinal target variable for new observations.
  Moreover, by means of the (permutation-based) variable importance measure of OF, it is also
  possible to rank the covariates with respect to their importance in the prediction of the 
  values of the ordinal target variable.
  OF is presented in Hornung (2020).
  NOTE: Starting with package version 2.4, it is also possible to obtain class probability 
  predictions in addition to the class point predictions. Moreover, the variable importance values
  can also be based on the class probability predictions. Preliminary results indicate that
  this might lead to a better discrimination between influential and non-influential covariates.
  The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() 
  (prediction of the target variable values of new observations).
  References:
  Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. 
  <doi:10.1007/s00357-018-9302-x>.
It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).
Fits ordinal regression models with elastic net penalty.
    Supported model families include cumulative probability, stopping ratio, 
    continuation ratio, and adjacent category. These families are a subset of 
    vector glm's which belong to a model class we call the elementwise link 
    multinomial-ordinal (ELMO) class. Each family in this class links a vector 
    of covariates to a vector of class probabilities. Each of these families 
    has a parallel form, which is appropriate for ordinal response data, as 
    well as a nonparallel form that is appropriate for an unordered categorical
    response, or as a more flexible model for ordinal data. The parallel model
    has a single set of coefficients, whereas the nonparallel model has a set of
    coefficients for each response category except the baseline category. It is 
    also possible to fit a model with both parallel and nonparallel terms, which 
    we call the semi-parallel model. The semi-parallel model has the flexibility 
    of the nonparallel model, but the elastic net penalty shrinks it toward the 
    parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) 
    <doi:10.18637/jss.v099.i06>.
Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.
We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm.
Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions.
             The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.
Selection, fusion, and/or smoothing of ordinally scaled independent variables using a group lasso, fused lasso or generalized ridge penalty, as well as non-linear principal components analysis for ordinal variables using a second-order difference/smoothing penalty.
Ordination comprises several multivariate exploratory and
    explanatory techniques with theoretical foundations in geometric data
    analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and
    applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for
    foundations.
    Greenacre (2010, ISBN:978-84-923846) shows how the most established of
    these, including principal components analysis, correspondence analysis,
    multidimensional scaling, factor analysis, and discriminant analysis,
    rely on eigen-decompositions or singular value decompositions of
    pre-processed numeric matrix data.
    These decompositions give rise to a set of shared coordinates along which
    the row and column elements can be measured. The overlay of their
    scatterplots on these axes, introduced by Gabriel (1971)
    <doi:10.1093/biomet/58.3.453>, is called a biplot.
    'ordr' provides inspection, extraction, manipulation, and visualization
    tools for several popular ordination classes supported by a set of recovery
    methods. It is inspired by and designed to integrate into 'tidyverse'
    workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.
Provides an alternative to R's built-in functionality for handling
    regular expressions, based on the Onigmo library. Offers first-class
    compiled regex objects, partial matching and function-based substitutions,
    amongst other features.
The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) <doi:10.1122/1.3544591>).
A connector to ORES (<https://ores.wmflabs.org/>), an AI project to provide edit scoring for content
             on Wikipedia and other Wikimedia projects. This lets a researcher identify
             if edits are likely to be reverted, damaging, or made in good faith.
An implementation of the Ordered Forest estimator as developed 
    in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly
    estimates the conditional probabilities of models with ordered categorical
    outcomes (so-called ordered choice models). Additionally to common machine 
    learning algorithms the 'orf' package provides functions for estimating
    marginal effects as well as statistical inference thereof and thus provides
    similar output as in standard econometric models for ordered choice. The
    core forest algorithm relies on the fast C++ forest implementation from
    the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.
Automates and standardizes the import of raw data from Oregon RFID (radio-frequency identification) ORMR (Oregon RFID Multi-Reader) and ORSR (Oregon RFID Single Reader) antenna readers. Compiled data can then be combined within multi-reader arrays for further analysis, including summarizing tag and reader detections, determining tag direction, and calculating antenna efficiency.
A system to help you organize projects. Most analyses have three (or more) main sections: code, results, and data, each with different requirements (version control/sharing/encryption). You provide folder locations and 'org' helps you take care of the details.
Provides functions for quickly creating R and Python scripts, as 
    well as 'Rmarkdown' or Quarto documents with automatically assigned name 
    prefixes. Prefixes are either file counts (e.g. "001") or dates 
    (e.g. "2022-09-26").
Organic/biological mass spectrometry data analysis.
Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc).  Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects.  Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours.  Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files.
Helper functions for Org files (<http://orgmode.org/>):
  a generic function 'toOrg' for transforming R objects into Org
  markup (most useful for data frames; there are also methods for
  Dates/POSIXt) and a function to read Org tables into data frames.
A user-friendly R-based software package for
        gene clustering. Clusters are given by genes matched to
        prespecified profiles across various ordered treatment groups.
        It is particularly useful for analyzing data obtained from
        short time-course or dose-response microarray experiments.
Representations, conversions and display of orientation SO(3) data.
 See the orientlib help topic for details.
A general framework for the application of cross-validation schemes
    to particular functions. By allowing arbitrary lists of results, origami
    accommodates a range of cross-validation applications. This implementation
    was first described by Coyle and Hejazi (2018) <doi:10.21105/joss.00512>.
Automatically adding 'pkg::' to a function, i.e. mutate()
    becomes dplyr::mutate(). It is up to the user to determine which
    packages should be used explicitly, whether to include base R packages
    or use the functionality on selected text, a file, or a complete
    directory. User friendly logging is provided in the 'RStudio' Markers
    pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used
    for checking which packages are actually used in a project.
Functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations.
Algorithm of online regularized k-means to deal with online multi(single) view data.
 The philosophy of the package is described in Guo G. (2020) 
 <doi:10.1080/02331888.2020.1823979>.
Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. <DOI:10.1007/BF01592245>.
	     General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
Help and demo in Spanish of the orloca package. (Ayuda y demo en espanol del paquete orloca.) Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. <DOI: 10.1007/BF01592245>.
	     Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo adhoc Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
A big data version for fitting cumulative probability models using the orm() function.  See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.
An extension to the Regression Modeling Strategies package that
    facilitates plotting ordinal regression  model predictions together with
    confidence intervals for each dependent variable level. 
    It also adds a functionality to plot the  model summary as a modifiable 
    object.
Data input/output functions for data that conform to the
    Digital Imaging and Communications in Medicine (DICOM) standard, part
    of the Rigorous Analytics bundle.
Functions for the input/output and visualization of
    medical imaging data that follow either the 'ANALYZE', 'NIfTI' or 'AFNI'
    formats.  This package is part of the Rigorous Analytics bundle.
Image analysis techniques for positron emission tomography
    (PET) that form part of the Rigorous Analytics bundle.
A data set package with the "Orsi" and "Park/Durand" fronts as
    'SpatialLinesDataFrame' objects. The Orsi et al. (1995) fronts are published at
    the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the 'SEANOE' 
    Altimetry-derived Antarctic Circumpolar Current fronts page, please see package CITATION for details.
Convert odds ratio to relative risk in cohort studies with partial data information (Wang (2013) <doi:10.18637/jss.v055.i05>).
Utilize an orthogonality constrained optimization algorithm of
    Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of
    dimension reduction problems in the semiparametric framework, such as
    Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) 
    <DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <arXiv:1704.05046>
    and Zhou, Zhu & Zeng (2021) <arXiv:1802.06156>. It also serves as a general
    purpose optimization solver for problems with orthogonality constraints.
    Parallel computing for approximating the gradient is enabled
    through 'OpenMP'.
Represents the basis functions for B-splines in a simple matrix
    formulation that facilitates, taking integrals, derivatives, and
    making orthogonal the basis functions.
Implements the orthogonal reparameterization
    approach recommended by Lancaster (2002) to estimate dynamic panel
    models with fixed effects (and optionally: panel specific
    intercepts). The approach uses a likelihood-based estimator and
    produces estimates that are asymptotically unbiased as N goes to
    infinity, with a T as low as 2.
A collection of functions to construct sets of orthogonal
        polynomials and their recurrence relations. Additional
        functions are provided to calculate the derivative, integral,
        value and roots of lists of polynomial objects.
Connects to Google cloud vision <https://cloud.google.com/vision> to perform label detection and repurpose this feature for image classification.
A first implementation of automated parsing of user stories, when used 
  to defined functional requirements for operational research mathematical 
  models. It allows reading user stories, splitting them on the who-what-why template,
  and classifying them according to the parts of the mathematical model that 
  they represent. Also provides semantic grouping of stories, 
  for project management purposes.
Microarray probe ID is not convenient for further enrichment analysis and target gene selection.
    The package is created for the rice microarray probe ID conversion.
    This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research.
    For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes.
    Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.
Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance).
Optimal Subset Cardinality Regression (OSCAR) models offer
    regularized linear regression using the L0-pseudonorm, conventionally
    known as the number of non-zero coefficients. The package estimates an
    optimal subset of features using the L0-penalization via
    cross-validation, bootstrapping and visual diagnostics. Effective
    Fortran implementations are offered along the package for finding
    optima for the DC-decomposition, which is used for transforming the
    discrete L0-regularized optimization problem into a continuous
    non-convex optimization task. These optimization modules include DBDC
    ('Double Bundle method for nonsmooth DC optimization' as described in
    Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited
    Memory Bundle Method for large-scale nonsmooth optimization' as
    in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The
    OSCAR models are comprehensively exemplified in Halkola et al. (2023) 
    <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families
    are supported: Cox, logistic, and Gaussian.
The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.
Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.
Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.
A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011)<doi:10.18637/jss.v043.i11>.
Provides routines for finding an Optimal System of Distinct Representatives (OSDR),
 as defined by D.Gale (1968) <doi:10.1016/S0021-9800(68)80039-0>.
An interface for interacting with 'OSF' (<https://osf.io>). 'osfr'
    enables you to access open research materials and data, or create and
    manage your own private or public projects.
Expands quoted language by recursively replacing any symbol that
    points to quoted language with the language it points to.  The recursive
    process continues until only symbols that point to non-language objects
    remain.  The resulting quoted language can then be evaluated normally.  This
    differs from the traditional 'quote'/'eval' pattern because it resolves
    intermediate language objects that would interfere with evaluation.
Aims to support all features of the system credential store,
    including non-portable ones. Supports 'Keychain' on 'macOS', and
    'Credential Manager' on 'Windows'. See the 'keyring' package if you
    need a portable 'API'.
Function library for the identification and separation of exponentially
    decaying signal components in continuous-wave optically stimulated luminescence measurements.
    A special emphasis is laid on luminescence dating with quartz, which is known for
    systematic errors due to signal components with unequal physical behaviour.
    Also, this package enables an easy to use signal decomposition of
    data sets imported and analysed with the R package 'Luminescence'.
    This includes the optional automatic creation of HTML reports. Further information and tutorials
    can be found at <https://luminescence.de>.
Download and import of 'OpenStreetMap' ('OSM') data as 'sf'
    or 'sp' objects.  'OSM' data are extracted from the 'Overpass' web
    server (<https://overpass-api.de/>) and processed with very fast 'C++'
    routines for return to 'R'.
Match, download, convert and import Open Street Map data extracts 
    obtained from several providers.
Functionality to handle and project lat-long coordinates, easily download background maps
    and add a correct scale bar to 'OpenStreetMap' plots in any map projection.
A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization.
    To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge
    that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was
    introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri-
    Factorization" which will be submitted to BBRC.
Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package or solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See <arXiv:1711.08013> for details.
An interface between R and the 'OSRM' API. 'OSRM' is a routing
    service based on 'OpenStreetMap' data. See <http://project-osrm.org/> for more
    information. This package enables the computation of routes, trips, isochrones and
    travel distances matrices (travel time and kilometric distance).
Wrapper around the 'Open Source Routing Machine (OSRM)'
        API <http://project-osrm.org/>. 'osrmr' works with API versions
        4 and 5 and can handle servers that run locally as well as the
        'OSRM' webserver.
A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power.
Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>.
O-statistics, or overlap statistics, measure the degree of community-level trait overlap. 
    They are estimated by fitting nonparametric kernel density functions to each species’ trait 
    distribution and calculating their areas of overlap. For instance, the median pairwise overlap 
    for a community is calculated by first determining the overlap of each species pair 
    in trait space, and then taking the median overlap of each species pair in a community. 
    This median overlap value is called the O-statistic (O for overlap).
    The Ostats() function calculates separate univariate overlap statistics for each trait, 
    while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. 
    O-statistics can be evaluated against null models to obtain standardized effect sizes. 
    'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale 
    drivers of biodiversity across the National Ecological Observatory Network (NEON)." 
    For more information on this project, see the Macrosystems Biodiversity Website 
    (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in
    Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the
    underlying biological concepts at an undergraduate level is described in Grady et al.
    (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.
Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakowski', 'Zardad Khan', and 'Berthold Lausen' (2020) <arXiv:2005.09043>) is given. 
             The trees are grown by the method of random survival forest ('Marvin Wright', 'Andreas Ziegler' (2017) <doi:10.18637/jss.v077.i01>). 
             The survival trees grown are assessed for both individual and collective performances. 
             The ensemble can give promising results on fewer survival trees selected in the final ensemble.
Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.
Sample from the limiting distributions of empirical Wasserstein
    distances under the null hypothesis and under the alternative. Perform a 
    two-sample test on multivariate data using these limiting distributions and 
    binning.
Generating and validating One-time Password based on 
    Hash-based Message Authentication Code (HOTP) 
    and Time Based One-time Password (TOTP)
    according to RFC 4226 <https://tools.ietf.org/html/rfc4226> and
    RFC 6238 <https://tools.ietf.org/html/rfc6238>.
A wrapper for the 'OpenTripPlanner' <http://www.opentripplanner.org/>
    REST API. Queries are submitted to the relevant 'OpenTripPlanner' API resource, the response
    is parsed and useful R objects are returned.
In the context of data fusion, the package provides a set of functions dedicated to the solving of 'recoding problems' using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.
Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.
Provide methods for estimating optimal treatment regimes in survival contexts 
    with Kaplan-Meier-like estimators when no unmeasured confounding assumption is 
    satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) <doi:10.1111/rssb.12201>) 
    and when no unmeasured confounding assumption fails to hold and a binary instrument 
    is available (Xia, J., Zhan, Z., Zhang, J. (2022) <arXiv:2210.05538>).
A penalized regression framework that can simultaneously estimate 
    the optimal treatment strategy and identify important variables. 
    Appropriate for either censored or uncensored continuous response.
Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter
             et al. (2012) <doi:10.1109/SSP.2012.6319708>, SD-EWMA and TSSD-EWMA see H. Raza et al. 
             (2015) <doi:10.1016/j.patcog.2014.07.028>, KNN-CAD see E. Burnaev et al. (2016)
             <arXiv:1608.04585>, KNN-LDCD see V. Ishimtsev et al. (2017) <arXiv:1706.03412> and 
             CAD-OSE see M. Smirnov (2018) <https://github.com/smirmik/CAD>. The first three 
             algorithms belong to prediction-based techniques and the last three belong to 
             window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms 
             designed to work in stationary environments, while the other four 
             are algorithms designed to work in non-stationary environments.
An implementation of several functions for feature extraction in 
    ordinal time series datasets. Specifically, some of the features proposed by
    Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed.  
    These features can be used to perform inferential tasks or to feed machine
    learning algorithms for ordinal time series, among others. The package also includes some
    interesting datasets containing financial time series. Practitioners from a 
    broad variety of fields could benefit from the general framework provided 
    by 'otsfeatures'.
An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports 
    grading R scripts, R Markdown documents, and R Jupyter Notebooks.
Tools for converting Open-Source Tools for Training Resources (OTTR)
  courses into Leanpub or Coursera courses. 'ottrpal' is for use with the OTTR Template repository to create courses.
Summarizes the taxonomic composition, diversity contribution of the rare and abundant community by using OTU (operational taxonomic unit) table which was generated by analyzing pipeline of 'QIIME' or 'mothur'. The rare biosphere in this package is subset by the relative abundance threshold (for details about rare biosphere please see Lynch and Neufeld (2015) <doi:10.1038/nrmicro3400>).
Analyses of OTU tables produced by 16S rRNA gene amplicon sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2017) "Bacterial community composition and dynamics spanning five years in freshwater bog lakes," <doi: 10.1128/mSphere.00169-17>.
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
Bayesian reconstruction of disease outbreaks using epidemiological
    and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser
    C and Ferguson N. 2014. <doi:10.1371/journal.pcbi.1003457>. Campbell, F,
    Cori A, Ferguson N, Jombart T. 2019. <doi:10.1371/journal.pcbi.1006930>.
Empirical or simulated disease outbreak data, provided either as
    RData or as text files.
Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) <https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.
High-dimensional regression using outer product models.  Research on the methods is currently under investigation and published resources will be posted as they are available.  As the method is new, the website is the best resource for understanding the principals. Some of the core ideas are based on Plumlee and coauthors' work on analysis of grid-structured  experiments described in Plumlee (2014) <doi:10.1080/01621459.2014.900250> and  Plumlee, Erickson, Ankenman, Lawrence (2021) <doi:10.1093/biomet/asaa084>.  Some additional textbooks for additional information on Gaussian processes are Rasmussen and Williams (2005) <doi:10.7551/mitpress/3206.001.0001> and  Gramacy (2022) <doi:10.1201/9780367815493>.
Provides a random forest based implementation of the method
    described in Chapter 7.1.2 (Regression model based anomaly detection)
    of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as
    follows: Each numeric variable is regressed onto all other variables
    by a random forest. If the scaled absolute difference between observed
    value and out-of-bag prediction of the corresponding random forest is
    suspiciously large, then a value is considered an outlier. The package
    offers different options to replace such outliers, e.g. by realistic
    values found via predictive mean matching. Once the method is trained
    on a reference data, it can be applied to new data.
Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using 
    Item Response Theory. Existing outlier ensemble methods from Schubert et al (2012) <doi:10.1137/1.9781611972825.90>, 
    Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002> and Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549>
    are also included.
A collection of some tests commonly used for identifying
        outliers.
Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'.
Outlier detection method that flags suspicious values within observations,
  constrasting them against the normal values in a user-readable format, potentially
  describing conditions within the data that make a given outlier more rare.
  Full procedure is described in Cortes (2020) <arXiv:2001.00636>.
  Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
Functions to process ''outranking'' ELECTRE methods existing in the literature. See, e.g.,
			 <http://en.wikipedia.org/wiki/ELECTRE> about the outranking approach and the foundations of ELECTRE methods.
Create regression tables for publication.
    Currently supports 'lm', 'glm', 'survreg', and 'ivreg' outputs.
Estimates rates for continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al (2012).
Detection of overdispersion in count data for multiple regression analysis.
    Log-linear count data regression is one of the most popular techniques for predictive 
    modeling where there is a non-negative discrete quantitative dependent variable. In 
    order to ensure the inferences from the use of count data models are appropriate, 
    researchers may choose between the estimation of a Poisson model and a negative binomial
    model, and the correct decision for prediction from a count data estimation is directly
    linked to the existence of overdispersion of the dependent variable, conditional to the 
    explanatory variables. Based on the studies of Cameron and Trivedi (1990)
    <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), 
    the overdisp() command is a contribution to researchers, providing a fast and secure 
    solution for the detection of overdispersion in count data. Another advantage is that 
    the installation of other packages is unnecessary, since the command runs in the basic 
    R language.
Provides functions to fit kernel density functions to
 data on temporal activity patterns of animals; estimate coefficients
 of overlapping of densities for two species; and calculate bootstrap
 estimates of confidence intervals.
Functions for estimating the overlapping area of two or more kernel density estimations from empirical data.
Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.
Simplifies MCMC setup by automatically looping through sampling 
    functions and saving the results.  Reduces the memory footprint of running 
    MCMC and saves samples to disk as the chain runs.  Allows samples from the 
    chain to be analyzed while the MCMC is still running.  Provides functions 
    for commonly performed operations such as calculating Metropolis acceptance 
    ratios and creating adaptive Metropolis samplers.  References: Roberts and 
    Rosenthal (2009) <doi:10.1198/jcgs.2009.06134>.
Makes it easy to display descriptive information on a data
    set.  Getting an easy overview of a data set by displaying and
    visualizing sample information in different tables (e.g., time and
    scope conditions).  The package also provides publishable 'LaTeX' code
    to present the sample information.
This tool was designed to assess the sensitivity of research findings to omitted variables when estimating causal effects using propensity score (PS) weighting. This tool produces graphics and summary results that will enable a researcher to quantify the impact an omitted variable would have on their results. Burgette et al. (2021) describe the methodology behind the primary function in this package, ov_sim. The method is demonstrated in Griffin et al. (2020) <doi:10.1016/j.jsat.2020.108075>.
Open the current working directory (or a given directory path) in your computer's file manager.
Tools for collecting municipal-level data <http://www.transparencia.gov.br/swagger-ui.html> from several Brazilian governmental social programs.
An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models.
    They are Crossover model with subject dropout, crossover model with proportional
    first order residual effects and interference model. You can use it to find either
    A-opt or D-opt approximate designs. Exact designs can be automatically rounded
    from approximate designs and relative efficiency is provided as well.
Evaluates the Owen Q-function for an integer value of the
    degrees of freedom, by applying Owen's algorithm (1965)
    <doi:10.1093/biomet/52.3-4.437>.  It is useful for the calculation of
    the power of equivalence tests.
Search and download the data used in over 3,000 Our World in Data charts.
Accesses OpenWeatherMap's (owm) <https://openweathermap.org/> API.
   'owm' itself is a service providing weather data in the past, in the future and now.
   Furthermore, 'owm' serves weather map layers usable in frameworks like 'leaflet'.
   In order to access the API, you need to sign up for an API key. There are free and paid plans.
   Beside functions for fetching weather data from 'owm', 'owmr' supplies
   tools to tidy up fetched data (for fast and simple access) and to show it on leaflet maps.
Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service
 (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes,
 and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. Partial support is provided for the Web Map 
 Service (WMS). The purpose is to add support for additional OGC service standards such as Web Coverage Processing Service (WCPS), the Sensor 
 Observation Service (SOS), or even new standard services emerging such OGC API or SensorThings.
Short hand if-else function to easily switch the values depending
  on a logical condition.
A set of tools that enables using 'OxCal' from within R. 'OxCal' (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. 'OxcAAR' allows simple calibration with 'Oxcal' and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.
Provides utilities for processing of Oxy-Bisulfite microarray data 
  (e.g. via the Illumina Infinium platform, <http://www.illumina.com>) 
  with tandem arrays, one using conventional
  bisulfite conversion, the other using oxy-bisulfite conversion.
Collects a list of your third party R packages, and
    scans them with the 'OSS' Index provided by 'Sonatype', reporting back
    on any vulnerabilities that are found in the third party packages you
    use.
Functions for plotting Australia's coastline and state
  boundaries.
Maps of Australian coastline and administrative regions. Data 
 can be drawn or accessed directly as simple features objects. Includes
 simple functions for country or state maps of Australia and in-built data
 sets of administrative regions from the Australian Bureau of Statistics 
 <https://www.abs.gov.au/>. Layers include electoral divisions and local 
 government areas, simplified from the original sources but with sufficient 
 detail to allow mapping of a local municipality.
Contains functions for data preparation,
	prediction of transition probabilities,
	estimating semi-parametric regression models
	and for implementing nonparametric estimators
	for other quantities. See Meira-Machado and
	Roca-Pardiñas (2011) <doi:10.18637/jss.v038.i03>.
It provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.
Pabon Lasso is a graphical method for monitoring the efficiency of different wards of a hospital or different hospitals.Pabon Lasso graph is divided into 4 parts which are created after drawing the average of BTR and BOR. The part in the left-down side is Zone I, left-up side is Zone II, Right-up side part is Zone III and the last part is Zone IV.
Implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.
Functions to easily convert data to binary formats other programs/machines can understand.
It provides utility functions for investigating changes within R
    packages. The pkgInfo() function extracts package information such as
    exported and non-exported functions as well as their arguments. The
    pkgDiff() function compares this information for two versions of a package
    and creates a diff file viewable in a browser.
Search for R packages on CRAN directly from the R console, based on the packages' titles, short and long descriptions, or other fields. Combine multiple keywords with logical operators ('and', 'or'), view detailed information on any package and keep track of the latest package contributions to CRAN. If you don't want to search from the R console, use the comfortable R Studio add-in.
Compute and visualize the cross-sectional and longitudinal number
    and rank percentile of package downloads from RStudio's CRAN mirror.
Allows to get and cache R package download log files
  from RStudio's CRAN mirror for analyzing package usage.
Algorithms to find arrangements of non-overlapping circles.
A collection of functions to construct Markov model for model-based 
    cost-effectiveness analysis. This includes creating Markov model (both time
    homogenous and time dependent models), decision analysis, sensitivity 
    analysis (deterministic and probabilistic). The package allows estimation 
    of parameters for the Markov model from a given individual patient level 
    data, provided the data file follows some standard data entry rules.
Enforces good practice and provides convenience functions to make work with 'JavaScript' 
  not just easier but also scalable. It is a robust wrapper to 'NPM', 'yarn', and 'webpack' that 
  enables to compartmentalize 'JavaScript' code, leverage 'NPM' and 'yarn' packages, include
  'TypeScript', 'React', or 'Vue' in web applications, and much more.
Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...
Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134).
Manage the R packages your project depends on in an isolated,
    portable, and reproducible way.
An implementation of both the equality and inequality constrained lasso
    functions for the algorithm described in "Penalized and Constrained Optimization"
    by James, Paulson, and Rusmevichientong (Journal of the American Statistical Association, 2019;
    see <http://www-bcf.usc.edu/~gareth/research/PAC.pdf> for a full-text version of the paper). 
    The algorithm here is designed to allow users to define linear constraints (either equality
    or inequality constraints) and use a penalized regression approach to solve the constrained
    problem. The functions here are used specifically for constraints with the lasso formulation,
    but the method described in the PaC paper can be used for a variety of scenarios. In addition
    to the simple examples included here with the corresponding functions, complete code to 
    entirely reproduce the results of the paper is available online through the Journal of the
    American Statistical Association.
Tools to more conveniently perform tasks associated with
        add-on packages. pacman conveniently wraps library and package
        related functions and names them in an intuitive and consistent
        fashion.  It seeks to combine functionality from lower level
        functions which can speed up workflow.
Procrustes analyses to infer co-phylogenetic
    matching between pairs of phylogenetic trees.
Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) <doi:10.1214/22-EJS2051>). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings.
Supplementary utils for CRAN maintainers and R packages developers.
  Validating the library, packages and lock files.
  Exploring a complexity of a specific package like evaluating its size in bytes with all dependencies.
  The shiny app complexity could be explored too.
  Assessing the life duration of a specific package version.
  Checking a CRAN package check page status for any errors and warnings.
  Retrieving a DESCRIPTION or NAMESPACE file for any package version. 
  Comparing DESCRIPTION or NAMESPACE files between different package versions.
  Getting a list of all releases for a specific package.
  The Bioconductor is partly supported.
Provides a broad-view perspective on data via
    linear mapping of data onto a radial coordinate system. The package
    contains functions to visualize the residual values of linear
    regression and Cartesian data in the defined radial scheme. See the
    'pacviz' documentation page for more information:
    <https://pacviz.sriley.dev/>.
Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2020) <doi:10.1186/s12859-020-3475-0>.
Given a vector of Taylor series coefficients of sufficient length
    as input, the function returns the numerator and denominator coefficients
    for the Padé approximant of appropriate order (Baker, 1975)
    <ISBN:9780120748556>.
Transforms datetime data into a format ready for analysis.
    It offers two core functionalities; aggregating data to a higher level interval
    (thicken) and imputing records where observations were absent (pad).
Provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.
Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.
Provides functions to read, process and visualize pairwise sequence 
 alignments in the 'PAF' format used by 'minimap2' and other whole-genome aligners. 
 'minimap2' is described by Li H. (2018) <doi:10.1093/bioinformatics/bty191>.
Use the paged media properties in CSS and the JavaScript
  library 'paged.js' to split the content of an HTML document into discrete
  pages. Each page can have its page size, page numbers, margin boxes, and
  running headers, etc. Applications of this package include books, letters,
  reports, papers, business cards, resumes, and posters.
Quickly and easily add a mini map to your 'rmarkdown' html documents.
A simple way to add page numbers to base/ggplot/lattice graphics.
Pageview data from the 'Wikimedia' sites, such as
    'Wikipedia' <https://www.wikipedia.org/>, from entire projects to per-article
    levels of granularity, through the new RESTful API and data source <https://wikimedia.org/api/rest_v1/?doc>.
Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
Provides an encapsulated, object-oriented class system for
  analyzing bacterial pangenomes. For a definition of this concept, see
  Tettelin, et al. (2005) <doi:10.1073/pnas.0506758102>. It uses the R6
  package as backend. It was designed in order to facilitate and speed-up
  the comparative analysis of multiple bacterial genomes, standardizing and
  optimizing routine tasks performed everyday. There are a handful of things
  done everyday when working with bacterial pangenomes: subset, summarize,
  extract, visualize and store data. So, 'pagoo' is intended to facilitate these
  tasks as much as possible. For a description of the implemented data structure
  and methods, see Ferres & Iraola (2020), <doi:10.1101/2020.07.29.226951>.
Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.
XKCD described a supposedly "bad" colormap that it called a "Painbow" (see <https://xkcd.com/2537/>). But simple tests demonstrate that under some circumstances, the colormap can perform very well, and people can find information that is difficult to detect with the ggplot2 default and even supposedly "good" colormaps like viridis. This library let's you use the Painbow in your own ggplot graphs.
Functions for creating color palettes, visualizing
    palettes, modifying colors, and assigning colors for plotting.
Color palettes generated from paintings.
Plots matrices of colours as grids of coloured squares - aka heatmaps, 
	guaranteeing legible row and column names, 
	without transformation of values, 
	without re-ordering rows or columns,
	and without dendrograms.
Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis tests are provided for analysing paired data with S4 class.
Creates an interactive scatterplot matrix using the D3 JavaScript
    library. See <https://d3js.org/> for more information on D3.
Improving graphics by ameliorating order effects, using Eulerian tours 
    and Hamiltonian decompositions of  graphs. References for the methods presented 
    here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and 
    C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.
Performs the explicit calculation
    -- not estimation! -- of the Rasch item parameters for dichotomous and
    polytomous item responses, using a pairwise comparison approach. Person
    parameters (WLE) are calculated according to Warm's weighted likelihood
    approach.
Calculation of the parametric, nonparametric confidence intervals
 for the difference or ratio of location parameters, nonparametric confidence interval
 for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial
 proportions for comparison of independent samples. Common wrapper functions to split 
 data sets and apply confidence intervals or tests to these subsets.
 A by-statement allows calculation of CI separately for the levels of further factors. 
 CI are not adjusted for multiplicity.
The goal of 'pak' is to make package installation faster and
    more reliable. In particular, it performs all HTTP operations in parallel,
    so metadata resolution and package downloads are fast. Metadata and package
    files are cached on the local disk as well. 'pak' has a dependency solver,
    so it finds version conflicts before performing the installation. This
    version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.
Provides data sets and functions for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>).
Provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab, Pakistan.
Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Several tests of quantitative palaeoenvironmental reconstructions 
  from microfossil assemblages, including the null model tests of the 
  statistically significant of reconstructions developed by Telford and Birks
  (2011) <doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of 
  spatial autocorrelation on transfer function model performance using methods 
  from Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and 
  Trachsel and Telford (2016) <doi:10.5194/cp-12-1215-2016>. Age-depth models with 
  generalized mixed-effect regression from Heegaard et al (2005)
  <doi:10.1191/0959683605hl836rr> are also included.
Provides functionality to support data preparation and exploration for
  palaeobiological analyses, improving code reproducibility and accessibility. The
  wider aim of 'palaeoverse' is to bring the palaeobiological community together 
  to establish agreed standards. The package currently includes functionality for 
  data cleaning, binning (time and space), exploration, summarisation and 
  visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart>)
  and auxiliary functions are also provided. Details can be found in:
  Jones et al., (2022) <doi:10.31223/X5Z94Q>.
Implements sparse regression with paired covariates (Rauschenberger et al. 2020 <doi:10.1007/s11634-019-00375-6>). For the optional shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).
Implementation of the Partitioned Local Depth (PaLD) 
    approach which provides a measure of local depth and the cohesion of a point 
    to another which  (together with a universal threshold for distinguishing 
    strong and weak ties) may be used to reveal local and global structure in 
    data, based on methods described in Berenhaut, Moore, and Melvin (2022)
    <doi:10.1073/pnas.2003634119>. No extraneous inputs, distributional 
    assumptions, iterative procedures nor optimization criteria are employed. 
    This package includes functions for computing local depths and cohesion as
    well as flexible functions for plotting community networks and displays of 
    cohesion against distance.
Includes 19 functions to wrap each endpoint of
    the PaleobioDB API, plus 8 functions to visualize and process the fossil
    data. The API documentation for the Paleobiology Database can be found in
    <http://paleobiodb.org/data1.1/>.
Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.
Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses.
This extension of the poems pattern-oriented modeling (POM) framework
    provides a collection of modules and functions customized for paleontological
    time-scales, and optimized for single-generation transitions and large populations,
    across multiple generations.
Provides tools for transforming, a posteriori time-scaling, and
    modifying phylogenies containing extinct (i.e. fossil) lineages. In particular,
    most users are interested in the functions timePaleoPhy, bin_timePaleoPhy,
    cal3TimePaleoPhy and bin_cal3TimePaleoPhy, which date cladograms of
    fossil taxa using stratigraphic data. This package also contains a large number
    of likelihood functions for estimating sampling and diversification rates from
    different types of data available from the fossil record (e.g. range data,
    occurrence data, etc). paleotree users can also simulate diversification and
    sampling in the fossil record using the function simFossilRecord, which is a
    detailed simulator for branching birth-death-sampling processes composed of
    discrete taxonomic units arranged in ancestor-descendant relationships. Users
    can use simFossilRecord to simulate diversification in incompletely sampled
    fossil records, under various models of morphological differentiation (i.e.
    the various patterns by which morphotaxa originate from one another), and
    with time-dependent, longevity-dependent and/or diversity-dependent rates of
    diversification, extinction and sampling. Additional functions allow users to
    translate simulated ancestor-descendant data from simFossilRecord into standard
    time-scaled phylogenies or unscaled cladograms that reflect the relationships
    among taxon units.
Facilitates analysis of paleontological sequences of trait values.  
    Functions are provided to fit, using maximum likelihood, simple 
    evolutionary models (including unbiased random walks, directional 
    evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and 
    complex models (punctuation, mode shifts).
The choices of color palettes in R can be quite
    overwhelming with palettes spread over many packages with many
    different API's. This packages aims to collect all color palettes
    across the R ecosystem under the same package with a streamlined API.
Streamlines the steps for adding colour scales and associated legends 
         when working with base R graphics, especially for interactive use. Popular
         palettes are included and pretty legends produced when mapping a large 
         variety of vector classes to a colour scale. An additional helper for 
         adding axes and grid lines complements the base::plot() work flow.
Provides a comprehensive library for colour vectors and colour
    palettes using a new family of colour classes (palettes_colour and
    palettes_palette) that always print as hex codes with colour previews.
    Capabilities include: formatting, casting and coercion, extraction and
    updating of components, plotting, colour mixing arithmetic, and colour
    interpolation.
A set of palettes imported from 'Gimp' distributed 
  under GPL3 (<https://www.gimp.org/about/COPYING>), and 'Inkscape' 
  distributed under GPL2 (<https://inkscape.org/about/license/>).
Use Pokemon(R) inspired palettes with additional 'ggplot2' scales.
    Palettes are the colours in each Pokemon's sprite, ordered by how common
    they are in the image. The first 386 Pokemon are currently provided.
R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means for all latitudes.
Functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) <DOI:10.1002/bimj.200610339>, maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) <DOI:10.1002/sim.8046>. The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 <DOI:10.1111/biom.12983>). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys.
Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program.
R Analysis suite for viral RNA dependent RNA polymerase (RdRP). 
    Statistical and meta-data analysis of 'palmscan' output and 'palmDB'/
    'DIAMOND' alignment files. Cross reference an input RNA virus against
    145,000 RdRP identified in the Serratus project.
It is a platform for analyzing longitudinal data from bulk as well as single cell datasets. It allows to identify variations in molecular features within and across donors over longitudinal time points. The analysis can be done on bulk expression dataset without known cell type information or single cell with cell type/user-defined groups. It allows to infer stable and variable features in given donor and each cell type (or user defined group). The outlier analysis can be performed to identify technical/biological perturbed samples in donor/participant. Further, differential analysis can be performed to decipher time-wise changes in gene expression in a cell type.
This is an implementation of model-based trees with global model
  parameters (PALM trees). The PALM tree algorithm is an extension to the MOB
  algorithm (implemented in the 'partykit' package), where some parameters are
  fixed across all groups. Details about the method can be found in Seibold,
  Hothorn, Zeileis (2016) <arXiv:1612.07498>. The package offers coef(),
  logLik(), plot(), and predict() functions for PALM trees.
Colour palettes for data, based on some well known public data
    sets. Includes helper functions to map absolute values to known palettes, and 
    capture the work of image colour mapping as raster data sets.
A comprehensive collection of color palettes, colormaps, and tools to evaluate them.
Functions for easily reading and processing binary data files created by
    'Pamguard' (<https://www.pamguard.org/>). All functions for directly reading the
    binary data files are based on 'MATLAB' code written by Michael Oswald.
Correspondence Analysis of Contingency Tables with Simple
        and Double Structures Superimposed Representations, Intra
        Blocks Correspondence Analysis (IBCA), Weighted Intra Blocks
        Correspondence Analysis (WIBCA).
We propose a pair of summary measures for the predictive power of a prediction
    function based on a regression model. The regression model can be linear
    or nonlinear, parametric, semi-parametric, or nonparametric, and correctly
    specified or mis-specified. The first measure, R-squared, is an extension of
    the classical R-squared statistic for a linear model, quantifying the prediction
    function's ability to capture the variability of the response. The second
    measure, L-squared, quantifies the prediction function's bias for predicting the
    mean regression function. When used together, they give a complete summary of
    the predictive power of a prediction function. Please refer to Gang Li and Xiaoyan Wang (2016) <arXiv:1611.03063> for more details.
Data are partitioned (clustered) into k clusters "around medoids", which is
    a more robust version of K-means implemented in the function pam() in the 'cluster' package.
    The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
    Please refer to the pam() function documentation for more references.
    Clustered data is plotted as a split heatmap allowing visualisation of representative
    "group-clusters" (medoids) in the data as separated fractions of the graph while those
    "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.
Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages.
A collection of miscellaneous functions for passive acoustics. 
    Much of the content here is adapted to R from code written by other people.
    If you have any ideas of functions to add, please contact Taiki Sakai.
The Piece-wise exponential (Additive Mixed) Model
    (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a
    powerful model class for the analysis of survival (or time-to-event) data,
    based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated, competing risks and recurrent events data.
    pammtools provides tidy workflow for survival analysis with PAMMs,
    including data simulation, transformation and other functions for data
    preprocessing and model post-processing as well as visualization.
Tools for loading and processing passive acoustic data. Read in data
    that has been processed in 'Pamguard' (<https://www.pamguard.org/>), apply a suite
    processing functions, and export data for reports or external modeling tools. Parameter 
    calculations implement methods by Oswald et al (2007) <doi:10.1121/1.2743157>,
    Griffiths et al (2020) <doi:10.1121/10.0001229> and Baumann-Pickering et al (2010)
    <doi:10.1121/1.3479549>.
Implements the Panel Data Approach Method for program evaluation as developed in Hsiao, Ching and Ki Wan (2012). pampe estimates the effect of an intervention by comparing the evolution of the outcome for a unit affected by an intervention or treatment to the evolution of the unit had it not been affected by the intervention.
Some functions for sample classification in microarrays.
It provides functions and examples for maximum likelihood estimation for
              generalized linear mixed models and Gibbs sampler for multivariate linear
              mixed models with incomplete data, as described in Schafer JL (1997)
              "Imputation of missing covariates under a multivariate linear mixed model".
              Technical report 97-04, Dept. of Statistics, The Pennsylvania State University.
Provides the tools needed to benchmark the R2 value corresponding to a certain acceptable noise level while also providing a rescaling function based on that noise level yielding a new value of R2 we refer to as R2k which is independent of both the number of degrees of freedom and the noise distribution function.
Provides function for performing Bayesian survival regression using 
    Horseshoe prior in the accelerated failure time model with log normal assumption 
    in order to achieve high dimensional pan-cancer variable selection as developed in
    Maity et. al. (2019) <doi:10.1111/biom.13132>.
Contains some functions catching all messages, 'stdout' and other
    useful information while evaluating R code and other helpers to return user
    specified text elements (like: header, paragraph, table, image, lists etc.)
    in 'pandoc' markdown or several type of R objects similarly automatically
    transformed to markdown format. Also capable of exporting/converting (the
    resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This
    latter reporting feature is supported in brew syntax or with a custom reference
    class with a smarty caching 'backend'.
Provides a set of tools to install, manage and run several
    'Pandoc' versions.
The document converter 'pandoc' <https://pandoc.org/> is widely used
    in the R community. One feature of 'pandoc' is that it can produce and consume
    JSON-formatted abstract syntax trees (AST). This allows to transform a given
    source document into JSON-formatted AST, alter it by so called filters and pass
    the altered JSON-formatted AST back to 'pandoc'. This package provides functions
    which allow to write such filters in native R code. 
    Although this package is inspired by the Python package 'pandocfilters' 
    <https://github.com/jgm/pandocfilters/>, it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a 
    report generator. Since 'pandocfilters' inherits most of it's functionality
    from 'pandoc' it can create documents in many formats 
    (for more information see <https://pandoc.org/>) but is also bound to the same
    limitations as 'pandoc'.
Aggregate Business Tendency Survey Data (and other qualitative
    surveys) to time series at various aggregation levels. Run aggregation of
    survey data in a speedy, re-traceable and a easily deployable way.
    Aggregation is substantially accelerated by use of data.table.
    This package intends to provide an interface that is less general and abstract than data.table but rather geared towards
    survey researchers.
A high performance package implementing random effects and/or
    sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2022) <doi:10.2139/ssrn.2702053>.
Understanding the dynamics of potentially heterogeneous variables is important in statistical applications.
    This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis.
    The methods are developed by Okui and Yanagi (2019) <doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020) <doi:10.1093/ectj/utz019>.
Implements a set of methodological tools
	     that enable researchers to apply matching methods to
	     time-series cross-sectional data. Imai, Kim, and Wang
	     (2021) <http://web.mit.edu/insong/www/pdf/tscs.pdf> 
	     proposes a nonparametric generalization of the
	     difference-in-differences estimator, which does not rely
	     on the linearity assumption as often done in
	     practice. Researchers first select a method of matching
	     each treated observation for a given unit in a
	     particular time period with control observations from
	     other units in the same time period that have a similar
	     treatment and covariate history. These methods include
	     standard matching methods based on propensity score and
	     Mahalanobis distance, as well as weighting methods. Once 
	     matching is done, both short-term and long-term average 
	     treatment effects for the treated can be estimated with 
	     standard errors. The package also offers a visualization 
	     technique that allows researchers to assess the quality 
	     of matches by examining the resulting covariate balance.
Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.
Provides an object type and associated tools for storing and 
  wrangling panel data. Implements several methods for creating regression
  models that take advantage of the unique aspects of 
  panel data. Among other capabilities, automates the "within-between" 
  (also known as "between-within" and "hybrid") panel regression specification
  that combines the desirable aspects of both fixed effects and random effects 
  econometric models and fits them as multilevel models 
  (Allison, 2009 <doi:10.4135/9781412993869.d33>; 
  Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be 
  estimated via generalized estimating equations 
  (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian 
  estimation is (optionally) supported via 'Stan'. 
  Supports estimation of asymmetric effects models via first differences
  (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized
  linear model extension thereof using GEE.
Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. 'panelsummary' extends 'modelsummary' (Arel-Bundock, V. (2022) <doi:10.18637/jss.v103.i01>) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from 'modelsummary' to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation.
We extend two general methods of moment estimators to panel vector 
    autoregression models (PVAR) with p lags of endogenous variables, predetermined 
    and strictly exogenous variables. This general PVAR model contains the first 
    difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, 
    Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator 
    by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also 
    provide specification tests (Hansen overidentification test, lag selection 
    criterion and stability test of the PVAR polynomial) and classical structural 
    analysis for PVAR models such as orthogonal and generalized impulse response 
    functions, bootstrapped confidence intervals for impulse response analysis and 
    forecast error variance decompositions.
Visualizes panel data. It has three main functionalities: (1) it visualizes treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of an outcome variable (or any variable); (3) it visualizes bivariate relationships of two variables by unit or in aggregate.
Leading/lagging a panel, creating dummy variables,
             taking panel differences, looking for panel autocorrelations,
             and more. Implemented via a 'data.table' back end.
Tools to interact with the 'Pangaea' Database
    (<https://www.pangaea.de>), including functions for searching for data,
    fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea'
    'OAI-PMH' service.
A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an  explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations.  In contrast to the classic power-transformations, the methods in this package allows for  theoretical driven user input and the possibility to compare with a non-parametric transformation.
An interface to the API for 'Pan-STARRS1', a data archive of
    the PS1 wide-field astronomical survey.  The package allows access to
    the PS1 catalog and to the PS1 images.  (see
    <https://outerspace.stsci.edu/display/PANSTARRS/> for more
    information).  You can use it to plan astronomical observations, make
    guidance pictures, find magnitudes in five broadband filters (g, r, i,
    z, y) and more.
Sankey diagrams are a powerfull and visually attractive way
  to visualize the flow of conservative substances through a system.
  They typically consists of a network of nodes, and fluxes between them,
  where the total balance in each internal node is 0, i.e. input equals output.
  Sankey diagrams are typically used to display energy systems, material flow accounts etc.
  Unlike so-called alluvial plots, Sankey diagrams also allow for cyclic flows:
  flows originating from a single node can, either direct or indirect, contribute to the input of that same node.
  This package, named after the Greek aphorism Panta Rhei (everything flows),
  provides functions to create publication-quality diagrams, using data in tables (or spread sheets)
  and a simple syntax.
Tools to create dynamic, submission-ready manuscripts, which
  conform to American Psychological Association manuscript guidelines. We
  provide R Markdown document formats for manuscripts (PDF and Word) and
  revision letters (PDF). Helper functions facilitate reporting statistical
  analyses or create publication-ready tables and plots.
Positive predictive value (PPV) defined as the conditional probability of clinical trial assay (CTA) being positive given Companion diagnostic device (CDx) being positive is a key performance parameter for evaluating the clinical validity utility of a companion diagnostic test in clinical bridging studies. When bridging study patients are enrolled based on CTA assay results, Binomial-based confidence intervals (CI) may are not appropriate for PPV CI estimation. Bootstrap CIs which are not restricted by the Binomial assumption may be used for PPV CI estimation only when PPV is not 100%. Bootstrap CI is not valid when PPV is 100% and becomes a single value of [1, 1]. We proposed a risk ratio-based method for constructing CI for PPV. By simulation we illustrated that the coverage probability of the proposed CI is close to the nominal value even when PPV is high and negative percent agreement (NPA) is close to 100%. There is a lack of R package for PPV CI calculation. we developed a publicly available R package along with this shiny app to implement the proposed approach and some other existing methods.
A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based
	     reports and to prettify the output of various estimated models.
This is a data only package, that provides distances from a paper plane experiment.
A simple interface in the form of R6 classes for executing tasks in
    parallel, tracking their progress, and displaying accurate progress bars.
Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.
Define parameter spaces, constraints and
    dependencies for arbitrary algorithms, to program on such spaces. Also
    includes statistical designs and random samplers. Objects are
    implemented as 'R6' classes.
A fast parallelized alternative to R's native 'dist' function to
    calculate distance matrices for continuous, binary, and multi-dimensional
    input matrices, which supports a broad variety of 41 predefined distance
    functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user-
    defined functions written in C++. For ease of use, the 'parDist' function
    extends the signature of the 'dist' function and uses the same parameter
    naming conventions as distance methods of existing R packages. The package
    is mainly implemented in C++ and leverages the 'RcppParallel' package to
    parallelize the distance computations with the help of the 'TinyThread'
    library. Furthermore, the 'Armadillo' linear algebra library is used for
    optimized matrix operations during distance calculations. The curiously
    recurring template pattern (CRTP) technique is applied to avoid virtual
    functions, which improves the Dynamic Time Warping calculations while
    the implementation stays flexible enough to support different DTW step
    patterns and normalization methods.
Parallel computing, multi-core CPU is used to efficiently compute
    and process multi-dimensional soil data.This package includes the 
    parallelized 'Quantile Regression Forests' algorithm for Digital Soil Mapping 
    and is mainly dependent on the package 'quantregForest' and 'snowfall'.
    Detailed references to the R package and the web site are described in the
    methods, as detailed in the method documentation.
Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk,
  and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in
  parallel).
Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package.  For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.
Unified parallelization framework for multiple
    back-end, designed for internal package and interactive usage.  The
    main operation is parallel mapping over lists.  Supports 'local',
    'multicore', 'mpi' and 'BatchJobs' mode.  Allows tagging of the
    parallel operation with a level name that can be later selected by the
    user to switch on parallel execution for exactly this operation.
See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
By sampling your data, running the provided classifier on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular machine learning algorithm and possibly even more accurate predictions.
Application of the Partitioning-Around-Medoids (PAM) clustering algorithm described in Schubert, E. and Rousseeuw, P.J.:
        "Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms." Information Systems,
        vol. 101, p. 101804, (2021). <doi:10.1016/j.is.2021.101804>.
	It uses a binary format for storing and retrieval of matrices developed for the 'jmatrix' package but the functionality of 'jmatrix'
	is included here, so you do not need to install it. Also, it is used by package 'scellpam', so if you have installed it, you do not need
	to install this package.
	PAM can be applied to sets of data whose dissimilarity matrix can be very big. It has been tested with up to 100.000 points.
	It does this with the help of the code developed for other package, 'jmatrix', which allows the matrix not to be loaded in 'R' memory (which
	would force it to be of double type) but it gets from disk, which allows using float (or even smaller data types). Moreover, the
	dissimilarity matrix is calculated in parallel if the computer has several cores so it can open many threads. The initial part
	of the PAM algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has been implemented in parallel. The optimization
	phase implements the FastPAM1 algorithm, also in parallel. Finally, calculation of silhouette is available and also implemented in parallel.
Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the 'pcalg' package but will be much more efficient.
Create a parallel coordinates plot, using 'htmlwidgets' package and 'd3.js'.
Utilities for processing the parameters of various
    statistical models. Beyond computing p values, CIs, and other indices
    for a wide variety of models (see list of supported models using the
    function 'insight::supported_models()'), this package implements
    features like bootstrapping or simulating of parameters and models,
    feature reduction (feature extraction and variable selection) as well
    as functions to describe data and variable characteristics (e.g.
    skewness, kurtosis, smoothness or distribution).
Allows specification and fitting of some parameter estimation
    examples inspired by time-resolved spectroscopy via a Shiny GUI.
Functions for parameter descriptions and operations
    in black-box optimization, tuning and machine learning. Parameters can
    be described (type, constraints, defaults, etc.), combined to
    parameter sets and can in general be programmed on. A useful OptPath
    object (archive) to log function evaluations is also provided.
Performs statistical tests to compare coefficients and residual 
    variance across models. Also provides graphical methods for 
    assessing heterogeneity in coefficients and residuals. Currently supports 
    linear and generalized linear models.
NOTE: 'PARAMLINK' HAS BEEN SUPERSEDED BY THE 'PED SUITE'
    PACKAGES (<https://magnusdv.github.io/pedsuite/>). 'PARAMLINK' IS
    MAINTAINED ONLY FOR LEGACY PURPOSES AND SHOULD NOT BE USED IN NEW
    PROJECTS. A suite of tools for analysing pedigrees with marker data,
    including parametric linkage analysis, forensic computations,
    relatedness analysis and marker simulations. The core of the package
    is an implementation of the Elston-Stewart algorithm for pedigree
    likelihoods, extended to allow mutations as well as complex
    inbreeding. Features for linkage analysis include singlepoint LOD
    scores, power analysis, and multipoint analysis (the latter through a
    wrapper to the 'MERLIN' software). Forensic applications include
    exclusion probabilities, genotype distributions and conditional
    simulations. Data from the 'Familias' software can be imported and
    analysed in 'paramlink'. Finally, 'paramlink' offers many utility
    functions for creating, manipulating and plotting pedigrees with or
    without marker data (the actual plotting is done by the 'kinship2'
    package).
Parametric linkage analysis of monogenic traits in medical
    pedigrees. Features include singlepoint analysis, multipoint analysis
    via 'MERLIN' (Abecasis et al. (2002) <doi:10.1038/ng786>),
    visualisation of log of the odds (LOD) scores and summaries of linkage
    peaks. Disease models may be specified to accommodate phenocopies,
    reduced penetrance and liability classes. 'paramlink2' is part of the
    'ped suite' package ecosystem, presented in 'Pedigree Analysis in R'
    (Vigeland, 2021, ISBN:9780128244302).
An interface to simplify organizing parameters used in a package,
    using external configuration files. This attempts to provide a cleaner
    alternative to options().
This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.
Run simulations or other functions while easily varying parameters
    from one iteration to the next. Some common use cases would be grid search
    for machine learning algorithms, running sets of simulations (e.g.,
    estimating statistical power for complex models), or bootstrapping under
    various conditions. See the 'paramtest' documentation for more information
    and examples.
An implementation of Horn's technique for numerically and graphically evaluating the components or factors retained in a principle components analysis (PCA) or common factor analysis (FA). Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention.
Fast, flexible framework for implementing Bayesian optimization of model 
	hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>.
	The package allows the user to run scoring function in parallel, save intermediary 
	results, and tweak other aspects of the process to fully utilize the computing resources
	available to the user.
Complex graphical representations of data are best explored using interactive elements.
  'parcats' adds interactive graphing capabilities to the 'easyalluvial' package.
  The 'plotly.js' parallel categories diagrams offer a good framework for
  creating interactive flow graphs that allow manual drag and drop sorting of dimensions
  and categories, highlighting single flows and displaying mouse over information. The
  'plotly.js' dependency is quite heavy and therefore is outsourced into a separate package.
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU
Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution
    that are useful for reinsurance pricing. In particular, the package provides
    a non-trivial algorithm that can be used to match the expected losses of a 
    tower of reinsurance layers with a layer-independent collective risk model.
    The theoretical background of the matching algorithm and most other methods
    are described in Ulrich Riegel (2018) <doi:10.1007/s13385-018-0177-3>.
Statistical functions to describe a Pareto Positive Stable (PPS) 
    distribution and fit it to real data. Graphical and statistical tools to 
    validate the fits are included.
Fits Parametric Frailty Models by maximum marginal likelihood.
             Possible baseline hazards:
                 exponential, Weibull, inverse Weibull (Fréchet),
                 Gompertz, lognormal, log-skew-normal, and loglogistic.
             Possible Frailty distributions:
                gamma, positive stable, inverse Gaussian and lognormal.
Provides a parallel estimation method for generalized 
  linear models without compiling with a multithreaded LAPACK or BLAS.
It computes the All-Resolution Inference method in the permutation framework, i.e., simultaneous lower confidence bounds for the number of true discoveries. <arXiv:2012.00368>.
Provides various tools for analysing UK political data, including 
    election result datasets, hexagonal cartograms and functions to 
    retrieve council member data.
Provision of a set of models and methods for use in the allocation and management of capital in financial portfolios.
Parallel estimation of the mutual information based on entropy
    estimates from k-nearest neighbors distances and algorithms for the
    reconstruction of gene regulatory networks
    (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>).
Executes simple parametric models for right-censored 
    survival data.  Functionality emulates capabilities in 'Minitab', including
    fitting right-censored data, assessing fit, plotting survival functions,
    and summary statistics and probabilities.
Enable optimization of parameters of ordinary differential equations.
             Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) <doi:10.1145/1089014.1089020>).
             Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) <doi:10.1155/2018/9160793> and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) <doi:10.3390/make1010010>).
Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.
Collection of functions to get files in parquet format.
    Parquet is a columnar storage file format <https://parquet.apache.org/>. 
    The files to convert can be of several formats 
    ("csv", "RData", "rds", "RSQLite", 
    "json", "ndjson", "SAS", "SPSS"...).
Implements tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.
Parse dates automatically, without the need of
    specifying a format. Currently it includes the git date parser.
    It can also recognize and parse all ISO 8601 formats.
A system to increase the efficiency of dynamic web-scraping with 'RSelenium'
    by leveraging parallel processing. You provide a function wrapper for your 'RSelenium' 
    scraping routine with a set of inputs, and 'parsel' runs it in several browser instances. 
    Chunked input processing as well as error catching and logging ensures seamless 
    execution and minimal data loss, even when unforeseen 'RSelenium' errors occur. You can additionally 
    build safe scraping functions with minimal coding by utilizing constructor functions that act as wrappers 
    around 'RSelenium' methods.
An implementation of a formal grammar and parser for R Markdown documents
    using the Boost Spirit X3 library. It also includes a collection of high level
    functions for working with the resulting abstract syntax tree.
Functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R.
             The package also provides helper functions to manipulate data and execute common procedures
             such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database
             from the downloaded images. All functionalities are parallelized for fast and efficient analyses.
Perform flexible simulation studies using one or multiple computer cores.
          The package is set up to be usable on high-performance clusters in addition
          to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.
A common interface is provided to allow users to specify a
    model without having to remember the different argument names across
    different functions or computational engines (e.g. 'R', 'Spark',
    'Stan', 'H2O', etc).
It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.
A novel tool for generating a piecewise
        constant estimation list of increasingly complex predictors
        based on an intensive and comprehensive search over the entire
        covariate space.
Provides a 'partialised' class that extends the partialising 
    function of 'purrr' by making it easier to change the arguments. This is 
    similar to the function-like object in 'Julia' 
    (<https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects>).
Tests for a comparison of two partially overlapping samples.
  A comparison of means using the partially overlapping samples t-test: 
  See Derrick, Russ, Toher and White (2017), Test 
  statistics for the comparison of means for two samples which include 
  both paired observations and independent observations, Journal of 
  Modern Applied Statistical Methods, 16(1). 
  A comparison of proportions using the partially overlapping samples z-test:
  See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics
  for comparing two proportions with partially overlapping samples.
  Journal of Applied Quantitative Methods, 10(3).
A toolbox to create a particle swarm optimisation (PSO), the package contains two classes: the Particle and the Particle Swarm, this two class is used to run the PSO with methods to easily print, plot and save the result.
Simulating particle movement in 2D space has many application. The
    'particles' package implements a particle simulator based on the ideas 
    behind the 'd3-force' 'JavaScript' library. 'particles' implements all 
    forces defined in 'd3-force' as well as others such as vector fields, traps, 
    and attractors.
A fast and flexible framework for agglomerative
    partitioning. 'partition' uses an approach called
    Direct-Measure-Reduce to create new variables that maintain the
    user-specified minimum level of information. Each reduced variable is
    also interpretable: the original variables map to one and only one
    variable in the reduced data set. 'partition' is flexible, as well:
    how variables are selected to reduce, how information loss is
    measured, and the way data is reduced can all be customized.
    'partition' is based on the Partition framework discussed in Millstein
    et al. (2020) <doi: 10.1093/bioinformatics/btz661>.
A collection of functions that can be used to estimate selection and complementarity effects,
	sensu Loreau & Hector (2001) <doi:10.1038/35083573>, even in cases where data are only available for
	a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the 
	statistical corrections used here is available in Clark et al. (2019) <doi:10.1111/2041-210X.13285>.
Provides several measures ((dis)similarity, distance/metric,
    correlation, entropy) for comparing two partitions of the same set of
    objects. The different measures can be assigned to three different
    classes: Pair comparison (containing the famous Jaccard and Rand
    indices), set based, and information theory based.
    Many of the implemented measures can be found in
    Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006)
    <doi:10.1007/s00357-006-0017-z> and
    Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>.
    Partitions are represented by vectors of class labels which allow a
    straightforward integration with existing clustering algorithms
    (e.g. kmeans()). The package is mostly based on the S4 object system.
partitionMetric computes a distance between two partitions
        of a set.
Additive partitions of integers.  Enumerates the
  partitions, unequal partitions, and restricted partitions of an
  integer; the three corresponding partition functions are also given.
  Set partitions and now compositions and riffle shuffles are
  included.
Miscellaneous utilities for parallelizing large
   computations.  Alternative to MapReduce.
   File splitting and distributed operations such as sort and aggregate.
   "Software Alchemy" method for parallelizing most statistical methods,
   presented in N. Matloff, Parallel Computation for Data Science,
   Chapman and Hall, 2015.  Includes a debugging aid.
Partitioning the R2 of GLMMs into variation explained by each 
    predictor and combination of predictors using semi-partial (part) R2 and
    inclusive R2. Methods are based on the R2 for GLMMs described in
    Nakagawa & Schielzeth (2013) <doi:10.1111/j.2041-210x.2012.00261.x> and
    Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>.
Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.
Datetimes and timestamps are invariably an imprecise notation, with any
    partial representation implying some amount of uncertainty. To handle this,
    'parttime' provides classes for embedding partial missingness as a central
    part of its datetime classes. This central feature allows for more ergonomic
    use of datetimes for challenging datetime computation, including
    calculations of overlapping date ranges, imputations, and more thoughtful
    handling of ambiguity that arises from uncertain time zones. This package was
    developed first and foremost with pharmaceutical applications in mind, but
    aims to be agnostic to application to accommodate general use cases just as
    conveniently.
A computational toolbox for recursive partitioning.
  The core of the package is ctree(), an implementation of
  conditional inference trees which embed tree-structured 
  regression models into a well defined theory of conditional
  inference procedures. This non-parametric class of regression
  trees is applicable to all kinds of regression problems, including
  nominal, ordinal, numeric, censored as well as multivariate response
  variables and arbitrary measurement scales of the covariates. 
  Based on conditional inference trees, cforest() provides an
  implementation of Breiman's random forests. The function mob()
  implements an algorithm for recursive partitioning based on
  parametric models (e.g. linear models, GLMs or survival
  regression) employing parameter instability tests for split
  selection. Extensible functionality for visualizing tree-structured
  regression models is available. The methods are described in
  Hothorn et al. (2006) <doi:10.1198/106186006X133933>,
  Zeileis et al. (2008) <doi:10.1198/106186008X319331> and 
  Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
A toolkit with infrastructure for representing, summarizing, and
  visualizing tree-structured regression and classification models. This
  unified infrastructure can be used for reading/coercing tree models from
  different sources ('rpart', 'RWeka', 'PMML') yielding objects that share
  functionality for print()/plot()/predict() methods. Furthermore, new and improved
  reimplementations of conditional inference trees (ctree()) and model-based
  recursive partitioning (mob()) from the 'party' package are provided based
  on the new infrastructure. A description of this package was published
  by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.
Parse messy geographic coordinates from various character formats
    to decimal degree numeric values. Parse coordinates into
    their parts (degree, minutes, seconds); calculate hemisphere
    from coordinates; pull out individually degrees,
    minutes, or seconds; add and subtract degrees, minutes,
    and seconds. C++ code herein originally inspired from code
    written by Jeffrey D. Bogan, but then completely re-written.
An R package for polygenic trait analysis.
Anomaly detection method based on the paper "Truth will out: Departure-based process-level detection of stealthy attacks on control systems" from Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>. Also referred to the following implementation: <https://github.com/rahulrajpl/PyPASAD>.
Provide summary table of daily physical activity and per-person/grouped heat map for accelerometer data from SenseWear Armband. See <https://templehealthcare.wordpress.com/the-sensewear-armband/> for more information about SenseWear Armband.
Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.
Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. Here we 
	present an 'R' package, 'PASSED', that performs power and sample size calculations for 
	the test of two-sample means or ratios with data following beta, 
	gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal, 
	Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric, and 
	negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>) distributions.
An implementation of the unified framework for assessing partial association 
            between ordinal variables after adjusting for a set of covariates (Dungang Liu, Shaobo 
            Li, Yan Yu and Irini Moustaki (2020), accepted by the Journal of the American 
            Statistical Association). This package provides a set of tools to quantify, visualize, 
            and test partial associations between multiple ordinal variables. It can produce a number
            of $phi$ measures, partial regression plots, 3-D plots, and $p$-values for testing 
            $H_0: phi=0$ or $H_0: phi <= delta$.
Smooths the process of working with country names and codes via 
    powerful parsing, standardization, and conversion utilities arranged in a 
    simple, consistent API. Country name formats include multiple sources 
    including the Unicode Common Locale Data 
    Repository (CLDR, <http://cldr.unicode.org/>) common-sense standardized 
    names in hundreds of languages.
Simulates judgments of frequency and duration based on
    the Probability Associator Time (PASS-T) model. PASS-T is a memory
    model based on a simple competitive artificial neural network. It 
    can imitate human judgments of frequency and duration, which have
    been extensively studied in cognitive psychology
    (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010)
    <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model
    is an extension of the PASS model (Sedlmeier, 2002,
    ISBN:0198508638). The package provides an easy way to run
    simulations, which can then be compared with empirical data in
    human judgments of frequency and duration.
Create random passwords of letters, numbers and punctuation.
Methods to easily extract and manipulate palaeoclimate
  reconstructions for ecological and anthropological analyses, as described
  in Leonardi et al. (2023) <doi:10.1111/ecog.06481>.
Provides a 'RStudio' addin allowing to paste the content of the clipboard as a comment block or as 'roxygen' lines. This is very useful to insert an example in the 'roxygen' block.
Regularisation, decomposition and analysis of space-time series.
  The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff
  <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.
Functions and data sets for the text Probability and Statistics
    with R.
Functions and data sets for the text Probability and Statistics
    with R, Second Edition.
Functions to patch specials in '.dvi' files,
        or entries in '.synctex' files.  Works with concordance=TRUE 
        in Sweave, knitr or R Markdown to link sources to previews.
This utility eases the debugging of literate documents
	     ('noweb' files) by patching the synchronization information
	     (the '.synctex(.gz)' file) produced by 'pdflatex' with
	     concordance information produced by 'Sweave' or 'knitr' and
	     'Sweave' or 'knitr' ; this allows for bilateral communication
	     between a text editor (visualizing the 'noweb' source) and
	     a viewer (visualizing the resultant 'PDF'), thus bypassing
	     the intermediate 'TeX' file.
The 'ggplot2' package provides a strong API for sequentially 
    building up a plot, but does not concern itself with composition of multiple
    plots. 'patchwork' is a package that expands the API to allow for 
    arbitrarily complex composition of plots by, among others, providing 
    mathematical operators for combining multiple plots. Other packages that try 
    to address this need (but with a different approach) are 'gridExtra' and 
    'cowplot'.
Converts TXT and XML data curated by the United States Patent and
    Trademark Office (USPTO). Allows conversion of bulk data after downloading
    directly from the USPTO bulk data website, eliminating need for users to
    wrangle multiple data formats to get large patent databases in tidy,
    rectangular format. Data details can be found on the USPTO website
    <https://bulkdata.uspto.gov/>. Currently, all 3 formats: 1. TXT data
    (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data
    (2005-current) can be converted to rectangular, CSV format.
    Relevant literature that uses data from USPTO includes Wada (2020)
    <doi:10.1007/s11192-020-03674-4> and Plaza & Albert (2008)
    <doi:10.1007/s11192-007-1763-3>.
Provides functions to simplify the 'PatentsView' API
    (<https://patentsview.org/apis/purpose>) query language,
    send GET and POST requests to the API's seven endpoints, and parse the data
    that comes back.
Provides path_chain class and functions, which facilitates loading and saving 
             directory structure in YAML configuration files via 'config' package. 
             The file structure you created during exploration can be transformed 
             into legible section in the config file, and then easily loaded for further usage.
Implementation of simple functions to draw
    basic path diagrams just for visualization purposes.
Enrichment analysis enables researchers to uncover mechanisms 
    underlying a phenotype. However, conventional methods for enrichment 
    analysis do not take into account protein-protein interaction information, 
    resulting in incomplete conclusions. pathfindR is a tool for enrichment 
    analysis utilizing active subnetworks. The main function identifies active 
    subnetworks in a protein-protein interaction network using a user-provided 
    list of genes and associated p values. It then performs enrichment analyses 
    on the identified subnetworks, identifying enriched terms (i.e. pathways or, 
    more broadly, gene sets) that possibly underlie the phenotype of interest.
    pathfindR also offers functionalities to cluster the enriched terms and 
    identify representative terms in each cluster, to score the enriched terms 
    per sample and to visualize analysis results. The enrichment, clustering and 
    other methods implemented in pathfindR are described in detail in 
    Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR: An R Package for 
    Comprehensive Identification of Enriched Pathways in Omics Data Through 
    Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
This is a data-only package, containing data needed to run the CRAN 
    package 'pathfindR', a package for enrichment analysis utilizing active 
    subnetworks. This package contains protein-protein interaction network data, 
    data related to gene sets and example input/output data.
This wrapper houses PathLit API endpoints for R. The usage of
    these endpoints require the use of an API key which can be obtained at
    <https://www.pathlit.io/docs/cli/>.
Functions to compute and display the area-based deviation between spatial paths and to compute a mapping based on minimizing area and distance-based cost.  For details, see: Mueller, S. T., Perelman, B. S., & Veinott, E. S. (2016) <DOI:10.3758/s13428-015-0562-7>.
Functions for computing fit indices for 
    evaluating the path component of latent variable structural equation models. 
    Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated 
    by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by 
    O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) 
    <doi:10.1177/1094428117736137>. Also included are fit indices described by 
    Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.
In causal mediation analysis with multiple causally ordered mediators, a set of path-specific
  effects are identified under standard ignorability assumptions. This package implements an imputation
  approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis
  (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating 
  path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified 
  using the nonparametric bootstrap.
Tools to import, clean, and visualize movement data,
    particularly from motion capture systems such as Optitrack's 
    'Motive', the Straw Lab's 'Flydra', or from other sources. We provide 
    functions to remove artifacts, standardize tunnel position and tunnel 
    axes, select a region of interest, isolate specific trajectories, fill
    gaps in trajectory data, and calculate 3D and per-axis velocity. For 
    experiments of visual guidance, we also provide functions that use 
    subject position to estimate perception of visual stimuli.
A systematic bioinformatics tool to develop a new pathway-based gene panel for tumor mutational burden (TMB) assessment (pathway-based tumor mutational burden, PTMB), using somatic mutations files in an efficient manner from either The Cancer Genome Atlas sources or any in-house studies as long as the data is in mutation annotation file (MAF) format. Besides, we develop a multiple machine learning method using the sample's PTMB profiles to identify cancer-specific dysfunction pathways, which can be a biomarker of prognostic and predictive for cancer immunotherapy.
Creation of patient profile visualizations for
  exploration, diagnostic or monitoring purposes during a clinical trial.
  These static visualizations display a patient-specific overview
  of the evolution during the trial time frame of 
  parameters of interest (as laboratory, ECG, vital signs),
  presence of adverse events, exposure to a treatment; 
  associated with metadata patient information, 
  as demography, concomitant medication.
  The visualizations can be tailored for specific domain(s) or endpoint(s) of interest.
  Visualizations are exported into patient profile report(s)
  or can be embedded in custom report(s).
This is an extension of the 'testthat' package that
    lets you add parameters to your unit tests. Parameterized unit tests
    are often easier to read and more reliable, since they follow the DNRY
    (do not repeat yourself) rule.
Provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see <https://imagej.net> for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width.
Quantification of variation in organismal color patterns as
    obtained from image data. Patternize defines homology between pattern positions
    across images either through fixed landmarks or image registration. Pattern
    identification is performed by categorizing the distribution of colors using RGB
    thresholds or image segmentation.
Creates aesthetically pleasing and informative pie charts, ring charts, bar charts and box plots with colors, patterns, and images.
A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.
A cohesive framework for the spectral and spatial analysis of 
    colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) 
    <doi:10.1111/2041-210X.12069> and Maia, Gruson, Endler & White (2019)
    <doi:10.1111/2041-210X.13174>.
This is a collection of functions to process, format and store accelerometer data.
Interface to Amazon Web Services <https://aws.amazon.com>,
    including storage, database, and compute services, such as 'Simple
    Storage Service' ('S3'), 'DynamoDB' 'NoSQL' database, and 'Lambda'
    functions-as-a-service.
Interface to 'Amazon Web Services' 'analytics' services,
    including 'Elastic MapReduce' 'Hadoop' and 'Spark' big data service,
    'Elasticsearch' search engine, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' application integration
    services, including 'Simple Queue Service' ('SQS') message queue,
    'Simple Notification Service' ('SNS') publish/subscribe messaging, and
    more <https://aws.amazon.com/>.
Functions for making low-level API requests to Amazon Web Services
    <https://aws.amazon.com>. The functions handle building, signing, and
    sending requests, and receiving responses. They are designed to help build 
    higher-level interfaces to individual services, such as Simple Storage
    Service (S3).
Interface to 'Amazon Web Services' compute services,
    including 'Elastic Compute Cloud' ('EC2'), 'Lambda'
    functions-as-a-service, containers, batch processing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' cost management services,
    including cost and usage reports, budgets, pricing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' customer engagement
    services, including 'Simple Email Service', 'Connect' contact center
    service, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' database services,
    including 'Relational Database Service' ('RDS'), 'DynamoDB' 'NoSQL'
    database, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' developer tools services,
    including version control, continuous integration and deployment, and
    more <https://aws.amazon.com/products/developer-tools/>.
Interface to 'Amazon Web Services' end user computing
    services, including collaborative document editing, mobile intranet,
    and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' machine learning services,
    including 'SageMaker' managed machine learning service, natural
    language processing, speech recognition, translation, and more
    <https://aws.amazon.com/machine-learning/>.
Interface to 'Amazon Web Services' management and governance
    services, including 'CloudWatch' application and infrastructure
    monitoring, 'Auto Scaling' for automatically scaling resources, and
    more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' networking and content
    delivery services, including 'Route 53' Domain Name System service,
    'CloudFront' content delivery, load balancing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' security, identity, and
    compliance services, including the 'Identity & Access Management'
    ('IAM') service for managing access to services and resources, and
    more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' storage services,
    including 'Simple Storage Service' ('S3') and more
    <https://aws.amazon.com/>.
Parametric bootstrap (PB) has been used for 
             three-way ANOVA model with unequal group variances.
A lightweight package that adds
  progress bar to vectorized R functions
  ('*apply'). The implementation can easily be added
  to functions where showing the progress is
  useful (e.g. bootstrap). The type and style of the
  progress bar (with percentages or remaining time)
  can be set through options.
  Supports several parallel processing backends including future.
This R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. Both the data analysis and power calculations have command line and graphical interfaces using tcltk.
Generates a position balanced or nearly position balanced block design with given parameters.  This package can also convert a given proper and equireplicate block design into a position balanced or nearly position balanced block design.
Design and implementation of Percentile-based Shewharts Control Charts for continuous data. Faraz (2019) <doi:10.1002/qre.2384>.
Conducts maximum likelihood analysis and simulation of the
    protracted birth-death model of diversification. See
    Etienne, R.S. & J. Rosindell 2012 <doi:10.1093/sysbio/syr091>;
    Lambert, A., H. Morlon & R.S. Etienne 2014, <doi:10.1007/s00285-014-0767-x>;
    Etienne, R.S., H. Morlon & A. Lambert 2014, <doi:10.1111/evo.12433>.
An efficient interface to MPI by utilizing S4
        classes and methods with a focus on Single Program/Multiple Data
        ('SPMD')
        parallel programming style, which is intended for batch parallel
        execution.
Utilizing scalable linear algebra packages mainly
        including 'BLACS', 'PBLAS', and 'ScaLAPACK' in double precision via
        'pbdMPI' based on 'ScaLAPACK' version 2.0.2.
'ZeroMQ' is a well-known library for high-performance
    asynchronous messaging in scalable, distributed applications.  This
    package provides high level R wrapper functions to easily utilize
    'ZeroMQ'. We mainly focus on interactive client/server programming
    frameworks. For convenience, a minimal 'ZeroMQ' library (4.2.2)
    is shipped with 'pbdZMQ', which can be used if no system installation
    of 'ZeroMQ' is available.  A few wrapper functions compatible with
    'rzmq' are also provided.
The PBIB designs are important type of incomplete block designs having wide area of their applications for example in agricultural experiments, in plant breeding, in sample surveys etc. This package constructs various series of PBIB designs and assists in checking all the necessary conditions of PBIB designs and the association scheme on which these designs are based on. It also assists in calculating the efficiencies of PBIB designs with any number of associate classes. The package also constructs Youden-m square designs which are Row-Column designs for the two-way elimination of heterogeneity. The incomplete columns of these Youden-m square designs constitute PBIB designs. With the present functionality, the package will be of immense importance for the researchers as it will help them to construct PBIB designs, to check if their PBIB designs and association scheme satisfy various necessary conditions for the existence, to calculate the efficiencies of PBIB designs based on any association scheme and to construct Youden-m square designs for the two-way elimination of heterogeneity. R. C. Bose and K. R. Nair (1939) <http://www.jstor.org/stable/40383923>.
A set of datasets and functions used in the book
  'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'.
  Datasets either come from real studies or are created to be as similar 
  as possible to real studies.
Make statistical inference on the probability of being in response, 
             the duration of response, and the cumulative response rate up to a given time point. 
             The method can be applied to analyze phase II randomized clinical trials with the endpoints 
             being time to treatment response and time to progression or death.
Provides a vectorized R function for calculating
    probabilities from a standard bivariate normal CDF.
Access data and metadata from 'Microsoft' 'Power BI' ('.pbix', <https://powerbi.microsoft.com>) documents with R. The 'pbixr' package enables one to extract 'Power Query M' formulas (<https://docs.microsoft.com/en-us/power-query/>) 'Data Analysis Expressions' queries ('DAX', <https://docs.microsoft.com/en-us/dax/>) and their properties, report layout and style, and data and data models.
Computes p-values based on (a) Satterthwaite or
    Kenward-Rogers degree of freedom methods and (b) parametric bootstrap
    for mixed effects models as implemented in the 'lme4'
    package. Implements parametric bootstrap test for generalized linear
    mixed models as implemented in 'lme4' and generalized linear
    models. The package is documented in the paper by Halekoh and
    Højsgaard, (2012, <doi:10.18637/jss.v059.i09>).  Please see
    'citation("pbkrtest")' for citation details.
Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>. Hoang H. Nguyen et al. (2015) <doi:10.3390/s150510481>. After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression.
A light-weight package helps you track and visualize
  the progress of parallel version of vectorized R functions (mc*apply).
  Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to
  the lack of fork() functionality, which is essential for mc*apply, on Windows.
Permutation based
    non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) <doi:10.1186/s12864-017-3938-5>: A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper.
Following the method of Bailey et al., computes for a collection
    of candidate models the probability of backtest overfitting, the
    performance degradation and probability of loss, and the stochastic
    dominance.
Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.
Provides three ways to estimate the probability of being in response function (PBRF) 
             The estimates are presented in Tsai, Luo and Crowley (2017) <doi: 10.1007/978-981-10-0126-0_10>.
A collection of software provides R support for 'ADMB'
             (Automatic Differentiation Model Builder) and a 'GUI'
             interface facilitates the conversion of 'ADMB' template
             code to 'C code' followed by compilation to a binary executable.
             Stand-alone functions can also be run by users
             not interested in clicking a 'GUI'.
Functions for solving systems of delay differential equations by
   interfacing with numerical routines written by Simon N. Wood, including
   contributions from Benjamin J. Cairns. These numerical routines first
   appeared in Simon Wood's 'solv95' program. This package includes a vignette
   and a complete user's guide. 'PBSddesolve' originally appeared on CRAN under
   the name 'ddesolve'. That version is no longer supported. The current name
   emphasizes a close association with other 'PBS' packages, particularly
   'PBSmodelling'.
This software has evolved from fisheries research conducted at the
   Pacific Biological Station (PBS) in 'Nanaimo', British Columbia, Canada. It
   extends the R language to include two-dimensional plotting features similar
   to those commonly available in a Geographic Information System (GIS).
   Embedded C code speeds algorithms from computational geometry, such as
   finding polygons that contain specified point events or converting between
   longitude-latitude and Universal Transverse Mercator (UTM) coordinates.
   Additionally, we include 'C++' code developed by Angus Johnson for the
   'Clipper' library, data for a global shoreline, and other data sets in the
   public domain. Under the user's R library directory '.libPaths()',
   specifically in './PBSmapping/doc', a complete user's guide is offered and
   should be consulted to use package functions effectively.
Provides software to facilitate the design, testing, and operation
   of computer models. It focuses particularly on tools that make it easy to
   construct and edit a customized graphical user interface ('GUI'). Although our
   simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk'
   package, a user does not need to know 'Tcl/Tk'. Examples illustrate models
   built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'. 
   A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package
   effectively.
Computes probabilities of the bivariate normal distribution
    in a vectorized R function (Drezner & Wesolowsky, 1990, 
    <doi:10.1080/00949659008811236>).
To seek for a contemporaneous linear transformation for
    a multivariate time series such that the transformed series is segmented
    into several lower-dimensional subseries, and those subseries are
    uncorrelated with each other both contemporaneously and serially.
Methods to detect genetic markers involved in biological
    adaptation. 'pcadapt' provides statistical tools for outlier detection based on
    Principal Component Analysis. Implements the method described in (Luu, 2016)
    <DOI:10.1111/1755-0998.12592>.
A suite of non-parametric, visual tools for assessing differences in data structures
    for two datasets that contain different observations of the same variables. These tools are all 
    based on Principal Component Analysis (PCA) and thus effectively address differences in the structures
    of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, 
    intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue
    (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The
    angle plot presents the information loss when moving from the PCA decomposition of one dataset to the 
    PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby
    presenting the relative weighting and importance of the variables from the original dataset.
Calibrate p-values under a robust perspective using the methods developed
    by Sellke, Bayarri, and Berger (2001) <doi:10.1198/000313001300339950> and obtain 
    measures of the evidence provided by the data in favor of point null hypotheses 
    which are safer and more straightforward to interpret.
Implementations of several methods for principal component analysis 
        using the L1 norm.  The package depends on COIN-OR Clp version >= 
        1.17.4.  The methods implemented are 
        PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, 
        L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, 
        L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>, 
        L1-PCAhp (Visentin, Prestwich and Armagan 2016) 
                 <DOI:10.1007/978-3-319-46227-1_37>, 
        wPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>,
        awPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>,
        PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and
        SharpEl1-PCA (Brooks and Dula, submitted).
Functions for causal structure
  learning and causal inference using graphical models. The main algorithms
  for causal structure learning are PC (for observational data without hidden
  variables), FCI and RFCI (for observational data with hidden variables),
  and GIES (for a mix of data from observational studies
  (i.e. observational data) and data from experiments
  involving interventions (i.e. interventional data) without hidden
  variables). For causal inference the IDA algorithm, the Generalized
  Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC)
  and some related functions are implemented. Functions for incorporating
  background knowledge are provided.
Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, <doi:10.1146/annurev-statistics-031017-100307>). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test.
Compute the price of different types of call using different methods. The types available are Vanilla European Calls, Vanilla American Calls and American Digital Calls. Available methods are Montecarlo Simulation, Montecarlo Simulation with Antithetic Variates, Black-Scholes and the Binary Tree.
Matches cases to controls based on genotype principal components (PC). 
      In order to produce better results, matches are based on the weighted 
      distance of PCs where the weights are equal to the % variance explained 
      by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987)
      <DOI:10.1016/0031-3203(87)90066-5>) is used to determine matches.
Implements principal component analysis, orthogonal rotation and multiple
    factor analysis for a mixture of quantitative and qualitative variables.
Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2022) <doi:10.1101/2022.05.25.493261>, where we implement and propose two RSVD methods. One is based on Yu (2017) <arXiv:1704.07669> single pass RSVD but with power iteration scheme. The other is our new window based RSVD.
Provides functions for robust PCA by projection pursuit.
    The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>,
    Croux et al. (2013) <doi:10.1080/00401706.2012.727746>,
    Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
Provides an R interface to the 'PCATS' API
    <https://pcats.research.cchmc.org/api/__docs__/>,
    allowing R users to submit tasks and retrieve results.
An implementation of the pediatric complex chronic conditions (CCC)
    classification system using R and C++.
Implements methods to automate the Auer-Gervini graphical
  Bayesian approach for determining the number of significant
  principal components. Automation uses clustering, change points, or
  simple statistical models to distinguish "long" from "short" steps
  in a graph showing the posterior number of components as a function
  of a prior parameter. See <doi:10.1101/237883>.
Method extends multivariate and functional dynamic principal components
    to periodically correlated multivariate time series. This package allows you to
    compute true dynamic principal components in the presence of periodicity. 
    We follow implementation guidelines as described in Kidzinski, Kokoszka and
    Jouzdani (2017), in Principal component analysis of periodically correlated
    functional time series <arXiv:1612.00040>.
Contains the functions for generating patterns of segregation, association, complete spatial randomness (CSR))
    and Uniform data in one, two and three dimensional cases, for testing these patterns based on two invariants of
    various families of the proximity catch digraphs (PCDs) (see (Ceyhan (2005) ISBN:978-3-639-19063-2).
    The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011)
    <doi:10.1080/03610921003597211>) and arc density (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>;
    Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>) of for two-dimensional data for visualization of PCDs for one,
    two and three dimensional data. The PCD families considered are Arc-Slice PCDs,
    Proportional-Edge PCDs and Central Similarity PCDs.
Functions for generating progressively Type-II censored data in
    a mixture structure and fitting models using a constrained EM algorithm. It can also
    create a progressive Type-II censored version of a given real dataset to be considered for
    model fitting.
Principal component of explained variance (PCEV) is a statistical
    tool for the analysis of a multivariate response vector. It is a dimension-
    reduction technique, similar to Principal component analysis (PCA), that seeks
    to maximize the proportion of variance (in the response vector) being explained
    by a set of covariates.
Provides convenience functions and pre-programmed
    Stan models related to the paired comparison factor model. Its purpose
    is to make fitting paired comparison data using Stan easy. This
    package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.
We provide several algorithms to compute the genotype ancestry scores (such as eigenvector projections) in the case where highly correlated individuals are involved.
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.
Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm,
             with specific conditional independence tests and modified orientation rules. pcgen extends 
			 the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of 
			 direct genetic effects.
Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.
Piecewise constant hazard models for survival data. 
    The package allows for right-censored, left-truncated, and interval-censored data.
Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then 
			 applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. 
			 The relevant papers are 
			 a) Tsagris M. (2021). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>. 
			 b) Tsagris M. (2022). The FEDHC Bayesian Network Learning Algorithm. Mathematics 2022, 10(15): 2604. <doi:10.3390/math10152604>.
Provides a work-alike to R's POSIXct class which implements
  360- and 365-day calendars in addition to the gregorian calendar.
Estimates the multidimensional polytomous Rasch model (Rasch, 1961) with conditional maximum likelihood estimation.
We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <arXiv:2009.10982>.
A method for fitting the entire regularization path 
    of the principal components lasso for linear and
    logistic regression models. The algorithm uses cyclic coordinate descent
    in a path-wise fashion. See URL below for more information on the algorithm.
    See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression'
    <arXiv:1810.04651>.
Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.
Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019) <doi:10.5506/APhysPolBSupp.12.25>. The suggested geiger package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/geiger/>, suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/distory/>, suggested to take latest version.
Phylogenetic comparative methods represent models of continuous trait 
  data associated with the tips of a phylogenetic tree. Examples of such models 
  are Gaussian continuous time branching stochastic processes such as Brownian 
  motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the 
  tips of the tree as an observed (final) state of a Markov process starting from 
  an initial state at the root and evolving along the branches of the tree. The 
  PCMBase R package provides a general framework for manipulating such models. 
  This framework consists of an application programming interface for specifying 
  data and model parameters, and efficient algorithms for simulating trait evolution 
  under a model and calculating the likelihood of model parameters for an assumed
  model and trait data. The package implements a growing collection of models, 
  which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed 
  Gaussian models, in which different types of the above models can be associated 
  with different branches of the tree. The PCMBase package is limited to 
  trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic 
  models. The PCMFit package provides functionality for inference of 
  these models to tree and trait data. The package web-site 
  <https://venelin.github.io/PCMBase/>
  provides access to the documentation and other resources.
Provides a C++ backend for multivariate phylogenetic comparative
  models implemented in the R-package 'PCMBase'. Can be used in combination
  with 'PCMBase' to enable fast and parallel likelihood calculation. Implements
  the pruning likelihood calculation algorithm described in Mitov et al. (2018) 
  <arXiv:1809.09014>. Uses the 'SPLITT' C++ library for parallel tree traversal 
  described in Mitov and Stadler (2018) <doi:10.1111/2041-210X.13136>.
Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> .  PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.
Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes
 (Zhang et al., 2014 <doi:10.1177/1740774513498322>;
  Lin et al., 2017 <doi:10.18637/jss.v080.i05>).
Computation of the Pedroni (1999) panel cointegration test statistics.  Reported are the empirical and the standardized values.
Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) 
             criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019)
             <https://hal.archives-ouvertes.fr/hal-02002275>. 
             It can be used with univariate and multivariate data.
An implementation of the parameter cascade method in Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.
Fits penalized models for both time-independent and time-dependent survival data. It fully implements elastic net and uses proximal gradient descent to solve the optimization problem. The package is an implementation of Steve Cygu and Benjamin M. Bolker. (2021) <arXiv:2102.02297>.
Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).
Calculates the amplification efficiency and curves from real-time 
  quantitative PCR (Polymerase Chain Reaction) data. Estimates the relative 
  expression from PCR data using the double delta CT and the standard curve 
  methods Livak & Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Tests for 
  statistical significance using two-group tests and linear regression 
  Yuan et al. (2006) <doi: 10.1186/1471-2105-7-85>.
A collection of functions and data sets that support teaching
  a quantitative finance MS level course on Portfolio Construction and Risk
  Analysis, and the writing of a textbook for such a course.  The package is
  unique in providing several real-world data sets that may be used for problem
  assignments and student projects.  The data sets include cross-sections of
  stock data from the Center for Research on Security Prices, LLC (CRSP),
  corresponding factor exposures data from S&P Global, and several SP500 data
  sets.
Extracts features from amplification curve data of quantitative 
    Polymerase Chain Reactions (qPCR) (Pabinger S. et al. (2014) 
    <doi:10.1016/j.bdq.2014.08.002>) for machine learning purposes. Helper 
    functions prepare the amplification curve data for processing as functional 
    data (e.g., Hausdorff distance) or enable the plotting of amplification 
    curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() 
    functions (Burdukiewicz M. et al. (2018) <doi:10.1016/j.bdq.2018.08.001>) 
    can be used to predict amplification curves with an hook effect-like 
    curvature. The pcrfit_single() function can be used to extract features 
    from an amplification curve.
Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
A function to estimate
        panel-corrected standard errors. Data may contain balanced or
        unbalanced panels.
Parallel Constraint Satisfaction (PCS) models are an increasingly
    common class of models in Psychology, with applications to reading and word
    recognition (McClelland & Rumelhart, 1981), judgment and decision making
    (Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several
    other fields (e.g. Read, Vanman, & Miller, 1997). In each of these fields,
    they provide a quantitative model of psychological phenomena, with precise
    predictions regarding choice probabilities, decision times, and often the degree
    of confidence. This package provides the necessary functions to create and
    simulate basic Parallel Constraint Satisfaction networks within R.
Defines functions to describe regression models using only
    pre-computed summary statistics (i.e. means, variances, and covariances)
    in place of individual participant data.
    Possible models include linear models for linear combinations, products, 
    and logical combinations of phenotypes.
    Implements methods presented in 
    Wolf et al. (2021) <doi:10.1101/2021.03.08.433979>
    Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and 
    Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.
The Prize-Collecting Steiner Tree problem asks to find a subgraph
    connecting a given set of vertices with the most expensive nodes and least
    expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm
    does not exist. This package provides convenient functionality for obtaining an
    approximate solution to this problem using loopy belief propagation algorithm.
Functions and example data to teach and
  increase the reproducibility of the methods and code underlying 
  the Propensity to Cycle Tool (PCT), a research project and web application 
  hosted at <https://www.pct.bike/>. 
  For an academic paper on the methods,
  see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.
Classes and methods for modelling and simulation of
    periodically correlated (PC) and periodically integrated time
    series.  Compute theoretical periodic autocovariances and related
    properties of PC autoregressive moving average models. Some original
    methods including Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
    <doi:10.1111/j.1467-9892.1996.tb00281.x>.
Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.
A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, Poisson regression for count outcome, or multi-categorical regression for nominal or ordinal outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
The PDE (Pdf Data Extractor) allows the extraction of 
    information and tables optionally based on search words from 
    PDF (Portable Document Format) files and enables the visualization 
    of the results, both by providing a convenient user-interface.
Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.
Cluster analysis via nonparametric density 
   estimation is performed. Operationally, the kernel method is used throughout to estimate
   the density. Diagnostics methods for evaluating the quality of the clustering 
   are available. The package includes also a routine to estimate the 
   probability density function obtained by the kernel method, given a set of
   data with arbitrary dimensions.
Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A multivariate nonparametric density estimator based on the maximum-entropy method.  Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.
Download economic and financial time series from public sources, 
  including the St Louis Fed's FRED system, Yahoo Finance, the US Bureau of Labor Statistics, 
  the US Energy Information Administration, the World Bank, Eurostat, the European Central Bank,
  the Bank of England, the UK's Office of National Statistics, Deutsche Bundesbank, and INSEE.
Provides an interface to 'PDFMiner' <https://github.com/pdfminer/pdfminer.six> 
    a 'Python' package for extracting information from 'PDF'-files. 
    'PDFMiner' has the goal to get all information available in a 'PDF'-file, 
    position of the characters, font type, font size and informations about lines. 
    Which makes it the perfect starting point for extracting tables from 'PDF'-files.
    More information can be found in the package 'README'-file.
Includes functions for keyword search of pdf files. There is
    also a wrapper that includes searching of all files within a single
    directory.
Allows the user to convert PDF tables to formats more amenable to
  analysis ('.csv', '.xml', or '.xlsx') by wrapping the PDFTables API.
  In order to use the package, the user needs to sign up for an API account
  on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>).
  The package works by taking a PDF file as input, uploading it to PDFTables,
  and returning a file with the extracted data.
Utilities based on 'libpoppler' for extracting text, fonts, attachments and 
    metadata from a PDF file. Also supports high quality rendering of PDF documents into
    PNG, JPEG, TIFF format, or into raw bitmap vectors for further processing in R.
Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.
Computes the euclidean distance between rows of a matrix X
        and rows of another matrix Y.  Previously, this could be done
        by binding the two matrices together and calling 'dist', but
        this creates unnecessary computation by computing the distances
        between a row of X and another row of X, and likewise for Y.
        pdist strictly computes distances across the two matrices, not
        within the same matrix, making computations significantly
        faster for certain use cases.
Measures real distances in pictures. With PDM() function, you can choose one '*.jpg' file, select the measure in mm of scale, starting and and finishing point in the graphical scale, the name of the measure, and starting and and finishing point of the measures. After, ask the user for a new measure.
Fits heterogeneous panel data models with interactive effects for linear regression, logistic, count, probit, quantile, and clustering. Based on Ando, T. and Bai, J. (2015) "A simple new test for slope homogeneity in panel data models with interactive effects" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" <doi: 10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" <doi: 10.1002/jae.2467>, Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" <doi: 10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" <doi: 10.1016/j.jeconom.2020.11.013.>.
Fits a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
Building patient level networks for prediction of 
        medical outcomes and draw the cluster of network.        
        This package is based on paper Personalized disease 
        networks for understanding and predicting cardiovascular 
        diseases and other complex processes 
        (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).
A general framework for constructing partial dependence (i.e.,
        marginal effect) plots from various types machine learning models
        in R.
Looks for amino acid and/or nucleotide patterns and/or small
    ligands coordinated to a given prosthetic centre. Files have to be in the local
    file system and contain proper extension.
Create, transform, and summarize custom random
    variables with distribution functions (analogues of 'p*()', 'd*()',
    'q*()', and 'r*()' functions from base R). Two types of distributions
    are supported: "discrete" (random variable has finite number of output
    values) and "continuous" (infinite number of values in the form of
    continuous random variable). Functions for distribution
    transformations and summaries are available. Implemented approaches
    often emphasize approximate and numerical solutions: all distributions
    assume finite support and finite values of density function; some
    methods implemented with simulation techniques.
A collection of tools for approximating the 'PDQ' functions
    (respectively, the cumulative distribution, density, and quantile) of
    probability distributions via classical expansions involving moments and
    cumulants.
Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.
Compute and tune some positive definite and sparse covariance
  estimators.
Interactive shiny application for working with Probability Distributions. Calculations and  Graphs are provided.
An implementation of data analysis tools for samples of symmetric or 
  Hermitian positive definite matrices, such as collections of covariance matrices 
  or spectral density matrices. The tools in this package can be used to perform: (i) 
  intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive 
  definite matrices with applications to dimension reduction, denoising and clustering in the 
  space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference 
  for samples of positive definite matrices by means of intrinsic data depth functions and 
  rank-based hypothesis tests in the space of Hermitian positive definite matrices.
Permutation (randomisation) test for single-case phase design data with 
    two phases (e.g., pre- and post-treatment). Correction for dependency of observations 
    is done through stepwise resampling the time series while varying 
    the distance between observations. The required distance 0,1,2,3.. is determined 
    based on repeated dependency testing while stepwise increasing the distance.
    In preparation: Vroegindeweij et al. "A Permutation distancing test 
    for single-case observational AB phase design data: A Monte Carlo simulation study".
The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation. 
	     The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation. 
	     Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different 
	     data transformation and modeling tasks such as: 
	     imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and 
	     information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering, 
	     area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity, 
	     heterogeneity, discriminatory and predictive power of the model.
An engaging collection of datasets from Portland Parks and Recreation. The city of Portland inventoried every tree in over 170 parks and along the streets in 96 neighborhoods.
Linear dynamic panel data modeling based on linear and
    nonlinear moment conditions as proposed by
    Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>,
    Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>,
    and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>.
    Estimation of the model parameters relies on the Generalized
    Method of Moments (GMM), numerical optimization (when nonlinear
    moment conditions are employed) and the computation of closed
    form solutions (when estimation is based on linear moment
    conditions). One-step, two-step and iterated estimation is
    available. For inference and specification
    testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005>
    and doubly corrected standard errors
    (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>)
    are available. Additionally, serial correlation tests, tests for
    overidentification, and Wald tests are provided. Functions for
    visualizing panel data structures and modeling results obtained
    from GMM estimation are also available. The plot methods include
    functions to plot unbalanced panel structure, coefficient ranges
    and coefficient paths across GMM iterations (the latter is
    implemented according to the plot shown in
    Hansen and Lee, 2021 <doi:10.3982/ECTA16274>).
    For a more detailed description of the functionality, please
    see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>.
These are useful tools and data sets for the study of quantitative 
    peace science. The goal for this package is to include tools and data sets
    for doing original research that mimics well what a user would have to previously
    get from a software package that may not be well-sourced or well-supported.
    Those software bundles were useful the extent to which they encourage replications 
    of long-standing analyses by starting the data-generating process from scratch. However, 
    a lot of the functionality can be done relatively quickly and more transparently
    in the R programming language.
A meta gene set analysis tool developed based on principles of Pareto dominance (William B T Mock (2011) <doi:10.1007/978-1-4020-9160-5_341>). It is designed to combine gene set analysis p-values from multiple transcriptome datasets (e.g., microarray and RNA-Seq). The novel Pareto method for p-value combination allows PEACH to properly model heterogeneity and correlation in Omics datasets.
The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample
             test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, 
             are provided to compute the test statistics in two and three dimensional spaces, respectively. 
             Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is 
             a variant of the Peacock test.
Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series.
Chromatin immunoprecipitation DNA sequencing results in genomic
    tracks that show enriched regions or peaks where proteins are bound.
    This package implements fast C code that computes the true and false
    positives with respect to a database of annotated region labels.
When working with big data sets, RAM conservation is critically
    important. However, it is not always enough to just monitor the
    size of the objects created. So-called "copy-on-modify" behavior,
    characteristic of R, means that some expressions or functions may
    require an unexpectedly large amount of RAM overhead. For example,
    replacing a single value in a matrix duplicates that matrix in the
    back-end, making this task require twice as much RAM as that used
    by the matrix itself. This package makes it easy to monitor the total
    and peak RAM used so that developers can quickly identify and
    eliminate RAM hungry code.
Disk-based implementation of
 Functional Pruning Optimal Partitioning with up-down constraints
 <doi:10.18637/jss.v101.i10> for single-sample peak calling
 (independently for each sample and genomic problem),
 can handle huge data sets (10^7 or more).
A quadratic time dynamic programming algorithm
 can be used to compute an approximate solution to the problem of
 finding the most likely changepoints
 with respect to the Poisson likelihood, subject
 to a constraint on the number of segments, and the changes which must
 alternate: up, down, up, down, etc. For more info read
 <http://proceedings.mlr.press/v37/hocking15.html>
 "PeakSeg: constrained optimal segmentation and supervised penalty learning
 for peak detection in count data" by TD Hocking et al,
 proceedings of ICML2015.
Jointly segment several ChIP-seq samples to find the peaks 
 which are the same and different across samples. The fast approximate
 maximum Poisson likelihood algorithm is described in
 "PeakSegJoint: fast supervised peak detection via joint segmentation
 of multiple count data samples"
 <arXiv:1506.01286> by TD Hocking and G Bourque.
Computes optimal changepoint models using the
 Poisson likelihood for non-negative count data,
 subject to the PeakSeg constraint:
 the first change must be up, second change down, third change up, etc.
 For more info about the models and algorithms,
 read "A log-linear time algorithm for constrained changepoint detection"
 <arXiv:1703.03352> by TD Hocking et al.
Supports maximum likelihood inference for the Pearson VII
    distribution with shape parameter 3/2 and free location and scale
    parameters. This distribution is relevant when estimating the velocity of
    processive motor proteins with random detachment.
Implementation of the Pearson distribution system, including full
  support for the (d,p,q,r)-family of functions for probability distributions 
  and fitting via method of moments and maximum likelihood method.
The Pearson-ICA algorithm is a mutual information-based
        method for blind separation of statistically independent source
        signals. It has been shown that the minimization of mutual
        information leads to iterative use of score functions, i.e.
        derivatives of log densities. The Pearson system allows
        adaptive modeling of score functions. The flexibility of the
        Pearson system makes it possible to model a wide range of
        source distributions including asymmetric distributions. The
        algorithm is designed especially for problems with asymmetric
        sources but it works for symmetric sources as well.
Validation of risk predictions obtained from survival models and
    competing risk models based on censored data using inverse weighting and
    cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.
It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.
Data files and documentation for PEDiatric vALidation oF vAriableS
    in TBI (PEDALFAST).  The data was used in "Functional Status Scale in
    Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett,
    Dixon, et al  (2016) <doi:10.1097/PCC.0000000000000934>.
Data and utilities for estimating pediatric blood pressure
    percentiles by sex, age, and optionally height (stature) as described in
    Martin et.al. (2022) <doi:10.1001/jamanetworkopen.2022.36918>.
    Blood pressure percentiles for children under one year of age come from Gemelli
    et.al. (1990) <doi:10.1007/BF02171556>.  Estimates of blood pressure
    percentiles for children at least one year of age are informed by
    data from the National Heart, Lung, and Blood Institute (NHLBI) and the
    Centers for Disease Control and Prevention (CDC)
    <doi:10.1542/peds.2009-2107C> or from Lo et.al. (2013)
    <doi:10.1542/peds.2012-1292>.  The flowchart for selecting the informing
    data source comes from Martin et.al. (2022)
    <doi:10.1542/hpeds.2021-005998>.
Reconstruct pedigrees from genotype data, by optimising the likelihood 
    over all possible pedigrees subject to given restrictions. Tailor-made plots
    facilitate evaluation of the output. This package is part of the 'ped suite'
    ecosystem for pedigree analysis. In particular, it imports 'pedprobr' for 
    calculating pedigree likelihoods and 'forrel' for estimating pairwise relatedness.
Gene-level variant association tests with disease status for pedigree data: kernel and burden association statistics.
Provides functions to estimate mixed probit models using, for 
    instance, pedigree data like in <doi:10.1002/sim.1603>. The models are also 
    commonly called liability threshold models. The approximation is 
    based on direct log marginal likelihood approximations like the randomized 
    Quasi-Monte Carlo suggested by <doi:10.1198/106186002394> with a similar 
    procedure to approximate the derivatives. The minimax tilting method 
    suggested by <doi:10.1111/rssb.12162> is also supported. Graph-based methods 
    are also provided that can be used to simplify pedigrees.
A collection of functions for modelling mutations in
    pedigrees with marker data, as used e.g. in likelihood computations
    with microsatellite data. Implemented models include equal,
    proportional and stepwise models, as well as random models for
    experimental work, and custom models allowing the user to apply any
    valid mutation matrix.  Allele lumping is done following the
    lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.
An R implementation of methods employed in the field of pedometrics, soil science
    discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil
    using statistical and computational methods. The methods found here include the calibration of
    linear regression models using covariate selection strategies, computation of summary validation
    statistics for predictions, generation of summary plots, evaluation of the local quality of a
    geostatistical model of uncertainty, and so on. Other functions simply extend the
    functionalities of or facilitate the usage of functions from other packages that are commonly
    used for the analysis of soil data. Formerly available versions of suggested packages no longer
    available from CRAN can be obtained from the CRAN archive
    <https://cran.r-project.org/src/contrib/Archive/>.
An implementation of the Elston-Stewart algorithm for
    calculating pedigree likelihoods given genetic marker data (Elston and
    Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is
    extended to allow inbred founders. 'pedprobr' is part of the 'ped
    suite', a collection of packages for pedigree analysis in R. In
    particular, 'pedprobr' depends on 'pedtools' for pedigree
    manipulations and 'pedmut' for mutation modelling. For more
    information, see 'Pedigree Analysis in R' (Vigeland, 2021,
    ISBN:9780128244302).
Provides an interface to access public economic and financial data for 
    economic research and quantitative analysis. The data sources including 
    NBS, FRED, 163, Sina, Eastmoney and etc. It also provides quantitative 
    functions for trading strategies based on the 'data.table', 'TTR', 
    'PerformanceAnalytics' and etc packages.
Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes.
   Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>;
   Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.
The 'ped suite' is a collection of packages for pedigree
    analysis, covering applications in forensic genetics, medical genetics
    and more. A detailed presentation of the 'ped suite' is given in the
    book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN: 9780128244302).
A comprehensive collection of tools for creating,
    manipulating and visualising pedigrees and genetic marker data.
    Pedigrees can be read from text files or created on the fly with
    built-in functions. A range of utilities enable modifications like
    adding or removing individuals, breaking loops, and merging pedigrees.
    An online tool for creating pedigrees interactively, based on
    'pedtools', is available at <https://magnusdv.shinyapps.io/quickped>.
    'pedtools' is the hub of the 'ped suite', a collection of packages for
    pedigree analysis. A detailed presentation of the 'ped suite' is given
    in the book 'Pedigree Analysis in R' (Vigeland, 2021,
    ISBN:9780128244302).
Enables direct cloud access to health care decision models hosted on the PRISM server of the Peer Models Network.
Provides functions to perform the peer performance
    analysis of funds' returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.
Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.
The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.
Functions and mined database from 'UniProt' focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2022) <bioRxiv:10.1101/2022.11.09.515610>.
Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, 'ART' and 'SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.
Bayesian supervised predictive classifiers, hypothesis testing, and parametric estimation under Partition Exchangeability are implemented. The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities.
Historic Pell grant data as provided by the US Department of Education. 
    This package contains data about how much pell grant was awarded by which institution in 
    which year. 
    This data comes from the US Department of Education. Raw data can be downloaded from here: <https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html>.
An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.
Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper
    (2023) <doi:10.31234/osf.io/6phs5>. In meta-analysis, there are
    often between-study differences. These can be coded as moderator variables,
    and controlled for using meta-regression. However, if the number of
    moderators is large relative to the number of studies, such an analysis may
    be overfit. Penalized meta-regression is useful in these cases, because
    it shrinks the regression slopes of irrelevant moderators towards zero.
Utility functions for the handling, analysis and visualisation 
    of data from portable emissions measurement systems ('PEMS') and other 
    similar mobile activity monitoring devices. The package includes a dedicated 
    'pems' data class that manages many of the quality control, unit handling 
    and data archiving issues that can hinder efforts to standardise 'PEMS' 
    research.
We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in Tian et al. (2023) <arXiv:2302.02310>.
The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022+) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, to appear in Statistics in Medicine <doi:10.1002/sim.9264>.
Fitting possibly high dimensional penalized
        regression models. The penalty structure can be any combination
        of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
        positivity constraint on the regression coefficients. The
        supported regression models are linear, logistic and Poisson
        regression and the Cox Proportional Hazards model.
        Cross-validation routines allow optimization of the tuning
        parameters.
Utilize the CDF penalty function to estimate a penalized linear model. 
  It enables you to display some graphical representations and determine whether the Karush-Kuhn-Tucker conditions are met. 
  For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) <arXiv:2212.08582>.
Implements the penalized LDA proposal of "Witten and Tibshirani (2011), Penalized classification using Fisher's linear discriminant, to appear in Journal of the Royal Statistical Society, Series B".
Support Vector Machine (SVM) classification with simultaneous feature selection using penalty
        functions is implemented. The smoothly clipped absolute deviation (SCAD),
        'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
        SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
        parameters can be found using either a fixed grid or a interval
        search.
Implementations of algorithms from 
 Learning Sparse Penalties for Change-point Detection
 using Max Margin Interval Regression, by
 Hocking, Rigaill, Vert, Bach
 <http://proceedings.mlr.press/v28/hocking13.html>
 published in proceedings of ICML2013.
Computes penalized regression calibration (PRC), a
    statistical method that allows to predict survival from
    high-dimensional longitudinal predictors. PRC is described in
    Signorelli et al. (2021) <doi:10.1002/sim.9178>.
Estimating Non-Simplified Vine Copulas Using Penalized Splines.
A regularization approach for Cox Frailty Models by penalization methods is provided.
Estimation of univariate (conditional) densities using penalized B-splines with automatic selection of optimal smoothing parameter.
Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines.
Fits single- and multiple-group penalized factor analysis models 
    via a trust-region algorithm with integrated automatic multiple tuning 
    parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). 
    Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.
Currently incorporate the generalized odds-rate model (a type of linear 
    transformation model) for interval-censored data based on penalized monotonic B-Spline. 
    More methods under other semiparametric models such as cure model or additive model will
    be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. 
    (2019) <arXiv:1912.11703>.
Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).
Implementation of the semi-parametric proportional-hazards (PH) of Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x> extended to time-varying covariates. Estimation and variable selection are based on the methodology described in Beretta and Heuchenne (2019) <doi:10.1080/02664763.2018.1554627>; confidence intervals of the parameter estimates may be computed using a bootstrap approach. Moreover, data following the PH cure model may be simulated using a method similar to Hendry (2014) <doi:10.1002/sim.5945>, where the event-times are generated on a continuous scale from a piecewise exponential distribution conditional on time-varying covariates.
A set of tools that enables efficient estimation of penalized 
    Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models 
    that feature one or more sets of high-dimensional fixed effects. The methodology is based on 
    Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> 
    and takes advantage of the method of alternating projections of Gaure (2013) 
    <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as 
    the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) 
    <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out 
    cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) 
    <doi:10.1080/07350015.2015.1102733>.
Robust penalized (adaptive) elastic net S and M estimators for
    linear regression. The methods are proposed in
    Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
    (2019) <https://projecteuclid.org/euclid.aoas/1574910036>.
    The package implements the extensions and algorithms described in
    Kepplinger, D. (2020) <doi:10.14288/1.0392915>.
Simulation of continuous, correlated high-dimensional data with 
    time to event or binary response, and parallelized functions for Lasso, 
    Ridge, and Elastic Net penalized regression with repeated starts and 
    two-dimensional tuning of the Elastic Net.
Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".
Data sets for statistical inference modeling related to People Analytics.  
  Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' 
  by Keith McNulty (2020).
Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor.
Designed for prediction error estimation
        through resampling techniques, possibly accelerated by parallel
        execution on a compute cluster. Newly developed model fitting
        routines can be easily incorporated. Methods used in the package are detailed in
        Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062>
        and were used, for instance, in
        Porzelius Ch., Schumacher M.and  Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.
Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. Details of the implemented model can be found in Demarqui et al. (2008) <doi:10.1007/s10985-008-9086-0>.
A PEP, or Portable Encapsulated Project, is a dataset that 
    subscribes to the PEP structure for organizing metadata. It is written using
    a simple YAML + CSV format, it is your one-stop solution to metadata 
    management across data analysis environments. This package reads this 
    standardized project configuration structure into R.
An implementation of the data processing and data analysis portion
    of a pipeline named the PepSAVI-MS which is currently under development by
    the Hicks laboratory at the University of North Carolina.  The statistical
    analysis package presented herein provides a collection of software tools
    used to facilitate the prioritization of putative bioactive peptides from a
    complex biological matrix.  Tools are provided to deconvolute mass
    spectrometry features into a single representation for each peptide charge
    state, filter compounds to include only those possibly contributing to the
    observed bioactivity, and prioritize these remaining compounds for those
    most likely contributing to each bioactivity data set.
Evaluation of diversity in peptide libraries, including NNN, NNB,
    NNK/S, and 20/20 schemes. Custom encoding schemes can also be defined.
    Metrics for evaluation include expected coverage, relative efficiency, and
    the functional diversity of the library. Peptide-level inclusion
    probabilities are computable for both the native and custom encoding
    schemes.
Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' output files from the 'GROMACS' molecular dynamics package.
Moderated regression with mean and residual centering and simple slopes analysis.
Perform permutation-based hypothesis testing for randomized
    experiments as suggested in Ludbrook & Dudley (1998) <doi:10.2307/2685470>
    and Ernst (2004) <doi:10.1214/088342304000000396>, introduced in Pham et al.
    (2022) <doi:10.1016/j.chemosphere.2022.136736>.
To find the certainty of dominance interactions with indirect
    interactions being considered.
Contains functions which allow the user to compute confidence intervals quickly using the double bootstrap-based percentile calibrated ('perc-cal') method for linear regression coefficients. 'perccal_interval()' is the primary user-facing function within this package.
An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.
Calculate (stratified) percentiles on a data.frame
    Stratification will split the data.frame into subgroups and calculate percentiles for each independently.
Reconstructs perfect phylogeny at a user-given focal point and to depict and test association in a genomic region based on the reconstructed partitions. Charith B Karunarathna and Jinko Graham (2019) <bioRxiv:10.1101/674523>.
Several person-fit statistics 
  (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) 
  are offered. These statistics allow assessing whether
  individual response patterns to tests or questionnaires are (im)plausible given 
  the other respondents in the sample or given a specified item response theory model. Some PFSs apply to 
  dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs 
  (personal biserial correlation, caution index, (normed) number of Guttman errors, 
  agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include
  extensions of lz, U3, and (normed) number of Guttman errors.
Implementation of different performance measures for classification and ranking tasks  including  Area Under the Receiving Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC), precision at a given recall, F-score for single and multiple classes.
Utilities for computing measures to assess model quality,
    which are not directly provided by R's 'base' or 'stats' packages.
    These include e.g. measures like r-squared, intraclass correlation
    coefficient (Nakagawa, Johnson & Schielzeth (2017)
    <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to
    check models for overdispersion, singularity or zero-inflation and
    more. Functions apply to a large variety of regression models,
    including generalized linear models, mixed effects models and Bayesian
    models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.
Collection of econometric functions for performance and risk 
    analysis. In addition to standard risk and performance metrics, this 
    package aims to aid practitioners and researchers in utilizing the latest
    research in analysis of non-normal return streams.  In general, it is most 
    tested on return (rather than price) data on a regular scale, but most 
    functions will work with irregular return data as well, and increasing
    numbers of functions will work with P&L or price data where possible.
An infra-structure for estimating the predictive performance of
    predictive models. In this context, it can also be used to compare and/or select
    among different alternative ways of solving one or more predictive tasks. The
    main goal of the package is to provide a generic infra-structure to estimate
    the values of different metrics of predictive performance using different
    estimation procedures. These estimation tasks can be applied to any solutions
    (workflows) to the predictive tasks. The package provides easy to use standard
    workflows that allow the usage of any available R modeling algorithm together
    with some pre-defined data pre-processing steps and also prediction post-
    processing methods. It also provides means for addressing issues related with
    the statistical significance of the observed differences.
Provides tools for linkage mapping in polyploids.
    It implements the method PERGOLA, which is a fast, deterministic method to
    calculate the order of markers in a linkage group.
Functions for generating variants of curves:
    restricted cubic spline, periodic restricted cubic spline,
    periodic cubic spline. Periodic splines can be used to model data
    that has periodic nature / seasonality.
Provides a dataset containing properties for chemical elements.
    Helper functions are also provided to access some atomic properties.
An enterprise-targeted scalable and UI-standardized 'shiny' framework 
    including a variety of developer convenience functions with the goal of both 
    streamlining robust application development while assisting with creating a 
    consistent user experience regardless of application or developer.
A Shiny Web Application to predict and visualize concentrations of pharmaceuticals in the aqueous environment. 
    Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) <https://www.ssrn.com/abstract=4306129>.
Perform Exact or Asymptotic permutation tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].
This version of the permutational algorithm generates a
        dataset in which event and censoring times are conditional on
        an user-specified list of covariates, some or all of which are
        time-dependent.
Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.
Permutational group-sequential tests for time-to-event data based on the log-rank test statistic. Supports exact permutation test when the censoring distributions are equal in the treatment and the control group and approximate imputation-permutation methods when the censoring distributions are different.
An add-on to the 'party' package, with a faster implementation 
   of the partial-conditional permutation importance for random forests. The 
   standard permutation importance is implemented exactly the same as in 
   the 'party' package. The conditional permutation importance can be 
   computed faster, with an option to be backward compatible to the 'party' 
   implementation. The package is compatible with random forests fit using the 
   'party' and the 'randomForest' package. The methods are described in
   Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and 
   Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.
Can be used to carry out permutation based gene expression pathway analysis. This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C).
The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions' or 'Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the 'dabestr' (>=0.3.0) R package.
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).
Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different permutation types for two-way designs.
Manipulates invertible functions from a finite set to
             itself.  Can transform from word form to cycle form and
             back.  To cite the package in publications please use
             Hankin (2020) "Introducing the permutations R package",
             SoftwareX, volume 11 <doi:10.1016/j.softx.2020.100453>.
A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Helps you determine the analysis window to use when analyzing densely-sampled
    time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007)
    <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints
    where significance of an effect begins and ends, and the results can be plotted in various
    types of heatmap for reporting. Mixed-effects models are supported using an implementation of
    the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.
Tools that allow developers to write functions for prediction
    error estimation with minimal programming effort and assist users with
    model selection in regression problems.
Examples for integrating package 'perry' for prediction error estimation into regression models.
Recommend the optimal biomarker for disease screening or diagnosis based on patients' individual characteristics.
Enables the interoperability between the Perseus platform for omics data analysis (Tyanova et al. 2016) <doi:10.1038/nmeth.3901> and R. It provides the foundation for developing and running Perseus plugins implemented in R by providing all required input and output handling, including data and parameter parsing as described in Rudolph and Cox 2018 <doi:10.1101/447268>.
Translate words from English to Persian (Over 67,000
        words)
Allows users to stem Persian texts for text analysis.
Automated identification of printed array positions from high content 
    microscopy images and the export of those positions as individual images 
    written to output as multi-layered tiff files.
Provides functions for fitting and validation of models for subgroup
    identification and personalized medicine / precision medicine under the general subgroup
    identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>.
    This package is intended for use for both randomized controlled trials and
    observational studies and is described in detail in Huling and Yu (2021) 
    <doi:10.18637/jss.v098.i05>.
Implements the methodology of Huling, Smith, and 
    Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification 
    for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part 
    modeling framework to handle semi-continuous data by separately modeling the positive part 
    of the outcome and an indicator of whether each outcome is positive, but still results in a 
    single treatment rule. High dimensional data is handled with a cooperative lasso penalty, 
    which encourages the coefficients in the two models to have the same sign.
Functions to setup a personal R package that attaches given
    libraries and exports personal helper functions.
An R-package-version of an open online science-based personality
    test from <https://openpsychometrics.org/tests/IPIP-BFFM/>,
    providing a better-designed interface and a more detailed report.
    The core command launch_test() opens a personality test in your browser,
    and generates a report after you click "Submit". In this report, your results
    are compared with other people's, to show what these results mean.
    Other people's data is from <https://openpsychometrics.org/_rawdata/BIG5.zip>.
Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.
Interface to the 'Perspective' API, which can be found at the following URL: <https://github.com/conversationai/perspectiveapi#perspective-comment-analyzer-api>. 
    The 'Perspective' API uses machine learning models to score the perceived impact a comment might have on a conversation (i.e. TOXICITY, INFLAMMATORY, etc.).     
    'peRspective' provides access to the API and returns tidy data frames with results of the specified machine learning model(s).
The perturbR() function incrementally perturbs network edges (using the   rewireR function)and compares the resulting community detection solutions 
    from the rewired networks with the solution found for the original network. 
    These comparisons aid in understanding the stability of the 
    original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.
A friendly API for sequence iteration and set comprehension.
Automatic estimation of number of principal components in PCA
    with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse
    'Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood' 
    (2017) <doi:10.1080/10618600.2017.1340302>.
Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) <doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019) <doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) <doi:10.1080/10807039.2015.1133242>.
Set of tools to automatize extraction of data on pests from 'EPPO
    Data Services' and 'EPPO Global Database' and to put them into tables with
    human readable format. Those function use 'EPPO database API', thus you 
    first need to register on <https://data.eppo.int> (free of charge).
    Additional helpers allow to download, check and connect to
    'SQLite EPPO database'.
Wrapper of the 'Petfinder API' <https://www.petfinder.com/developers/v2/docs/> that implements 
  methods for interacting with and extracting data from the 'Petfinder' database. The 'Petfinder 
  REST API' allows access to the 'Petfinder' database, one of the largest online databases of 
  adoptable animals and animal welfare organizations across North America.
Calculates the relative growth rate (RGR) of a series of individuals by building a life table and solving the Lotka-Birch equation. (See Birch, L. C. 1948. The intrinsic rate of natural increase of an insect population. - Journal of Animal Ecology 17: 15-26) <doi:10.2307/1605>.
Functions for the construction of Petri Nets. Petri Nets can be replayed by firing enabled transitions.
     Silent transitions will be hidden by the execution handler. Also includes functionalities for the visualization of Petri Nets and
     export of Petri Nets to PNML (Petri Net Markup Language) files.
Reproducible, programmatic retrieval of survey datasets from the
    Pew Research Center.
Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.
Eco-phylogenetic and community phylogenetic analyses.
    Keeps community ecological and phylogenetic data matched up and
    comparable using 'comparative.comm' objects. Wrappers for common
    community phylogenetic indices ('pez.shape', 'pez.evenness',
    'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation
    of Cavender-Bares (2004) correlation of phylogenetic and
    ecological matrices ('fingerprint.regression'). Phylogenetic
    Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives &
    Helmus (2011) and Rafferty & Ives (2013). Simulation of null
    assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').
Estimate the false discovery proportion (FDP) by Principal Factor Approximation method with general known and unknown covariance dependence.
Performs smoothed (and non-smoothed) principal/independent components analysis
  of functional data. Various functional pre-whitening approaches are implemented as 
  discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional 
  settings", <doi:10.1002/sta4.516>. Further whitening representations of functional 
  data can be derived in terms of a few principal components, providing an avenue 
  to explore hidden structures in low dimensional settings: see Vidal,
  Rosso and Aguilera (2021) “Bi-smoothed functional independent component 
  analysis for EEG artifact removal”, <doi:10.3390/math9111243>.
Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to 
    Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>,
    Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>,
    Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>,
    Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>,
    Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and
    Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.
This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.
Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.
Fits penalized generalized estimating equations to longitudinal data with high-dimensional covariates.
Perform simultaneous estimation and variable selection for correlated bivariate
    mixed outcomes (one continuous outcome and one binary outcome per cluster) using
    penalized generalized estimating equations. In addition, clustered Gaussian and binary
    outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported.
    Cross-validation can be performed to find the optimal regularization parameter(s).
Set of tools for reading, writing and transforming spatial and seasonal data, model selection and specific statistical tests for ecologists. It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points and convert a bounding box in a spatial object. It also provides miscellaneous functions for field ecologists such as spatial statistics and inference on diversity indexes, writing data.frame with Chinese characters.
Estimation of panel models for glm-like models:
             this includes binomial models (logit and probit), count models (poisson and negbin)
	     and ordered models (logit and probit), as described in:
             Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7,
	     Hsiao (2014) Analysis of Panel Data  <doi:10.1017/CBO9781139839327> and
	     Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.
Construction method of nested resolvable designs from 
    a projective geometry defined on Galois field of order 2. The obtained
    Resolvable designs are used to build uniform design. The presented results
    are based on <https://eudml.org/doc/219563> and A. Boudraa et al. (See references).
This is an R and C code implementation of the New-SP and New-JGL method of Gao et al. (2016) <DOI:10.1214/16-EJS1135> to perform model-based clustering and multiple graph estimation.
Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.
Evaluation of the pdf and the cdf of the univariate,
        noncentral, p-generalized normal distribution. Sampling from
        the univariate, noncentral, p-generalized normal distribution
        using either the p-generalized polar method, the p-generalized
        rejecting polar method, the Monty Python method, the Ziggurat
        method or the method of Nardon and Pianca. The package also
        includes routines for the simulation of the bivariate,
        p-generalized uniform distribution and the simulation of the
        corresponding angular distribution.
Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<http://cs.brown.edu/people/pfelzens/dt/index.html>).
Implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.
Provides functions to aid the identification of probable/possible
    duplicates in Plant Genetic Resources (PGR) collections using
    'passport databases' comprising of information records of each constituent
    sample. These include methods for cleaning the data, creation of a
    searchable Key Word in Context (KWIC) index of keywords associated with
    sample records and the identification of nearly identical records with
    similar information by fuzzy, phonetic and semantic matching of keywords.
Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.
Computes the generalized synthetic control estimator described in
    Powell (2017) <doi:10.7249/WR1142>.  Provides both point estimates, and hypothesis testing.
Create PostgreSQL statements/scripts from R, optionally executing the SQL statements.
    Common SQL operations are included, although not every configurable option is available at this time. 
    SQL output is intended to be compliant with PostgreSQL syntax specifications. PostgreSQL documentation is available here
    <https://www.postgresql.org/docs/current/index.html>.
Reproducible cleaning of bio-medical laboratory data using methods of visualization,error correction and transformation implemented as interactive R-notebooks.
The purpose of 'PH1XBAR' is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, and Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>.
An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior
  probability (Thall and Simon (1994) <doi:10.2307/2533377>) and predictive probability (Lee and Liu (2008) <doi:10.1177/1740774508089279>).
Calculate the Bayesian posterior/predictive probability and
    determine the sample size and stopping boundaries for single-arm Phase II design.
Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).
Fits right-truncated meta-analysis (RTMA), a bias correction for
  the joint effects of p-hacking (i.e., manipulation of results within studies
  to obtain significant, positive estimates) and traditional publication bias
  (i.e., the selective publication of studies with significant, positive
  results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for
  p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication
  bias alone, p-hacking that favors significant, positive results (termed
  "affirmative") can distort the distribution of affirmative results. To
  bias-correct results from affirmative studies would require strong assumptions
  on the exact nature of p-hacking. In contrast, joint p-hacking and publication
  bias do not distort the distribution of published nonaffirmative results when
  there is stringent p-hacking (e.g., investigators who hack always eventually
  obtain an affirmative result) or when there is stringent publication bias
  (e.g., nonaffirmative results from hacked studies are never published). This
  means that any published nonaffirmative results are from unhacked studies.
  Under these assumptions, RTMA involves analyzing only the published
  nonaffirmative results to essentially impute the full underlying distribution
  of all results prior to selection due to p-hacking and/or publication bias.
  The package also provides diagnostic plots described in Mathur (2022).
Allows for estimation of phylogenetic trees and networks
    using Maximum Likelihood, Maximum Parsimony, distance methods and
    Hadamard conjugation (Schliep 2011). Offers methods for tree comparison, 
    model selection and visualization of phylogenetic networks as described in
    Schliep et al. (2017).
Enhanced RTF wrapper written in R for use with existing R tables
    packages such as 'Huxtable' or 'GT'. This package fills a gap where tables in
    certain packages can be written out to RTF, but cannot add certain metadata
    or features to the document that are required/expected in a report for a
    regulatory submission, such as multiple levels of titles and footnotes,
    making the document landscape, and controlling properties such as margins.
The 'pharmaverse' is a set of packages that compose multiple pathways 
    through clinical data generation and reporting in the pharmaceutical industry.
    This package is designed to guide users to our work-spaces on 'GitHub', 'Slack' 
    and 'LinkedIn' as well as our website and examples.
    Learn more about the 'pharmaverse' at <https://pharmaverse.org>.
Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R.
Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) <doi:10.1016/0022-5193(78)90022-X>, Lomb-Scargle periodogram: Lomb (1976) <doi:10.1007/BF00648343>, Scargle (1982) <doi:10.1086/160554> and Ruf (1999) <doi:10.1076/brhm.30.2.178.1422>, and Autocorrelation: Eijzenbach et al. (1986) <doi:10.1111/j.1440-1681.1986.tb00943.x>. Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) <doi:10.1021/ac60214a047>) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) <doi:10.1016/j.cub.2020.04.025>, Harrisingh method: Harrisingh et al. (2007) <doi:10.1523/JNEUROSCI.3680-07.2007>, and Stoleru method: Stoleru et al. (2004) <doi:10.1038/nature02926>. Rose plots and circular analysis are based on methods from - Batschelet (1981) <ISBN:0120810506> and Zar (2010) <ISBN:0321656865>.
Contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design.
Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding.
Implements Bayesian phase I repeated measurement design that 
    accounts for multidimensional toxicity endpoints and longitudinal efficacy 
    measure from multiple treatment cycles. The package provides flags to 
    fit a variety of model-based phase I design, including 1 stage models with
    or without individualized dose modification, 3-stage models with or without
    individualized dose modification, etc. Functions are provided to recommend 
    dosage selection based on the data collected in the available patient cohorts 
    and to simulate trial characteristics given design parameters. 
    Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Performs a qualitative analysis of one- and two-dimensional
   autonomous ordinary differential equation systems, using phase plane methods.
   Programs are available to identify and classify equilibrium points, plot the
   direction field, and plot trajectories for multiple initial conditions. In
   the one-dimensional case, a program is also available to plot the phase
   portrait. Whilst in the two-dimensional case, programs are additionally
   available to plot nullclines and stable/unstable manifolds of saddle points.
   Many example systems are provided for the user. For further details can be
   found in Grayling (2014) <doi:10.32614/RJ-2014-023>.
Functions to perform Bayesian inference on absorption time data for
             Phase-type distributions. The methods of Bladt et al (2003)
             <doi:10.1080/03461230110106435> and Aslett (2012)
             <https://www.louisaslett.com/PhD_Thesis.pdf> are provided.
General implementation of core function from phase-type
    theory. 'PhaseTypeR' can be used to model continuous and discrete
    phase-type distributions, both univariate and multivariate. The
    package includes functions for outputting the mean and (co)variance of
    phase-type distributions; their density, probability and quantile
    functions; functions for random draws; functions for
    reward-transformation; and functions for plotting the distributions as
    networks. For more information on these functions please refer to
    Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro
    (2019)
    <https://orbit.dtu.dk/en/publications/order-statistics-and-multivariate-discrete-phase-type-distributio>.
PHATE is a tool for visualizing high dimensional single-cell data
  with natural progressions or trajectories. PHATE uses a novel conceptual framework
  for learning and visualizing the manifold inherent to biological systems in which
  smooth transitions mark the progressions of cells from one state to another.
  To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
  stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology
  at <doi:10.1038/s41587-019-0336-3>.
Clustering analysis for sparse microbiome data, based on a Poisson hurdle model.
Provides permutation methods for testing in high-dimensional linear models.
    The tests are often robust against heteroscedasticity and non-normality 
    and usually perform well under anti-sparsity.
    See Hemerik, Thoresen and Finos (2021) <doi:10.1080/00949655.2020.1836183>.
Implementation of heatmaps that offers more control
    over dimensions and appearance.
Implement surrogate-assisted feature extraction (SAFE) and
             common machine learning approaches to train and validate phenotyping models.
             Background and details about the methods can be found at 
             Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>,
             Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and 
             Liao et al. (2015) <doi:10.1136/bmj.h1885>.
To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree.
Functions to calculate commonly used public health statistics and 
    their confidence intervals using methods approved for use in the production  
    of Public Health England indicators such as those presented via Fingertips 
    (<http://fingertips.phe.org.uk/>). It provides functions for the generation 
    of proportions, crude rates, means, directly standardised rates, indirectly 
    standardised rates, standardised mortality ratios, slope and relative index
    of inequality and life expectancy. 
    Statistical methods are referenced in the following publications. 
    Breslow NE, Day NE (1987) <doi:10.1002/sim.4780080614>.
    Dobson et al (1991) <doi:10.1002/sim.4780100317>. 
    Armitage P, Berry G (2002) <doi:10.1002/9780470773666>.
    Wilson EB. (1927) <doi:10.1080/01621459.1927.10502953>.
    Altman DG et al (2000, ISBN: 978-0-727-91375-3).
    Chiang CL. (1968, ISBN: 978-0-882-75200-6).
    Newell C. (1994, ISBN: 978-0-898-62451-9).
    Eayres DP, Williams ES (2004) <doi:10.1136/jech.2003.009654>.
    Silcocks PBS et al (2001) <doi:10.1136/jech.55.1.38>.
    Low and Low (2004) <doi:10.1093/pubmed/fdh175>.
Generates Weibull-parameterized estimates of phenology for any percentile of 
    a distribution using the framework established in Cooke (1979)
    <doi:10.1093/biomet/66.2.367>. Extensive testing against other 
    estimators suggest the weib_percentile() function is especially useful in 
    generating more accurate and less biased estimates of onset and offset 
    (Belitz et al. 2020 <doi.org:10.1111/2041-210X.13448>. Non-parametric 
    bootstrapping can be used to generate confidence intervals around those 
    estimates, although this is computationally expensive. Additionally, this 
    package offers an easy way to perform non-parametric bootstrapping to 
    generate confidence intervals for quantile estimates, mean estimates, 
    or any statistical function of interest.
Provides some easy-to-use functions for 
	spatial analyses of (plant-) phenological data 
	sets and satellite observations of vegetation.
Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984), bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices.
Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) <doi:10.1007/s00484-016-1161-8>.
Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.
Provides some easy-to-use functions for time series
        analyses of (plant-) phenological data sets. These functions
        mainly deal with the estimation of combined phenological time
        series and are usually wrappers for functions that are already
        implemented in other R packages adapted to the special
        structure of phenological data and the needs of phenologists.
        Some date conversion functions to handle Julian dates are also
        provided.
Programmatic interface to the 'PhenoCam' web services (<https://phenocam.nau.edu/webcam>).
    Allows for easy downloading of 'PhenoCam' data directly to your R workspace
    or your computer and provides post-processing routines for consistent and easy
    timeseries outlier detection, smoothing and estimation of phenological transition dates.
    Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.
Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.
The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and 
    growing season dividing method and a practical snow elimination method 
    based on Whittaker were proposed. 7 curve fitting methods and 4 phenology 
    extraction methods were provided. Parameters boundary are considered for 
    every curve fitting methods according to their ecological meaning. 
    And 'optimx' is used to select best optimization method for different 
    curve fitting methods.
    Reference:
    Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction 
    package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>;
    Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains 
    the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness 
    Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. 
    <doi:10.1029/2020JG005636>;
    Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing 
    global MODIS EVI time series on the Google Earth Engine. 
    ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24;
    Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology 
    on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). 
    Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.
Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.
This tool projects annual phenology metrics and long-term phenology trends, following methodologies described in John (2016) <https://etda.libraries.psu.edu/catalog/13521clj5135>.
A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages.
The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.
Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.
Simulation is a critical part of method development and assessment
    in quantitative genetics. 'PhenotypeSimulator' allows for the flexible 
    simulation of phenotypes under different models, including genetic variant 
    and  infinitesimal genetic effects (reflecting population structure) as well 
    as non-genetic covariate effects, observational noise and additional 
    correlation effects. The different phenotype components are combined into a 
    final phenotype while controlling for the proportion of variance explained 
    by each of the components. For each effect component, the number of 
    variables, their distribution and the design of their effect across traits 
    can be customised. For the simulation of the genetic effects, external 
    genotype data from a number of standard software ('plink', 'hapgen2'/
    'impute2', 'genome', 'bimbam', simple text files) can be imported. The final 
    simulated phenotypes and its components can be automatically saved into .rds 
    or .csv files. In addition, they can be saved in formats compatible with 
    commonly used genetic association software ('gemma', 'bimbam', 'plink', 
    'snptest', 'LiMMBo').
Use phenotype risk scores based on linked clinical and genetic data
  to study Mendelian disease and rare genetic variants. See Bastarache et al.
  2018 <doi:10.1126/science.aal4043>.
Provides tools for the evaluation of the goodness of fit and the predictive capacity of the proportional hazards model.
Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) <doi:10.1101/2020.06.15.20131458>.
Analysis of terms in linear, generalized and mixed linear models, 
	on the basis of multiple comparisons of factor contrasts.  Specially suited 
	for the analysis of interaction terms.
Analysis of features by phi delta diagrams. In particular, functions for reading data and calculating phi and delta as well as the functionality to plot it. Moreover it is possible to do further analysis on the data by generating rankings. For more information on phi delta diagrams, see also Giuliano Armano (2015) <doi:10.1016/j.ins.2015.07.028>.
Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.
Control Philips Hue smart lighting. Use this package to
    connect to a Hue bridge on your local network (remote authentication
    not yet supported) and control your smart lights through the Philips
    Hue API. All API V1 endpoints are supported. See API documentation at
    <https://developers.meethue.com/>.
Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.
A bioinformatics method developed for analyzing the heterogeneity of single-cell populations. Phitest provides an objective and automatic method to evaluate the performance of clustering and quality of cell clusters.
Functions to extract and handle commonly occurring principal phrases
    obtained from collections of texts.
Convert English letters to numbers or numbers to English
    letters as on a telephone keypad. When converting letters to numbers,
    a character vector is returned with "A," "B," or "C" becoming 2, "D,"
    "E", or "F" becoming 3, etc. When converting numbers to letters, a
    character vector is returned with multiple elements (i.e., "2" becomes
    a vector of "A," "B," and "C").
There are a lot of different typical tasks that have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in 'Praat' TextGrids (this is one of the sound annotation standards provided by 'Praat' software, see Boersma & Weenink 2020 <https://www.fon.hum.uva.nl/praat/>), creating an html table with annotations and spectrograms, and converting multiple formats ('Praat' TextGrid, 'ELAN', 'EXMARaLDA', 'Audacity', subtitles '.srt', and 'FLEx' flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). 'phonfieldwork' provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can also compare the functionality with other packages: 'rPraat' <https://CRAN.R-project.org/package=rPraat>, 'textgRid' <https://CRAN.R-project.org/package=textgRid>.
Provides a collection of phonetic algorithms including
    Soundex, Metaphone, NYSIIS, Caverphone, and others.  The package is
    documented in <doi:10.18637/jss.v095.i08>.
Tools for phoneticians and phonologists, including functions for normalization and plotting of vowels.
Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.
Use 'Phosphor' icons in 'shiny' applications or 'rmarkdown' documents. Icons are available in
    5 different weights and can be customized by setting color, size, orientation and more.
Definitions of classes, methods, operators and functions for use 
    in photobiology and radiation meteorology and climatology. Calculation of
    effective (weighted) and not-weighted irradiances/doses, fluence rates,
    transmittance, reflectance, absorptance, absorbance and diverse ratios and 
    other derived quantities from spectral data. Local maxima and minima: peaks,
    valleys and spikes. Conversion between energy-and photon-based units. 
    Wavelength interpolation. Astronomical calculations related solar angles and 
    day length. Colours and vision. This package is part of the 'r4photobiology' 
    suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral 'transmittance' data for frequently used filters and 
    similar materials. Plastic sheets and films; photography filters; 
    theatrical gels; machine-vision filters; various types of window glass;
    optical glass and some laboratory plastics and glassware. Spectral 
    reflectance data for frequently encountered materials. Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Functions for reading, and in some cases writing, foreign files 
    containing spectral data from spectrometers and their associated software, 
    output from daylight simulation models in common use, and some spectral 
    data repositories. As well as functions for exchange of spectral data with 
    other R packages. Part of the 'r4photobiology' suite, 
    Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral emission data for some frequently used lamps including 
    bulbs and flashlights based on led emitting diodes (LEDs) but excluding
    LEDs available as electronic components. Original spectral irradiance data 
    for incandescent-, LED- and discharge lamps are included. They are 
    complemented by data on the effect of temperature on the emission by 
    fluorescent tubes. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) 
    <doi:10.19232/uv4pb.2015.1.14>.
Spectral emission data for some frequently used light emitting
    diodes available as electronic components. Part of the 'r4photobiology' 
    suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Provides functions for quantifying visible (VIS) and ultraviolet
    (UV) radiation in relation to the photoreceptors Phytochromes,
    Cryptochromes, and UVR8 which are present in plants. It also includes data 
    sets on the optical properties of plants. Part of the 'r4photobiology' 
    suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral response data for broadband ultraviolet and visible
    radiation sensors. Different data sources were used: author-supplied data
    from scientific research papers, sensor-manufacturer supplied data, and
    published sensor specifications. Part of the 'r4photobiology' suite
    Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Data for the extraterrestrial solar spectral irradiance and ground 
    level solar spectral irradiance and irradiance. In addition data for 
    shade light under vegetation and irradiance time series.  Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Constructors of waveband objects for commonly used biological
    spectral weighting functions (BSWFs) and for different wavebands describing
    named ranges of wavelengths in the ultraviolet (UV), visible (VIS)
    and infrared (IR) regions of the electromagnetic spectrum.  Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Connect R to the PhotosynQ platform (<https://photosynq.org>). It allows to login and logout,
    as well as receive project information and project data. Further it transforms the received JSON objects
    into a data frame, which can be used for the final data analysis.
A geochemical modeling program developed by the US Geological
    Survey that is designed to perform a wide variety of aqueous geochemical
    calculations, including speciation, batch-reaction, one-dimensional 
    reactive-transport, and inverse geochemical calculations.
Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. <arXiv:2101.09197>.
Assessment of habitat selection by means of the permutation-based combination of sign tests (Fattorini et al., 2014 <DOI:10.1007/s10651-013-0250-7>). To exemplify the application of this procedure, habitat selection is assessed for a population of European Brown Hares settled in central Italy.
A collection of several pharmacovigilance signal detection methods extended to the multiple comparison setting.
Calls 'Phoenix NLME' (non-linear mixed effects), a population
    modeling and simulation software, for pharmacokinetics and pharmacodynamics
    analyses and conducts post-processing of the results. This includes creation of
    various diagnostic plots, bootstrap and visual predictive checks. See <http://
    www.certara.com/software/pkpd-modeling-and-simulation/phoenix-nlme/> for more
    information about 'Phoenix NLME'.
Phylogenetic clustering (phyloclustering) is an evolutionary
        Continuous Time Markov Chain model-based approach to identify
        population structure from molecular data without assuming
        linkage equilibrium. The package phyclust (Chen 2011) provides a
        convenient implementation of phyloclustering for DNA and SNP data,
        capable of clustering individuals into subpopulations and identifying
        molecular sequences representative of those subpopulations. It is
        designed in C for performance, interfaced with R for visualization,
        and incorporates other popular open source programs including
        ms (Hudson 2002) <doi:10.1093/bioinformatics/18.2.337>,
        seq-gen (Rambaut and Grassly 1997)
        <doi:10.1093/bioinformatics/13.3.235>,
        Hap-Clustering (Tzeng 2005) <doi:10.1002/gepi.20063> and
        PAML baseml (Yang 1997, 2007) <doi:10.1093/bioinformatics/13.5.555>,
        <doi:10.1093/molbev/msm088>,
        for simulating data, additional analyses, and searching the best tree.
        See the phyclust website for more information, documentations and
        examples.
Based on (but not identical to) the no-longer-maintained package 'phyext', provides enhancements to 'phylobase' classes, specifically for use by package 'SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in 'SigTree'); also provides support for a few other extra features.
The spatial interpolation of genetic distances between
	     samples is based on a modified kriging method that
	     accepts a genetic distance matrix and generates a map of
	     probability of lineage presence. This package also offers
	     tools to generate a map of  potential contact zones
	     between groups with user-defined thresholds in the tree
	     to account for old and recent divergence. Additionally,
	     it has functions for IDW interpolation using genetic data
	     and midpoints.
Provides a base S4 class for comparative methods, incorporating
    one or more trees and trait data.
Create and customize interactive phylogenetic trees using the 'phylocanvas' JavaScript library and the 'htmlwidgets' package. These trees can be used directly from the R console, from 'RStudio', in Shiny apps, and in R Markdown documents.  See <http://phylocanvas.org/>  for more information on the 'phylocanvas' library.
Implements some methods in phyloclimatic modeling: 
  estimation of ancestral climatic niches, age-range-correlation, 
  niche equivalency test and background-similarity test.
Interface to 'Phylocom' (<https://phylodiversity.net/phylocom/>),
    a library for analysis of 'phylogenetic' community structure and
    character evolution. Includes low level methods for interacting with
    the three executables, as well as higher level interfaces for methods
    like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more.
Implementation of the automatic shift detection method for
    Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
    phylogenies. Some tools to handle equivalent shifts configurations are also
    available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and
    Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.
Manipulation and analysis of phylogenetically simulated
        data sets and phylogenetically based analyses using GLS.
Contains functions for developing phylogenetic trees as
    deeply-nested lists ("dendrogram" objects).
    Enables bi-directional conversion between dendrogram and
    "phylo" objects
    (see Paradis et al (2004) <doi:10.1093/bioinformatics/btg412>),
    and features several tools for command-line tree
    manipulation and import/export via Newick parenthetic text.
Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.
Provides functions to estimate statistical errors of phylogenetic
    metrics particularly to detect binary trait influence on diversification, as
    well as a function to simulate trees with fixed number of sampled taxa and trait
    prevalence.
A comprehensive and easy to use R implementation of confirmatory
    phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer
    (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.
Computational infrastructure for biogeography, community ecology,
    and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>.
    It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>.
    The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013>
    on patterns and processes of biogeographical regionalization. Additionally, the package
    contains fast and efficient functions to compute more standard conservation measures
    such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness
    and global endangerment, as well as compositional turnover (e.g., beta diversity).
Implements novel tools that estimate the probability of true transmission between two cases given 
    phylogenetic linkage and the expected number of true transmission links in a sample. Methods described in
    Wohl, Giles, and Lessler (2021) <doi:10.1371/journal.pcbi.1009182>.
Provides functions for graph-based multiple-sample
    testing and visualization of microbiome data, in particular data
    stored in 'phyloseq' objects. The tests are based on those
    described in Friedman and Rafsky (1979)
    <http://www.jstor.org/stable/2958919>, and the tests are described
    in more detail in Callahan et al. (2016)
    <doi:10.12688/f1000research.8986.1>.
A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.
Functions to read and write APE-compatible phylogenetic
  trees in NEXUS and Newick formats, while preserving annotations.
A collection of tools for building RAxML supermatrix using
             PHYLIP or aligned FASTA files. These functions will be
             useful for building large phylogenies using multiple markers.
Tools for calculating and viewing topological properties of phylogenetic trees.
Analyzis and filtering of phylogenomics datasets. 
	It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify
	what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) <doi:10.1101/2021.09.08.459421>), a generalization of classical multidimensional scaling to multiple distance matrices.
A collection of functions to do model-based phylogenetic analysis. 
    It includes functions to calculate community phylogenetic diversity,
    to estimate correlations among functional traits while accounting for 
    phylogenetic relationships, and to fit phylogenetic generalized linear
    mixed models. The Bayesian phylogenetic generalized linear mixed models
    are fitted with the 'INLA' package (<https://www.r-inla.org>).
Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true  sequence of binary splits).
Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active.  The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively.  The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package.  Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation.
It provides a function "wearingMarking" for classification of monitor
    wear and nonwear time intervals in accelerometer data collected to assess
    physical activity. The package also contains functions for making plot for 
    accelerometer data and obtaining the summary of various information including 
    daily monitor wear time and the mean monitor wear time during valid days.      
    "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail;
    "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete 
    days and removing low activity days; "markPAI" can categorize physical activity
    intensity level based on user-defined cut-points of accelerometer counts. It also
    supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions.
A variety of formulae are provided for estimation
    of physiologic characteristics of infants, children, and adults.
    Calculations include: body surface area, ideal weight, airway
    dead-space, the alveolar gas equation, and GFR.  Each formula is
    referenced to the original publication. Future functions will cover
    more material with a focus on anaesthesia, critical care and
    peri-operative medicine.
Screens and sorts phylogenetic trees in both traditional and
    extended Newick format. Allows for the fast and flexible screening (within
    a tree) of Exclusive clades that comprise only the target taxa and/or Non-
    Exclusive clades that includes a defined portion of non-target taxa.
A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research.
Most price indexes are made with a two-step procedure, where period-over-period elemental indexes are first calculated for a collection of elemental aggregates at each point in time, and then aggregated according to a price index aggregation structure. These indexes can then be chained together to form a time series that gives the evolution of prices with respect to a fixed base period. This package contains a collections of functions that revolve around this work flow, making it easy to build standard price indexes, and implement the methods described by Balk (2008, ISBN:978-1-107-40496-0), von der Lippe (2001, ISBN:3-8246-0638-0), and the CPI manual (2020, ISBN:978-1-51354-298-0) for bilateral price indexes.
Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.
Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.
Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.
Performant interactive scatterplot for ~ 1 million points. Zoom, pan,
    and pick points. Includes tooltips, labels, a grid overlay, legend, and
    coupled interactions across multiple plots.
Computation of predictive information criteria (PIC) from select model object classes 
    for model selection in predictive contexts. In contrast to the more widely used Akaike 
    Information Criterion (AIC), which are derived under the assumption that target(s) of prediction 
    (i.e. validation data) are independently and identically distributed to the fitting data, the PIC
    are derived under less restrictive assumptions and thus generalize AIC to the more practically 
    relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) <https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST> "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".
A collection of scripts and data files for the statistics text: 
 "Process Improvement using Data" <https://learnche.org/pid> and the online 
 course "Experimentation for Improvement" found on Coursera. The package 
 contains code for designed experiments, data sets and other convenience 
 functions used in the book.
This function fits a reversible jump Bayesian piecewise exponential model that also includes the intensity of each event considered along with the rate of events.
Tokenizers break text into pieces that are more usable by machine 
    learning models. Many tokenizers share some preparation steps. This package
    provides those shared steps, along with a simple tokenizer.
Functions to make board game graphics with the 'ggplot2', 'grid', 'rayrender', 'rayvertex', and 'rgl' packages.  Specializes in game diagrams, animations, and "Print & Play" layouts for the 'piecepack' <https://www.ludism.org/ppwiki> but can make graphics for other board game systems.  Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, 'piecepack', playing cards, etc.
Implements piecewise structural equation modeling from a single
    list of structural equations, with new methods for non-linear, latent, and
    composite variables, standardized coefficients, query-based prediction and
    indirect effects. See <http://jslefche.github.io/piecewiseSEM/> for more.
Extends 'ggplot2' to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. The pie glyphs are independent of the axes and plot dimensions, to prevent distortions when the plot dimensions are changed.
Because larger (> 50 MB) data files cannot easily be committed to git,
  a different approach is required to manage data associated with an analysis in a 
  GitHub repository.  This package provides a simple work-around by allowing larger
  (up to 2 GB) data files to piggyback on a repository as assets attached to individual
  GitHub releases.  These files are not handled by git in any way, but instead are
  uploaded, downloaded, or edited directly by calls through the GitHub API. These
  data files can be versioned manually by creating different releases.  This approach
  works equally well with public or private repositories.  Data can be uploaded
  and downloaded programmatically from scripts. No authentication is required to
  download data from public repositories.
Fits models of gene expression evolution to expression data from
    coregulated groups of genes, assuming inverse gamma distributed rate
    variation.
Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) <DOI:10.1016/0041-5553(72)90115-2>.
Provides 'pillar' and 'colonnade' generics designed
    for formatting columns of data using the full range of colours
    provided by modern terminals.
Fit a probabilistic index model as described in 
    Thas et al, 2012: <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the 
    modeling function has changed in this new version. The old version is
    still available at R-Forge.
An implementation of prediction intervals for random-effects meta-analysis:
  Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017)
  <doi:10.1002/sim.7140>, and Nagashima et al. (2019) <doi:10.1177/0962280218773520>,
  <arXiv:1804.01054>.
Genomic data for the plant pathogen "Phytophthora infestans." It
    includes a variant file ('VCF'), a sequence file ('FASTA') and an annotation file
    ('GFF'). This package is intended to be used as example data for packages that
    work with genomic data.
To assist you with troubleshooting internet connection issues and
    assist in isolating packet loss on your network. It does this by allowing you to 
    retrieve the top trace route destinations your internet provider uses, and recursively 
    ping each server in series while capturing the results and writing them to a log file. 
    Each iteration it queries the destinations again, before shuffling the sequence of 
    destinations to ensure the analysis is unbiased and consistent across each trace route.
Check if a remote computer is up. It can either
    just call the system ping command, or check a specified
    TCP port.
Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.
Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.
Packages data about the victims of the Pinochet regime as compiled by the Chilean National Commission for Truth and Reconciliation Report (1991, ISBN:9780268016463).
A 'PNAS'-alike style for 'rmarkdown', derived from the
 'Proceedings of the National Academy of Sciences of the United States
 of America' ('PNAS', see <https://www.pnas.org>) 'LaTeX' style, and
 adapted for use with 'markdown' and 'pandoc'.
Publish data sets, models, and other R objects, making it
    easy to share them across projects and with your colleagues. You can
    pin objects to a variety of "boards", including local folders (to
    share on a networked drive or with 'DropBox'), 'RStudio' connect,
    Amazon S3, and more.
Provides a robust approach for omics data integration and disease subtyping. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et al. (2017)<DOI: 10.1101/gr.215129.116>, Nguyen et al. (2021)<DOI: 10.3389/fonc.2021.725133>).
A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.
Collect  marketing data from Pinterest Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
    Use four spaces when indenting paragraphs within the Description.
Convert Chinese characters into Pinyin (the official romanization system for Standard Chinese in mainland China, Malaysia, Singapore, and Taiwan. See <https://en.wikipedia.org/wiki/Pinyin> for details), Sijiao (four or five numerical digits per character. See <https://en.wikipedia.org/wiki/Four-Corner_Method>.), Wubi (an input method with five strokes. See <https://en.wikipedia.org/wiki/Wubi_method>) or user-defined codes.
Implements the Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials as described in Mander AP, Sweeting MJ (2015) <DOI:10.1002/sim.6434>.
Provides a simple function to bind a piped object to a placeholder
    symbol to enable complex function evaluation with the base R |> pipe.
To take nested function calls and convert them to a more readable form using pipes from package 'magrittr'.
Code for various permutation p-values estimation methods for gene set test. The description of corresponding methods can be found in the dissertation of Yu He(2016) "Efficient permutation P-value estimation for gene set tests" <https://searchworks.stanford.edu/view/11849351>. One of the methods also corresponds to the paper "Permutation p-value approximation via generalized Stolarsky invariance" <arXiv:1603.02757>.
A framework for defining 'pipelines' of functions for applying data transformations, 
  model estimation and inverse-transformations, resulting in predicted value generation (or 
  model-scoring) functions that automatically apply the entire pipeline of functions required to go
  from input to predicted output.
Provides various styles of function chaining methods: Pipe
    operator, Pipe object, and pipeline function, each representing a distinct
    pipeline model yet sharing almost a common set of features: A value can be
    piped to the first unnamed argument of a function and to dot symbol in an
    enclosed expression. The syntax is designed to make the pipeline more
    readable and friendly to a wide range of operations.
An implementation of the generated effect modifier (GEM) method. This method constructs composite variables by linearly combining pre-treatment scalar patient characteristics to create optimal treatment effect modifiers in linear models. The optimal linear combination is called a GEM. Treatment is assumed to have been assigned at random. For reference, see E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics (First published online: July 27, 2016, <doi:10.1093/biostatistics/kxw035>).
Calculate and optimize dynamic performance ratings of association football 
  teams competing in matches, in accordance with the method used in 
  the research paper "Determining the level of ability of football teams by 
  dynamic ratings based on the relative discrepancies in scores between adversaries", 
  by Constantinou and Fenton (2013) 
  <doi:10.1515/jqas-2012-0036>    
  This dynamic rating system has proven to provide superior 
  results for predicting association football outcomes.
Implements multiple state-of-the-art prediction interval methodologies for random forests. 
	These include: quantile regression intervals, out-of-bag intervals, bag-of-observations intervals, 
	one-step boosted random forest intervals, bias-corrected intervals, high-density intervals, and 
	split-conformal intervals. The implementations include a combination of novel adjustments to the 
	original random forest methodology and novel prediction interval methodologies. All of these 
	methodologies can be utilized using solely this package, rather than a collection of separate 
	packages. Currently, only regression trees are supported. Also capable of handling high dimensional data. 
	Roy, Marie-Helene and Larocque, Denis (2019) <doi:10.1177/0962280219829885>.
	Ghosal, Indrayudh and Hooker, Giles (2018) <arXiv:1803.08000>.
	Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) <arXiv:1905.10101>.
	Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) <doi:10.1080/00031305.2019.1585288>.
	Meinshausen, Nicolai (2006) <http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>.
	Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) <arXiv:1905.03222>.
	Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) <doi:10.13140/2.1.2500.8002>.
Theoretical biologists are interested in measuring the extent
    at which we can measure the truth. This package allows to
    create a Bayesian posterior from a phylogeny that depicts
    the true evolutionary relationships. The given and true phylogeny
    can than be compared to the posterior phylogenies.
    Richèl J. C. Bilderbeek, Giovanni Laudanno, Rampal S. Etienne (2020)
    "Quantifying the impact of an inference model in Bayesian phylogenetics"
    <doi:10.1111/2041-210X.13514>.
Scored responses and responses times from the Canadian subsample of the PISA 2018 assessment, accessible as the "Cognitive items total time/visits data file" by OECD (2020) <https://www.oecd.org/pisa/data/2018database/>.
With 'pitchRx', one can easily obtain Major League Baseball Advanced
    Media's 'Gameday' data (as well as store it in a remote database). The
    'Gameday' website hosts a wealth of data in XML format, but perhaps most
    interesting is 'pitchfx'. Among other things, 'pitchfx' data can be used to
    recreate a baseball's flight path from a pitcher's hand to home plate. With
    pitchRx, one can easily create animations and interactive 3D 'scatterplots'
    of the baseball's flight path. 'pitchfx' data is also commonly used to
    generate a static plot of baseball locations at the moment they cross home
    plate. These plots, sometimes called strike-zone plots, can also refer to a
    plot of event probabilities over the same region. 'pitchRx' provides an easy
    and robust way to generate strike-zone plots using the 'ggplot2' package.
A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating
    Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement
    their own grammars and easily expose them in R packages.
Collection of pivotal algorithms 
             for: relabelling the MCMC chains in order to undo the label 
             switching problem in Bayesian mixture models;
             fitting sparse finite mixtures;
             initializing the centers of the classical k-means algorithm 
             in order to obtain a better clustering solution. 
             For further details see
             Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.
'Pivotal Tracker' <https://www.pivotaltracker.com> is a project
    management software-as-a-service that provides a REST API. This package
    provides an R interface to that API, allowing you to query it and work with
    its responses.
Create regular pivot tables with just a few lines of R.  
    More complex pivot tables can also be created, e.g. pivot tables
    with irregular layouts, multiple calculations and/or derived 
    calculations based on multiple data frames.  Pivot tables are
    constructed using R only and can be written to a range of
    output formats (plain text, 'HTML', 'Latex' and 'Excel'), 
    including with styling/formatting.
Run Queries against the API of 'Piwik Pro' <https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>. The result is a tibble.
Data about Disney Pixar films provided by Wikipedia. This
    package contains data about the films, the people involved, and their
    awards.
Contains functions to classify the pixels of an image file by its colour. It implements a simple form of the techniques known as Support Vector Machine adapted to this particular problem.
Puzzle game that can be played in the R console.
  Restore the pixel art by shifting rows.
Provides tools to show and draw image pixels using 'HTML' widgets 
  and 'Shiny' applications.  It can be used to visualize the 'MNIST' dataset
  for handwritten digit recognition or to create new image recognition datasets.
The introduction of the 'broom' package has made converting model
    objects into data frames as simple as a single function. While the 'broom'
    package focuses on providing tidy data frames that can be used in advanced
    analysis, it deliberately stops short of providing functionality for reporting
    models in publication-ready tables. 'pixiedust' provides this functionality with
    a programming interface intended to be similar to 'ggplot2's system of layers
    with fine tuned control over each cell of the table. Options for output include
    printing to the console and to the common markdown formats (markdown, HTML, and
    LaTeX). With a little 'pixiedust' (and happy thoughts) tables can really fly.
Functions for import, export, plotting and other
  manipulations of bitmapped images.
Estimation of pharmacokinetic parameters using non-compartmental theory.
Pharmacokinetics is the study of drug absorption, distribution, 
   metabolism, and excretion. The pharmacokinetics model explains that how the 
   drug concentration change as the drug moves through the different compartments
   of the body. For pharmacokinetic modeling and analysis, it is essential
   to understand the basic pharmacokinetic parameters. All parameters are 
   considered, but only some of parameters are used in the model. Therefore, 
   we need to convert the estimated parameters to the other parameters after 
   fitting the specific pharmacokinetic model. This package is developed to help
   this converting work. For more detailed explanation of pharmacokinetic 
   parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001";
   "Benet and Zia-Amirhosseini" (1995) <DOI: 10.1177/019262339502300203>; 
   "Mould and Upton" (2012) <DOI: 10.1038/psp.2012.4>;
   "Mould and Upton" (2013) <DOI: 10.1038/psp.2013.14>.
Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses. 
    This package provides functions to standardize infusion and bolus dose data while
    linking it to drug level or concentration data.
Provides functions used to build R packages. Locates
    compilers needed to build R packages on various platforms and ensures
    the PATH is configured appropriately so R can use them.
Metadata and package cache for CRAN-like repositories.  This
    is a utility package to be used by package management tools that want
    to take advantage of caching.
This provides utilities for creating classed error and warning
  conditions based on where the error originated.
Set configuration options on a per-package basis.
    Options set by a given package only apply to that package,
    other packages are unaffected.
Find recursive dependencies of 'R' packages from various
    sources. Solve the dependencies to obtain a consistent set of packages
    to install. Download packages, and install them. It supports packages
    on 'CRAN', 'Bioconductor' and other 'CRAN-like' repositories,
    'GitHub', package 'URLs', and local package trees and files. It caches
    metadata and package files via the 'pkgcache' package, and performs
    all 'HTTP' requests, downloads, builds and installations in parallel.
    'pkgdepends' is the workhorse of the 'pak' package.
Statically determine and visualize the function dependencies
    within and across packages. This may be useful for managing function 
    dependencies across a code base of multiple R packages.
Generate an attractive and useful website from a source
    package.  'pkgdown' converts your documentation, vignettes, 'README',
    and more to 'HTML' making it easy to share information about your
    package online.
Manage optional data for your package. The data can be hosted anywhere, and you have to give a Uniform Resource Locator (URL) for each file. File integrity checks are supported. This is useful for package authors who need to ship more than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive Network (CRAN).
Interactively explore various dependencies of a package(s) (on the Comprehensive R Archive Network Like repositories) and perform analysis using tidy philosophy. Most of the functions return a 'tibble' object (enhancement of 'dataframe') which can be used for further analysis. The package offers functions to produce 'network' and 'igraph' dependency graphs. The 'plot' method produces a static plot based on 'ggnetwork' and 'plotd3' function produces an interactive D3 plot based on 'networkD3'.
Provides a function kitten() which creates cute little 
 packages which pass R package checks. This sets it apart from 
 package.skeleton() which it calls, and which leaves imperfect files 
 behind. As this is not exactly helpful for beginners, kitten() offers 
 an alternative. Unit test support can be added via the 'tinytest'
 package (if present), and documentation-creation support can be
 added via 'roxygen2' (if present).
A tool, grammar, and standard to represent and exchange
    R package source code as text files. Converts one or more source
    packages to a text file and restores the package structures from the file.
Simulates the process of installing a package and then
    attaching it. This is a key part of the 'devtools' package as it
    allows you to rapidly iterate while developing a package.
Provides some low-level utilities to use for package
    development. It currently provides managers for multiple package specific
    options and registries, vignette, unit test and bibtex related utilities.
    It serves as a base package for packages like 'NMF', 'RcppOctave', 'doRNG', and
    as an incubator package for other general purposes utilities, that will
    eventually be packaged separately.
    It is still under heavy development and changes in the interface(s) are
    more than likely to happen.
A new metric named 'dependency heaviness' is proposed that measures the number 
    of additional dependency packages that a parent package brings to its child package 
    and are unique to the dependency packages imported by all other parents.  
    The dependency heaviness analysis is visualized by a customized heatmap. 
    The package is described in <doi:10.1093/bioinformatics/btac449>. We have also performed 
    the dependency heaviness analysis on the CRAN/Bioconductor package ecosystem and the results 
    are implemented as a web-based database which provides comprehensive tools for 
    querying dependencies of individual R packages.
Tools from the domain of graph theory can be used to quantify the complexity
             and vulnerability to failure of a software package. That is the guiding philosophy
             of this package. 'pkgnet' provides tools to analyze the dependencies between functions
             in an R package and between its imported packages.  See the pkgnet website for vignettes 
             and other supplementary information.
Read R package news files, regardless of whether or not the package
  is installed.
Search CRAN metadata about packages by keyword, popularity,
    recent activity, package name and more. Uses the 'R-hub' search server,
    see <https://r-pkg.org> and the CRAN metadata database, that
    contains information about CRAN packages. Note that this is _not_
    a CRAN project.
Static code analyses for R packages using the external code-tagging
    libraries "ctags" and "gtags". Static analyses enable packages to be
    analysed very quickly, generally a couple of seconds at most. The package
    also provides access to a database generating by applying the main function
    to the full CRAN archive, enabling the statistical properties of any package
    to be compared with all other CRAN packages.
Build your own universe of packages similar to the 'tidyverse'
    package <https://tidyverse.org/> with this meta-package creator. Create a
    package-verse, or meta package, by supplying a custom name for the
    collection of packages and the vector of desired package names to include–
    and optionally supply a destination directory, an indicator of whether to
    keep the created package directory, and/or a vector of verbs implement via
    the 'usethis' <http://usethis.r-lib.org/> package.
Public Key Infrastucture functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing Completely At Random) in the context of missing data as introduced in Michel et al. (2021)  <arXiv:2109.10150>.
We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.
Compute standard Non-Compartmental Analysis (NCA) parameters for
    typical pharmacokinetic analyses and summarize them.
Simulate dose regimens for pharmacokinetic-pharmacodynamic (PK-PD)
        models described by differential equation (DE) systems. Simulation using 
        ADVAN-style analytical equations is also supported (Abuhelwa et al. (2015) 
        <doi:10.1016/j.vascn.2015.03.004>).
Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software.
             Some features are
             1) CDISC SDTM terms
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
Fitting and testing probabilistic knowledge structures,
  especially the basic local independence model (BLIM, Doignon & Flamagne,
  1999), using the minimum discrepancy maximum likelihood (MDML) method
  (Heller & Wickelmaier, 2013 <doi:10.1016/j.endm.2013.05.145>).
A tool for inferring kinase activity changes from 
    phosphoproteomics data. 'pKSEA' uses kinase-substrate 
    prediction scores to weight observed changes in 
    phosphopeptide abundance to calculate a phosphopeptide-level 
    contribution score, then sums up these contribution scores by 
    kinase to obtain a phosphoproteome-level kinase activity 
    change score (KAC score). 'pKSEA' then assesses the 
    significance of changes in predicted substrate abundances for 
    each kinase using permutation testing. This results in a 
    permutation score (pKSEA significance score) reflecting the 
    likelihood of a similarly high or low KAC from random chance, 
    which can then be interpreted in an analogous manner to an 
    empirically calculated p-value. 'pKSEA' contains default 
    databases of kinase-substrate predictions from 'NetworKIN' 
    (NetworKINPred_db) <http://networkin.info> 
    Horn, et. al (2014) <doi:10.1038/nmeth.2968>
    and of known kinase-substrate links from 'PhosphoSitePlus' 
    (KSEAdb) <https://www.phosphosite.org/>
    Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.
Applying the global sensitivity analysis workflow to investigate 
    the parameter uncertainty and sensitivity in physiologically based 
    kinetic (PK) models, especially the physiologically based 
    pharmacokinetic/toxicokinetic model with multivariate outputs. 
    The package also provides some functions to check the convergence 
    and sensitivity of model parameters. The workflow was first mentioned 
    in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further 
    refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).
Tools to process legacy format summary redistricting data files
    produced by the United States Census Bureau pursuant to P.L. 94-171. These
    files are generally available earlier but are difficult to work with as-is.
A semi-parametric estimation method for the Cox model
    with left-truncated data using augmented information
    from the marginal of truncation times.
Assessment of the prevalence of plastic
    debris in bird nests based on bootstrap replicates. The package allows for 
    calculating bootstrapped 95% confidence intervals for the estimated prevalence 
    of debris. Combined with a Bayesian approach, the resampling simulations can 
    be also used to define appropriate sample sizes to detect prevalence of plastics. 
    The method has wide application, and can also be applied to estimate confidence 
    intervals and define sample sizes for the prevalence of plastics ingested by any 
    other organisms. The method is described in Tavares et al. (Submitted).
Clusters GPS coordinates into places (i.e., meaningful stops). Additionally, categorizes places into types (e.g., home, cafe, gym). Places are identified as home using a rules-based algorithm defining home as the stop occurring most frequently during the night. Other places (e.g., cafe, gym) are identified using the Google Maps API.
Functions to prepare rankings data and fit the Plackett-Luce model
    jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce
    (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized
    to accommodate ties of any order in the ranking. Partial rankings, in which
    only a subset of items are ranked in each ranking, are also accommodated in
    the implementation. Disconnected/weakly connected networks implied by the
    rankings may be handled by adding pseudo-rankings with a hypothetical item.
    Optionally, a multivariate normal prior may be set on the log-worth
    parameters and ranker reliabilities may be incorporated as proposed by
    Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a
    posteriori estimation is used when priors are set. Methods are provided to
    estimate standard errors or quasi-standard errors for inference as well as
    to fit Plackett-Luce trees. See the package website or vignette for further
    details.
Provides methods for plotting potentially large (raster) images
    interactively on a plain HTML canvas. In contrast to package 'mapview'
    data are plotted without background map, but data can be projected to
    any spatial coordinate reference system.
    Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick'
    (from package 'raster') as well as 'png' files located on disk. 
    Interactivity includes zooming, panning, and mouse location information. 
    In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots 
    are created (similar to 'raster::plotRGB' - but interactive).
Supports the creation of 'burndown' charts and 'gantt' diagrams.
An extensive set of plane geometry routines. Provides R6 classes representing triangles, circles, circular arcs, ellipses, elliptical arcs and lines, and their plot methods. Also provides R6 classes representing transformations: rotations, reflections, homotheties, scalings, general affine transformations, inversions, Möbius transformations.
Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge  Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.
It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API <https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>. 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System.
The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets.
Coupled leaf gas exchange model, A-Ci curve simulation and
    fitting, Ball-Berry stomatal conductance models, 
    leaf energy balance using Penman-Monteith, Cowan-Farquhar
    optimization, humidity unit conversions. 
    See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
Provides wrapping functions to add to capabilities to 'plantecophys' 
    (Duursma, 2015, <doi:10.1371/journal.pone.0143346>). Key added capabilities 
    include temperature responses of mesophyll conductance (gm, gmeso), apparent 
    Michaelis-Menten constant for rubisco carboxylation in air (Km, Kcair),and
    photorespiratory CO2 compensation point (GammaStar) for fitting A-Ci or A-Cc
    curves for C3 plants (for temperature responses of gm, Km, & GammaStar,  see 
    Bernacchi et al., 2002, <doi:10.1104/pp.008250>; for theory on fitting A-Ci 
    or A-Cc curves, see Farquhar et al., 1980; <doi:10.1007/BF00386231>, von 
    Caemmerer, 2000, ISBN:064306379X; Ethier & Livingston, 2004 
    <doi:10.1111/j.1365-3040.2004.01140.x>; and Gu et al., 2010, 
    <doi:10.1111/j.1365-3040.2010.02192.x>). Includes the ability to fit the 
    Arrhenius and modified Arrhenius temperature response functions (see Medlyn 
    et al., 2002, <doi:10.1046/j.1365-3040.2002.00891.x>) for maximum rubisco 
    carboxylation rates (Vcmax) and maximum electron transport rates (Jmax) (see
    Farquhar et al., 1980; <doi:10.1007/BF00386231>).
Extracts growth, survival, and local neighborhood density 
  information from repeated, fine-scale maps of organism occurrence. Further 
  information about this package can be found in our journal article, 
  "plantTracker: An R package to translate maps of plant occurrence into 
  demographic data" published in 2022 in Methods in Ecology and Evolution 
  (Stears, et al., 2022) <doi:10.1111/2041-210X.13950>.
Estimation, prediction, thresholding, transformation, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
Contains tools for supervised analyses of incomplete, overlapping
  multiomics datasets. Applies partial least squares in multiple steps to find
  models that predict survival outcomes. See Yamaguchi et al. (2023)
  <doi:10.1101/2023.03.10.532096>.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele
    frequency at each position to the measured PER in control samples.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis using barcoding. Mutations are assessed by comparing the minor-allele frequency at each position to the measured PER in control samples.
    This package has been used for Kjersti Tjensvoll, Morten Lapin, Bjørnar Gilje, Herish Garresori, Satu Oltedal, Rakel Brendsdal Forthun, Anders Molven, Yves Rozenholc and Oddmund N\o{o}rdgaard (2022) <https://www.nature.com/articles/s41598-022-09698-5>.
Contains functions developed to combine the results of querying a plasmid database using
    short-read sequence typing with the results of a blast analysis against the query results.
Tools for interacting with data from experiments done in microtiter
    plates. Easily read in plate-shaped data and convert it to tidy format, 
    combine plate-shaped data with tidy data, and view tidy data in plate shape.
Collection of functions for working with multi-well microtitre
    plates, mainly 96, 384 and 1536 well plates.
Design parameters of the optimal two-period multiarm platform design
    (controlling for either family-wise error rate or pair-wise error rate) can be calculated
    using this package, allowing pre-planned deferred arms to be added during the trial. More
    details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J.
    (2022) "An optimal two-period multiarm platform design with new experimental arms added
    during the trial". Manuscript submitted for publication. For additional references: Dunnett,
    C. W. (1955) <doi:10.2307/2281208>.
Data and analysis from an experiment with improving touch 
    typing speed, using the tDCS PlatoWork headset produced by PlatoScience.
It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch.
We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology (Yermanos A, et al (2021) <doi:10.1093/nargab/lqab023>).
Provides functions to visualise sports data. Converts data into a 
    format suitable for plotting charts. Helps to ease the process of working with
    messy sports data to a more user friendly format. Football data is accessed 
    through 'worldfootballR' '<https://github.com/JaseZiv/worldfootballR>' which 
    gets data from 'FBref' <https://fbref.com/en>, 'Transfermarkt' <https://www.transfermarkt.com/>, 
    'Understat' <https://understat.com/>, and 'fotmob' <https://www.fotmob.com/>.
Implements schemes for estimating player or 
  team skill based on dynamic updating. Implemented methods include 
  Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation 
  of a reproducible analysis using approximately two million chess 
  matches. Also contains an Elo based method for multi-player games
  where the result is a placing or a score. This includes zero-sum
  games such as poker and mahjong.
We present a penalized log-density estimation method using Legendre polynomials with lasso penalty to adjust estimate's smoothness. Re-expressing the logarithm of the density estimator via a linear combination of Legendre polynomials, we can estimate parameters by maximizing the penalized log-likelihood function. Besides, we proposed an implementation strategy that builds on the coordinate decent algorithm, together with the Bayesian information criterion (BIC).
Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.
It performs a fast multi-trait genome-wide association analysis based on seemingly unrelated regressions. It tests for pleiotropic effects based on a series of Intersection-Union Wald tests. The package can handle large and unbalanced data and plot results.
Log-multiplicative association models (LMA) are
    models for cross-classifications of categorical variables
    where interactions are represented by products of category
    scale values and an association parameter. Maximum
    likelihood estimation (MLE) fails for moderate to large
    numbers of categorical variables. The 'pleLMA' package
    overcomes this limitation of MLE by using pseudo-likelihood
    estimation to fit the models to small or large
    cross-classifications dichotomous or multi-category variables.
    Originally proposed by Besag (1974,
    <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood
    estimation takes large complex models and breaks it down
    into smaller ones. Rather than maximizing the likelihood
    of the joint distribution of all the variables, a
    pseudo-likelihood function, which is the product likelihoods
    from conditional distributions, is maximized. LMA models can
    be derived from a number of different frameworks including
    (but not limited to) graphical models and uni-dimensional
    and multi-dimensional item response theory models. More
    details about the models and estimation can be found in
    the vignette.