Implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values.
    Bhoj, D. S. (1991) <doi:10.1002/bimj.4710330108> "Testing equality of means in the presence of correlation and missing data".
    Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) <doi:10.22237/jmasm/1020254460> "Rank-based procedures for mixed paired and two-sample designs".
    Einsporn, R. L. and Habtzghi, D. (2013) <https://pdfs.semanticscholar.org/89a3/90bafeb2bc41ed4414533cfd5ab84a6b54b6.pdf> "Combining paired and two-sample data using a permutation test".
    Ekbohm, G. (1976) <doi:10.1093/biomet/63.2.299> "On comparing means in the paired case with incomplete data on both responses".
    Lin, P. E. and Stivers, L. E. (1974) <doi:10.1093/biomet/61.2.325> On difference of means with incomplete data".
    Maritz, J. S. (1995) <doi:10.1111/j.1467-842x.1995.tb00649.x> "A permutation paired test allowing for missing values".
Suite of functions to study animal incubation.
    At the core of incR 
    lies an algorithm that allows for the scoring of 
    incubation behaviour. Additionally, several functions 
    extract biologically relevant metrics of incubation such as off-bout number 
    and off-bout duration - for a review of avian incubation studies, 
    see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) 
    edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.
Tools for estimating incidence from biomarker data in cross-
    sectional surveys, and for calibrating tests for recent infection. 
    Implements and extends the method of Kassanjee et al. (2012)
    <doi:10.1097/EDE.0b013e3182576c07>.
Fit parametric models for time-to-event data that show an initial
    'incubation period', i.e., a variable delay phase where the hazard is zero. The
    delayed Weibull distribution serves as foundational data model. The
    specific method of 'MPSE' (maximum product of spacings estimation) and MLE-based methods are used for parameter
    estimation. Bootstrap confidence intervals for parameters and significance
    tests in a two group setting are provided.
Genome-wide gene insertion and deletion rates can be modelled in a maximum 
    likelihood framework with the additional flexibility of modelling potential missing 
    data using the models included within. These models simultaneously estimate insertion 
    and deletion (indel) rates of gene families and proportions of "missing" data for 
    (multiple) taxa of interest. The likelihood framework is utilized for parameter 
    estimation. A phylogenetic tree of the taxa and gene presence/absence patterns 
    (with data ordered by the tips of the tree) are required. See Dang et al.
    (2016) <doi:10.1534/genetics.116.191973> for more details.
Estimates weights to make a continuous-valued exposure statistically independent of a vector of pre-treatment covariates using the method proposed in Huling, Greifer, and Chen (2021) <arxiv:2107.07086>.
Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://mi.mathnet.ru/eng/ppi797>). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.
Extract and replace elements using indices that start from zero (rather than one), as is common in mathematical notation and other programming languages.
Derivation of indexes for benchmarking purposes. A methodology with flexible number of constituents is implemented. Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) <doi:10.1016/j.jempfin.2018.08.004>.
We provide an R tool for teaching in Social Sciences. It allows the computation of index numbers. It is a measure of the evolution of a fixed magnitude for only a product of for several products. It is very useful in Social Sciences. Among others, we obtain simple index numbers (in chain or in serie), index numbers for not only a product or weighted index numbers as the Laspeyres index (Laspeyres, 1864), the Paasche index (Paasche, 1874) or the Fisher index (Lapedes, 1978).
A set of utilities for manipulating index numbers series including chain-linking, re-referencing, and computing growth rates.
Computes bilateral and multilateral index numbers. 
    It has support for many standard bilateral indexes as well as
    multilateral index number methods such as GEKS, GEKS-Tornqvist 
    (or CCDI), Geary-Khamis and the weighted time product dummy
    (for details on these methods see Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>). 
    It also supports updating of multilateral indexes using 
    several splicing methods.
Helps with the thoughtful saving, reading, and management of result
  files (using 'rds' files). The core functions take a list of parameters that are
  used to generate a unique hash to save results under. Then, the same
  parameter list can be used to read those results back in. This
  is helpful to avoid clunky file naming when running a large number of
  simulations. Additionally, helper functions are available for compiling a flat
  file of parameters of saved results, monitoring result usage, and cleaning up
  unwanted or unused results. For more information, visit the 'indexr' homepage 
  <https://lharris421.github.io/indexr/>.
Quick indexation of any type of vector or of any combination of those. Indexation turns a vector into an integer vector going from 1 to the number of unique elements. Indexes are important building blocks for many algorithms. The method is described at <https://github.com/lrberge/indexthis/>.
Allows the construction selection indices based on estimated breeding values in animal and plant breeding and to calculate several analytic measures around to assess its impact on genetic and phenotypic progress. The methodology thereby allows to analyze genetic gain of traits in the breeding goal which are not part of the actual index and automatically computes several analytic measures. It further allows to retrospectively derive realized economic weights from observed genetic trends. The framework is described in Simianer, H., Heise, J., Rensing, S., Pook, T. Geibel, J. and Reimer, C. (2023) <doi:10.1186/s12711-023-00807-0>.
Computation of test statistics of independence between  (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.
Set of routines for influence diagnostics by using case-deletion in ordinary least 
    squares, nonlinear regression [Ross (1987). <doi:10.2307/3315198>], ridge estimation [Walker and Birch (1988). <doi:10.1080/00401706.1988.10488370>] 
    and least absolute deviations (LAD) regression [Sun and Wei (2004). <doi:10.1016/j.spl.2003.08.018>].
Different functions includes constructing composite indicators, imputing missing data, and evaluating imputation techniques. Additionally, different tools for data normalization. Detailed methodologies of  'Indicator' package are: OECD/European Union/EC-JRC (2008), "Handbook on Constructing Composite Indicators: Methodology and User Guide", OECD Publishing, Paris, <DOI:10.1787/533411815016>, Matteo Mazziotta & Adriano Pareto, (2018) "Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices" <DOI:10.1007/s11205-017-1577-5> and De Muro P., Mazziotta M., Pareto A. (2011), "Composite Indices  of Development and Poverty: An Application to MDGs" <DOI:10.1007/s11205-010-9727-z>.
Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].
Simplifies the generation of customized R Markdown PDF templates.
    A template may include an individual logo, typography, geometry or color
    scheme. The package provides a skeleton with detailed instructions for
    customizations. The skeleton can be modified by changing defaults in the
    'YAML' header, by adding additional 'LaTeX' commands or by applying dynamic
    adjustments in R. Individual corporate design elements, such as a title page, can be added as R functions that produce 'LaTeX' code.
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence,  and functions for generating and analysing  different types of dependence.
Companion package to the book "industRial data science", 
    J.Ramalho (2021) <https://j-ramalho.github.io/industRial/>. 
    Provides data sets and functions to complete the case studies and contains 
    the book original Rmd files and tutorials.
Provides access to granular socioeconomic indicators from the
    Spanish Statistical Office (INE) Household Income Distribution Atlas.
    The package downloads and processes data from a companion 'GitHub'
    repository (<https://github.com/pablogguz/ineAtlas.data/>) which contains
    processed versions of the official INE Atlas data. Functions are
    provided to fetch data at multiple geographic levels (municipalities,
    districts, and census tracts), including income indicators,
    demographic characteristics, and inequality metrics. The data
    repository is updated every year when new releases are published by
    INE.
It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecología AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.
Provides functions to download and parse information from INEGI
    (Official Mexican statistics agency). To learn more about the API, see <https://www.inegi.org.mx/servicios/api_indicadores.html>.
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Decomposition of income inequality by groups formed of individuals 
    possessing similar characteristics (e.g., sex, education, age) and their 
    income sources at the same time.
    Decomposition of the Theil index is based on Giammatteo, M. (2007) 
    <https://www.lisdatacenter.org/wps/liswps/466.pdf>.
    Decomposition of the squared coefficient of variation is based on
    Garcia-Penalosa, C., & Orgiazzi, E. (2013) <doi:10.1111/roiw.12054>.
Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) <doi:10.1400/209575> Zenga M. (2015) <doi:10.1400/246627> Zenga M., Valli I. (2017) <doi:10.26350/999999_000005> Zenga M., Valli I. (2018) <doi:10.26350/999999_000011>.
Performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the 'hdi' package.
It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration" <doi:10.1007/s00180-024-01536-8>.
Provides simple functions to compute and plot two types 
    (sample-size- and coverage-based) rarefaction and extrapolation curves for species
    diversity (Hill numbers) based on individual-based abundance data or sampling-unit-
    based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent
    theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution)
    for an introduction of the R package.
Biodiversity is a multifaceted concept covering different levels of organization from
            genes to ecosystems. 'iNEXT.3D' extends 'iNEXT' to include three dimensions (3D) 
            of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional 
            diversity (FD). This package provides functions to compute standardized 3D diversity estimates 
            with a common sample size or sample coverage. A unified framework based on Hill numbers 
            and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates 
            are in the same units of species/lineage equivalents and can be meaningfully compared. 
            The package features size- and coverage-based rarefaction and extrapolation sampling 
            curves to facilitate rigorous comparison of 3D diversity across individual assemblages. 
            Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) 
            <doi:10.1111/2041-210X.13682> for more details.
Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis:
       STEP 1: Assessment of sample completeness profiles.
       STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to 
       determine whether the asymptotic diversity can be accurately estimated. 
       STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. 
       STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves.
       STEP 4: Assessment of evenness profiles. 
       The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) <doi:10.1111/1440-1703.12102> for statistical background.
As a sequel to 'iNEXT', the 'iNEXT.beta3D' package provides functions to compute 
              standardized taxonomic, phylogenetic, and functional diversity (3D) estimates 
              with a common sample size (for alpha and gamma diversity) or sample coverage 
              (for alpha, beta, gamma diversity as well as dissimilarity or turnover indices). 
              Hill numbers and their generalizations are used to quantify 3D and to make 
              multiplicative decomposition (gamma = alpha x beta). The package also features 
              size- and coverage-based rarefaction and extrapolation sampling curves to 
              facilitate rigorous comparison of beta diversity across datasets.  
              See Chao et al. (2023) <doi:10.1002/ecm.1588> for more details.
Provides functions to access real-time infectious disease data from the 'disease.sh API',
    including COVID-19 global, US states, continent, and country statistics, vaccination coverage,
    influenza-like illness data from Centers for Disease Control and Prevention (CDC), and more. Also includes curated datasets on a variety
    of infectious diseases such as influenza, measles, dengue, Ebola, tuberculosis, meningitis,
    AIDS, and others. The package supports epidemiological research and data analysis by
    combining API access with high-quality historical and survey datasets on infectious diseases. For more details on the 'disease.sh API', see <https://disease.sh/>.
The objective of this package is to perform inference using
    an expressive statistical grammar that coheres with the tidy design
    framework.
An R package for inferring cell-type specific gene regulatory network from single-cell RNA data.
Functions to make inference about the 
        standardized mortality ratio (SMR) when evaluating the
        effect of a screening program. The package is
        based on methods described in Sasieni (2003) 
        <doi: 10.1097/00001648-200301000-00026> and 
        Talbot et al. (2011) <doi: 10.1002/sim.4334>.
Provides methods for estimating causal effects in the presence of interference described in  B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of
    statistical tests provided in 'stats' package by including additional data types as inputs, expanding and
    restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.
A set of functions for the modeling of data derived from the
    Minidisc Infiltrometer device.  It calculates cumulative infiltration
    and square root of time. Also, it calculates the A parameter based on
    soil physical properties.
Sampler and post-processing functions for semi-parametric Bayesian infinite factor models, motivated by the Multiplicative Gamma Shrinkage Prior of Bhattacharya and Dunson (2011) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419391/>. Contains component C++ functions for building samplers for linear and 2-way interaction factor models using the multiplicative gamma and Dirichlet-Laplace shrinkage priors. The package also contains post processing functions to return matrices that display rotational ambiguity to identifiability through successive application of orthogonalization procedures and resolution of column label and sign switching. This package was developed with the support of the National Institute of Environmental Health Sciences grant 1R01ES028804-01.
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
Contains a number of infix binary operators that may be useful in day to day practices.
Infix functions in R are those that comes between its
  arguments such as %in%, +, and *. These are useful in R 
  programming when manipulating data, performing logical operations, 
  and making new functions. 'infixit' extends the infix functions 
  found in R to simplify frequent tasks, such as finding elements
  that are NOT in a set, in-line text concatenation, augmented assignment
  operations, additional logical and control flow operators, and 
  identifying if a number or date lies between two others.
Provides access to core inflation functions. Four different core inflation 
 functions are provided. The well known trimmed means, exclusion and double weighing methods, 
 alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.
Implementation of methods Extremum Surface Estimator (ESE) and 
   Extremum Distance Estimator (EDE) to identify the inflection point of a curve .
   Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. 
    McCracken (2022) <doi:10.1101/2022.12.30.522131>.
Provides a collection of tools for
        detecting influential cases in generalized mixed effects
        models. It analyses models that were estimated using 'lme4'. The
        basic rationale behind identifying influential data is that
        when single units are omitted from the data, models
        based on these data should not produce substantially different
        estimates. To standardize the assessment of how influential a
        (single group of) observation(s) is, several measures of
        influence are common practice, such as Cook's Distance. 
        In addition, we provide a measure of percentage change of the fixed point 
        estimates and a simple procedure to detect changing levels of significance.
A set of tools for evaluating several measures of case influence for structural equation models.
Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.
Provides functionality to compute various node centrality measures on networks.
    Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
    SNAP library), implementations of constraint and effective network size by Burt (2000) <doi:10.1016/S0191-3085(00)22009-1>; algorithm 
    to identify key players by Borgatti (2006) <doi:10.1007/s10588-006-7084-x>; and the 
    bridging algorithm by Valente and Fujimoto (2010) <doi:10.1016/j.socnet.2010.03.003>. 
    On Unix systems, the betweenness, Key Players, and
    bridging implementations are parallelized with OpenMP, which may run
    faster on systems which have OpenMP configured.
Contains functions for the classification and ranking of top candidate features, reconstruction of networks from
    adjacency matrices and data frames, analysis of the topology of the network 
    and calculation of centrality measures, and identification of the most
    influential nodes. Also, a function is provided for running SIRIR model, which 
    is the combination of leave-one-out cross validation technique and the conventional SIR model, on a network to unsupervisedly rank the true influence of vertices. Additionally, some functions have been provided for the assessment 
    of dependence and correlation of two network centrality measures as well as 
    the conditional probability of deviation from their corresponding means in opposite direction.
    Fred Viole and David Nawrocki (2013, ISBN:1490523995).
    Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695.
    Adopted algorithms and sources are referenced in function document.
'InfluxDB' 2.x time-series database client. Supports both 'InfluxDB' OSS (<https://portal.influxdata.com/downloads/>) and Cloud (<https://cloud2.influxdata.com/>) version.
An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.
The main purpose of this package is to generate the structure of the analysis of variance 
            (ANOVA) table of the two-phase experiments. The user only need to input the design and the 
              relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, 
              this package can then quickly generate the structure of the ANOVA table with the 
              coefficients of the variance components for the expected mean squares.
              Thus, the balanced incomplete block design and provides the efficiency
              factors of the fixed effects can also be studied and compared much easily.
Performs exploratory data analysis and variable screening for
    binary classification models using weight-of-evidence (WOE) and information
    value (IV). In order to make the package as efficient as possible, aggregations
    are done in data.table and creation of WOE vectors can be distributed across
    multiple cores. The package also supports exploration for uplift models (NWOE
    and NIV).
Multiple Imputation for Informative Censoring.
    This package implements two methods. Gamma Imputation
    described in <DOI:10.1002/sim.6274> and Risk Score Imputation
    described in <DOI:10.1002/sim.3480>.
Calculation of informative simultaneous confidence intervals
    for graphical described multiple test procedures and given information
    weights. Bretz et al. (2009) <doi:10.1002/sim.3495> and Brannath et
    al. (2024) <doi:10.48550/arXiv.2402.13719>. Furthermore, exploration of the
    behavior of the informative bounds in dependence of the information
    weights.  Comparisons with compatible bounds are possible.
    Strassburger and Bretz (2008) <doi:10.1002/sim.3338>.
After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test.  The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>.  The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374  <doi:10.1093/biomet/79.2.367>.
Estimation of the most-left informative set of gross returns 
             (i.e., the informative set).
             The procedure to compute the informative set adjusts the method 
             proposed by 
             Mariani et al. (2022a) <doi:10.1007/s11205-020-02440-6> 
             and 
             Mariani et al. (2022b) <doi:10.1007/s10287-022-00422-2> 
             to gross returns of financial assets. 
             This is accomplished through an adaptive algorithm
             that identifies sub-groups of gross returns in 
             each iteration by approximating their distribution with a
             sequence of two-component log-normal mixtures. 
             These sub-groups emerge when a significant change
             in the distribution occurs below the median of the 
             financial returns, with their boundary termed as
             the “change point" of the mixture. 
             The process concludes when no further change points are detected.
             The outcome encompasses parameters of the leftmost mixture 
             distributions and change points of the
             analyzed financial time series.
             The functionalities of the INFOSET package include: (i) modelling asset distribution
             detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial
             series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function)
             (iv) portfolio construction (ptf_construction function).
             The package also provide a specific function to construct rolling windows of different length size and overlapping time.
Implements various measures of information theory based on several entropy estimators.
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
Facilitates the generation of input files for infraFDTD and processes snapshot output. infraFDTD is a finite-difference model written by Keehoon Kim for simulating infrasound that considers topography and a 1-D atmosphere (see Kim et al., 2015 <doi:10.1002/2015GL064466>).
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated. The package implements more advanced methods than the ones first described in: Rousset, Gouy, Almoyna and Courtiol  (2017) <doi:10.1111/1755-0998.12627>.
Collection of tools for assessment of feature importance and feature effects.
    Key functions are:
    feature_importance() for assessment of global level feature importance,
    ceteris_paribus() for calculation of the what-if plots,
    partial_dependence() for partial dependence plots,
    conditional_dependence() for conditional dependence plots,
    accumulated_dependence() for accumulated local effects plots,
    aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,
    generic print() and plot() for better usability of selected explainers,
    generic plotD3() for interactive, D3 based explanations, and
    generic describe() for explanations in natural language.
    The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Parse simple '.ini' configuration files to an structured list. Users
  can manipulate this resulting list with lapply() functions. This same
  structured list can be used to write back to file after modifications.
R dependency injection framework. Dependency injection allows
    a program design to follow the dependency inversion principle. The user
    delegates to external code (the injector) the responsibility of providing its
    dependencies. This separates the responsibilities of use and construction.
Calculate the injury severity score (ISS)
    based on the dictionary in 'ICDPIC' from <https://ideas.repec.org/c/boc/bocode/s457028.html>. The original code was written in 
    'STATA 11'. The original 'STATA' code was written by David Clark, 
    Turner Osler and David Hahn. I implement the same logic for easier access.
    Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. 
    "ICDPIC: Stata module to provide methods for translating International 
    Classification of Diseases (Ninth Revision) diagnosis codes into standard injury 
    categories and/or scores," Statistical Software Components S457028, 
    Boston College Department of Economics, revised 29 Oct 2010.
Sports Injury Data analysis aims to identify and describe the
  magnitude of the injury problem, and to gain more insights (e.g. determine
  potential risk factors) by statistical modelling approaches. The 'injurytools'
  package provides standardized routines and utilities that simplify such
  analyses. It offers functions for data preparation, informative visualizations 
  and descriptive and model-based analyses.
Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <https://www.r-inla.org>.
Facilitates spatial and general latent Gaussian modeling using
  integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>).
  Additionally, extends the GAM-like model class to more general nonlinear predictor
  expressions, and implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data.
  Model components are specified with general inputs and mapping methods to the
  latent variables, and the predictors are specified via general R expressions,
  with separate expressions for each observation likelihood model in
  multi-likelihood models. A prediction method based on fast Monte Carlo sampling
  allows posterior prediction of general expressions of the latent variables.
  Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019)
  <doi:10.1111/2041-210X.13168>.
Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).
Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.
Prepare objects to implement models over spatial and 
  spacetime domains with the 'INLA' package (<https://www.r-inla.org>).
  These objects contain data to for the 'cgeneric' interface in
  'INLA', enabling fast parallel computations.
  We implemented the spatial barrier model, see Bakka et. al. (2019) 
  <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal 
  models proposed in Lindgren et. al. (2023) 
  <https://www.idescat.cat/sort/sort481/48.1.1.Lindgren-etal.pdf>. 
  Details are provided in the available vignettes and from the URL bellow.
Contain code to work with latent Gaussian
  Markov random field (GMRF) models. Queries for the 
  'cgeneric' interface, specified as a way to implement
  new GMRF models to be fitted as model components
  in the 'INLA' package (<https://www.r-inla.org>).
  The implemented functionalities leverage the use 
  of 'cgeneric' models and provide a way to debug 
  the code as well to work with the prior for the 
  model parameters and to sample from it. 
  A Kronecker product method is also implemented to 
  work with the four possible combinations between 
  a 'cgeneric' and a 'rgeneric' model.
A collection of functions for creating color schemes.
    Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory Project Office.
A collection of analysis-ready datasets for the
    U.S. Geological Survey - Idaho National Laboratory (USGS-INL)
    groundwater and surface-water monitoring networks, administered by the
    USGS-INL Project Office in cooperation with the U.S. Department of Energy.
    The data collected from wells and surface-water stations at the
    Idaho National Laboratory and surrounding areas have been used to describe
    the effects of waste disposal on water contained in the
    eastern Snake River Plain aquifer, located in the southeastern part of
    Idaho, and the availability of water for long-term consumptive and
    industrial use. The package includes long-term monitoring records dating
    back to measurements from 1922. Geospatial data describing the areas from
    which samples were collected or observations were made are also included in
    the package. Bundling this data into a single package significantly reduces
    the magnitude of data processing for researchers and provides a way to
    distribute the data along with its documentation in a standard format.
    Geospatial datasets are made available in a common projection and datum, and
    geohydrologic data have been structured to facilitate analysis.
Functionality to dynamically define R functions and S4 methods
 with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling
 conventions.
Generates Rd files from R source code with comments.
 The main features of the default syntax are that
 (1) docs are defined in comments near the relevant code,
 (2) function argument names are not repeated in comments, and
 (3) examples are defined in R code, not comments.
 It is also easy to define a new syntax.
Contains bibliographic information for the U.S. Geological Survey
    (USGS) Idaho National Laboratory (INL) Project Office.
Interpretation methods for analyzing the behavior and individual
    predictions of modern neural networks in a three-step procedure: Converting 
    the model, running the interpretation method, and visualizing the results. 
    Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004)
    <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance
    propagation ('LRP') described by Bach et al. (2015)
    <doi:10.1371/journal.pone.0130140>, deep learning important features
    ('DeepLIFT') described by Shrikumar et al.  (2017) <doi:10.48550/arXiv.1704.02685>
    and gradient-based methods like 'SmoothGrad' described by Smilkov et
    al. (2017) <doi:10.48550/arXiv.1706.03825>, 'Gradient x Input' 
    or 'Vanilla Gradient'.
    Details can be found in the accompanying scientific paper: Koenen & Wright 
    (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).
Infix operators to detect, subset, and replace the elements matched by a given condition.
  The functions have several variants of operator types, including subsets, ranges, regular expressions and others.
  Implemented operators work on vectors, matrices, and lists.
A set of functions to analyse and compare texts, using classical 
  text mining	functions, as well as those from theoretical ecology.
It provides in-place operators for R 
    that are equivalent to '+=', '-=', '*=', '/=' in C++. 
    Those can be applied on integer|double vectors|matrices.
    You have also access to sweep operations (in-place).
Non-parametric resampling-based inference tests for ExPosition.
Collection of functions for quality control (QC) of climatological daily time series (e.g. the ECA&D station data).
For environmental chemists, ecologists, researchers and agricultural scientists to understand the dissipation kinetics, calculate the half-life periods and rate constants of compounds, pesticides, contaminants in different matrices.
A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) 
    assays in pancreatic beta cells or islets.
    The package allows the user to import several sets of experiments from different spreadsheets 
    and to perform subsequent steps: summarise in a tidy format, visualise data quality 
    and compare experimental conditions without omitting to account for technical confounders 
    such as the date of the experiment or the technician.
    Together, insane is a comprehensive method that optimises pre-processing and analyses of 
    GSIS experiments in a friendly-user interface.
    The Shiny App was initially designed for EndoC-betaH1 cell line following method described 
    in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).
Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.
Using embedded sdmx queries, get the data of more than 150 000 insee series from 'bdm' macroeconomic database.
A tool to provide an easy, intuitive and consistent
    access to information contained in various R models, like model
    formulas, model terms, information about random effects, data that was
    used to fit the model or data from response variables. 'insight'
    mainly revolves around two types of functions: Functions that find
    (the names of) information, starting with 'find_', and functions that
    get the underlying data, starting with 'get_'. The package has a
    consistent syntax and works with many different model objects, where
    otherwise functions to access these information are missing.
Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.
A collection of utilities for columnwise summary, comparison and visualisation of data frames.  Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage.
A 'shiny' application to assist in phytosanitary inspections. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling).
Utility functions that implement and automate common sets of validation tasks. 
    These functions are particularly useful to validate inputs, intermediate objects and output 
    values in user-defined functions, resulting in tidier and less verbose functions.
This is a shiny app used for the statistical classifying and analysing
    pre-clinical tumour responses.
Collect  marketing data from Instagram Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
The function 'install_load' checks the local R library(ies) to see
    if the required package(s) is/are installed or not. If the package(s)
    is/are not installed, then the package(s) will be installed along with
    the required dependency(ies). This function pulls source or
    binary packages from the Posit/RStudio-sponsored CRAN mirror. Lastly, the
    chosen package(s) is/are loaded. The function 'load_package' simply loads
    the provided package(s). If this package does not fit your needs, then you
    may want to consider these other R packages: 'needs', 'easypackages',
    'pacman', 'pak', 'anyLib', and/or 'librarian'.
R is great for installing software.  Through the 'installr'
    package you can automate the updating of R (on Windows, using updateR())
    and install new software. Software installation is initiated through a
    GUI (just run installr()), or through functions such as: install.Rtools(),
    install.pandoc(), install.git(), and many more. The updateR() command
    performs the following: finding the latest R version, downloading it,
    running the installer, deleting the installation file, copy and updating
    old packages to the new R installation.
Similar to 'rstantools' for 'rstan',
  the 'instantiate' package builds pre-compiled
  'CmdStan' models into CRAN-ready statistical modeling R packages.
  The models compile once during installation,
  the executables live inside the file systems of their respective packages,
  and users have the full power and convenience of
  'cmdstanr' without any additional compilation after package installation.
  This approach saves time and helps R package developers
  migrate from 'rstan' to the more modern 'cmdstanr'.
  Packages 'rstantools', 'cmdstanr', 'stannis', and
  'stanapi' are similar Stan clients with different objectives.
Provides an interface to the Instagram API <https://instagram.com/
    developer/>, which allows R users to download public pictures filtered by
    hashtag, popularity, user or location, and to access public users' profile data.
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
Functions to build, evaluate, and visualize insurance rating 
    models. It simplifies the process of modeling premiums, and allows to 
    analyze insurance risk factors effectively. The package employs a 
    data-driven strategy for constructing insurance tariff classes, drawing on 
    the work of Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>.
It facilitates the calculation of 40 different insulin sensitivity indices based on fasting, oral glucose tolerance test (OGTT), lipid (adipose), and tracer (palmitate and glycerol rate) and dxa (fat mass) measurement values. It enables easy and accurate assessment of insulin sensitivity, critical for understanding and managing metabolic disorders like diabetes and obesity. Indices calculated are described in Gastaldelli (2022). <doi:10.1002/oby.23503> and Lorenzo (2010). <doi:10.1210/jc.2010-1144>.
Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
The methods in this package adds to the functionality of  the 'intamap' package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.
Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi{10.1002/sim.7350} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
The kernel of this 'Rcpp' based package is an efficient implementation of the generalized gradient projection method for spline function based constrained maximum likelihood estimator for interval censored survival data (Wu, Yuan; Zhang, Ying. Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 <doi:10.1214/12-AOS1016>). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC).
Provides a systematic framework for
 integrating multiple modalities of assays profiled on the same set of
 samples. The goal is to identify genes that are altered in cancer
 either marginally or consistently across different assays. The
 heterogeneity among different platforms and different samples are
 automatically adjusted so that the overall alteration magnitude can
 be accurately inferred. See Tong and Coombes (2012) 
 <doi:10.1093/bioinformatics/bts561>.
Generates a 'Graphviz' graph of the most significant 3-way 
    interaction gains (i.e. conditional information gains) based on a provided 
    discrete data frame. Various output formats are supported ('Graphviz', SVG, 
    PNG, PDF, PS). For references, see the webpage of Aleks Jakulin 
    <http://stat.columbia.edu/~jakulin/Int/>.
Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
Four datasets are provided here from the 'Intendo' game
    'Super Jetroid'. It is data from the 2015 year of operation and it comprises
    a revenue table ('all_revenue'), a daily users table ('users_daily'), a user
    summary table ('user_summary'), and a table with data on all user sessions
    ('all_sessions'). These core datasets come in different sizes, and, each of
    them has a variant that was intentionally made faulty (totally riddled with
    errors and inconsistencies). This suite of tables is useful for testing with
    packages that focus on data validation and data documentation.
Electricity is not made equal and it vary in its carbon footprint (or carbon intensity) 
    depending on its source. This package enables to access and query data provided by the 
    Carbon Intensity API (<https://carbonintensity.org.uk/>). National Grid’s Carbon Intensity API 
    provides an indicative trend of regional carbon intensity of the electricity system in Great Britain.
Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. 
    This package is based on the following research: Eckardt and Mateu (2018) <doi:10.1080/10618600.2017.1391695>. Eckardt and Mateu (2021) <doi:10.1007/s11749-020-00720-4>.
Power analysis for regression models which test the interaction of
    two or three independent variables on a single dependent variable. Includes options 
    for correlated interacting variables and specifying variable reliability. 
    Two-way interactions can include continuous, binary, or ordinal variables.
    Power analyses can be done either analytically or via simulation.  Includes 
    tools for simulating single data sets and visualizing power analysis results.
    The primary functions are power_interaction_r2() and power_interaction() for two-way
    interactions, and power_interaction_3way_r2() for three-way interactions. 
    Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
    Olino TM (2023). "Tutorial: Power analyses for interaction effects in 
    cross-sectional regressions." <doi:10.1177/25152459231187531>.
Produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>]. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).
A tool to calculate and plot estimates from models 
    in which an interaction between the main predictor and a continuous covariate has been specified.
    Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); 
    Durrleman S, Simon R. (1989) <doi:10.1002/sim.4780080504>; Greenland S. (1995) <doi:10.1097/00001648-199507000-00005>.
A suite of functions for conducting and interpreting analysis 
  of statistical interaction in regression models that was formerly part of the 
  'jtools' package. Functionality includes visualization of two- and three-way
  interactions among continuous and/or categorical variables as well as 
  calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., 
  Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These
  capabilities are implemented for generalized linear models in addition to the 
  standard linear regression context.
Implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.
Chi-square tests are computed with corrections.
Various functions and a Shiny app to enrich the results of Multiple Correspondence Analysis with interpretive axes and planes (see Moschidis, Markos, and Thanopoulos, 2022; <doi:10.1108/ACI-07-2022-0191>).
Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.
Provides a runtime type system, allowing users to define and implement interfaces, enums, typed data.frame/data.table, as well as typed functions. This package enables stricter type checking and validation, improving code structure, robustness and reliability.
Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.
A dataframe validation framework for package builders who use
  dataframes as function parameters. It performs checks on column names, coerces
  data-types, and checks grouping to make sure user inputs conform to a
  specification provided by the package author. It provides a mechanism for
  package authors to automatically document supported dataframe inputs and
  selectively dispatch to functions depending on the format of a dataframe much
  like S3 does for classes. It also contains some developer tools to make
  working with and documenting dataframe specifications easier. It helps package
  developers to improve their documentation and simplifies parameter validation
  where dataframes are used as function parameters.
Performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator.
Functions implemented in this package allow to coerce (i.e.
	convert) network data between classes provided by other R packages.
	Currently supported classes are those defined in packages: network and
	igraph.
Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.
Allows an interactive assessment of the timing of interim analyses. The algorithm simulates both the recruitment and treatment/event phase of a clinical trial based on the package 'interim'.
Converts matrices and lists of matrices into a single vector by interleaving 
    their values. That is, each element of the result vector is filled from the input 
    matrices one row at a time. This is the same as transposing a matrix, then removing the 
    dimension attribute, but is designed to operate on matrices in nested list structures.
Interlinearized glossed texts (IGT) are used in descriptive linguistics for
 representing a morphological analysis of a text through a morpheme-by-morpheme gloss.
 'InterlineaR' provide a set of functions that targets several popular formats of IGT
 ('SIL Toolbox', 'EMELD XML') and that turns an IGT into a set of data frames following
 a relational model (the tables represent the different linguistic units: texts,
 sentences, word, morphems).
 The same pieces of software ('SIL FLEX', 'SIL Toolbox') typically produce dictionaries
 of the morphemes used in the glosses. 'InterlineaR' provide a function for turning
 the LIFT XML dictionary format into a set of data frames following a relational model
 in order to represent the dictionary entries, the sense(s) attached to the entries,
 the example(s) attached to senses, etc.
Intervention analysis is used to investigate structural changes in data resulting from external events. Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. <doi:10.1016/j.hazadv.2023.100325> and Paul and Yeasin <doi:10.1371/journal.pone.0272999>.
Bivariate data interpolation on regular and irregular
  grids, either linear or using splines are the main part of this
  package.  It is intended to provide FOSS replacement functions for
  the ACM licensed akima::interp and tripack::tri.mesh functions.
  Linear interpolation is implemented in 
  interp::interp(..., method="linear"), this corresponds to the call 
  akima::interp(..., linear=TRUE) which is the default setting and 
  covers most of akima::interp use cases in depending packages.  
  A re-implementation of Akimas irregular grid spline
  interpolation (akima::interp(..., linear=FALSE)) is now also
  available via interp::interp(..., method="akima").
  Estimators for partial derivatives are now also available in 
  interp::locpoly(), these are a prerequisite for the spline interpolation.  
  The basic part is a GPLed triangulation algorithm (sweep hull 
  algorithm by David Sinclair) providing the starting point for the
  irregular grid interpolator. As side effect this algorithm is also
  used to provide replacements for almost all functions of the tripack
  package which also suffers from the same ACM license restrictions.  
  All functions are designed to be backward compatible with their 
  akima / tripack counterparts.
Computational topology, which enables topological data analysis
    (TDA), makes pervasive use of abstract mathematical objects called
    simplicial complexes; see Edelsbrunner and Harer (2010)
    <doi:10.1090/mbk/069>.
    Several R packages and other software libraries used through an R interface
    construct and use data structures that represent simplicial complexes,
    including mathematical graphs viewed as 1-dimensional complexes.
    This package provides coercers (converters) between these data structures.
    Currently supported structures are complete lists of simplices as used by
    'TDA'; the simplex trees of Boissonnat and Maria (2014)
    <doi:10.1007/s00453-014-9887-3> as implemented in 'simplextree' and in
    Python GUDHI (by way of 'reticulate'); and the graph classes of 'igraph' and
    'network', by way of the 'intergraph' package.
Plots the conditional coefficients ("marginal effects") of
    variables included in multiplicative interaction terms.
Spatial interpolation toolkit designed for environmental and geospatial applications. It includes a range of methods, from traditional techniques to advanced machine learning approaches, ensuring accurate and efficient estimation of values in unobserved locations.
Provides two different methods, linear and nonlinear, to
    interpolate a bivariate function, scalar-valued or vector-valued. 
    The interpolated data are not necessarily gridded. The algorithms 
    are performed by the 'C++' library 'CGAL' (<https://www.cgal.org/>).
Some interpolation methods taken from 'Boost': barycentric
    rational interpolation, modified Akima interpolation, PCHIP (piecewise
    cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom
    splines.
Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
Estimate confidence intervals for mean, proportion, mean difference 
   for unpaired and paired samples and proportion difference. Plot the confidence 
   intervals. Generate documents explaining the statistical result step by step.
High resolution mass spectrometry yields often large data sets of 
    spectra from compounds which are not present in available libraries. These 
    spectra need to be annotated and interpreted.
    'InterpretMSSpectrum' provides a set of functions to perform such tasks for 
    Electrospray-Ionization and Atmospheric-Pressure-Chemical-Ionization derived 
    data in positive and negative ionization mode.
Compute permutation- based performance measures and create partial
    dependence plots for (cross-validated) 'randomForest' and 'ada' models.
Provides access to low-level operating system mechanisms for
    performing atomic operations on shared data structures. Mutexes provide
    shared and exclusive locks. Semaphores act as counters. Message queues
    move text strings from one process to another. All these interprocess
    communication (IPC) tools can optionally block with or without a timeout.
    Implemented using the cross-platform 'boost' 'C++' library
    <https://www.boost.org/doc/libs/release/libs/interprocess/>.
A set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1017/pan.2021.19>.
Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.
Provides an R version of the 'InterVA4' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Provides an R version of the 'InterVA5' software (<http://www.byass.uk/interva/>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].
Perform fast and memory efficient time-weighted averaging of values 
    measured over intervals into new arbitrary intervals. 
	This package is useful in the context of data measured or represented
	as constant values over intervals on a one-dimensional discrete axis 
	(e.g. time-integrated averages of a curve over defined periods). 
	This package was written specifically to deal with air 
	pollution data recorded or predicted as averages over sampling periods. 
	Data in this format often needs to be shifted to non-aligned periods 
	or averaged up to periods of longer duration (e.g. averaging data measured over 
	sequential non-overlapping periods to calendar years).
A user-friendly toolbox for doing the statistical analysis of
  interval-valued responses in questionnaires measuring intrinsically
  imprecise human attributes or features (attitudes, perceptions, opinions,
  feelings, etc.). In particular, this package provides S4 classes, methods,
  and functions in order to compute basic arithmetic and statistical operations
  with interval-valued data; prepare customized plots; associate each
  interval-valued response to its equivalent Likert-type and visual analogue
  scales answers through the minimum theta-distance and the mid-point criteria;
  analyze the reliability of respondents' answers from the internal consistency
  point of view by means of Cronbach's alpha coefficient; and simulate
  interval-valued responses in this type of questionnaires. The package also
  incorporates some real-life data that can be used to illustrate its working
  with several non-trivial reproducible examples. The methodology used in this
  package is based in many theoretical and applied publications from
  SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and
  Comparison of Distributions of Random Elements) Research Group
  (<https://bellman.ciencias.uniovi.es/smire+codire/>)
  from the University of Oviedo (Spain).
Tools for working with and comparing sets of points and intervals.
Manipulate integer-bounded intervals including finding overlaps, piling and merging.
Performs hypothesis testing
    using the interval estimates (e.g., confidence intervals). The
    non-overlapping interval estimates indicates the statistical
    significance. References to these procedures can be found at
    Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>,
    Bonett and Seier (2003) <doi:10.1198/0003130032323>, and
    Lemm (2006) <doi:10.1300/J082v51n02_05>.
Creation of tables of summary statistics or counts for clinical data (for 'TLFs'). 
  These tables can be exported as in-text table (with the 'flextable' package) for a Clinical Study Report 
  (Word format) or a 'topline' presentation (PowerPoint format), 
  or as interactive table (with the 'DT' package) to an html document for clinical data review.
The 'inti' package is part of the 'inkaverse' project for developing 
    different procedures and tools used in plant science and experimental designs. 
    The mean aim of the package is to support researchers during the planning of
    experiments and data collection (tarpuy()), data analysis and graphics (yupana())
    , and technical writing. 
    Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
An interval-valued extension of ordinary and simple kriging.
    Optimization of the function is based on a generalized interval distance.
    This creates a non-differentiable cost function that requires a
    differentiable approximation to the absolute value function. This
    differentiable approximation is optimized using a Newton-Raphson algorithm
    with a penalty function to impose the constraints. Analyses in the package
    are driven by the 'intsp' and 'intgrd' 
    classes, 
    which are interval-valued extensions of
    'SpatialPointsDataFrame' and 'SpatialPixelsDataFrame' respectively. 
    The package includes several wrappers to functions in the 
    'gstat' and 'sp' packages.
Provides a key-value store data structure. The keys are
    integers and the values can be any R object. This is like a list but
    indexed by a set of integers, not necessarily contiguous and possibly
    negative. The implementation uses a 'R6' class. These containers are 
    not faster than lists but their usage can be more convenient for 
    certain situations.
Implementing the interventional effects for mediation analysis for up to 3 mediators.
    The methods used are based on VanderWeele, Vansteelandt and Robins (2014) <doi:10.1097/ede.0000000000000034>, 
    Vansteelandt and Daniel (2017) <doi:10.1097/ede.0000000000000596> and Chan and Leung (2020; unpublished manuscript, available on request from the author of this package).
    Linear regression, logistic regression and Poisson regression are used for continuous, binary 
    and count mediator/outcome variables respectively.
Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.
Models, analyzes, and forecasts financial intraday signals. This package
    currently supports a univariate state-space model for intraday trading volume provided
    by Chen (2016) <doi:10.2139/ssrn.3101695>.
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.  Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).
Performs Goodness of Fit for regression models 
  using Integrated Regression method. Works for several 
  different fitting techniques.
Estimate the proportions of the null and the reproducibility and
    non-reproducibility of the signal group for the input data set. The Bayes factor
    calculation and EM (Expectation Maximization) algorithm procedures are also
    included.
Provides functions to estimate the intrinsic dimension of a dataset 
  via likelihood-based approaches. Specifically, the package implements the 
  'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. 
  In addition, the first reference contains an extended vignette on the usage of 
  the 'TWO-NN' and 'Hidalgo' models. References: 
    Denti (2023, <doi:10.18637/jss.v106.i09>); 
    Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); 
    Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>);
    Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); 
    Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.
Functions for evaluating and testing asset pricing models, including
    estimation and testing of factor risk premia, selection of "strong" risk 
    factors (factors having nonzero population correlation with test asset
    returns), heteroskedasticity and autocorrelation robust covariance matrix
    estimation and testing for model misspecification and identification. 
    The functions for estimating and testing factor risk 
    premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass 
    approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) 
    <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk
    premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The 
    functions for selecting the "strong" risk factors are based on the Oracle
    estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the 
    factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. 
    The functions for evaluating model misspecification implement the HJ
    model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>,
    which is a modification of the prominent Hansen-Jagannathan (1997)
    <doi:10.1111/j.1540-6261.1997.tb04813.x> distance.
    The functions for testing model identification 
    specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> 
    and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test 
    to the regression coefficient matrix of test asset returns on risk factors.
    Finally, the function for heteroskedasticity and autocorrelation robust 
    covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912>
    covariance estimator.
Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics – A novel and practical method for estimating the “intrinsic” value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>.
Evaluating if values
  of vectors are within different open/closed intervals
  (`x %[]% c(a, b)`), or if two closed
  intervals overlap (`c(a1, b1) %[]o[]% c(a2, b2)`).
  Operators for negation and directional relations also implemented.
Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.
Integration of disparate datasets is needed in order to make efficient use of all available data and thereby address the issues currently threatening biodiversity.
   Data integration is a powerful modeling framework which allows us to combine these datasets together into a single model, yet retain the strengths of each individual dataset.
   We therefore introduce the package, 'intSDM': an R package designed to help ecologists develop a reproducible workflow of integrated species distribution models, using data both provided from the user as well as data obtained freely online.
   An introduction to data integration methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
Contains implementations of
    integrative survival analysis routines, including
    regular Cox cure rate model proposed by
    Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531>
    via an EM algorithm proposed by
    Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>,
    regularized Cox cure rate model with elastic net penalty following
    Masud et al. (2018) <doi:10.1177/0962280216677748>, and
    Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and
    weighted concordance index for cure models proposed by
    Asano and Hirakawa (2017) <doi:10.1080/10543406.2017.1293082>.
Provides tools for importing, merging, and analysing data from 
  international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).
Enum-type representation of vectors and representation
  of intervals, including a method of coercing variables in data frames.
Provides an up-to-date version of the 'InvaCost' database 
    (<doi:10.6084/m9.figshare.12668570>) in R, and
    several functions to analyse the costs of invasive alien species 
    (<doi:10.1111/2041-210X.13929>).
Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.
Vector operations between grapes: An infix-only package! The 'invctr' functions perform common and less common operations on vectors, data frames matrices and list objects:
    - Extracting a value (range), or, finding the indices of a value (range).
    - Trimming, or padding a vector with a value of your choice.
    - Simple polynomial regression.
    - Set and membership operations.
    - General check & replace function for NAs, Inf and other values.
Simulate inventory policies with and without forecasting, facilitate inventory analysis calculations such as  stock levels and re-order points,pricing and promotions calculations. 
  The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations.
    The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization.
  The functions are referenced from :
  1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. 
  2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition.
  3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. 
  4-Ballou, R.H. Business Logistics Management. 
  5-MIT Micromasters Program. 
  6- Columbia University  course for supply and demand analysis.
  8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W
  For further details or correspondence :<www.linkedin.com/in/haythamomar>, <www.rescaleanalytics.com>.
Reverse engineer a regular expression pattern for the characters
    contained in an R object. Individual characters can be categorised into
    digits, letters, punctuation or spaces and encoded into run-lengths. This
    can be used to summarise the structure of a dataset or identify non-standard
    entries. Many non-character inputs such as numeric vectors and data frames
    are supported.
Provides composable invertible transforms for
    (sparse) matrices.
Functions to facilitate inverse estimation (e.g., calibration) in
    linear, generalized linear, nonlinear, and (linear) mixed-effects models. A
    generic function is also provided for plotting fitted regression models with
    or without confidence/prediction bands that may be of use to the general
    user. For a general overview of these methods, see Greenwell and Schubert 
    Kabban (2014) <doi:10.32614/RJ-2014-009>.
Light weight implementation of the standard distribution
  functions for the inverse gamma distribution, wrapping those for the gamma
  distribution in the stats package.
Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) <http://rcea.org/RePEc/pdf/wp23-11.pdf> .  
    The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.
Fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
Takes in vivo toxicokinetic concentration-time data and fits
    parameters of 1-compartment and 2-compartment models for each
    chemical. These methods are described in detail in "Informatics for Toxicokinetics" (submitted).
Contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019)  <doi:10.1007/s42519-018-0027-2>. The package can also be used as a teaching demo for introductory Bayesian courses.
Interaction and analysis of multiple response data,
    along with other tools for analysing these types of data including
    missing value analysis and calculation of standard errors for
    a range of covariance matrix results (proportions, multinomial,
    independent samples, and multiple response).
Simple plotting function(s) for exploratory data analysis with flexible options allowing for easy plot customisation. The goal is to make it easy for beginners to start exploring a dataset through simple R function calls, as well as provide a similar interface to summary statistics and inference information. Includes functionality to generate interactive HTML-driven graphs. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a suite of functions to use with regression models, including summaries, residual plots, and factor comparisons. Used as part of the Model Fitting module of 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding.
Provides a collection of functions for working with time series data, including functions for drawing, decomposing, and forecasting. Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) <doi:10.1016/j.ijforecast.2003.09.015>, Winters (1960) <doi:10.1287/mnsc.6.3.324>, Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".
One function to read files. One function to write files. One
    function to direct plots to screen or file. Automatic file format inference
    and directory structure creation.
Calculates fundamental IO matrices (Leontief, Wassily W. (1951) <doi:10.1038/scientificamerican1051-15>); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) <doi:10.1080/09535319200000013>, Blair and Miller (2009) <ISBN:978-0-521-73902-3>, Antras et al (2012) <doi:10.3386/w17819>, Hummels, Ishii, and Yi (2001) <doi:10.1016/S0022-1996(00)00093-3>); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) <doi:10.1080/09535319300000017>, Sonis, Hewings, and Guo (2006) <doi:10.1080/09535319600000002>); and a variety of table operators.
The data analysis module for the Iterative Optimization Heuristics
    Profiler ('IOHprofiler'). This module provides statistical analysis methods for the 
    benchmark data generated by optimization heuristics, which can be visualized through a 
    web-based interface. The benchmark data is usually generated by the 
    experimentation module, called 'IOHexperimenter'. 'IOHanalyzer' also supports
    the widely used 'COCO' (Comparing Continuous Optimisers) data format for benchmarking.
Addresses the 'log of zero' by developing a new family of 
    estimators called iterated Ordinary Least Squares. 
    This family nests standard approaches such as log-linear and 
    Poisson regressions, offers several computational advantages, 
    and corresponds to the correct way to perform the popular 
    log(Y + 1) transformation. For more details about how to use it, 
    see the notebook at: <https://www.davidbenatia.com/>.
Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.
Network functionalities specialized for data generated from input-output tables.
Collection of functions for IO Psychologists.
Density, spectral density, and regression estimation using infinite
    order flat-top kernels.
Pre-processing and basic analytical tasks related to working
    with Eurostat's symmetric input-output tables and provide basic
    input-output economics calculations. The package is part of rOpenGov
    <http://ropengov.github.io/> to open source open government initiatives.
Routines and tools for assessing the quality of content
    analysis on the basis of the Iota Reliability Concept. The concept is
    inspired by item response theory and can be applied to any kind of
    content analysis which uses a standardized coding scheme and discrete
    categories.  It is also applicable for content analysis conducted by
    artificial intelligence.  The package provides reliability measures
    for a complete scale as well as for every single category. Analysis of
    subgroup-invariance and error corrections are implemented. This
    information can support the development process of a coding scheme and
    allows a detailed inspection of the quality of the generated data.
    Equations and formulas working in this package are part of Berding et
    al. (2022)<doi:10.3389/feduc.2022.818365> and Berding and Pargmann
    (2022) <doi:10.30819/5581>.
Basic I/O tools for streaming and data parsing.
Provides S4 classes for Internet Protocol (IP) versions 4 and 6 addresses and efficient methods for 'IP' addresses comparison, arithmetic, bit manipulation and lookup. Both 'IPv4' and 'IPv6' arbitrary ranges are also supported as well as internationalized ('IDN') domain lookup with and 'whois' query.
Enables the user to find the country, region, district, city, coordinates, zip code, time zone, ISP, domain name, connection type, area code, weather, Mobile Country Code, Mobile Network Code, mobile brand name, elevation, usage type, address type, IAB category and Autonomous system information that any IP address or hostname originates from. Supported IPv4 and IPv6.
        Please visit <https://www.ip2location.com> to learn more. You may also want to visit <https://lite.ip2location.com> for free database download.
        This package requires 'IP2Location Python' module. At the terminal, please run 'pip install IP2Location' to install the module.
A system for submitting multiple IP information queries to 'IP2Location.io'’s IP Geolocation API and storing the resulting data in a dataframe. You provide a vector of IP addresses and your 'IP2Location.io' API key. The package returns a dataframe with one row per IP address and a column for each available data field (data fields not included in your API plan will contain NAs). This is the second submission of the package to CRAN.
Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too.
    This package uses the 'IP2Location.io' API to query this data. To get started with a free API key, sign up here <https://www.ip2location.io/sign-up?ref=1>.
Enable user to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits.
    The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit <https://lite.ip2location.com> for free database download.
Easily implement the checking of 'WHOIS' information for a particular domain. 'IP2WHOIS' supports the query for 1113 Top-level Domains(TLDs) and 634 Country Code Top-level Domains(ccTLDs).
    To get started with a free API key, you may sign up at here <https://www.ip2whois.com/register>.
Converts character vectors between phonetic
    representations.  Supports IPA (International Phonetic Alphabet),
    X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and
    ARPABET (used by the CMU Pronouncing Dictionary).
Classes and functions for working with IP (Internet Protocol)
    addresses and networks, inspired by the Python 'ipaddress' module.
    Offers full support for both IPv4 and IPv6 (Internet Protocol versions
    4 and 6) address spaces. It is specifically designed to work well with
    the 'tidyverse'.
A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) <doi:10.1101/2020.03.21.001206>.
Read data from 'LimeSurvey'
    (<https://www.limesurvey.org/>)
    in a comfortable way.
    Heavily inspired by 'limer'
    (<https://github.com/cloudyr/limer/>),
    which lacked a few comfort features for me.
An R client for the 'ipbase.com' IP Geolocation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://ipbase.com/docs> .
Provides tools for passing messages between R processes. 
    Shiny examples are provided showing how to perform useful tasks such as: 
    updating reactive values from within a future, progress bars for long running 
    async tasks, and interrupting async tasks based on user input.
An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.
Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials.
Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2024) <doi:10.48550/arXiv.2410.09665>.
An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. ’s digitize package (2011)  <doi:10.32614/RJ-2011-004> . For more complex and tangled together graphs, digitizing software, such as 'DigitizeIt' (for MAC or windows) or 'ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) <doi:10.1186/1471-2288-12-9> with some modifications.
Functions are provided to interpolate geo-referenced point data via
    Inverse Path Distance Weighting. Useful for coastal marine applications where
    barriers in the landscape preclude interpolation with Euclidean distances.
Allows direct access to the macroeconomic, 
             financial and regional database maintained by 
             Brazilian Institute for Applied Economic Research ('Ipea').
             This R package uses the 'Ipeadata' API. For more information, 
             see <http://www.ipeadata.gov.br/>.
Convenient functions to create 'ggplot2' graphics following the 
             editorial guidelines of the  Institute for Applied Economic 
             Research (Ipea).
Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) <doi:10.1002/9780470316757> and Ratkowsky and Reddy (2017) <doi:10.1093/aesa/saw098> for details.
Contains data on Post-Secondary Institution Statistics in 2020 <https://nces.ed.gov/ipeds/use-the-data>. The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice.
Starting from user-supplied institutional data, these scripts 
    transform, aggregate, and reshape the information to produce 
    key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) 
    through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes.  
    Final files are saved locally to a location of the user's choice. 
    User-friendly readable files can also be produced for purposes of data review and validation.
A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.
A fast (C) implementation of the iterative proportional fitting
    procedure.
Performs iterative proportional updating given a seed table and
  an arbitrary number of marginal distributions. This is commonly used in
  population synthesis, survey raking, matrix rebalancing, and other
  applications. For example, a household survey may be weighted to match the
  known distribution of households by size from the census. An origin/
  destination trip matrix might be balanced to match traffic counts.
  The approach used by this package is based on a paper from
  Arizona State University (Ye, Xin, et. al. (2009)
  <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf>).
  Some enhancements have been made to their work including primary and 
  secondary target balance/importance, general marginal agreement, and weight 
  restriction.
Algorithms and utility functions for indoor positioning using fingerprinting techniques. 
    These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data 
    sets, estimation of positions,comparison of the performance of different models, and graphical 
    visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or
    probabilistic fingerprinting are implemented in this package to perform analysis
    and estimations over RSSI data sets.
When you want to install R package or download file from GitHub, 
    but you can't access GitHub, this package helps you install R packages or 
    download file from GitHub via the proxy website <https://gh-proxy.com/> 
    or <https://ghfast.top/>, which is in real-time sync with GitHub.
Combining genomic prediction with Monte Carlo simulation, three different 
    strategies are implemented to select parental lines for multiple traits in plant 
    breeding. The selection strategies include (i) GEBV-O considers only genomic 
    estimated breeding values (GEBVs) of the candidate individuals; (ii) GD-O 
    considers only genomic diversity (GD) of the candidate individuals; and (iii) 
    GEBV-GD considers both GEBV and GD. The above method can be seen in Chung PY, 
    Liao CT (2020) <doi:10.1371/journal.pone.0243159>. Multi-trait genomic best 
    linear unbiased prediction (MT-GBLUP) model is used to simultaneously estimate 
    GEBVs of the target traits, and then a selection index is adopted to evaluate 
    the composite performance of an individual.
An R client for the 'iplookupapi.com' IP Lookup API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://iplookupapi.com/docs> .
Provides functions and data sets to accompany the  book 'Integrated Population Models: Theory and Ecological Applications with R and JAGS' by Michael Schaub and Marc Kéry (ISBN: 9780128205648).
Flexibly implements Integral Projection Models using a
  mathematical(ish) syntax. This package will not help with the vital rate
  modeling process, but will help convert those regression models into an
  IPM. 'ipmr' handles density dependence and environmental stochasticity, with a
  couple of options for implementing the latter. In addition, provides functions
  to avoid unintentional eviction of individuals from models. Additionally, 
  provides model diagnostic tools, plotting functionality, 
  stochastic/deterministic simulations, and analysis tools.
  Integral projection models are described in depth by Easterling et al. (2000) 
  <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) 
  <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>,
  and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. 
  Williams et al. (2012) <doi:10.1890/11-2147.1> discuss the problem of 
  unintentional eviction.
Calculation of key bacterial growth curve parameters using fourth degree polynomial functions. 
    Six growth curve parameters are provided including peak growth rate, doubling time, lag time, maximum growth, and etc.
    'ipolygrowth' takes time series data from individual biological samples (with technical replicates) or multiple samples.
Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.
Let us consider a sample of patients who can suffer from several diseases simultaneously, in a given set of diseases. The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient.
Improved predictive models by indirect classification and
  bagging for classification, regression and survival problems 
  as well as resampling based estimators of prediction error.
Provides methods to perform and analyse I-prior regression models.
    Estimation is done either via direct optimisation of the log-likelihood or 
    an EM algorithm.
Immunotherapy has revolutionized cancer treatment, but predicting patient
    response remains challenging. Here, we presented Intelligent Predicting
    Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a
    novel network-based model that integrates multiple data types to predict
    immunotherapy outcomes. It incorporates gene expression, biological
    functional network, tumor microenvironment characteristics, immune-related
    pathways, and clinical data to provide a comprehensive view of factors
    influencing immunotherapy efficacy. By identifying key genetic and
    immunological factors, it provides an insight for more personalized
    treatment strategies and combination therapies to overcome resistance
    mechanisms.
Functions that wrap popular phylogenetic software for sequence
    alignment, masking of sequence alignments, and estimation of phylogenies and
    ancestral character states.
Estimates the density of a spatially distributed animal population 
  sampled with an array of passive detectors, such as traps. Models incorporating 
  distance-dependent detection are fitted by simulation and inverse prediction 
  as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.
Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets.
Contains data sets, programmes and illustrations 
    discussed in the book, "Introduction to Probability, Statistics and R:
    Foundations for Data-Based Sciences."  Sahu (2024, isbn:9783031378645)
    describes the methods in detail.
An easy way to work with census, survey, and geographic data
    provided by IPUMS in R. Generate and download data through the IPUMS
    API and load IPUMS files into R with their associated metadata to
    make analysis easier. IPUMS data describing 1.4 billion individuals
    drawn from over 750 censuses and surveys is available free of charge
    from the IPUMS website <https://www.ipums.org>.
Generate plots based on the Item Pool Visualization concept for
    latent constructs. Item Pool Visualizations are used to display the
    conceptual structure of a set of items (self-report or psychometric).
    Dantlgraber, Stieger, & Reips (2019) <doi:10.1177/2059799119884283>.
Functions to estimate the probability to receive the observed treatment, based on
    individual characteristics. The inverse of these probabilities can be used as weights when
	estimating causal effects from observational data via marginal structural models. Both point
	treatment situations and longitudinal studies can be analysed. The same functions can be used to
	correct for informative censoring.
Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or  modelled  using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.
An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighted (IPW) estimation of average treatment effect (ATE) with misclassified binary outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals.
An implementation of the MaxLFQ algorithm by
    Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive
    pipeline for processing proteomics data in data-independent acquisition mode
    (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>).
    It offers additional options for protein quantification using
    the N most intense fragment ions, using all fragment ions, and
    a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160).
    In general, the tool can be used to integrate multiple 
    proportional observations into a single quantitative value.
Builds statistical control charts with exact limits for
        univariate and multivariate cases.
Functions to import and handle infrared spectra (import from '.csv' and
  Thermo Galactic's '.spc', baseline correction, binning, clipping,
  interpolating, smoothing, averaging, adding, subtracting, dividing, 
  multiplying, atmospheric correction, 'tidyverse' methods, plotting).
Iterated race is an extension of the Iterated F-race method for
             the automatic configuration of optimization algorithms, that is,
             (offline) tuning their parameters by finding the most appropriate
             settings given a set of instances of an optimization problem.
             M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle,
             and M. Birattari (2016) <doi:10.1016/j.orp.2016.09.002>.
Graphical visualization tools for analyzing the data produced by 'irace'. The 'iraceplot' package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of 'irace' and their performance. The functions just require the log file generated by 'irace' and, in some cases, they can be used with user-provided data.
Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.
Provides implementation of various correlation coefficients of common use in
  Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient
  as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008)
  <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b
  developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.
An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>.
    Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.
Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.
Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.
Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.
Provides examples of code for analyzing data or accomplishing tasks that may be useful to institutional or educational researchers.
A comprehensive suite of tools for managing, processing, and analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports quality control, geospatial analysis, and preparation of IFCB data for publication in databases like <https://www.gbif.org>, <https://www.obis.org>, <https://emodnet.ec.europa.eu/en>, <https://shark.smhi.se/>, and <https://www.ecotaxa.org>. The package integrates with the MATLAB 'ifcb-analysis' tool, which is described in Sosik and Olson (2007) <doi:10.4319/lom.2007.5.204>, and provides features for working with raw, manually classified, and machine learning–classified image datasets. Key functionalities include image extraction, particle size distribution analysis, taxonomic data handling, and biomass concentration calculations, essential for plankton research.
Fits a double logistic function to NDVI time series and calculates 
             instantaneous rate of green (IRG) according to methods described
             in Bischoff et al. (2012) <doi:10.1086/667590>.
Provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.
Classes and functions for metrics calculation as part of the
             'EarthScope MUSTANG' project. The functionality in this package 
             builds upon the base classes of the 'IRISSeismic' package.
             Metrics include basic statistics as well as higher level
             'health' metrics that can help identify problematic seismometers.
Provides classes and methods for seismic data analysis. The
             base classes and methods are inspired by the python code found in
             the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and 
             methods support data returned by web services provided by the 'IRIS DMC'
             <http://service.iris.edu/>.
The R kernel for the 'Jupyter' environment executes R code which the front-end
    ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
Fast and memory efficient methods for truncated singular value
    decomposition and principal components analysis of large sparse and dense
    matrices.
The Row-column designs are widely recommended for experimental situations when there are two well-identified factors that are cross-classified representing known sources of variability. These designs are expected to result a gain in accuracy of estimating treatment comparisons in an experiment as they eliminate the effects of the row and column factors. However, these designs are not readily available when the number of treatments is more than the levels of row and column blocking factors. This package named 'iRoCoDe' generates row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs. (Mcsorley, J.P., Phillips, N.C., Wallis, W.D. and Yucas, J.L. (2005).<doi:10.1007/s10623-003-6149-9>).
Imbalanced domain learning has almost exclusively focused on solving 
    classification tasks, where the objective is to predict cases labelled with a 
    rare class accurately. Such a well-defined approach for regression tasks lacked 
    due to two main factors. First, standard regression tasks assume that each value 
    is equally important to the user. Second, standard evaluation metrics focus on 
    assessing the performance of the model on the most common cases. This package 
    contains methods to tackle imbalanced domain learning problems in regression 
    tasks, where the objective is to predict extreme (rare) values.
    The methods contained in this package are: 1) an automatic and non-parametric 
    method to obtain such relevance functions; 2) visualisation tools; 3) suite of 
    evaluation measures for optimisation/validation processes; 4) the squared-error 
    relevance area measure, an evaluation metric tailored for imbalanced regression tasks.
    More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.
Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson's A, Kendall's W, Cohen's Kappa, ...
Implements a 'Shiny Item Analysis' module and functions for computing false positive rate 
  and other binary classification metrics from inter-rater reliability based on Bartoš & Martinková (2024) 
   <doi:10.1111/bmsp.12343>.
Calculates various chance-corrected agreement coefficients (CAC) among 2 or more raters are provided. 
	Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 
	coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. All of these statistical 
	procedures are described in details in Gwet, K.L. (2014,ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
	Advanced Analytics, LLC.
Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).
Calculates various intraclass correlation coefficients used to quantify 
  inter-rater and intra-rater reliability.  The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented
  in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
  Advanced Analytics, LLC.
Provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi{10.1037/1082-989X.1.1.30} and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi{10.1214/aoms/1177732186}.
A collection of Item Response Theory (IRT) and Computerized 
     Adaptive Testing (CAT) functions that are used in psychometrics.
Dichotomous and polytomous data analysis and their scoring using the unidimensional Item Response Theory model (Chalmers (2012) <doi:10.18637/jss.v048.i06>) with user-friendly graphic User Interface. Suitable for beginners who are learning item response theory.
Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) <doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) <doi:10.1177/21582440211052556>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.
Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.
Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm 
    (Bock & Aitkin, 1981 <doi:10.1007/BF02293801>). 
    Within parameter estimation algorithm, several methods for latent distribution estimation are available.
    Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution.
Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.
IRT-M is a semi-supervised approach based on Bayesian Item Response Theory
    that produces theoretically identified underlying dimensions from input data
    and a constraints matrix. The methodology is fully described in 'Morucci et al. (2024),
    "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'. 
    Details are available at <https://www.cambridge.org/core/journals/american-political-science-review/article/measurement-that-matches-theory-theorydriven-identification-in-item-response-theory-models/395DA1DFE3DCD7B866DC053D7554A30B>.
A collection of functions useful in learning and practicing IRT,
    which can be combined into larger programs. Provides basic CTT analysis,
    a simple common interface to the estimation of item
    parameters in IRT models for binary responses with three different programs
    (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible 
    values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord,
    and the complete Hebaera method), and a rich array of parametric and 
    non-parametric (kernel) plots. Estimates and plots Haberman's interaction model
    when all items are dichotomously scored.
Implementation of analytical and sampling-based power analyses for
    the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied
    to item response theory (IRT) models that are fitted using marginal maximum
    likelihood estimation. The methods are described in our paper
    (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).
Fit unidimensional item response theory (IRT) models to a mixture 
    of dichotomous and polytomous data, calibrate online item parameters 
    (i.e., pretest and operational items), estimate examinees' abilities, 
    and examine the IRT model-data fit on item-level in different ways 
    as well as provide useful functions related to IRT analyses such as 
    IRT model-data fit evaluation and differential item functioning analysis.
    The bring.flexmirt() and write.flexmirt() functions were written by modifying 
    the read.flexmirt() function (Pritikin & Falk (2022) <doi:10.1177/0146621620929431>).
    The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() 
    and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>).
    The bisection() function was written by modifying the bisection() function 
    (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve 
    scoring in the est_score() function was written by modifying the irt.eq.tse() function 
    (González (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted 
    likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions
    of the catR package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).
Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software.
Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
Interactive shiny application for running Item Response Theory
    analysis. Provides graphics for characteristic and information curves.
The ISA is a biclustering algorithm that finds modules 
  in an input matrix. A module or bicluster is a block of the
  reordered input matrix.
We provide data sets used in the forthcoming textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2024). The package currently contains sixteen datasets and should be published in early 2024.
These are data and functions to support quantitative peace science
    research. The data are important state-year information on democracy and
    wealth, which require periodic updates and regular maintenance. The
    functions permit some exploratory and diagnostic assessment of the kinds of
    data in demand by the community, but do not impose many dependencies on the
    user.
Reads the output of the 'PerkinElmer InForm' software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.
ISA is a metadata framework to manage an increasingly diverse set 
    of life science, environmental and biomedical experiments. In isatabr 
    methods for reading, modifying and writing of files in the ISA-Tab format
    are implemented. It also contains methods for processing assay data.
The Inductive Subgroup Comparison Approach ('ISCA') offers a way to compare groups that are internally differentiated and heterogeneous. It starts by identifying the social structure of a reference group against which a minority or another group is to be compared, yielding empirical subgroups to which minority members are then matched based on how similar they are. The modelling of specific outcomes then occurs within specific subgroups in which majority and minority members are matched. 'ISCA' is characterized by its data-driven, probabilistic, and iterative approach and combines fuzzy clustering, Monte Carlo simulation, and regression analysis. ISCA_random_assignments() assigns subjects probabilistically to subgroups. ISCA_clustertable() provides summary statistics of each cluster across iterations. ISCA_modeling() provides Ordinary Least Squares regression results for each cluster across iterations. For further details please see Drouhot (2021) <doi:10.1086/712804>.
Implementation of functions to assign corresponding common job prestige scores (SIOPS, ISEI), the official job or group title and the ISCO-88 code to given ISCO-08 codes. ISCO-08 is the latest version of the International Standard Classification of Occupations which is used to organise information on labour and jobs.
Implementation of a KL-based scoring rule to assess the quality of different missing value imputations in the broad sense as introduced in Michel et al. (2021)  <arXiv:2106.03742>.
Provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).
Tools for parsing 'NOAA' Integrated Surface Data ('ISD') files,
    described at <https://www.ncdc.noaa.gov/isd>. Data includes for example,
    wind speed and direction, temperature, cloud data, sea level pressure,
    and more. Includes data from approximately 35,000 stations worldwide,
    though best coverage is in North America/Europe/Australia. Data is stored
    as variable length ASCII character strings, with most fields optional.
    Included are tools for parsing entire files, or individual lines of data.
Characterisation and calibration of single or multiple Ion Selective Electrodes (ISEs); 
    activity estimation of experimental samples. Implements methods described in:
	Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) <doi:10.1002/elan.201100510>,  
	Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) <doi:10.1109/ICSENS.2017.8233898>,
	Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) <doi:10.3390/s19204544>, and
	Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020)  <doi:10.1021/acssensors.9b02133>.
The implement of integrative analysis methods based on a two-part penalization, which realizes dimension reduction analysis and mining the heterogeneity and association of multiple studies with compatible designs. The software package provides the integrative analysis methods including integrative sparse principal component analysis (Fang et al., 2018), integrative sparse partial least squares (Liang et al., 2021) and integrative sparse canonical correlation analysis, as well as corresponding individual analysis and meta-analysis versions. References: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). Integrative sparse principal component analysis. Journal of Multivariate Analysis, <doi:10.1016/j.jmva.2018.02.002>. (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). Integrative sparse partial least squares. Statistics in Medicine, <doi:10.1002/sim.8900>.
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
A toolbox for constructing potential landscapes for Ising
    networks. The parameters of the networks can be directly supplied by
    users or estimated by the 'IsingFit' package by van Borkulo and
    Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from
    empirical data. The Ising model's Boltzmann distribution is preserved
    for the potential landscape function. The landscape functions can be
    used for quantifying and visualizing the stability of network states,
    as well as visualizing the simulation process.
Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided.
Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.
Develops stochastic models based on the Theory of Island
    Biogeography (TIB) of MacArthur and Wilson (1967) <doi:10.1023/A:1016393430551>
    and extensions. It implements methods to estimate colonization and
    extinction rates (including environmental variables) given presence-absence
    data, simulates community assembly, and performs model selection.
An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.
The development of ISM was made by Warfield in 1974. 
      ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure.
      To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's.
      Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524>
      Warfield,J.N. (1974, E-ISSN:2168-2909).
Functions to support the computations carried out in
  `An Introduction to Statistical Modeling of Extreme Values' by
  Stuart Coles. The functions may be divided into the following 
  groups; maxima/minima, order statistics, peaks over thresholds
  and point processes.
Paquete creado con el fin de facilitar el cálculo y distribución del índice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodología completa está disponible en "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)].
The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For a tutorial, download at <https://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Check if an 'externalptr' is a null pointer.
    R does currently not have a native function for that purpose.
    This package contains a C function that returns TRUE in case of a null pointer.
Linear order and unimodal order (univariate)
	     isotonic regression; bivariate isotonic regression
	     with linear order on both variables.
Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers.
    The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal 'dot' format, e.g., 3E7.1CBE991A14. These are referred
    to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary
    structure (see <https://en.wikipedia.org/wiki/ISO_11784_and_ISO_11785>). There are two 'flavours' of this format, a left and a right variation. Which flavour
    a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on 
    the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically.
Functions to parse strings with ISO8601 dates, times, and
    date-times into R-objects.  Additionally, there are functions to determine
    the type of ISO8601 string and to standardise ISO8601 strings.
A fast C++ implementation to generate contour lines
    (isolines) and contour polygons (isobands) from regularly spaced grids
    containing elevation data.
In classification problems a monotone relation between some
  predictors and the classes may be assumed. In this package 'isoboost' 
  we propose new boosting algorithms, based on LogitBoost, that 
  incorporate this isotonicity information, yielding more accurate 
  and easily interpretable rules.
A set of functions to run simple and composite box-models to describe the 
    dynamic or static distribution of stable isotopes in open 
    or closed systems. The package also allows the sweeping of many 
    parameters in both static and dynamic conditions.
    The mathematical models used in this package are derived from Albarede, 1995, 
    Introduction to Geochemical Modelling, Cambridge University Press,
    Cambridge <doi:10.1017/CBO9780511622960>.
Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology <doi:10.1111/gcb.16407>.
This resource provides tools to create, compare, and post-process 
    spatial isotope assignment models of animal origin. It generates 
    probability-of-origin maps for individuals based on user-provided tissue and 
    environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] 
    <doi:10.1111/2041-210X.12147>) using the framework established in Bowen et al.
    (2010) <doi:10.1146/annurev-earth-040809-152429>). The package 'isocat' can then 
    quantitatively compare and cluster these maps to group individuals by 
    similar origin. It also includes techniques for applying four approaches 
    (cumulative sum, odds ratio, quantile only, and quantile simulation) with 
    which users can summarize geographic origins and probable distance traveled 
    by individuals. Campbell et al. [2020] establishes several of the functions
    included in this package <doi:10.1515/ami-2020-0004>.
A bunch of functions to deal with circular data under order restrictions.
ISO language, territory, currency, script and character codes.
  Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217
  currency codes, ISO 15924 script codes, and the ISO 8859 character codes
  as well as the UN M.49 area codes.
Analyzing Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) 
    measurement data to evaluate isotope ratios (IRs) is a complex process. 
    The 'IsoCor' package facilitates this process and renders it reproducible 
    by providing a function to run a 'Shiny'-App locally in any web browser. 
    In this App the user can upload data files of various formats, select ion 
    traces, apply peak detection and perform calculation of IRs and delta 
    values. Results are provided as figures and tables and can be exported. 
    The App, therefore, facilitates data processing of ICP-MS experiments to 
    quickly obtain optimal processing parameters compared to traditional 'Excel'
    worksheet based approaches. A more detailed description can be found in the 
    corresponding article <doi:10.1039/D2JA00208F>. The most recent version 
    of 'IsoCor' can be tested online at <https://apps.bam.de/shn00/IsoCor/>.
ISO 3166-1 country codes and ISO 4217 currency codes provided
    by the International Organization for Standardization.
Distributional regression under stochastic order restrictions for
    numeric and binary response variables and partially ordered covariates. See
    Henzi, Ziegel, Gneiting (2020) <arXiv:1909.03725>.
This toolbox makes working with oxygen,
    carbon, and clumped isotope data reproducible and straightforward.
    Use it to quickly calculate isotope fractionation factors,
    and apply paleothermometry equations.
Implementation of Isolation kernel (Qin et al. (2019) <doi:10.1609/aaai.v33i01.33014755>).
API wrapper that contains functions to retrieve data from the 'IsoMemo' partnership databases. Web services for API: <https://isomemodb.com/api/v1/iso-data>.
Read and process isotopocule data from an Orbitrap Isotope Solutions mass spectrometer. Citation: Kantnerova et al. (Nature Protocols, 2024).
Clustering algorithm developed for use with plot inventories of species. It groups plots by subsets of diagnostic species rather than overall species composition. There is an unsupervised and a supervised mode, the latter accepting suggestions for species with greater weight and cluster medoids.
Ternary plots made simple. This package allows to create
    ternary plots using 'graphics'. It provides functions to display the
    data in the ternary space, to add or tune graphical elements and to
    display statistical summaries. It also includes common ternary
    diagrams which are useful for the archaeologist (e.g. soil texture
    charts, ceramic phase diagram).
Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington', 'Ludwig' and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.
Provides a graphical user interface to the 'IsoplotR' package for radiometric geochronology. The GUI runs in an internet browser and can either be used offline, or hosted on a server to provide online access to the 'IsoplotR' toolbox.
Deconvolution of mixed tumour profiles into normal and cancer for each patient, using 
	the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires 
	mixed tumour profiles and a set of unmatched "basis" normal profiles.
Building isoscapes using mixed models and inferring the geographic
  origin of samples based on their isotopic ratios. This package is essentially a
  simplified interface to several other packages which implements a new
  statistical framework based on mixed models. It uses 'spaMM' for fitting and
  predicting isoscapes, and assigning an organism's origin depending on its
  isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for
  plotting the maps produced with 'terra' using 'lattice'.
IsoSpec is a fine structure calculator used for obtaining the most
    probable masses of a chemical compound given the frequencies of the composing
    isotopes and their masses. It finds the smallest set of isotopologues with
    a given probability. The probability is assumed to be that of the product of
    multinomial distributions, each corresponding to one particular element and
    parametrized by the frequencies of finding these elements in nature. These
    numbers are supplied by IUPAC - the International Union of Pure and Applied
    Chemistry. See: Lacki, Valkenborg, Startek (2020) <DOI:10.1021/acs.analchem.0c00959>
    and Lacki, Startek, Valkenborg, Gambin (2017) <DOI:10.1021/acs.analchem.6b01459>
    for the description of the algorithms used.
Nonparametric estimation on survival analysis under order-restrictions.
Contains two main functions: one for
        solving general isotone regression problems using the
        pool-adjacent-violators algorithm (PAVA); another one provides
        a framework for active set methods for isotone optimization
        problems with arbitrary order restrictions. Various types of
        loss functions are prespecified.
Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Implements Bayesian models to analyze data from tracer addition
    experiments. The implemented method was originally described in the article
    "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from
    Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020)
    <doi:10.1086/708546>.
Fast and multi-threaded implementation of
	isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>),
	extended isolation forest (Hariri, Kind, Brunner (2018) <doi:10.48550/arXiv.1811.02141>),
	SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>),
	fair-cut forest (Cortes (2021) <doi:10.48550/arXiv.2110.13402>),
	robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>),
	and customizable variations of them, for isolation-based outlier detection, clustered outlier detection,
	distance or similarity approximation (Cortes (2019) <doi:10.48550/arXiv.1910.12362>),
	isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>),
	and imputation of missing values (Cortes (2019) <doi:10.48550/arXiv.1911.06646>),
	based on random or guided decision tree splitting, and providing different metrics for
	scoring anomalies based on isolation depth or density (Cortes (2021) <doi:10.48550/arXiv.2111.11639>).
	Provides simple heuristics for fitting the model to categorical columns and handling missing data,
	and offers options for varying between random and guided splits, and for using different splitting criteria.
The wiDB...() functions provide an interface to the public API 
    of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: 
    build, check and submit queries, and receive and 
    unpack responses. Data analysis functions support Bayesian 
    inference of the source and source isotope composition of water 
    samples that may have experienced evaporation. Algorithms 
    adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).
This is an substitute for the %V and %u formats which are
        not implemented on Windows. In addition, the package offers
        functions to convert from standard calender format yyyy-mm-dd
        to and from ISO 8601 week format yyyy-Www-d.
A collection of several functions related to construction and analysis of incomplete split-plot designs. The package contains functions to obtain  and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are  incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments.
Allows access to data from the Rio de Janeiro Public Security Institute (ISP), such as criminal statistics, data on gun seizures and femicide. The package also contains the spatial data of Pacifying Police Units (UPPs) and Integrated Public Safety Regions, Areas and Circumscriptions.
We use the  ISR to handle with PCA-based missing data with high  correlation,  and the DISR to handle with distributed PCA-based missing data.  The philosophy of the package is described in Guo G. (2024)  <doi:10.1080/03610918.2022.2091779>.
This is the central location for data and tools for the development,
    maintenance, analysis, and deployment of the International Soil Radiocarbon Database
    (ISRaD). ISRaD was developed as a collaboration between the U.S. Geological Survey
    Powell Center and the Max Planck Institute for Biogeochemistry. This R package provides
    tools for accessing and manipulating ISRaD data, compiling local data using the ISRaD
    data structure, and simple query and reporting functions for ISRaD. For more detailed
    information visit the ISRaD website at: <https://soilradiocarbon.org/>.
Methodology for subgroup selection in the context of isotonic regression including methods for sub-Gaussian errors, classification, homoscedastic Gaussian errors and quantile regression. See the documentation of ISS(). Details can be found in the paper by Müller, Reeve, Cannings and Samworth (2023) <arXiv:2305.04852v2>.
Manage a 'GitHub' problem using R: wrangle issues, labels and
    milestones. It includes functions for storing, prioritizing (sorting),
    displaying, adding, deleting, and selecting (filtering) issues based
    on qualitative and quantitative information. Issues (labels and
    milestones) are written in lists and categorized into the S3 class to
    be easily manipulated as datasets in R.
You can access to open data published in Instituto Canario De Estadistica (ISTAC) APIs at <https://datos.canarias.es/api/estadisticas/>.
Download data from Istat (Italian Institute of Statistics) database, both old and new provider (respectively, <http://dati.istat.it/> and <https://esploradati.istat.it/databrowser/>). Additional functions for manipulating data are provided. Moreover, a 'shiny' application called 'shinyIstat' can be used to search, download and filter datasets in an easier way.
Launches a 'shiny' based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.
Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations.
Independent Surrogate Variable Analysis is an algorithm
        for feature selection in the presence of potential confounding
        factors (see Teschendorff AE et al 2011, <doi: 10.1093/bioinformatics/btr171>).
Data sets and scripts for text examples and exercises in 
  P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Provides two record linkage data sets on the Italian Survey on Household and Wealth, 2008 and 2010, a sample survey conducted by the Bank of Italy every two years. The 2010 survey covered 13,702 individuals, while the 2008 survey covered 13,734 individuals. The following categorical variables are included in this data set: year of birth, working status, employment status, branch of activity, town size, geographical area of birth, sex, whether or not Italian national, and highest educational level obtained. Unique identifiers are available to assess the accuracy of one’s method. Please see Steorts (2015) <DOI:10.1214/15-BA965SI> to find more details about the data set.
Functions for analyzing multiple choice items. These analyses include
  the convertion of student response into binaty data (correct/incorrect),
  the computation of the number of corrected responses and grade for each subject,
  the calculation of item difficulty and discrimination, the computation of the
  frecuency and point-biserial correlation for each distractor and the graphical
  analysis of each item.
Three methods for Individual Tree Crowns (ITCs) delineation on remote sensing data: one is based on LiDAR data in x,y,z format and one on imagery data in raster format.
The itdr() routine allows  for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) <doi:10.1198/016214506000000140>, convolution transformation methods proposed by Zeng and Zhu (2010) <doi:10.1016/j.jmva.2009.08.004>, and iterative Hessian transformation methods proposed by Cook and Li (2002) <doi:10.1214/aos/1021379861>. Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) <doi:10.5705/ss.202020.0312>, and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) <doi:10.1016/j.csda.2021.107380>.
Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales). The statistics reported in this package can be found in any measurement textbook such as Crocker and Algina (2006, ISBN:9780495395911).
Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented.
    ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively.
    For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.
Fits large-scale regression models with a penalty that 
  restricts the maximum number of non-zero regression coefficients
  to a prespecified value.  While Chu et al (2020) <doi:10.1093/gigascience/giaa044>
  describe the basic algorithm, this package uses Cyclops for an efficient implementation.
Know which loop iteration the code execution is up to by including
    a single, convenient function call inside the loop.
Support for iterators, which allow a programmer to traverse
    through all the elements of a vector, list, or other collection of data.
The iterLap (iterated Laplace approximation) algorithm approximates a
             general (possibly non-normalized) probability density on R^p, by repeated
             Laplace approximations to the difference between current approximation 
             and true density (on log scale). The final approximation is a mixture of
             multivariate normal distributions and might be used for example as a
             proposal distribution for importance sampling (eg in Bayesian applications). 
             The algorithm can be seen as a computational generalization of the Laplace 
             approximation suitable for skew or multimodal densities.
A fresh take on iterators in R. Designed to be cross-compatible with the 'iterators' package, but using the 'nextOr' method will offer better performance as well as more compact code. With batteries included: includes a collection of iterator constructors and combinators ported and refined from the 'iterators', 'itertools', and 'itertools2' packages.
Iterator for generating permutations and combinations. They
    can be either drawn with or without replacement, or with distinct/ non-distinct 
    items (multiset). The generated sequences are in lexicographical order 
    (dictionary order). The algorithms to generate permutations and combinations are 
    memory efficient. These iterative algorithms enable users to process all sequences 
    without putting all results in the memory at the same time. 
    The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer
    being maintained. Users are recommended to switch to 'arrangements'.
Various tools for creating iterators, many patterned after
        functions in the Python itertools module, and others patterned
        after functions in the 'snow' package.
A port of Python's excellent itertools module to R for efficient
    looping.
Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).
Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.
The 'Interactive Tree Of Life' <https://itol.embl.de/> online server can 
    edit and annotate trees interactively. The 'itol.toolkit' package can 
    support all types of annotation templates.
Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).
Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies."  Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>.  The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'.  The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>).  The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.
Implements the Interpolate, Truncate, Project (ITP) root-finding 
    algorithm developed by Oliveira and Takahashi (2021) <doi:10.1145/3423597>.
    The user provides the function, from the real numbers to the real numbers, 
    and an interval with the property that the values of the function at its 
    endpoints have different signs. If the function is continuous over this 
    interval then the ITP method estimates the value at which the function is 
    equal to zero. If the function is discontinuous then a point of 
    discontinuity at which the function changes sign may be found. 
    The function can be supplied using either an R function or an external 
    pointer to a C++ function. Tuning parameters of the ITP algorithm can be 
    set by the user. Default values are set based on arguments in Oliveira and 
    Takahashi (2021).
Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter 
    scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing 
    XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. 
    A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. 
    Multivariate methods are implemented with appropriate data transformation.
The improved trimmed weighted Hochberg procedure provides increased statistical power and relaxes the dependence assumptions for familywise error rate control compared to the original weighted Hochberg procedure. This package computes the boundaries required for implementing the proposed methodology and includes sample size optimization methods.
    See Gou, J., Chang, Y., Li, T., and Zhang, F.(2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report.
Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999)
    as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear
    regression analysis which is particularly useful for time course data such as
    EEG, pupil dilation, gaze data (eye tracking), and articulography recordings,
    but also for behavioral data such as reaction times and response data. As time
    course measures are sensitive to autocorrelation problems, GAMMs implements
    methods to reduce the autocorrelation problems. This package includes functions
    for the evaluation of GAMM models (e.g., model comparisons, determining regions
    of significance, inspection of autocorrelational structure in residuals)
    and interpreting of GAMMs (e.g., visualization of complex interactions, and
    contrasts).
Provides a wrapper around the same API <https://app.americansocceranalysis.com/api/v1/__docs__/>
    that powers the American Soccer Analysis app.
Collection of R functions to do purely presence-only species
    distribution modeling with isolation forest (iForest) and its
    variations such as Extended isolation forest and SCiForest. See the
    details of these methods in references: Liu, F.T., Ting, K.M. and
    Zhou, Z.H. (2008) <doi:10.1109/ICDM.2008.17>, Hariri, S., Kind, M.C.
    and Brunner, R.J. (2019) <doi:10.1109/TKDE.2019.2947676>, Liu, F.T.,
    Ting, K.M. and Zhou, Z.H. (2010) <doi:10.1007/978-3-642-15883-4_18>,
    Guha, S., Mishra, N., Roy, G. and Schrijvers, O. (2016)
    <https://proceedings.mlr.press/v48/guha16.html>, Cortes, D. (2021)
    <arXiv:2110.13402>. Additionally, Shapley values are used to explain
    model inputs and outputs. See details in references: Shapley, L.S.
    (1953) <doi:10.1515/9781400881970-018>, Lundberg, S.M. and Lee, S.I.
    (2017) <https://dl.acm.org/doi/abs/10.5555/3295222.3295230>, Molnar,
    C.  (2020) <ISBN:978-0-244-76852-2>, Štrumbelj, E. and Kononenko, I.
    (2014) <doi:10.1007/s10115-013-0679-x>. itsdm also provides functions
    to diagnose variable response, analyze variable importance, draw
    spatial dependence of variables and examine variable contribution. As
    utilities, the package includes a few functions to download
    bioclimatic variables including 'WorldClim' version 2.0 (see Fick,
    S.E. and Hijmans, R.J. (2017) <doi:10.1002/joc.5086>) and
    'CMCC-BioClimInd' (see Noce, S., Caporaso, L. and Santini, M. (2020)
    <doi:10.1038/s41597-020-00726-5>.
Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis.
Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the 'iucnrdata' package, available on GitHub <https://github.com/PaulESantos/iucnrdata>. To install the data package, use pak::pak('PaulESantos/iucnrdata').
Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.
A integrated variance correlation is proposed to measure the dependence between a categorical or continuous random variable and a continuous random variable or vector.
    This package is designed to estimate the new correlation coefficient with parametric and nonparametric approaches. 
    Test of independence for different problems can also be implemented via the new correlation coefficient with this package.
An implementation of randomization-based hypothesis 
    testing for three different estimands in a cluster-randomized 
    encouragement experiment. The three estimands include (1) testing
    a cluster-level constant proportional treatment effect (Fisher's
    sharp null hypothesis), (2) pooled effect ratio, and (3) average 
    cluster effect ratio. To test the third estimand, user needs to install
    'Gurobi' (>= 9.0.1) optimizer via its R API. Please refer to 
    <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.
Estimation and diagnostic tools for instrumental variables designs, which implements the guidelines proposed in Lal et al. (2023) <arXiv:2303.11399>, including bootstrapped confidence intervals, effective F-statistic, Anderson-Rubin test, valid-t ratio test, and local-to-zero tests.
Instrumental variable (IV) estimators for homogeneous and
    heterogeneous treatment effects with efficient machine learning instruments.
    The estimators are based on double/debiased machine learning allowing for
    nonlinear and potentially high-dimensional control variables. Details can 
    be found in Scheidegger, Guo and Bühlmann (2025) "Inference for 
    heterogeneous treatment effects with efficient instruments and machine 
    learning" <doi:10.48550/arXiv.2503.03530>.
Uses data and researcher's beliefs on measurement error and
    instrumental variable (IV) endogeneity to generate the space of consistent 
    beliefs across measurement error, instrument endogeneity, and instrumental 
    relevance for IV regressions. 
    Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.
Provides facilities of general to specific model selection for
    exogenous regressors in 2SLS models. Furthermore, indicator saturation
    methods can be used to detect outliers and structural breaks in the sample.
A method that estimates
    an IV-optimal individualized treatment rule. An individualized
    treatment rule is said to be IV-optimal if it minimizes the 
    maximum risk with respect to the putative IV and the set of
    IV identification assumptions. Please refer to 
    <arXiv:2002.02579> for more details on the methodology and 
    some theory underpinning the method. Function IV-PILE() uses
    functions in the package 'locClass'. Package 'locClass' can be
    accessed and installed from the 'R-Forge' repository via the following link: 
    <https://r-forge.r-project.org/projects/locclass/>.
    Alternatively, one can install the package by entering the following in R:
    'install.packages("locClass", repos="<http://R-Forge.R-project.org>")'.
Carries out instrumental variable
    estimation of causal effects, including power analysis, sensitivity analysis,
    and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.
The marginal treatment effect was introduced by Heckman and
    Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a
    choice-theoretic interpretation to instrumental variables models that
    maintain the monotonicity condition of Imbens and Angrist (1994)
    <doi:10.2307/2951620>. This interpretation can be used to extrapolate from
    the compliers to estimate treatment effects for other subpopulations. This
    package provides a flexible set of methods for conducting this
    extrapolation. It allows for parametric or nonparametric sieve estimation,
    and allows the user to maintain shape restrictions such as monotonicity. The
    package operates in the general framework developed by Mogstad, Santos and
    Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point
    identification or partial identification (bounds). In the partially
    identified case, bounds are computed using either linear programming
    or quadratically constrained quadratic programming. Support for
    four solvers is provided. Gurobi and the Gurobi R API
    can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained
    from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex'
    and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be
    obtained from <https://www.mosek.com/>. The lp_solve library is freely
    available from <http://lpsolve.sourceforge.net/5.5/>, and is included when
    installing its API 'lpSolveAPI', which is available from CRAN.
Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or 'Office' documents. Designed to work with pipes.
An implementation of the Invariance Partial Pruning (IVPP) approach 
  described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial 
  Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a 
  two-step method that first test for global network structural difference with 
  invariance test and then inspect specific edge difference with partial pruning.
Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.
Provides a library for generic interval manipulations using a
    new interval vector class. Capabilities include: locating various
    kinds of relationships between two interval vectors, merging overlaps
    within a single interval vector, splitting an interval vector on its
    overlapping endpoints, and applying set theoretical operations on
    interval vectors. Many of the operations in this package were inspired
    by James Allen's interval algebra, Allen (1983)
    <doi:10.1145/182.358434>.
An instrumental variable estimator under structural cumulative additive intensity model is fitted, that leverages initial randomization as the IV. The estimator can be used to fit an additive hazards model under time to event data which handles treatment switching (treatment crossover) correctly. We also provide a consistent variance estimate.
Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.
Drawing statistical inference on the coefficients
    of a short- or long-horizon predictive regression with persistent
    regressors by using the IVX method of Magdalinos and Phillips (2009)
    <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and
    Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.
For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. 
    Leaves are stacked vertically. At most twenty leaves are shown; For high frequency, each leaflet may represent more than 
    one observation with multiplicity declared in the subtitle.
Calculates irrigation water quality ratios and has functions that could be used to plot several popular diagrams for irrigation water quality classification.
Improved methods based on inverse probability weighting
        and outcome regression for causal inference and missing data
        problems.
Create and view tickets in 'gitea', a self-hosted git service <https://gitea.io>, using an 'RStudio' addin, and use helper functions to publish documentation and use git.
Code to specify, run, and then visualize and analyze the results of 
    Ixodidae (hard-bodied ticks) population and infection dynamics models. Such 
    models exist in the literature, but the source code to run them is not 
    always available. 'IxPopDyMod' provides an easy way for these models to be 
    written and shared.
Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and 
    likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) 
    <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate 
    random deviates from zero inflated or hurdle models and obtain  maximum likelihood estimate
    of unknown parameters in these models.
Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. This will return all datasets formatted as Excel files (.csv or .xlsx), as well as datasets that require an API key.
An estimation method that can use computer simulations to
    approximate maximum-likelihood estimates even when the likelihood function can not
    be evaluated directly. It can be applied whenever it is feasible to conduct many
    simulations, but works best when the data is approximately Poisson distributed.
    It was originally designed for demographic inference in evolutionary
    biology (Naduvilezhath et al., 2011 <doi:10.1111/j.1365-294X.2011.05131.x>,
    Mathew et al., 2013 <doi:10.1002/ece3.722>).
    It has optional support for conducting coalescent simulation using
    the 'coala' package.
Provides data about the possible adverse events/reactions
    resulting from being injected with a vaccine/experimental gene therapy.
    Currently, this data set only includes information from six reference
    sources. Refer to the CITATION.cff file for the complete citations of
    the reference sources. For information about vaccination$/immunization$
    hazards, visit <https://www.questionuniverse.com/rethink.html#vaccine>,
    <https://www.ecoccs.com/healing.html#vaccines>,
    <https://www.questionuniverse.com/rethink_current_crisis.html#cov_vaccin>,
    and <https://www.questionuniverse.com/vaccination.html>.
Calculate statistical significance of Jaccard/Tanimoto similarity
    coefficients for binary data.
Schur polynomials appear in combinatorics and zonal
    polynomials appear in random matrix theory. They are particular cases
    of Jack polynomials. This package allows to compute these polynomials
    and other symmetric multivariate polynomials: flagged Schur
    polynomials, factorial Schur polynomials, t-Schur polynomials,
    Hall-Littlewood polynomials, Macdonald polynomials, and modified
    Macdonald polynomials. In addition, it can compute the Kostka-Jack
    numbers, the Kostka-Foulkes polynomials, the Kostka-Macdonald
    polynomials, and the Hall polynomials.  Mainly based on Demmel &
    Koev's paper (2006) <doi:10.1090/S0025-5718-05-01780-1> and
    Macdonald's book (1995) <doi:10.1093/oso/9780198534891.003.0001>.
Simply and efficiently
    simulates (i) variants from reference genomes and (ii) reads from both Illumina 
    <https://www.illumina.com/>
    and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. 
    It can either read reference genomes from FASTA files or simulate new ones.
    Genomic variants can be simulated using summary statistics, phylogenies, 
    Variant Call Format (VCF) files, and coalescent simulations—the latter of which
    can include selection, recombination, and demographic fluctuations.
    'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, 
    as well as PacBio reads.
    These simulations include sequencing errors, mapping qualities, multiplexing,
    and optical/polymerase chain reaction (PCR) duplicates.
    Simulating Illumina sequencing is based on ART
    by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>.
    PacBio sequencing simulation is based on 
    SimLoRD  by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>.
    All outputs can be written to standard file formats.
Implements delete-d jackknife resampling for robust statistical estimation. 
    The package provides both weighted (HC3-adjusted) and unweighted versions of 
    jackknife estimation, with parallel computation support. Suitable for biomedical 
    research and other fields requiring robust variance estimation.
Provides method used to check whether data have outlier in efficiency measurement 
  of big samples with data envelopment analysis (DEA). In this jackstrap method, the package 
  provides two criteria to define outliers: heaviside and k-s test. The technique was developed 
  by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting 
  Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.
Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).
Evaluation of the Jacobi theta functions and related
    functions: Weierstrass elliptic function, Weierstrass sigma function,
    Weierstrass zeta function, Klein j-function, Dedekind eta function,
    lambda modular function, Jacobi elliptic functions, Neville theta
    functions, Eisenstein series, lemniscate elliptic functions, elliptic 
    alpha function, Rogers-Ramanujan continued fractions, and Dixon 
    elliptic functions. Complex values of the variable are supported.
Implements the classical Jacobi algorithm for the
    eigenvalues and eigenvectors of a real symmetric matrix, both in 
    pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example 
    for teaching purposes.
Uses the Jaccard similarity index to account for population
    structure in sequencing studies. This method was specifically
    designed to detect population stratification based on rare variants, hence it
    will be especially useful in rare variant analysis.
Contains procedures to estimate the nine condensed Jacquard genetic identity coefficients
              (Jacquard, 1974) <doi:10.1007/978-3-642-88415-3> by constrained least squares
              (Graffelman et al., 2024) <doi:10.1101/2024.03.25.586682> and by the method of moments
	      (Csuros, 2014) <doi:10.1016/j.tpb.2013.11.001>. These procedures require previous estimation of the
	      allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard
	      coefficients, such as individual inbreeding coefficients and kinship coefficients.
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Russell A. Stultz (“The option strategy desk reference: an essential reference for option traders (First edition.)”, 2019, ISBN: 9781949443912).
Bayesian methods for estimating developmental age from ordinal dental data. For an explanation of the model used, see Konigsberg (2015) <doi:10.3109/03014460.2015.1045430>. For details on the conditional correlation correction, see Sgheiza (2022) <doi:10.1016/j.forsciint.2021.111135>. Dental scoring is based on Moorrees, Fanning, and Hunt (1963) <doi:10.1177/00220345630420062701>.
All the data and functions used to produce the book. We do not expect
    most people to use the package for any other reason than to get simple access to the
    'JAGS' model files, the data, and perhaps run some of the simple examples.
    The authors of the book are David Lucy (now sadly deceased) and James Curran. It is 
    anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with
    bibliographic details to follow at that point. Until such time, further information
    can be obtained by emailing James Curran.
Tools are provided to streamline Bayesian analyses in 'JAGS' using 
    the 'jagsUI' package.  Included are functions for extracting output in 
    simpler format, functions for streamlining assessment of convergence, and 
    functions for producing summary plots of output.  Also included is a 
    function that provides a simple template for running 'JAGS' from 'R'.
    Referenced materials can be found at <DOI:10.1214/ss/1177011136>.
Bayesian data analysis usually incurs long runtimes
  and cumbersome custom code.
  A pipeline toolkit tailored to Bayesian statisticians,
  the 'jagstargets' R package is leverages
  'targets' and 'R2jags' to ease this burden.
  'jagstargets' makes it super easy to set up scalable
  JAGS pipelines that automatically parallelize the computation
  and skip expensive steps when the results are already up to date.
  Minimal custom code is required, and there is no need to manually
  configure branching, so usage is much easier than 'targets' alone.
  For the underlying methodology, please refer
  to the documentation of 'targets' <doi:10.21105/joss.02959> and 'JAGS'
  (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.
When relationships between sources of data can be represented by a tree, the generation of appropriate Markov Chain Monte Carlo modeling code to be used with 'JAGS' to run a Bayesian hierarchical model can be automatically generated by this package. Any admissible tree-structured data can be used, under the assumption that node counts are multinomial and branching probabilities are Dirichlet among sibling groups. The methodological basis used to create this package can be found in Flynn (2023) <http://hdl.handle.net/2429/86174>.
A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags').  A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.
The Jalaali calendar, also known as the Persian or Solar Hijri 
    calendar, is the official calendar of Iran and Afghanistan. It starts on 
    Nowruz, the spring equinox, and follows an astronomical system for 
    determining leap years. Each year consists of 365 or 366 days, divided into 
    12 months. This package provides functions for converting dates between the 
    Jalaali and Gregorian calendars. The conversion calculations are based on 
    the work of Kazimierz M. Borkowski (1996) (<doi:10.1007/BF00055188>), who 
    used an analytical model of Earth's motion to compute equinoxes from AD 550 
    to 3800 and determine leap years based on Tehran time.
Just analysis methods ('jam') base functions
    focused on bioinformatics.
    Version- and gene-centric alphanumeric sort,
    unique name and version assignment, colorized console and 'HTML' output,
    color ramp and palette manipulation,
    'Rmarkdown' cache import, styled 'Excel' worksheet import and export,
    interpolated raster output from smooth scatter and image plots,
    list to delimited vector, efficient list tools.
Provides an interface to 'Jamendo' API <https://developer.jamendo.com/v3.0>.
              Pull audio, features and other information for a given 
              'Jamendo' user (including yourself!) or enter an artist's -, album's -, 
              or track's name and retrieve the available information in seconds.
Fit latent space network cluster models using an expectation-maximization algorithm. Enables flexible modeling of unweighted or weighted network data (with or without noise edges), supporting both directed and undirected networks (with or without degree heterogeneity). Designed to handle large networks efficiently, it allows users to explore network structure through latent space representations, identify clusters (i.e., community detection) within network data, and simulate networks with varying clustering, connectivity patterns, and noise edges. Methodology for the implementation is described in Arakkal and Sewell (2025) <doi:10.1016/j.csda.2025.108228>.
Full texts for Jane Austen's 6 completed novels, ready for
    text analysis. These novels are "Sense and Sensibility", "Pride and
    Prejudice", "Mansfield Park", "Emma", "Northanger Abbey", and
    "Persuasion".
The main janitor functions can: perfectly format data.frame column
    names; provide quick counts of variable combinations (i.e., frequency
    tables and crosstabs); and explore duplicate records. Other janitor functions
    nicely format the tabulation results. These tabulate-and-report functions
    approximate popular features of SPSS and Microsoft Excel. This package
    follows the principles of the "tidyverse" and works well with the pipe function
    %>%. janitor was built with beginning-to-intermediate R users in mind and is
    optimized for user-friendliness.
Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using 'tensorflow'.
Provides tools for using the API of 'e-Stat' (<https://www.e-stat.go.jp/>), a portal site for Japanese government statistics.
    Includes functions for automatic query generation, data collection and formatting.
Provides a function collection to extract metadata, sectioned text and study characteristics from scientific articles in 'NISO-JATS' format. Articles in PDF format can be converted to 'NISO-JATS' with the 'Content ExtRactor and MINEr' ('CERMINE', <https://github.com/CeON/CERMINE>). For convenience, two functions bundle the extraction heuristics: JATSdecoder() converts 'NISO-JATS'-tagged XML files to a structured list with elements title, author, journal, history, 'DOI', abstract, sectioned text and reference list. study.character() extracts multiple study characteristics like number of included studies, statistical methods used, alpha error, power, statistical results, correction method for multiple testing, software used. An estimation of the involved sample size is performed based on reports within the abstract and the reported degrees of freedom within statistical results. In addition, the package contains some useful functions to process text (text2sentences(), text2num(), ngram(), strsplit2(), grep2()). See Böschen, I. (2021) <doi:10.1007/s11192-021-04162-z> Böschen, I. (2021) <doi:10.1038/s41598-021-98782-3> and Böschen, I (2023) <doi:10.1038/s41598-022-27085-y>.
Graphics device routing all graphics commands to a Java
	     program. The actual functionality of the JavaGD depends
	     on the Java-side implementation. Simple AWT and Swing
	     implementations are included.
Simplifies the process of estimating above ground biomass components for teak trees using a few basic inputs, based on the equations taken from the journal "Allometric equations for estimating above ground biomass and leaf area of planted teak (Tectona grandis) forests under agroforestry management in East Java, Indonesia" (Purwanto & Shiba, 2006) <doi:10.60409/forestresearch.76.0_1>. This function is most reliable when applied to trees from the same region where the equations were developed, specifically East Java, Indonesia. This function help to estimate the stem diameter at the lowest major living branch (DB) using the stem diameter at breast height with R^2 = 0.969. Estimate the branch dry weight (WB) using the stem diameter at breast height and tree height (R^2 = 0.979). Estimate the stem weight (WS) using the stem diameter at breast height and tree height (R^2 = 0.997. Also estimate the leaf dry weight (WL) using the stem diameter at the lowest major living branch (R^2 = 0.996).
An implementation of the Jaya optimization algorithm for both 
    single-objective and multi-objective problems. Jaya is a population-based, 
    gradient-free optimization algorithm capable of solving constrained and 
    unconstrained optimization problems without hyperparameters. This package 
    includes features such as multi-objective Pareto optimization, adaptive 
    population adjustment, and early stopping. For further details, see 
    R.V. Rao (2016) <doi:10.5267/j.ijiec.2015.8.004>.
Provides an R interface to the JBrowse 2 genome browser.
    Enables embedding a JB2 genome browser in a Shiny app or R Markdown
    document. The browser can also be launched from an interactive R console.
    The browser can be loaded with a variety of common genomics data types,
    and can be used with a custom theme.
This is a set of simple utility functions to perform mutual conversion between the current Japanese calendar system that Wareki, the old Japanese calendar system that the Kyureki calendar and the Julian and Gregorian calendar.
    To calculate each calendar method, it converts to the Julian Day Number.
Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather 
             types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.
Contains a selection of color palettes and 'ggplot2' themes designed by the package author.
Procedures for joint detection of changes in both expectation and variance in univariate sequences. Performs a statistical test of the null hypothesis of the absence of change points. In case of rejection performs an algorithm for change point detection. Reference - Bivariate change point detection - joint detection of changes in expectation and variance, Scandinavian Journal of Statistics, DOI 10.1111/sjos.12547.
The Impact Factor of a journal reported by Journal Citation Reports ('JCR') of  'Clarivate' 'Analytics' is provided. The impact factor is available for those journals only that were included Journal Citation Reports 'JCR'.
A calculation tool to obtain the 10-year risk of cardiovascular disease from various risk models (Hisayama, Suita, EPOCH Japan).
Tool for generating quality reports from cruncher outputs
    (and calculating series scores). The latest version of the cruncher
    can be downloaded here:
    <https://github.com/jdemetra/jwsacruncher/releases>.
A Wrapper for the Node.js 'Jdenticon' <https://jdenticon.com/> Library. Uses 'esbuild' <https://esbuild.github.io/> to reduce user dependencies.
Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) <doi:10.1086/260062>] or jump diffusion processes [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other.
Customized R Markdown templates for authoring articles
    for Journal of Data Science.
Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeek) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) <arXiv:1806.00548>.
Generates interactive Jellyfish plots to visualize spatiotemporal tumor
    evolution by integrating sample and phylogenetic trees into a unified plot.
    This approach provides an intuitive way to analyze tumor heterogeneity and
    evolution over time and across anatomical locations. The Jellyfish plot
    visualization design was first introduced by Lahtinen, Lavikka, et al.
    (2023, <doi:10.1016/j.ccell.2023.04.017>).
    This package also supports visualizing ClonEvol results, a tool developed
    by Dang, et al. (2017, <doi:10.1093/annonc/mdx517>), for analyzing clonal
    evolution from multi-sample sequencing data. The 'clonevol' package is not
    available on CRAN but can be installed from its GitHub
    repository (<https://github.com/hdng/clonevol>).
Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search.
Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.
On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.
The goal of 'jetty' is to execute R functions and code
    snippets in an isolated R subprocess within a 'Docker' container
    and return the evaluated results to the local R session. 'jetty'
    can install necessary packages at runtime and seamlessly propagates
    errors and outputs from the 'Docker' subprocess back to the main
    session. 'jetty' is primarily designed for sandboxed testing and
    quick execution of example code.
Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022) <doi:10.3390/math10213983>.
Provides statistical methods for auditing as implemented in JASP
    for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package
    makes it easy for an auditor to plan a statistical sample, select the sample
    from the population, and evaluate the misstatement in the sample compliant
    with international auditing standards. Second, the package provides
    statistical methods for auditing data, including tests of digit
    distributions and repeated values. Finally, the package includes methods for
    auditing algorithms on the aspect of fairness and bias. Next to classical
    statistical methodology, the package implements Bayesian equivalents of
    these methods whose statistical underpinnings are described in Derks et al.
    (2021) <doi:10.1111/ijau.12240>, Derks et al. (2024)
    <doi:10.2308/AJPT-2021-086>, Derks et al. (2022) <doi:10.31234/osf.io/8nf3e>
    Derks et al. (2024) <doi:10.31234/osf.io/tgq5z>, and Derks et al. (2025)
    <doi:10.31234/osf.io/b8tu2>.
Offer procedures to download financial-economic time series data and enhanced procedures for computing the investment performance indices of Bacon (2004) <DOI:10.1002/9781119206309>.
Provides functions to extract joint planes from 3D triangular mesh derived
             from point cloud and makes data available for structural analysis.
The Joint Graphical Lasso is a generalized method for
        estimating Gaussian graphical models/ sparse inverse covariance
        matrices/ biological networks on multiple classes of data.  We
        solve JGL under two penalty functions: The Fused Graphical
        Lasso (FGL), which employs a fused penalty to encourage inverse
        covariance matrices to be similar across classes, and the Group
        Graphical Lasso (GGL), which encourages similar network
        structure between classes.  FGL is recommended over GGL for
        most applications. Reference: Danaher P, Wang P, Witten DM. (2013) 
        <doi:10.1111/rssb.12033>.
Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS.
All datasets and functions used in the german book "Statistik mit R und RStudio" by grosse Schlarmann (2010-2024) <https://www.produnis.de/R/>.
An R package that implements the JICO algorithm [Wang, P., Wang, H., Li, Q., Shen, D., & Liu, Y. (2024). <Journal of Computational and Graphical Statistics, 33(3), 763-773>]. 
        It aims at solving the multi-group regression problem. The algorithm decomposes the responses from multiple groups into shared and group-specific
        components, which are driven by low-rank approximations of joint and individual structures from the covariates respectively.
Template engine powered by the 'inja' C++ library. Users
    write a template document, using syntax inspired by the 'Jinja' Python
    package, and then render the final document by passing data from R.
    The template syntax supports features such as variables, loops,
    conditions and inheritance.
Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.
Set of common functions used for manipulating colors,
    detecting and interacting with 'RStudio', modeling, formatting, determining
    users' operating system, feature scaling, and more!
An implementation of fast cluster-based permutation analysis
    (CPA) for densely-sampled time data developed in Maris & Oostenveld,
    2007 <doi:10.1016/j.jneumeth.2007.03.024>. Supports (generalized,
    mixed-effects) regression models for the calculation of timewise
    statistics. Provides both a wholesale and a piecemeal interface to the
    CPA procedure with an emphasis on interpretability and diagnostics.
    Integrates 'Julia' libraries 'MixedModels.jl' and 'GLM.jl' for
    performance improvements, with additional functionalities for
    interfacing with 'Julia' from 'R' powered by the 'JuliaConnectoR'
    package.
Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 <doi:10.1016/j.ymeth.2022.03.003>.
Shared parameter models for the joint modeling of longitudinal and time-to-event data.
Provides features that allow users to download 
  weather data published by the Japan Meteorological Agency (JMA) website 
  (<https://www.jma.go.jp/jma/index.html>). The data includes information 
  dating back to 1976 and aligns with the categories available on the website. 
  Additionally, users can process the best track data of typhoons and easily 
  handle earthquake record files.
A mainly instrumental package meant to allow other packages whose core is written in 'C++' to read, write
        and manipulate matrices in a binary format so that the memory used for them is no more than strictly needed. Its functionality
        is already inside 'parallelpam' and 'scellpam', so if you have installed any of these, you do not need to install 'jmatrix'.
        Using just the needed memory is not always true with 'R' matrices or vectors, since by default they are of double type. Trials
        like the 'float' package have been done, but to use them you have to coerce a matrix already loaded in 'R' memory to a float matrix,
        and then you can delete it. The problem comes when your computer has not memory enough to hold the matrix in the first place, so
        you are forced to load it by chunks. This is the problem this package tries to address (with partial success, but this is a
        difficult problem since 'R' is not a strictly typed language, which is anyway quite hard to get in an interpreted language).
	This package allows the creation and manipulation of full, sparse and symmetric matrices of any standard data type.
Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.
Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).
Provides model fitting, prediction, and plotting for joint models of longitudinal and multiple time-to-event data, including methods from Rizopoulos (2012) <doi:10.1201/b12208>. Useful for handling complex survival and longitudinal data in clinical research.
Provides analysis tools for big data where the sample size is very large. It offers
             a suite of functions for fitting and predicting joint models, which allow for the simultaneous
             analysis of longitudinal and time-to-event data. This statistical methodology is particularly 
             useful in medical research where there is often interest in understanding the relationship 
             between a longitudinal biomarker and a clinical outcome, such as survival or disease progression.
             This can be particularly useful in a clinical setting where it is important to be able to predict 
             how a patient's health status may change over time. Overall, this package provides a 
             comprehensive set of tools for joint modeling of BIG data obtained as survival and 
             longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility
             and flexibility make it a valuable resource for researchers in many different fields,
             particularly in the medical and health sciences.
Fit joint mean-covariance models for longitudinal data. The models
    and their components are represented using S4 classes and methods. The core
    computational algorithms are implemented using the 'Armadillo' C++ library
    for numerical linear algebra and 'RcppArmadillo' glue.
Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.
Performs power calculations for joint modeling of longitudinal 
  and survival data with k-th order trajectories when the variance-covariance 
  matrix, Sigma_theta, is unknown.
The main purpose of this package is to make it easy for userR's to interact with 'jMetrik' an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by 'jMetrik'. It also allows useR's to read *.jmetrik files (e.g. output from an analysis) for follow-up analysis in R. The *.jmetrik format is a flat file that includes a multiline header and the data as comma separated values. The header includes metadata about the file and one row per variable with the following information in each row: variable name, data type, item scoring, special data codes, and variable label.
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) <arXiv:2301.06584>.
             The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. 
             The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model 
             is estimated using an Expectation Maximization algorithm.
Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.
Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA
    algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly),
    and characteristic pattern discovery along with interpretable time series classification.
Performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test.  Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions.  Performs power calculations for the binomial test.
A suite of common statistical methods such as descriptives,
    t-tests, ANOVAs, regression, correlation matrices, proportion tests,
    contingency tables, and factor analysis. This package is also useable from
    the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more
    information).
Methods to access data sets from the 'jamovi' statistical
  spreadsheet (see <https://www.jamovi.org> for more information) from R.
A framework for creating rich interactive analyses for the jamovi
    platform (see <https://www.jamovi.org> for more information).
The free and open a statistical spreadsheet 'jamovi'
    (<https://www.jamovi.org>) aims to make statistical analyses easy and
    intuitive. 'jamovi' produces syntax that can directly be used in R (in
    connection with the R-package 'jmv'). Having import / export routines for
    the data files 'jamovi' produces ('.omv') permits an easy transfer of
    data and analyses between 'jamovi' and R.
Aids in the calculation and visualization of regions of non-significance using the 'Johnson-Neyman' technique and its extensions as described by Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5> to assess the influence of categorical and continuous moderators. Allows correcting for phylogenetic relatedness.
Call job::job({<code here>}) to run R code as an RStudio job and keep your console free in the meantime. This allows for a productive workflow while testing (multiple) long-running chunks of code. It can also be used to organize results using the RStudio Jobs GUI or to test code in a clean environment. Two RStudio Addins can be used to run selected code as a job.
Takes an R expression and returns a job object with a $stop() method
    which can be called to terminate the background job. Also provides timeouts
    and other mechanisms for automatically terminating a background job. The
    result of the expression is available synchronously via $result or
    asynchronously with callbacks or through the 'promises' package framework.
Computing and plotting joint confidence regions and intervals. Regions include classical ellipsoids, minimum-volume or minimum-length regions, and an empirical Bayes region. Intervals include the TOST procedure with ordinary or expanded intervals and a fixed-sequence procedure. Such regions and intervals are useful e.g., for the assessment of multi-parameter (bio-)equivalence. Joint confidence regions for the mean and variance of a normal distribution are available as well.
In a typical experiment for the intuitive judgment of frequencies (JoF) different stimuli with different frequencies are presented. The participants consider these stimuli with a constant duration and give a judgment of frequency. These judgments can be simulated by formal models: PASS 1 and PASS 2 based on Sedlmeier (2002, ISBN:978-0198508632), MINERVA 2 baesd on Hintzman (1984) <doi:10.3758/BF03202365> and TODAM 2 based on Murdock, Smith & Bai (2001) <doi:10.1006/jmps.2000.1339>. The package provides an assessment of the frequency by determining the core aspects of these four models (attention, decay, and presented frequency) that can be compared to empirical results.
Analysis of repeated measurements and time-to-event data via random
    effects joint models. Fits the joint models proposed by Henderson and colleagues
    <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and
    colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a
    single continuous repeated measure. The time-to-event data is modelled using a 
    (cause-specific) Cox proportional hazards regression model with time-varying 
    covariates. The longitudinal outcome is modelled using a linear mixed effects
    model. The association is captured by a latent Gaussian process. The model is 
    estimated using am Expectation Maximization algorithm. Some plotting functions 
    and the variogram are also included. This project is funded by the Medical 
    Research Council (Grant numbers G0400615 and MR/M013227/1).
Fits the joint model proposed by Henderson and colleagues (2000) 
    <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple 
    continuous longitudinal measures. The time-to-event data is modelled using a 
    Cox proportional hazards regression model with time-varying covariates. The 
    multiple longitudinal outcomes are modelled using a multivariate version of the 
    Laird and Ware linear mixed model. The association is captured by a multivariate
    latent Gaussian process. The model is estimated using a Monte Carlo Expectation 
    Maximization algorithm. This project was funded by the Medical Research Council 
    (Grant number MR/M013227/1).
Implements penalised multivariate regression (i.e., for multiple outcomes and many features) by stacked generalisation (<doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. For optional comparisons, install 'remMap' from GitHub (<https://github.com/cran/remMap>).
Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death.
 Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model.
 The methods are applicable for meta-analytic data containing individual-patient information from several studies.
 Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).
 Methodologies were published in
 Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>,
 Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>,
 Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>.
 See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>.
 Survival data from ovarian cancer patients are also available.
Joint analysis and imputation of incomplete data in the Bayesian
    framework, using (generalized) linear (mixed) models and extensions there of,
    survival models, or joint models for longitudinal and survival data, as
    described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>.
    Incomplete covariates, if present, are automatically imputed.
    The package performs some preprocessing of the data and creates a 'JAGS'
    model, which will then automatically be passed to 'JAGS' 
    <https://mcmc-jags.sourceforge.io/> with the help of 
    the package 'rjags'.
A small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) <doi:10.1080/01621459.1992.10475217>, the calibration for quantiles is based on Harms and Duchesne (2006) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019255>. The package uses standard calibration via the 'survey', 'sampling' or 'laeken' packages. In addition, entropy balancing via the 'ebal' package and empirical likelihood based on codes from Wu (2005) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9051-eng.pdf> can be used. See the paper by Beręsewicz and Szymkowiak (2023) for details <arXiv:2308.13281>.
Different algorithms to perform approximate joint diagonalization
	of a finite set of square matrices. Depending on the algorithm,
	orthogonal or non-orthogonal diagonalizer is found. These algorithms
	are particularly useful in the context of blind source separation. 
	Original publications of the algorithms can be found in 
	Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, 
	Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, 
	Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of 
	application in the context of Brain-Computer Interfaces EEG denoising
	can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.
Runs resampling-based tests jointly, e.g., sign-flip score tests from Hemerik et al., (2020) <doi:10.1111/rssb.12369>, to allow for multivariate testing, i.e., weak and strong control of the Familywise Error Rate or True Discovery Proportion.
Implementation of a parametric joint model for modelling recurrent
  and competing event processes using generalised survival models as described in
  Entrop et al., (2005) <doi:10.1002/bimj.70038>. The joint  model can 
  subsequently be used to predict the mean number of events in the
  presence of competing risks at different time points. Comparisons of the mean
  number of event functions, e.g. the differences in mean number of events
  between two exposure groups, are also available.
Jointly estimates two-group means and covariances
    for matrix-variate data and calculates test statistics.  
    This package implements the algorithms defined in 
    Hornstein, Fan, Shedden, and Zhou (2018) 
    <doi:10.1080/01621459.2018.1429275>.
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.
Estimate risk caused by two extreme and dependent forcing variables using bivariate extreme value models as described in Zheng, Westra, and Sisson (2013) <doi:10.1016/j.jhydrol.2013.09.054>; Zheng, Westra and Leonard (2014) <doi:10.1002/2013WR014616>; Zheng, Leonard and Westra (2015) <doi:10.2166/hydro.2015.052>.
Methods for fast segmentation of multivariate
    signals into piecewise constant profiles and for generating realistic
    copy-number profiles. A typical application is the joint segmentation of total
    DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism
    (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, 
    Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.
In the observational study design stage, matching/weighting methods are
    conducted. However, when many background variables are present, the decision as to 
    which variables to prioritize for matching/weighting is not trivial. Thus, the 
    joint treatment-outcome variable importance plots are created to guide variable
    selection. The joint variable importance plots enhance variable comparisons via
    unadjusted bias curves derived under the omitted variable bias framework. The 
    plots translate variable importance into recommended values for tuning parameters 
    in existing methods. Post-matching and/or weighting plots can also be used to 
    visualize and assess the quality of the observational study design. The method 
    motivation and derivation is presented in "Prioritizing Variables for Observational 
    Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) 
    <doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel 
    (2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.
Performs Joins and Minus Queries on 'Excel' Files
    fulljoinXL() Merges all rows of 2 'Excel' files based upon a common column in the files.
    innerjoinXL() Merges all rows from base file and join file when the join condition is met.
    leftjoinXL() Merges all rows from the base file, and all rows from the join file
    if the join condition is met.
    rightjoinXL() Merges all rows from the join file, and all rows from the base file if the join
    condition is met.
    minusXL() Performs 2 operations source-minus-target and target-minus-source
    If the files are identical all output files will be empty.
    Choose two 'Excel' files via a dialog box, and then follow prompts at the console to
    choose a base or source file and columns to merge or minus on.
Implements an S4 distribution system and estimation methods for
    parameters of common distribution families. The common d, p, q, r function
    family for each distribution is enriched with the ll, e, and v counterparts,
    computing the log-likelihood, performing estimation, and calculating the
    asymptotic variance - covariance matrix, respectively. Parameter estimation
    is performed analytically whenever possible.
Similarly to package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi:10.1002/9781119942283>.
 Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
Functions and data to reproduce all plots in the book "Practical Smoothing. 
 The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).
A Jordan algebra is an algebraic object originally
   designed to study observables in quantum mechanics.  Jordan
   algebras are commutative but non-associative; they satisfy the
   Jordan identity.  The package follows the ideas and notation of
   K. McCrimmon (2004, ISBN:0-387-95447-3) "A Taste of Jordan
   Algebras".  To cite the package in publications, please use
   Hankin (2023) <doi:10.48550/arXiv.2303.06062>.
Implementation of some unit and area level EBLUP estimators as well as the estimators of their MSE also under heteroscedasticity. The package further documents the publications Breidenbach and Astrup (2012) <DOI:10.1007/s10342-012-0596-7>, Breidenbach et al. (2016) <DOI:10.1016/j.rse.2015.07.026> and Breidenbach et al. (2018 in press). The vignette further explains the use of the implemented functions.
Josa in Korean is often determined by judging the previous word. 
            When writing reports using Rmd, a function that prints the 
            appropriate investigation for each case is helpful. 
            The 'josaplay' package then evaluates the previous word 
            to determine which josa is appropriate.
Read and write JSON Web Keys (JWK, rfc7517), generate and verify JSON
    Web Signatures (JWS, rfc7515) and encode/decode JSON Web Tokens (JWT, rfc7519)
    <https://datatracker.ietf.org/wg/jose/documents/>. These standards provide 
    modern signing and encryption formats that are natively supported by browsers
    via the JavaScript WebCryptoAPI <https://www.w3.org/TR/WebCryptoAPI/#jose>, 
    and used by services like OAuth 2.0, LetsEncrypt, and Github Apps.
Reproducible work requires a record of where every statistic originated.
    When writing reports, some data is too big to load in the same environment and some statistics take a while to compute.
    This package offers a way to keep notes on statistics, simple functions, and small objects.
    Notepads can be locked to avoid accidental updates.
    Notepads keep track of who added the notes and when the notes were added.
    A simple text representation is used to allow for clear version histories.
Since the reference management software (such as 'Zotero', 'Mendeley') exports Bib file journal abbreviation is not detailed enough, the 'journalabbr' package only abbreviates the journal field of Bib file, and then outputs a new Bib file for generating reference format with journal abbreviation on other software (such as 'texstudio'). The abbreviation table is from 'JabRef'. At the same time, 'Shiny' application is provided to generate 'thebibliography', a reference format that can be directly used for latex paper writing based on 'Rmd' files.
Implements under/oversampling for probability estimation.  To be
    used with machine learning methods such as AdaBoost, random forests, etc.
Tool for diagnosing table joins. It combines the speed of `collapse`
     and `data.table`, the flexibility of `dplyr`, and the diagnosis and features 
     of the `merge` command in `Stata`.
Read Japanese city codes (<https://www.e-stat.go.jp/municipalities/cities>) 
    to get city and prefecture names, or convert to city codes at different 
    points in time. In addition, it merges or splits wards of designated cities
    and gets all city codes at a specific point in time.
This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.
A Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.
Provides functions for grid square codes in Japan
    (<https://www.stat.go.jp/english/data/mesh/index.html>).
    Generates the grid square codes from longitude/latitude, geometries, and 
    the grid square codes of different scales, and vice versa.
Download and post process the infectious disease case data 
              from Japan Institute for Health Security. Also the package included 
              ready-to-analyse datasets. See the data source website for further details
              <https://id-info.jihs.go.jp/>.
Helpful functions for using mesh code (80km to 100m) data in Japan. Visualize mesh code using 'ggplot2' and 'leaflet', etc.
Provides tools to use API such as 'e-Stat' (<https://www.e-stat.go.jp/>), 
    the portal site for Japanese government statistics, and 
    'RESAS' (Regional Economy and Society Analyzing System, <https://resas.go.jp>).
Contains functions for fitting a joinpoint proportional hazards model to relative survival or cause-specific survival data, including estimates of joinpoint years at which survival trends have changed and trend measures in the hazard and cumulative survival scale. See Yu et al.(2009) <doi:10.1111/j.1467-985X.2009.00580.x>.
A highly configurable 'jQuery' plugin offering a simple
    interface to create complex queries/filters in 'Shiny'. The outputted
    rules can easily be parsed into a set of 'R' and/or 'SQL' queries and
    used to filter data. Custom parsing of the rules is also supported.
    For more information about 'jQuery QueryBuilder' see
    <https://querybuilder.js.org/>.
We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap.
Client for 'jq', a 'JSON' processor (<https://jqlang.github.io/jq/>), 
    written in C. 'jq' allows the following with 'JSON' data: index into, parse, 
    do calculations, cut up and filter, change key names and values, perform 
    conditionals and comparisons, and more.
Obtain any major version of 'jQuery' (<https://code.jquery.com/>) and use it in any webpage generated by 'htmltools' (e.g. 'shiny', 'htmlwidgets', and 'rmarkdown').
    Most R users don't need to use this package directly, but other R packages (e.g. 'shiny', 'rmarkdown', etc.) depend on this package to avoid bundling redundant copies of 'jQuery'.
An 'httpuv' based bridge between R and 'JavaScript'. Provides an easy way to exchange commands and data between a web page and a currently running R session.
These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). 
   The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, 
   as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area 
   in a conservation analysis.
   The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.
Psychometric analysis and scoring of judgment data using polytomous Item-Response Theory (IRT) models, as described in Myszkowski and Storme (2019) <doi:10.1037/aca0000225> and Myszkowski (2021) <doi:10.1037/aca0000287>. A function is used to automatically compare and select models, as well as to present a variety of model-based statistics. Plotting functions are used to present category curves, as well as information, reliability and standard error functions.
Implements the basic financial analysis
    functions similar to (but not identical to) what
    is available in most spreadsheet software. This
    includes finding the IRR and NPV of regularly
    spaced cash flows and annuities. Bond pricing and
    YTM calculations are included. In addition, Black
    Scholes option pricing and Greeks are also
    provided.
A set of utilities for working with JavaScript syntax in R.
    Includes tools to parse, tokenize, compile, validate, reformat, optimize 
    and analyze JavaScript code.
Provides a 'RStudio' addin to send some 'JavaScript' code to the 'V8' console. The user can send an entire 'JavaScript' file or only some selected lines. This is useful to test the code.
The jscore() function in the package calculates the J-Score metric between two clustering
    assignments. The score is designed to address some problems with existing common metrics such 
    as problem of matching. The details of J-score is described in Ahmadinejad and Liu. (2021) <arXiv:2109.01306>.
Fits joint species distribution models ('jSDM')
    in a hierarchical Bayesian framework (Warton and al. 2015
    <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written
    in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation
    efficiency.
The age is estimated by calculating the Dirichlet Normal Energy (DNE) on the whole auricular surface and the apex of the auricular surface. It involves three estimation methods: principal component discriminant analysis (PCQDA), and principal component logistic regression analysis (PCLR) methods, principal component regression analysis with Southeast Asian (A_PCR), and principal component regression analysis with multipopulation (M_PCR). The package is created with the data from the Louis Lopes Collection in Lisbon, the 21st Century Identified Human Remains Collection in Coimbra, and the CAL Milano Cemetery Skeletal Collection in Milan, and  the skeletal collection at Khon Kaen University (KKU) Human Skeletal Research Centre (HSRC), housed in the Department of Anatomy in the Faculty of Medicine at KKU in Khon Kaen.
Allow to run 'jshint' on 'JavaScript' files with a 'R'
    command or a 'RStudio' addin. The report appears in the 'RStudio'
    viewer pane.
The function 'jskm()' creates publication quality Kaplan-Meier plot with at risk tables below. 'svyjskm()' provides plot for weighted Kaplan-Meier estimator.
Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data. Refer to the Journal of Statistical Software article: <doi:10.18637/jss.v093.i02>.
A set of helper functions to conduct joint-significance tests
    for mediation analysis, as recommended by Yzerbyt, Muller, Batailler,
    & Judd. (2018) <doi:10.1037/pspa0000132>.
'RStudio' addins and 'Shiny' modules for descriptive statistics, regression and survival analysis.
The function get_parameters() is intended to be used within a docker container to read keyword arguments from a .json file automagically. A tool.yaml file contains specifications on these keyword arguments, which are then passed as input to containerized R tools in the [tool-runner framework](<https://github.com/hydrocode-de/tool-runner>). A template for a containerized R tool, which can be used as a basis for developing new tools, is available at the following URL: <https://github.com/VForWaTer/tool_template_r>.
Encode/Decode 'base64', with support for JSON format, using two functions: j_encode() and j_decode().    'Base64' is a group of similar binary-to-text encoding schemes that represent binary data in an ASCII string format by translating it into a radix-64 representation, used when there is a need to encode binary data that needs to be stored and transferred over media that are designed to deal with textual data, ensuring that the data will remain intact and without modification during transport. <https://developer.mozilla.org/en-US/docs/Web/API/WindowBase64/Base64_encoding_and_decoding>    On the other side, JSON (JavaScript Object Notation) is a lightweight data-interchange format. Easy to read, write, parse and generate. It is based on a subset of the JavaScript Programming Language. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. JSON structure is built around name:value pairs and ordered list of values. <https://www.json.org>    The first function, j_encode(), let you transform a data.frame or list to a 'base64' encoded JSON (or JSON string).    The j_decode() function takes a 'base64' string (could be an encoded JSON) and transform it to a data.frame (or list, depending of the JSON structure).
Conversions between 'R' objects and Javascript Object Notation (JSON) using 
    the 'rapidjsonr' library <https://CRAN.R-project.org/package=rapidjsonr>.
JSON-LD <https://www.w3.org/TR/json-ld/> is a light-weight syntax for 
    expressing linked data. It is primarily intended for web-based programming
    environments, interoperable web services and for storing linked data in 
    JSON-based databases. This package provides bindings to the JavaScript 
    library for converting, expanding and compacting JSON-LD documents.
A reasonably fast JSON parser and generator, optimized for statistical 
    data and the web. Offers simple, flexible tools for working with JSON in R, and
    is particularly powerful for building pipelines and interacting with a web API. 
    The implementation is based on the mapping described in the vignette (Ooms, 2014).
    In addition to converting JSON data from/to R objects, 'jsonlite' contains 
    functions to stream, validate, and prettify JSON data. The unit tests included 
    with the package verify that all edge cases are encoded and decoded consistently 
    for use with dynamic data in systems and applications.
Provides a function allowing to normalize a 'JSON' string,
    for example by adding double quotes around the keys when they are
    missing. Also provides 'RStudio' addins for the same purpose.
Interface to 'JSON-stat' <https://json-stat.org/>,
  a simple lightweight 'JSON' format for data dissemination.
Uses the node library 'is-my-json-valid' or 'ajv' to
    validate 'JSON' against a 'JSON' schema.  Drafts 04, 06 and 07 of
    'JSON' schema are supported.
Implementation of joint sparse optimization (JSparO) to infer the gene regulatory network for cell fate conversion. The proximal gradient method is implemented to solve different low-order regularization models for JSparO.
Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Provides tools to access the J-STAGE WebAPI and retrieve 
    information published on J-STAGE 
    <https://www.jstage.jst.go.jp/browse/-char/ja>.
Functions and helpers to import metadata, ngrams and full-texts 
    delivered by Data for Research by JSTOR.
Create and customize interactive trees using the
       'jQuery' 'jsTree' <https://www.jstree.com/> plugin
       library and the 'htmlwidgets' package. These trees can
       be used directly from the R console, from 'RStudio', in
       Shiny apps and R Markdown documents.
Creates interactive trees that can be included in 'Shiny'
    apps and R markdown documents. A tree allows to represent hierarchical
    data (e.g. the contents of a directory). Similar to the 'shinyTree'
    package but offers more features and options, such as the grid
    extension, restricting the drag-and-drop behavior, and settings for
    the search functionality. It is possible to attach some data to the
    nodes of a tree and then to get these data in 'Shiny' when a node is
    selected. Also provides a 'Shiny' gadget allowing to manipulate one or
    more folders, and a 'Shiny' module allowing to navigate in the server
    side file system.
Uses least squares optimisation to estimate the parameters of the best-fitting JohnsonSU distribution for a given dataset, with the possibility of the distributions corresponding to the limiting cases of the JohnsonSU distribution. The code for the Golden Section Search used in the optimisation has been adapted from E. Cai. This package has been created as an extension of my Master's thesis. E. Cai (2013, "Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization", <https://chemicalstatistician.wordpress.com/2013/04/22/using-r-to-implement-the-golden-bisection-method/>).
Fitting and analyzing a Joint Trait Distribution Model. The Joint Trait Distribution Model is implemented in the Bayesian framework using conjugate priors and posteriors, thus guaranteeing fast inference. In particular the package computes joint probabilities and multivariate confidence intervals, and enables the investigation of how they depend on the environment through partial response curves. The method implemented by the package is described in Poggiato et al. (2023) <doi:10.1111/geb.13706>.
Minimal and memory efficient implementation of the junction tree
	     algorithm using the Lauritzen-Spiegelhalter scheme;
	     S. L. Lauritzen and D. J. Spiegelhalter (1988) 
	     <https://www.jstor.org/stable/2345762?seq=1>. The jti package is
	     part of the paper <doi:10.18637/jss.v111.i02>.
This is a collection of tools for more efficiently understanding 
  and sharing the results of (primarily) regression analyses. There are also a
  number of miscellaneous functions for statistical and programming purposes. 
  Support for models produced by the survey and lme4 packages are points of 
  emphasis.
A long-term forecast model called "Jubilee-Tectonic model" is implemented to forecast future returns of the U.S. stock market, Treasury yield, and gold price. The five-factor model forecasts the 10-year and 20-year future equity returns with high R-squared above 80 percent. It is based on linear growth and mean reversion characteristics in the U.S. stock market. This model also enhances the CAPE model by introducing the hypothesis that there are fault lines in the historical CAPE, which can be calibrated and corrected through statistical learning. In addition, it contains a module for business cycles, optimal interest rate, and recession forecasts.
A convenience tool to create 'HTML' with inline styles using 
  'juicyjuice' and 'markdown' packages. It is particularly useful when working 
  on a content management system (CMS) whose code editor eliminates style and 
  link tags. The main use case of the package is the learning management system, 
  'Moodle'. Additional helper functions for teaching purposes are provided. Learn 
  more about 'juicedown' at <https://kenjisato.github.io/juicedown/>.
Provides a GUI interface for automating data extraction from 
  multiple images containing scatter and bar plots, semi-automated tools to tinker 
  with extraction attempts, and a fully-loaded point-and-click manual extractor 
  with image zoom, calibrator, and classifier. Also provides detailed and 
  R-independent extraction reports as fully-embedded .html records.
There are occasions where you need a piece of HTML with integrated
    styles. A prime example of this is HTML email. This transformation
    involves moving the CSS and associated formatting instructions from the
    style block in the head of your document into the body of the HTML. Many
    prominent email clients require integrated styles in HTML email; otherwise a
    received HTML email will be displayed without any styling. This package will
    quickly and precisely perform these CSS transformations when given HTML text
    and it does so by using the JavaScript 'juice' library.
Generates image data for fractals (Julia and Mandelbrot sets) on the complex plane in the given region and resolution. Benoit B Mandelbrot (1982).
Provides an R interface to 'Julia',
    which is a high-level, high-performance dynamic programming language
    for numerical computing, see <https://julialang.org/> for more information.
    It provides a high-level interface as well as a low-level interface.
    Using the high level interface, you could call any 'Julia' function just like
    any R function with automatic type conversion. Using the low level interface,
    you could deal with C-level SEXP directly while enjoying the convenience of
    using a high-level programming language like 'Julia'.
Allows to import functions and whole packages from 'Julia' in R.
    Imported 'Julia' functions can directly be called as R functions.
    Data structures can be translated between 'Julia' and R.
    More details can also be found in the corresponding article
    <doi:10.18637/jss.v101.i06>.
Metaprogramming utilities for converting R regression model
    formulae to equivalents in 'Julia' <doi:10.1137/141000671>, via
    modifications to the abstract syntax tree. Supports translations in
    zero correlation random effects syntax, protection of expressions to
    be evaluated as-is, interaction terms, and more. Accepts strings or R
    formula objects and returns modified R formula objects where
    possible (or a modified string, if not a valid formula in R).
Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), <doi:10.1093/bioinformatics/btad366>].
A set of functions to compute the Hodrick-Prescott (HP)
    filter with automatically selected jumps. The original
    HP filter extracts a smooth trend from a time series, and our version
    allows for a small number of automatically identified jumps.
    See Maranzano and Pelagatti (2024) <doi:10.2139/ssrn.4896170> for details.
Individual based simulations of hybridizing populations,
    where the accumulation of junctions is tracked. Furthermore,
    mathematical equations are provided to verify simulation outcomes.
    Both simulations and mathematical equations are based on Janzen
    (2018, <doi:10.1101/058107>) and Janzen (2022,
    <doi:10.1111/1755-0998.13519>).
Install packages without attaching them. If a package it is already installed, it will be skipped.
Leverages the 'yum' package to
             implement a 'YAML' ('YAML Ain't Markup Language', a human
             friendly standard for data serialization; see <https:yaml.org>)
             standard for documenting justifications, such as for decisions
             taken during the planning, execution and analysis of a study
             or during the development of a behavior change intervention
             as illustrated by Marques & Peters (2019)
             <doi:10.17605/osf.io/ndxha>. These justifications are both
             human- and machine-readable, facilitating efficient extraction
             and organisation.
Functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>).
Provides functions to standardize and whiten data, and to perform
  Principal Component Analysis (PCA).  The main advantage of this
  package over alternatives like prcomp() is, that jvcoords makes it
  easy to convert (additional) data between the original and the
  transformed coordinates.  The package also provides a class coords,
  which can represent affine coordinate transformations.  This class
  forms the basis of the transformations provided by the package, but
  can also be used independently.  The implementation has been
  optimized to be of comparable speed (and sometimes even faster) than
  existing alternatives.
Miscellaneous tools and functions,
    including: generate descriptive statistics tables,
    format output, visualize relations among variables or check
    distributions, and generic functions for residual and
    model diagnostics.
Quality of life functions for interactive programming.
    Shortcuts for common combinations of functions or different default
    arguments. Not to be used in production level scripts, but useful for
    exploring and quickly manipulating data for easy analysis. Also
    imports a variety of packages to facilitate the installation of those
    imported packages on the host machine.
Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' 
    and the piping syntax from 'magrittr'. Function 'kable()' is a light weight 
    table generator coming from 'knitr'. This package simplifies the way to 
    manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows 
    users to construct complex tables and customize styles using a readable 
    syntax.
Implementation of various kernel adaptive methods in nonparametric curve
    estimation like density estimation as introduced in Stute and Srihera (2011)
    <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013)
    <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression
    as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.
Search and download data from the API for Japanese Diet Proceedings (see the reference at <https://kokkai.ndl.go.jp/api.html>).
A toolkit for absolute and relative dating and analysis of
    chronological patterns. This package includes functions for
    chronological modeling and dating of archaeological assemblages from
    count data. It provides methods for matrix seriation. It also allows
    to compute time point estimates and density estimates of the
    occupation and duration of an archaeological site.
'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
Implements methods for clustering mixed-type data,
  specifically combinations of continuous and nominal data. Special attention
  is paid to the often-overlooked problem of equitably balancing the
  contribution of the continuous and categorical variables. This package
  implements KAMILA clustering, a novel method for clustering
  mixed-type data in the spirit of k-means clustering. It does not require
  dummy coding of variables, and is efficient enough to scale to rather large
  data sets. Also implemented is Modha-Spangler clustering, which uses a
  brute-force strategy to maximize the cluster separation simultaneously in the
  continuous and categorical variables. For more information, see Foss, Markatou,
  Ray, & Heching (2016) <doi:10.1007/s10994-016-5575-7> and Foss & Markatou
  (2018) <doi:10.18637/jss.v083.i13>.
Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel).
Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them.
One-way and two-way analysis of variance for replicated point
	patterns, grouped by one or two classification factors, on the
	basis of the corresponding K-functions.
Computes the Kantorovich distance between two probability
    measures on a finite set. The Kantorovich distance is also known as
    the Monge-Kantorovich distance or the first Wasserstein distance.
Sequences encoding by using the chaos game representation.
    Löchel et al. (2019) <doi:10.1093/bioinformatics/btz493>.
Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019) <doi:10.1080/03610918.2018.1538457> Jun-mo Nam (2003) <doi:10.1111/j.0006-341X.2003.00118.x> Jun-mo Nam (2005) <doi:10.1002/sim.2321>Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) <doi:10.2307/3315487> Allan Donner, Michael Eliasziw, Neil Klar (1996) <doi:10.2307/2533154>.
Estimate agreement of a group of raters with a gold standard rating
    on a nominal scale. For a single gold standard rater the average pairwise
    agreement of raters with this gold standard is provided. For a group of (gold
    standard) raters the approach of S. Vanbelle, A. Albert (2009)
    <doi:10.1007/s11336-009-9116-1> is implemented. Bias and standard error are
    estimated via delete-1 jackknife.
Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss' Kappa. The calculation of kappa statistics is done using the R package 'irr', so that 'KappaGUI' is essentially a Shiny front-end for 'irr'.
S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
Contains basic tools for sample size estimation in studies of interobserver/interrater agreement (reliability).  Includes functions for both the power-based and confidence interval-based methods, with binary or multinomial outcomes and two through six raters.
Attempts to remove vocals from a stereo '.wav' recording of a song.
This is the R implementation of Karel the robot, a programming 
  language created by Dr. R. E. Pattis at Stanford University in 1981. Karel is 
  an useful tool to teach introductory concepts about general programming, such 
  as algorithmic decomposition, conditional statements, loops, etc., in an 
  interactive and fun way, by writing programs to make Karel the robot achieve 
  certain tasks in the world she lives in. Originally based on Pascal, Karel 
  was implemented in many languages through these decades, including 'Java', 'C++', 
  'Ruby' and 'Python'. This is the first package implementing Karel in R.
This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing. 
  This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types. 
  Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.07.08.499353>.
Real-time quantitative polymerase chain reaction (qPCR) data
    sets by Karlen et al. (2007) <doi:10.1186/1471-2105-8-131>. Provides one
    single tabular tidy data set in long format, encompassing 32
    dilution series, for seven PCR targets and four biological samples. The
    targeted amplicons are within the murine genes: Cav1, Ccn2, Eln, Fn1, Rpl27,
    Hspg2, and Serpine1, respectively. Dilution series: scheme 1 (Cav1, Eln,
    Hspg2, Serpine1): 1-fold, 10-fold, 50-fold, and 100-fold; scheme 2 (Ccn2,
    Rpl27, Fn1): 1-fold, 10-fold, 50-fold, 100-fold and 1000-fold.
    For each concentration there are five replicates, except for the 1000-fold
    concentration, where only two replicates were performed. Each amplification
    curve is 40 cycles long. Original raw data file is Additional file 2 from
    "Statistical significance of quantitative PCR" by Y. Karlen, A. McNair,
    S. Perseguers, C. Mazza, and N. Mermod (2007)
    <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-8-131/MediaObjects/12859_2006_1503_MOESM2_ESM.ZIP>.
An R code with a GUI for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) <doi:10.1016/j.physrep.2006.11.001>) and filling methods (Moffat et al. (2007) <doi:10.1016/j.agrformet.2007.08.011>), as well as tools to manipulate easily time series and gap sets.
Analysis of DNA copy number in single cells using 
    custom genome-wide targeted DNA sequencing panels for the Mission Bio 
    Tapestri platform. Users can easily parse, manipulate, and visualize 
    datasets produced from the automated 'Tapestri Pipeline', with support for 
    normalization, clustering, and copy number calling. Functions are also 
    available to deconvolute multiplexed samples by genotype and parsing 
    barcoded reads from exogenous lentiviral constructs.
Convert latex math expressions to HTML and 'MathML' for use in 
    markdown documents or package manual pages. The rendering is done in 
    R using the V8 engine (i.e. server-side), which eliminates the need 
    for embedding the 'MathJax' library into your web pages. In addition
    a 'math-to-rd' wrapper is provided to automatically render beautiful
    math in R documentation files.
Provides data for Kaya identity variables (population, gross 
             domestic product, primary energy consumption, and energy-related 
             CO2 emissions) for the world and for individual nations, and 
             utility functions for looking up data,  plotting trends of 
             Kaya variables, and plotting the fuel mix for a given country
             or region. The Kaya identity (Yoichi Kaya and Keiichi Yokobori, 
             "Environment, Energy, and Economy: Strategies for Sustainability" 
             (United Nations University Press, 1998) and 
             <https://en.wikipedia.org/wiki/Kaya_identity>) expresses a nation's 
             or region's greenhouse gas emissions in terms of its population, 
             per-capita Gross Domestic Product, the energy intensity of its 
             economy, and the carbon-intensity of its energy supply.
Many data science problems reduce to operations on very tall,
    skinny matrices.  However, sometimes these matrices can be so tall that they
    are difficult to work with, or do not even fit into main memory.  One
    strategy to deal with such objects is to distribute their rows across
    several processors.  To this end, we offer an 'S4' class for tall, skinny,
    distributed matrices, called the 'shaq'.  We also provide many useful
    numerical methods and statistics operations for operating on these
    distributed objects.  The naming is a bit "tongue-in-cheek", with the class
    a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he
    starred in the film 'Kazaam'.
Computes Khattree-Bahuguna's univariate and multivariate skewness, principal-component-based Khattree-Bahuguna's multivariate skewness. It also provides several measures of univariate or multivariate skewnesses including, Pearson’s coefficient of skewness, Bowley’s univariate skewness and Mardia's multivariate skewness. See Khattree, R. and Bahuguna, M. (2019) <doi: 10.1007/s41060-018-0106-1>.
Implementation of the KCMeans regression estimator studied by 
    Wiemann (2023) <arXiv:2311.17021> for expectation function estimation conditional on 
    categorical variables. Computation leverages the unconditional KMeans 
    implementation in one dimension using dynamic programming algorithm of
    Wang and Song (2011) <doi:10.32614/RJ-2011-015>, allowing for global solutions in time polynomial in 
    the number of observed categories.
Implements approaches of non-parametric smooth test to 
            compare simultaneously K(K>1) copulas and non-parametric clustering 
            of multivariate populations with arbitrary sizes. 
            See Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2112.05623> and
            Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2211.06338>.
The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.
It predicts any attribute (categorical) given a set of input numeric predictor values. Note that only numeric input predictors should be given.
             The k value can be chosen according to accuracies provided. The attribute to be predicted can be selected from the dropdown provided (select categorical attribute).
             This is because categorical attributes cannot be given as inputs here. A 'handsontable' is also provided to enter the input predictor values.
Predicts any variable in any categorical dataset for given values of predictor variables.
    If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. 
    The user can upload their own datasets and select what variable they want to predict.
    A 'handsontable' is provided to enter the predictor values and also accuracy of the prediction is also shown.
Provides an efficient implementation of univariate local polynomial
    kernel density estimators that can handle bounded and discrete data. See 
    Geenens (2014) <doi:10.48550/arXiv.1303.4121>, 
    Geenens and Wang (2018) <doi:10.48550/arXiv.1602.04862>, 
    Nagler (2018a) <doi:10.48550/arXiv.1704.07457>, 
    Nagler (2018b) <doi:10.48550/arXiv.1705.05431>.
Provides fast implementations of kernel smoothing techniques for
    bivariate copula densities, in particular density estimation and resampling,
    see Nagler (2018) <doi:10.18637/jss.v084.i07>.
Provides methods for selecting the optimal bandwidth in kernel density estimation for dependent samples, such as those generated by Markov chain Monte Carlo (MCMC). Implements a modified biased cross-validation (mBCV) approach that accounts for sample dependence, improving the accuracy of estimated density functions.
Handles univariate non-parametric density estimation with 
    parametric starts and asymmetric kernels in a simple and flexible way. 
    Kernel density estimation with parametric starts involves fitting a
    parametric density to the data before making a correction with kernel 
    density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
    Asymmetric kernels make kernel density estimation more efficient on bounded
    intervals such as (0, 1) and the positive half-line. Supported asymmetric 
    kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
    the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
    copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
    User-supplied kernels, parametric starts, and bandwidths are supported.
Implements the vine copula based kernel density estimator of
    Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does
    not suffer from the curse of dimensionality and is therefore well suited for
    high-dimensional applications.
Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.
Density, distribution function, quantile function and random generation
             for the K-distribution. A plotting function that plots data on Weibull
             paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.
Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) <doi:10.1007/s00357-024-09493-z> for dkps() methodology, and Ghashti (2024) <doi:10.14288/1.0443975> for dkss() methodology.
A collection of personal helper functions to avoid redundancy
    in the spirit of the "Don't repeat yourself" principle of software
    development (<https://en.wikipedia.org/wiki/Don%27t_repeat_yourself>).
Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data (described in Arsalane Chouaib Guidoum (2020) <arXiv:2012.06102> [stat.CO]).
Provides arrays with flexible control over dimension dropping when subscripting.
Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.
Uses Bessel functions to calculate the
    fundamental and complementary analytic solutions to the
    Kelvin differential equation.
Computes the Kendall rank correlation and Mann-Kendall
        trend test. See documentation for use of block bootstrap when
        there is autocorrelation.
The computational complexity of the implemented algorithm for
    Kendall's correlation is O(n log(n)), which is faster than the base R
    implementation with a computational complexity of O(n^2). For small vectors
    (i.e., less than 100 observations), the time difference is negligible.
    However, for larger vectors, the speed difference can be substantial and the
    numerical difference is minimal. The references are
    Knight (1966) <doi:10.2307/2282833>,
    Abrevaya (1999) <doi:10.1016/S0165-1765(98)00255-9>,
    Christensen (2005) <doi:10.1007/BF02736122> and
    Emara (2024) <https://learningcpp.org/>.
    This implementation is described in
    Vargas Sepulveda (2024) <doi:10.48550/arXiv.2408.09618>.
Kendall random walks are a continuous-space Markov chains generated
             by the Kendall generalized convolution. This package provides tools
             for simulating these random walks and studying distributions
             related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) <arXiv:1412.0220>.
Miscellaneous functions and data used in psychological research and teaching. Keng 
    currently has a built-in dataset depress, and could (1) scale a vector; (2) compute the cut-off 
    values of Pearson's r with known sample size; (3) test the significance and compute the post-hoc
    power for Pearson's r with known sample size; (4) conduct prior power analysis and plan the 
    sample size for Pearson's r; (5) compare lm()'s fitted outputs using R-squared, f_squared, 
    post-hoc power, and PRE (Proportional Reduction in Error, also called partial R-squared or 
    partial Eta-squared); (6) calculate PRE from partial correlation, Cohen's f, or f_squared; 
    (7) conduct prior power analysis and plan the sample size for one or a set of predictors in 
    regression analysis; (8) conduct post-hoc power analysis for one or a set of predictors in 
    regression analysis with known sample size.
Demo and dataset accompaying the books :
	De l'analyse des réseaux expérimentaux à la méta-analyse: Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales (Published 2018-06-28, Quae, for french version) by David Makowski, Francois Piraux and Francois Brun - <https://www.quae.com/produit/1514/9782759228164/de-l-analyse-des-reseaux-experimentaux-a-la-meta-analyse>
	Knowledge Synthesis in Agriculture : from Experimental Network to Meta-Analysis (in preparation for 2018-06, Springer , for English version) by David Makowski, Francois Piraux and Francois Brun
	A full description of all the material is in both books.
	ACKNOWLEDGMENTS : The French network "RMT modeling and data analysis for agriculture" (<http://www.modelia.org>) have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France.
Provides an implementation of a kernel-embedding of probability test for elliptical distribution. This is an asymptotic test for elliptical distribution under general alternatives, and the location and shape parameters are assumed to be unknown. Some side-products are posted, including the transformation between rectangular and polar coordinates and two product-type kernel functions. See Tang and Li (2024) <doi:10.48550/arXiv.2306.10594> for details.
Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Interface to 'Keras' <https://keras.io>, a high-level neural
  networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Interface to 'Keras' <https://keras.io>, a high-level neural
  networks API. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both CPU and GPU devices.
'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans. 
             It aims at making the life of AI practitioners, hypertuner 
             algorithm creators and model designers as simple as possible by 
             providing them with a clean and easy to use API for hypertuning. 
             'Keras Tuner' makes moving from a base model to a hypertuned one quick and 
             easy by only requiring you to change a few lines of code.
A new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions.
    Song, H. and Wu, M.C. (2023) 
    <arXiv:2307.15268>.
Nonparametric kernel distribution function estimation is performed. Three bandwidth selectors are implemented: the plug-in selectors of Altman and Leger and of Polansky and Baker, and the cross-validation selector of Bowman, Hall and Prvan. The exceedance function, the mean return period and the return level are also computed. For details, see Quintela-del-Río and Estévez-Pérez (2012) <doi:10.18637/jss.v050.i08>.
An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.
Gaussian process regression with an emphasis on kernels.
    Quantitative and qualitative inputs are accepted. Some pre-defined
    kernels are available, such as radial or tensor-sum for
    quantitative inputs, and compound symmetry, low rank, group kernel
    for qualitative inputs. The user can define new kernels and
    composite kernels through a formula mechanism. Useful methods
    include parameter estimation by maximum likelihood, simulation,
    prediction and leave-one-out validation.
A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.
Smoothed bootstrap and functions for random generation from
             univariate and multivariate kernel densities. It does not
             estimate kernel densities.
Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.
In self-reported or anonymised data the user often encounters
    heaped data, i.e. data which are rounded (to a possibly different degree
    of coarseness). While this is mostly a minor problem in parametric density
    estimation the bias can be very large for non-parametric methods such as kernel
    density estimation. This package implements a partly Bayesian algorithm treating
    the true unknown values as additional parameters and estimates the rounding
    parameters to give a corrected kernel density estimate. It supports various
    standard bandwidth selection methods. Varying rounding probabilities (depending
    on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
    Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
    as well as data aggregated on areas is supported.
Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
Efficient implementation of Kernel SHAP, see Lundberg and Lee
    (2017), and Covert and Lee (2021)
    <http://proceedings.mlr.press/v130/covert21a>.  Furthermore, for up to
    14 features, exact permutation SHAP values can be calculated.  The
    package plays well together with meta-learning packages like
    'tidymodels', 'caret' or 'mlr3'.  Visualizations can be done using the
    R package 'shapviz'.
Producing kernel estimates of the unconditional and conditional hazard
        function for right-censored data including methods of bandwidth selection.
Kernel-based machine learning methods for classification,
        regression, clustering, novelty detection, quantile regression
        and dimensionality reduction.  Among other methods 'kernlab'
        includes Support Vector Machines, Spectral Clustering, Kernel
        PCA, Gaussian Processes and a QP solver.
Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) <doi:10.1016/j.spl.2024.110078>.The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels.
Provides wind energy practitioners with an effective machine learning-based
    tool that estimates a multivariate power curve and predicts the wind power output
    for a specific environmental condition.
Kernel Machine Score Test for Pathway Analysis in the Presence of 
    Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) 
    <doi: 10.1177/0962280216653427>.
Functions for kernel smoothing (and density estimation)
  corresponding to the book: 
  Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
Fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools. The kernel smoothing is based on methods described in Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.
Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). <doi:10.48550/arXiv.2208.12026>.
Kernel functions for diverse types of data (including, but not
    restricted to: nonnegative and real vectors, real matrices, categorical
    and ordinal variables, sets, strings), plus other utilities like kernel
    similarity, kernel Principal Components Analysis (PCA) and features'
    importance for Support Vector Machines (SVMs), which expand other 'R'
    packages like 'kernlab'.
New kernel-based test and fast tests for detecting change-points or changed-intervals where the distributions abruptly change. They work well particularly for high-dimensional data.
    Song, H. and Chen, H. (2022)
    <arXiv:2206.01853>.
New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data.
      Song, H. and Chen, H. (2023) 
      <arXiv:2011.06127>.
A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) <doi:10.1038/s41746-021-00519-z>.
Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2024) <doi:10.1111/ajps.12779>.
We developed a package 'Keyboard' for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The 'Keyboard' designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) <doi:10.1158/1078-0432.CCR-17-0220>, Li DH, Whitmore JB, Guo W, Ji Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>,  Liu S, Johnson VE (2016) <doi:10.1093/biostatistics/kxv040>, Zhou Y, Lee JJ, Yuan Y (2019) <doi:10.1002/sim.8475>, Pan H, Lin R, Yuan Y (2020) <doi:10.1016/j.cct.2020.105972>). The 'Keyboard' package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication.
Control your keyboard and mouse with R code by simulating key presses and mouse clicks. The input simulation is implemented with the Windows API.
A fast and computationally efficient algorithm designed to enable researchers to efficiently and quickly extract semantically-related keywords using a fitted embedding model. For more details about the methods applied, see Chester (2025). <doi:10.17605/OSF.IO/5B7RQ>.
Tools for keeping track of information, named
    "keys", about rows of data frame like objects. This is done by
    creating special attribute "keys" which is updated after every change
    in rows (subsetting, ordering, etc.).  This package is designed to
    work tightly with 'dplyr' package.
Efficient implementation of permutation tests for keyword analysis in corpus linguistics as described in Mildenberger (2023) <arXiv:2308.13383>.
Computes group centrality scores and identifies the most central group of players in a network.
Wait for a single key press at the 'R' prompt.
    This works in terminals, but does not currently work
    in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'),
    in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in
    'R Studio'. In these cases 'keypress' stops with an
    error message.
Platform independent 'API' to access the operating system's
    credential store. Currently supports: 'Keychain' on 'macOS',
    Credential Store on 'Windows', the Secret Service 'API' on 'Linux',
    and simple, platform independent stores implemented with environment
    variables or encrypted files.  Additional storage back-ends can be
    added easily.
Decrypts passwords stored in the Gnome Keyring, macOS Keychain and
    strings encrypted with the Windows Data Protection API.
Assign and listen to keyboard shortcuts in 'shiny' using the 
  'Mousetrap' Javascript library.
Convert keys and other values to memorable phrases. 
  Includes some methods to build lists of words.
Provides functions to identify plausible and replicable factor
  structures for a set of variables via k-fold cross validation. The process
  combines the exploratory and confirmatory factor analytic approach to scale
  development (Flora & Flake, 2017) <doi:10.1037/cbs0000069> with a cross validation
  technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009)
  <isbn:978-0-387-21606-5>. Also available are functions to determine k by drawing 
  on power analytic techniques for covariance structures (MacCallum, Browne, &
  Sugawara, 1996) <doi:10.1037/1082-989X.1.2.130>, generate model syntax, and
  summarize results in a report.
State space modelling is an efficient and flexible framework for 
    statistical inference of a broad class of time series and other data. KFAS 
    includes computationally efficient functions for Kalman filtering, smoothing, 
    forecasting, and simulation of multivariate exponential family state space models, 
    with observations from Gaussian, Poisson, binomial, negative binomial, and gamma 
    distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA).
    There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions.
    In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly.
    This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.
A streamlined cross-referencing system for R Markdown documents
    generated with 'knitr'. R Markdown is  an authoring format for generating
    dynamic content from R. 'kfigr' provides a hook for anchoring code
    chunks and a function to cross-reference document elements generated from
    said chunks, e.g. figures and tables.
A method for detecting outliers with a Kalman filter on impulsed 
  noised outliers and prediction on cleaned data. 'kfino' is a robust 
  sequential algorithm allowing to filter data with a large number of outliers. 
  This algorithm is based on simple latent linear Gaussian processes as in the 
  Kalman Filter method and is devoted to detect impulse-noised outliers. These 
  are data points that differ significantly from other observations. 'ML' 
  (Maximization Likelihood) and 'EM' (Expectation-Maximization algorithm) 
  algorithms were implemented in 'kfino'. The method is described in full 
  details in the following arXiv e-Print: <arXiv:2208.00961>.
Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.
Implementation for kernel functional partial least squares (KFPLS) method. KFPLS method is developed for functional nonlinear models, and the method does not require strict constraints for the nonlinear structures. The crucial function of this package is KFPLS().
Aids in identifying the Koeppen-Geiger (KG) climatic zone for 
    a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system
    to classify regions of the earth by their relative heat and humidity through the year, for 
    the benefit of human health, plant and agriculture and other human activity [1]. This climate
    zone classification system, applicable to all of the earths surface, has continued to be 
    developed by scientists up to the present day.  Recently one of use (FZ) has published updated,
    higher accuracy KG climate zone definitions [2]. In this package we use these updated 
    high-resolution maps as the data source [3]. We provide functions that return the KG climate zone 
    for a given longitude and lattitude, or for a given United States zip code. In addition
    the CZUncertainty() function will check climate zones nearby to check if the given location
    is near a climate zone boundary. In addition an interactive shiny app is provided to define 
    the KG climate zone for a given longitude and lattitude, or United States zip code. 
    Digital data, as well as animated maps, showing the shift of the climate zones are provided 
    on the following website <http://koeppen-geiger.vu-wien.ac.at>.
    This work was supported by the DOE-EERE SunShot award DE-EE-0007140.
     [1] W. Koeppen, (2011) <doi:10.1127/0941-2948/2011/105>.
     [2] F. Rubel and M. Kottek, (2010) <doi:10.1127/0941-2948/2010/0430>.
     [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) <doi:10.1127/metz/2016/0816>.
A unified software package simultaneously implemented in 'Python', 'R', and 'Matlab' providing a uniform and internally-consistent way of calculating stoichiometric equilibrium constants in modern and palaeo seawater as a function of temperature, salinity, pressure and the concentration of magnesium, calcium, sulphate, and fluorine.
The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <https://proceedings.mlr.press/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.
Metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios. The data is described in Auton et al. (2015) <doi:10.1038/nature15393> and Byrska-Bishop et al. (2022) <doi:10.1016/j.cell.2022.08.004>, and raw data is available at <http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/>. See Turner (2022) <doi:10.48550/arXiv.2210.00539> for more details.
Training and evaluating k-gram language models in R, 
        supporting several probability smoothing techniques, 
        perplexity computations, random text generation and more.
Knowledge graphs enable to efficiently visualize and gain insights into large-scale data analysis results, as p-values from multiple studies or embedding data matrices. The usual workflow is a user providing a data frame of association studies results and specifying target nodes, e.g. phenotypes, to visualize. The knowledge graph then shows all the features which are significantly associated with the phenotype, with the edges being proportional to the association scores. As the user adds several target nodes and grouping information about the nodes such as biological pathways, the construction of such graphs soon becomes complex. The 'kgraph' package aims to enable users to easily build such knowledge graphs, and provides two main features: first, to enable building a knowledge graph based on a data frame of concepts relationships, be it p-values or cosine similarities; second, to enable determining an appropriate cut-off on cosine similarities from a complete embedding matrix, to enable the building of a knowledge graph directly from an embedding matrix. The 'kgraph' package provides several display, layout and cut-off options, and has already proven useful to researchers to enable them to visualize large sets of p-value associations with various phenotypes, and to quickly be able to visualize embedding results. Two example datasets are provided to demonstrate these behaviors, and several live 'shiny' applications are hosted by the CELEHS laboratory and Parse Health, as the KESER Mental Health application <https://keser-mental-health.parse-health.org/> based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.
Restore underlining numeric data from rating history graph of 
    KGS (an online platform of the game of go, <http://www.gokgs.com/>). 
    A shiny application is also provided.
Provides a user-friendly interface for interacting with the District Health 
    Information Software 2 (DHIS2) instance. It streamlines data retrieval, 
    empowering researchers, analysts, and healthcare professionals to obtain and 
    utilize data efficiently.
The King's Health Questionnaire (KHQ) is a disease-specific, 
  self-administered questionnaire designed specific to assess the impact of 
  Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed 
  by Kelleher and collaborators (1997) <doi:10.1111/j.1471-0528.1997.tb11006.x>. 
  It is a simple, acceptable and reliable measure to use in the clinical setting 
  and a research tool that is useful in evaluating UI treatment outcomes. 
  The KHQ five dimensions (KHQ5D) is a condition-specific preference-based 
  measure developed by Brazier and collaborators (2008) <doi:10.1177/0272989X07301820>. 
  Although not as popular as the SF6D <doi:10.1016/S0895-4356(98)00103-6> and 
  EQ-5D <https://euroqol.org/>, the KHQ5D measures health-related quality of 
  life (HRQoL) specifically for UI, not general conditions like the others 
  two instruments mentioned. The KHQ5D ca be used in the clinical and economic 
  evaluation of health care. The subject self-rates their health in terms of 
  five dimensions: Role Limitation (RL), Physical Limitations (PL), Social 
  Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these 
  five dimensions are converted to a single utility index using country specific 
  value sets, which can be used in the clinical and economic evaluation of 
  health care as well as in population health surveys. This package provides 
  methods to calculate scores for each dimension of the KHQ; converts KHQ item 
  scores to KHQ5D scores; and also calculates the utility index of the KHQ5D.
Color schemes ready for each type of data (qualitative,
    diverging or sequential), with colors that are distinct for all
    people, including color-blind readers. This package provides an
    implementation of Paul Tol (2018) and Fabio Crameri (2018)
    <doi:10.5194/gmd-11-2541-2018> color schemes for use with 'graphics'
    or 'ggplot2'. It provides tools to simulate color-blindness and to
    test how well the colors of any palette are identifiable. Several
    scientific thematic schemes (geologic timescale, land cover, FAO
    soils, etc.) are also implemented.
Contains kidney care oriented functions.
			 Current version contains functions for calculation of:
			 - Estimated glomerular filtration rate by CKD-EPI (2021 and 2009), MDRD, CKiD, FAS, EKFC, etc.
			 - Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors.
			 - Citation: Bikbov B. kidney.epi: Kidney-Related Functions for Clinical and Epidemiological Research. Scientific-Tools.Org, <https://Scientific-Tools.Org>.  <doi:10.32614/CRAN.package.kidney.epi>.
Caches and then connects to a 'sqlite' database containing half a million pediatric drug safety signals. 
    The database is part of a family of resources catalogued at <https://nsides.io>. The database
    contains 17 tables where the description table provides a map between the fields the field's details. 
    The database was created by Nicholas Giangreco during his PhD thesis which you can read in Giangreco (2022) <doi:10.7916/d8-5d9b-6738>. 
    The observations are from the Food and Drug Administration's Adverse Event Reporting System. Generalized additive models estimated
    drug effects across child development stages for the occurrence of an adverse event when exposed to a drug compared to other drugs.
    Read more at the methods detailed in Giangreco (2022) <doi:10.1016/j.medj.2022.06.001>.
Optimized for handling complex datasets in environmental and ecological 
    research, this package offers functionality that is not fully met by 
    general-purpose packages. It provides two key functions, 'summarize_data()', 
    which summarizes datasets, and 'plot_means()', which creates plots with 
    error bars. The 'plot_means()' function incorporates error bars by default, 
    allowing quick visualization of uncertainties, crucial in ecological 
    studies. It also streamlines workflows for grouped datasets (e.g., 
    by species or treatment), making it particularly user-friendly and reducing 
    the complexity and time required for data summarization and visualization.
A collection of functions for analyzing data typically collected 
    or used by behavioral scientists. Examples of the functions include
    a function that compares groups in a factorial experimental design,
    a function that conducts two-way analysis of variance (ANOVA),
    and a function that cleans a data set generated by Qualtrics surveys.
    Some of the functions will require installing additional package(s).
    Such packages and other references are cited within the section
    describing the relevant functions. Many functions in this package
    rely heavily on these two popular R packages:
    Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>.
    Wickham et al. (2021) <https://CRAN.R-project.org/package=ggplot2>.
'Rcpp' implementation of the multivariate Kim filter, which combines the Kalman and Hamilton filters for state probability inference. 
  The filter is designed for state space models and can handle missing values and exogenous data in the observation and state equations.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
A collection of useful functions not found anywhere else,
    mainly for programming: Pretty intervals, generalized lagged
    differences, checking containment in an interval, and an alternative
    interface to assign().
Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific 
 cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are
 retrospectively built from relatives of probands for whom the genotype is known. Currently 
 the method of moments and marginal maximum likelihood are implemented. Confidence intervals 
 are calculated from bootstrap samples.
 Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee.
Functions for simulating and estimating kinship-related dispersal. Based
    on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. 
    Hoffmann (2019) <doi:10.1111/1755-0998.13043> "A genomic approach to inferring kinship 
    reveals limited intergenerational dispersal in the yellow fever mosquito".
    Assumes an additive variance model of dispersal in two dimensions, compatible with 
    Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, 
    as well as the functions needed to estimate parent-offspring dispersal for simulated or 
    empirical data, and to undertake sampling design for future field studies of dispersal. 
    For ease of use an integrated Shiny app is also included.
Allows analyzing time series representing two-dimensional movements.
    It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns,
    and returns several dynamical properties such as speed, acceleration or curvature.
Data on houses in and around Seattle WA are included. Basic 
    characteristics are given along with sale prices.
Methods for inference about relationships between contributors to a DNA mixture and 
  other individuals of known genotype: a basic example would be testing whether a contributor to 
  a mixture is the father of a child of known genotype. This provides most of the functionality
  of the 'KinMix' package, but with some loss of efficiency and restriction on problem size,
  as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'.
  The package implements the methods introduced in 
  Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and 
  Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>.
Routines to handle family data with a pedigree object. The initial purpose
	     was to create correlation structures that describe family relationships such 
	     as kinship and identity-by-descent, which can be used to model family data 
	     in mixed effects models, such as in the coxme function. Also includes a tool
	     for pedigree drawing which is focused on producing compact layouts without 
	     intervention. Recent additions include utilities to trim the pedigree object
	     with various criteria, and kinship for the X chromosome.
Evaluate specific panels in different aspects: i) Simulation tools related to pedigree researches; ii) calculation for systemic effectiveness indicators, such as probability of exclusion (PE).
Machine learning, containing several algorithms for supervised and unsupervised classification, in addition to a function that plots the Receiver Operating Characteristic (ROC) and Precision-Recall (PRC) curve graphs, and also a function that returns several metrics used for model evaluation, the latter can be used in ranking results from other packs.
Multi-modal magnetic resonance imaging ('MRI')
    data from the 'Kirby21' 'reproducibility' study
    <https://www.nitrc.org/projects/multimodal/>, including functional
    and structural imaging.
Functional magnetic resonance imaging ('fMRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
Structural T1 magnetic resonance imaging ('MRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
API Wrapper to use Korea Investment & Securities (KIS) trading 
             system that provides various financial services like stock price 
             check, orders and balance check 
             <https://apiportal.koreainvestment.com/>.
Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, deterministic or stochastic migration model. Kissmig runs on binary or quantitative suitability maps, which are pre-calculated with niche-based habitat suitability models (also called ecological niche models (ENMs) or species distribution models (SDMs)). Nobis & Normand (2014), <doi:10.1111/ecog.00930>.
Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.
Provides tools to calculate the theoretical hydrodynamic response
    of an aquifer undergoing harmonic straining or pressurization, or analyze
    measured responses. There are
    two classes of models here, designed for use with confined
    aquifers: (1) for sealed wells, based on the model of 
    Kitagawa et al (2011, <doi:10.1029/2010JB007794>), 
    and (2) for open wells, based on the models of
    Cooper et al (1965, <doi:10.1029/JZ070i016p03915>), 
    Hsieh et al (1987, <doi:10.1029/WR023i010p01824>), 
    Rojstaczer (1988, <doi:10.1029/JB093iB11p13619>), 
    Liu et al (1989, <doi:10.1029/JB094iB07p09453>), and
    Wang et al (2018, <doi:10.1029/2018WR022793>). Wang's 
    solution is a special exception which
    allows for leakage out of the aquifer 
    (semi-confined); it is equivalent to Hsieh's model
    when there is no leakage (the confined case).
    These models treat strain (or aquifer head) as an input to the
    physical system, and fluid-pressure (or water height) as the output. The
    applicable frequency band of these models is characteristic of seismic
    waves, atmospheric pressure fluctuations, and solid earth tides.
Create a kite-square plot for contingency tables using 'ggplot2', to display their relevant quantities in a single figure (marginal, conditional, expected, observed, chi-squared). The plot resembles a flying kite inside a square if the variables are independent, and deviates from this the more dependence exists.
A wrapper for querying 'WISKI' databases via the 'KiWIS' 'REST' API. 'WISKI' is an 'SQL' relational database 
  used for the collection and storage of water data developed by KISTERS and 'KiWIS' is a 'REST' service that provides
  access to 'WISKI' databases via HTTP requests (<https://www.kisters.eu/water-weather-and-environment/>). 
  Contains a list of default databases (called 'hubs') and also allows users to provide their own 'KiWIS' URL. 
  Supports the entire query process- from metadata to specific time series values. All data is returned as tidy tibbles.
Weighted k-Nearest Neighbors for Classification, Regression
    and Clustering.
A variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). “Kernel Knockoffs Selection for Nonparametric Additive Models”. arXiv preprint <arXiv:2105.11659>.
Miscellaneous functions for classification and visualization,
     e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, 
     an interface to 'svmlight' and stepclass() wrapper variable selection 
     for supervised classification, partimat() visualization of classification rules 
         and shardsplot() of cluster results as well as kmodes() clustering for categorical data, 
     corclust() variable clustering, variable extraction from different variable clustering models 
         and weight of evidence preprocessing.
Functions to search, retrieve, apply and update classifications 
  and codelists using Statistics Norway's API <https://www.ssb.no/klass> 
  from the system 'KLASS'. Retrieves classifications by date with options 
  to choose language, hierarchical level and formatting.
A set of functions designed to quickly generate results of a multiple choice
          test. Generates detailed global results, lists for anonymous feedback and
          personalised result feedback (in LaTeX and/or PDF format), as well as item
          statistics like Cronbach's alpha or disciminatory power. 'klausuR' also
          includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs for
          its basic features. The respective R package 'rkward' cannot be installed
          directly from a repository, as it is a part of RKWard. To make full use of this
          feature, please install RKWard from <https://rkward.kde.org> (plugins are
          detected automatically). Due to some restrictions on CRAN, the full package
          sources are only available from the project homepage.
Estimation algorithms for Kullback-Leibler divergence between two probability
    distributions, based on one or two samples, and including uncertainty quantification.
    Distributions can be uni- or multivariate and continuous, discrete or mixed.
Most importantly, calculates Kullback-Leibler Divergence (KLD), Turing's perspective estimator
 and their confidence intervals.
Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) <doi:10.1088/1742-6596/1955/1/012047>.
Kernel Learning Integrative Clustering (KLIC) is an algorithm that allows to combine multiple kernels, each representing a different measure of the similarity between a set of observations. The contribution of each kernel on the final clustering is weighted according to the amount of information carried by it. As well as providing the functions required to perform the kernel-based clustering, this package also allows the user to simply give the data as input: the kernels are then built using consensus clustering. Different strategies to choose the best number of clusters are also available. For further details please see Cabassi and Kirk (2020) <doi:10.1093/bioinformatics/btaa593>.
A 'shiny' application for forensic kinship testing, based on
    the 'pedsuite' R packages. 'KLINK' is closely aligned with the (non-R)
    software 'Familias' and 'FamLink', but offers several unique features,
    including visualisations and automated report generation. The
    calculation of likelihood ratios supports pairs of linked markers, and
    all common mutation models.
A comprehensive set of geostatistical, visual,
    and analytical methods, in conjunction with the expanded version of the
    acclaimed J.E. Klovan's mining dataset, are included in 'klovan'. This makes the
    package an excellent learning resource for Principal Component Analysis (PCA),
    Factor Analysis (FA), kriging, and other geostatistical techniques. Originally
    published in the 1976 book 'Geological Factor Analysis', the included mining
    dataset was assembled by Professor J. E. Klovan of the University of Calgary.
    Being one of the first applications of FA in the geosciences, this dataset has
    significant historical importance. As a well-regarded and published dataset, it
    is an excellent resource for demonstrating the capabilities of PCA, FA, kriging,
    and other geostatistical techniques in geosciences. For those interested in
    these methods, the 'klovan' datasets provide a valuable and illustrative resource.
    Note that some methods require the 'RGeostats' package. Please refer to the
    README or Additional_repositories for installation instructions. This material is
    based upon research in the Materials Data Science for Stockpile Stewardship Center
    of Excellence (MDS3-COE), and supported by the Department of Energy's National
    Nuclear Security Administration under Award Number DE-NA0004104.
An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) <DOI:10.1007/978-3-319-11257-2_20>, which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette.
Used to create dynamic, interactive 'D3.js' 
  based parallel coordinates and principal component plots in 'R'.
  The plots make visualizing k-means or other clusters simple and informative.
Computes various confidence intervals for the Kaplan-Meier
        estimator, namely: Peto's CI, Rothman CI, CI's based on
        Greenwood's variance, Thomas and Grunkemeier CI and the
        simultaneous confidence bands by Nair and Hall and Wellner.
Implements k-means like blockmodeling of one-mode and linked networks as presented in Žiberna (2020) <doi:10.1016/j.socnet.2019.10.006>. The development of this package is financially supported by the Slovenian Research Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).
Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015)<doi: 10.1007/s00180-015-0567-9> and Mai Zhou and Yifan Yang (2017) <doi: 10.1002/wics.1400>. More applications could be found in Mai Zhou (2015) <doi: 10.1201/b18598>.
Implementations of the kernel measure of multi-sample dissimilarity (KMD) between
    several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD 
    measures the dissimilarity between multiple samples, based on the observations from them.
    It converges to the population quantity (depending on the kernel) which is between 0 and 1.
    A small value indicates the multiple samples are from the same distribution, and a large value
    indicates the corresponding distributions are different. The population quantity is 0 if and only
    if all distributions are the same, and 1 if and only if all distributions are mutually singular.
    The package also implements the tests based on KMD for H0: the M distributions are equal
    against H1: not all the distributions are equal. Both permutation test and asymptotic test are
    available. These tests are consistent against all alternatives where at least two samples have
    different distributions. For more details on KMD and the associated tests, see Huang, Z. and
    B. Sen (2022) <arXiv:2210.00634>.
Implementation of Kmeans clustering algorithm and a supervised KNN (K Nearest Neighbors) learning method. It allows users to perform unsupervised clustering and supervised classification on their datasets. Additional features include data normalization, imputation of missing values, and the choice of distance metric. The package also provides functions to determine the optimal number of clusters for Kmeans and the best k-value for KNN: knn_Function(), find_Knn_best_k(), KMEANS_FUNCTION(), and find_Kmeans_best_k().
Algorithms of distance-based k-medoids clustering: simple and fast 
  k-medoids, ranked k-medoids, and increasing number of clusters in k-medoids. 
  Calculate distances for mixed variable data such as Gower, Podani, Wishart, 
  Huang, Harikumar-PV, and Ahmad-Dey. Cluster validation applies internal and 
  relative criteria. The internal criteria includes silhouette index and shadow 
  values. The relative criterium applies bootstrap procedure producing a heatmap 
  with a flexible reordering matrix algorithm such as complete, ward, or average 
  linkages. The cluster result can be plotted in a marked barplot or pca biplot.
Online, Semi-online, and Offline K-medians algorithms are
    given. For both methods, the algorithms can be initialized
    randomly or with the help of a robust hierarchical
    clustering. The number of clusters can be selected with the
    help of a penalized criterion. We provide functions to provide
    robust clustering. Function gen_K() enables to generate a sample
    of data following a contaminated Gaussian mixture.
    Functions Kmedians() and Kmeans() consists in a K-median and a
    K-means algorithms while Kplot() enables to produce graph for both
    methods. 
    Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. <doi:10.3150/11-BEJ390>.
    Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 <doi:10.1007/s11749-016-0519-x>.
    Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians"     <arXiv:2209.03597> 
    Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. <doi:10.1073/pnas.97.4.1423>.
Contains tools for rapidly computing distance matrices 
    and clustering large sequence datasets using fast alignment-free 
    k-mer counting and recursive k-means partitioning. 
    See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> 
    for a review of k-mer counting methods and applications for 
    biological sequence analysis.
A multi-purpose and flexible k-meric enrichment analysis software. 
    'kmeRtone' measures the enrichment of k-mers by comparing the population of 
    k-mers in the case loci with a carefully devised internal negative control 
    group, consisting of k-mers from regions close to, yet sufficiently distant 
    from, the case loci to mitigate any potential sequencing bias. This method 
    effectively captures both the local sequencing variations and broader 
    sequence influences, while also correcting for potential biases, thereby 
    ensuring more accurate analysis. The core functionality of 'kmeRtone' is 
    the SCORE() function, which calculates the susceptibility scores for k-mers in 
    case and control regions. Case regions are defined by the genomic coordinates 
    provided in a file by the user and the control regions can be constructed 
    relative to the case regions or provided directly. The k-meric susceptibility 
    scores are calculated by using a one-proportion z-statistic. 'kmeRtone' is 
    highly flexible by allowing users to also specify their target k-mer patterns
    and quantify the corresponding k-mer enrichment scores in the context of 
    these patterns, allowing for a more comprehensive approach to understanding 
    the functional implications of specific DNA sequences on a genomic scale
    (e.g., CT motifs upon UV radiation damage).
    Adib A. Abdullah, Patrick Pflughaupt, Claudia Feng, Aleksandr B. Sahakyan (2024) Bioinformatics (submitted).
Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010 <doi:10.1093/biostatistics/kxq018>).
An implementation of k-means specifically design
        to cluster longitudinal data. It provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical
        interface for choosing the 'best' number of clusters.
An implementation of k-means specifically design
        to cluster joint trajectories (longitudinal data on
        several variable-trajectories).
        Like 'kml', it provides facilities to deal with missing
        value, compute several quality criterion (Calinski and Harabatz,
        Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
        interface for choosing the 'best' number of clusters. In addition, the 3D graph
        representing the mean joint-trajectories of each cluster can be exported through
        LaTeX in a 3D dynamic rotating PDF graph.
The developed function is designed to facilitate the seamless conversion of KML (Keyhole Markup Language)
             files to Shapefiles while preserving attribute values. It provides a straightforward interface for users
             to effortlessly import KML data, extract relevant attributes, and export them into the widely compatible
             Shapefile format. The package ensures accurate representation of spatial data while maintaining the integrity
             of associated attribute information. For details see, Flores, G. (2021). <DOI:10.1007/978-3-030-63665-4_15>.
             Whether for spatial analysis, visualization, or data interoperability, it simplifies the conversion process and 
             empowers users to seamlessly work with geospatial datasets.
An implementation of the 'k-means--' algorithm proposed
    by Chawla and Gionis, 2013 in their paper,
    "k-means-- : A unified approach to clustering and outlier detection.
    SIAM International Conference on Data Mining (SDM13)",
    <doi:10.1137/1.9781611972832.21>
    and using 'ordering' described by Howe, 2013 in the thesis,
    Clustering and anomaly detection in tropical cyclones".
    Useful for creating (potentially) tighter clusters than
    standard k-means and simultaneously finding outliers inexpensively
    in multidimensional space.
Datasets and functions for Klein and Moeschberger (1997),
        "Survival Analysis, Techniques for Censored and Truncated
        Data", Springer.
Produce Kaplan–Meier plots in the style recommended
    following the KMunicate study by Morris et al. (2019)
    <doi:10.1136/bmjopen-2019-030215>. The KMunicate style consists of
    Kaplan-Meier curves with confidence intervals to quantify uncertainty
    and an extended risk table (per treatment arm) depicting the number of
    study subjects at risk, events, and censored observations over time.
    The resulting plots are built using 'ggplot2' and can be further
    customised to a certain extent, including themes, fonts, and colour
    scales.
The sampl.mcmc function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints.
Given a set of points around a knee curve,
    analyzes first and second derivatives to find knee points.
Provides the ability to create dynamic citations
    in which the bibliographic information is pulled from the web rather
    than having to be entered into a local database such as 'bibtex' ahead of
    time. The package is primarily aimed at authoring in the R 'markdown'
    format, and can provide outputs for web-based authoring such as linked
    text for inline citations.  Cite using a 'DOI', URL, or
    'bibtex' file key.  See the package URL for details.
Provides several helper functions for working with 'knitr' and 'LaTeX'.
  It includes 'xTab' for creating traditional 'LaTeX' tables, 'lTab' for generating
  'longtable' environments, and 'sTab' for generating a 'supertabular' environment.
  Additionally, this package contains a knitr_setup() function which fixes a
  well-known bug in 'knitr', which distorts the 'results="asis"' command when used
  in conjunction with user-defined commands; and a com command (<<com=TRUE>>=)
  which renders the output from 'knitr' as a 'LaTeX' command.
Provides a general-purpose tool for dynamic report generation in R
    using Literate Programming techniques.
A framework to create Bootstrap <https://getbootstrap.com/> HTML reports from 'knitr'
    'rmarkdown'.
Implements a data language engine for incorporating data directly in 
    'rmarkdown' documents so that they can be made completely standalone.
Provides a progress bar similar to 'dplyr' that can write progress out to a 
    variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown',
    and you still want to see progress of calculations in the terminal.
Convert an R Markdown documents into an '.xlsx' spreadsheet reports
    with the knitxl() function, which works similarly to knit() from the 
    'knitr' package. The generated report can be opened in 'Excel' or similar
    software for further analysis and presentation.
Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix.
    The implementation is based on cover trees introduced by
    Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.
This is a 'C++' mutual information (MI) library based on the k-nearest 
    neighbor (KNN) algorithm. There are three functions provided for computing MI 
    for continuous values, mixed continuous and discrete values, and conditional MI 
    for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) <doi:10.1103/PhysRevE.69.066138>, 
    BC Ross (2014)<doi:10.1371/journal.pone.0087357>, 
    and A. Tsimpiris (2012) <doi:10.1016/j.eswa.2012.05.014>, respectively.
Two main functionalities are provided. One of them is predicting values with 
    k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm.
    These are carried out in parallel using multiple threads.
An interactive document on  the topic of K-nearest neighbour (KNN) using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/KNNShiny/>.
k Nearest Neighbors with variable selection, combine grid search and forward selection to achieve variable selection in order to improve k Nearest Neighbors predictive performance.
Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.
Application of a Known Biomass Production Model (KBPM): (1) the fitting of KBPM to each stock; (2) the estimation of the effects of environmental variability; (3) the retrospective analysis to identify regime shifts; (4) the estimation of forecasts. For more details see Schaefer (1954) <https://www.iattc.org/GetAttachment/62d510ee-13d0-40f2-847b-0fde415476b8/Vol-1-No-2-1954-SCHAEFER,-MILNER-B-_Some-aspects-of-the-dynamics-of-populations-important-to-the-management-of-the-commercial-marine-fisheries.pdf>, Pella and Tomlinson (1969) <https://www.iattc.org/GetAttachment/9865079c-6ee7-40e2-9e30-c4523ff81ddf/Vol-13-No-3-1969-PELLA,-JEROME-J-,-and-PATRICK-K-TOMLINSON_A-generalized-stock-production-model.pdf> and MacCall (2002) <doi:10.1577/1548-8675(2002)022%3C0272:UOKBPM%3E2.0.CO;2>.
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
  when performing variable selection. 
  For more information, see the website below and the accompanying paper: Candes et al., 
  "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", 
  J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.
Identification of putative causal variants in genome-wide association studies using hybrid analysis of both the trio and population designs. The package implements the method in the paper: Yang, Y., Wang, Q., Wang, C., Buxbaum, J., & Ionita-Laza, I. (2024). KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. The American Journal of Human Genetics, in press.
Functions for identification of putative causal loci in whole-genome sequencing data. The functions allow genome-wide association scan. It also includes an efficient knockoff generator for genetic data.
Identification of putative causal variants in genome-wide association studies with trio and duo families. The package calculates the W feature statistics from KnockoffTrio and p-values from the family-based association test (FBAT) using trio and/or duo data. Compared to previous versions, a significant improvement has been made in Version 1.1.0 to allow the package to be applied not only to trio families but also to duo families. The package implements the methods in the paper: "Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z., & Ionita-Laza, I. (2022). KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10), 1761-1776."
Makes visually pleasing diagrams of knot projections using optimized Bezier curves.
It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.
Retrieve data from 'kintone' (<https://www.kintone.com/>) via its API.
    'kintone' is an enterprise application platform.
Wrapper for 'Kobotoolbox' APIs ver 2 mentioned at <https://support.kobotoolbox.org/api.html>, to download data from 'Kobotoolbox' to R. Small and simple package that adds immense convenience for the data professionals using 'Kobotoolbox'.
A self-guided, weakly supervised learning algorithm for feature extraction from noisy and 
  high-dimensional data. It facilitates the identification of patterns that reflect underlying group 
  structures across all samples in a dataset. The method incorporates a novel strategy to integrate 
  spatial information, improving the interpretability of results in spatially resolved data.
Read Swiss time series data from the 'KOF Data' API, <https://datenservice.kof.ethz.ch>. The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.
To test if a tensor time series following a Tucker-decomposition factor model has a Kronecker product structure. Supplementary functions for tensor reshape and its reversal are also included.
Provides a function that uses a genetic algorithm to search for a subset
 of size k from the integers 1:n, such that a user-supplied objective function 
 is minimized at that subset.  The selection step is done by tournament selection 
 based on ranks, and elitism may be used to retain a portion of the best solutions 
 from one generation to the next. Population objective function values may 
 optionally be evaluated in parallel.
Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government.
    See <https://nlftp.mlit.go.jp/index.html> for more information.
Assists researchers in choosing Key Opinion Leaders (KOLs) in a network to help disseminate or encourage adoption of an innovation by other network members. Potential KOL teams are evaluated using the ABCDE framework (Neal et al., 2025 <doi:10.31219/osf.io/3vxy9_v1>). This framework which considers: (1) the team members' Availability, (2) the Breadth of the team's network coverage, (3) the Cost of recruiting a team of a given size, and (4) the Diversity of the team's members, (5) which are pooled into a single Evaluation score.
Functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. 
  The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms.
  See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in   Salvucci & Goldberg (2000,\doi{10.1145/355017.355028}). 
  Two-means clustering is described in Hessels et al. (2017, \doi{10.3758/s13428-016-0822-1}). 
  The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010,\doi{10.3758/BRM.42.1.188}).
  See a demonstration in the URL.
Write beautiful yet customizable letters in R Markdown and
  directly obtain the finished PDF. Smooth generation of PDFs is realized by
  'rmarkdown', the 'pandoc-letter' template and the 'KOMA-Script' letter class.
  'KOMA-Script' provides enhanced replacements for the standard 'LaTeX' classes
  with emphasis on typography and versatility. 'KOMA-Script' is particularly
  useful for international writers as it handles various paper formats well, 
  provides layouts for many common window envelope types (e.g. German, US, 
  French, Japanese) and lets you define your own layouts. The package comes 
  with a default letter layout based on 'DIN 5008B'.
Statistical methods that quantify the conditions necessary to
    alter inferences, also known as sensitivity analysis, are becoming
    increasingly important to a variety of quantitative sciences. A series
    of recent works, including Frank (2000)
    <doi:10.1177/0049124100029002001> and Frank et al. (2013)
    <doi:10.3102/0162373713493129> extend previous sensitivity analyses by
    considering the characteristics of omitted variables or unobserved
    cases that would change an inference if such variables or cases were
    observed. These analyses generate statements such as "an omitted
    variable would have to be correlated at xx with the predictor of
    interest (e.g., the treatment) and outcome to invalidate an inference of a
    treatment effect". Or "one would have to replace pp percent of the
    observed data with nor which the treatment had no effect to invalidate the 
    inference".
    We implement these recent developments of sensitivity analysis and
    provide modules to calculate these two robustness indices and generate
    such statements in R. In particular, the functions konfound(),
    pkonfound() and mkonfound() allow users to calculate the robustness of
    inferences for a user's own model, a single published study and
    multiple studies respectively.
The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, <doi:10.1177/0962280220907355>). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions.
Matches a data set with semi-structured address data, 
 e.g., street and house number as a concatenated string,
 wrongly spelled street names or non-existing house numbers to a 
 reference index. The methods are specifically designed for German 
 municipalities ('KOR'-community) and German address schemes.
A set of tools to analyze texts. Includes, amongst others, functions for
          automatic language detection, hyphenation, several indices of lexical diversity
          (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability (e.g., Flesch,
          SMOG, LIX, Dale-Chall). Basic import functions for language corpora are also
          provided, to enable frequency analyses (supports Celex and Leipzig Corpora
          Collection file formats) and measures like tf-idf. Note: For full functionality
          a local installation of TreeTagger is recommended. It is also recommended to
          not load this package directly, but by loading one of the available language
          support packages from the 'l10n' repository
          <https://undocumeantit.github.io/repos/l10n/>. 'koRpus' also includes a plugin
          for the R GUI and IDE RKWard, providing graphical dialogs for its basic
          features. The respective R package 'rkward' cannot be installed directly from a
          repository, as it is a part of RKWard. To make full use of this feature, please
          install RKWard from <https://rkward.kde.org> (plugins are detected
          automatically). Due to some restrictions on CRAN, the full package sources are
          only available from the project homepage. To ask for help, report bugs, request
          features, or discuss the development of the package, please subscribe to the
          koRpus-dev mailing list (<https://korpusml.reaktanz.de>).
Adds support for the English language to the 'koRpus' package. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the
                    package, please consider subscribing to the koRpus-dev mailing list (<https://korpusml.reaktanz.de>).
Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <arXiv:1907.03153>.
API wrapper to download statistical information from the
             Korean Statistical Information Service (KOSIS)
             <https://kosis.kr/openapi/index/index.jsp>.
Cubic spline fitting along with knot selection, includes support for additional variables.
Implementations of two empirical versions the kernel partial correlation (KPC) coefficient
    and the associated variable selection algorithms. KPC is a measure of the strength of conditional
    association between Y and Z given X, with X, Y, Z being random variables taking values in
    general topological spaces. As the name suggests, KPC is defined in terms of kernels on
    reproducing kernel Hilbert spaces (RKHSs). The population KPC is a deterministic number
    between 0 and 1; it is 0 if and only if Y is conditionally independent of Z given X, and it is 1 if
    and only if Y is a measurable function of Z and X. One empirical KPC estimator is based on
    geometric graphs, such as K-nearest neighbor graphs and minimum spanning trees, and is
    consistent under very weak conditions. The other empirical estimator, defined using conditional
    mean embeddings (CMEs) as used in the RKHS literature, is also consistent under suitable
    conditions. Using KPC, a stepwise forward variable selection algorithm KFOCI (using the graph
    based estimator of KPC) is provided, as well as a similar stepwise forward selection algorithm
    based on the RKHS based estimator. For more details on KPC, its empirical estimators and its
    application on variable selection, see Huang, Z., N. Deb, and B. Sen (2022). “Kernel partial
    correlation coefficient – a measure of conditional dependence” (URL listed below). When X is
    empty, KPC measures the unconditional dependence between Y and Z, which has been described
    in Deb, N., P. Ghosal, and B. Sen (2020), “Measuring association on topological spaces using
    kernels and geometric graphs” <arXiv:2010.01768>, and it is implemented in the functions
    KMAc() and Klin() in this package. The latter can be computed in near linear time.
The kernelized version of principal component analysis (KPCA) has proven to be a valid nonlinear alternative for tackling the nonlinearity of biological sample spaces. However, it poses new challenges in terms of the interpretability of the original variables. 'kpcaIG' aims to provide a tool to select the most relevant variables based on the kernel PCA representation of the data as in Briscik et al. (2023) <doi:10.1186/s12859-023-05404-y>. It also includes functions for 2D and 3D visualization of the original variables (as arrows) into the kernel principal components axes, highlighting the contribution of the most important ones.
The number of clusters (k) is needed to start all the partitioning clustering algorithms. An optimal value of this input argument is widely determined by using some internal validity indices. Since most of the existing internal indices suggest a k value which is computed from the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary,  the package 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.
Provides an easy way to create interactive KPI (key performance indicator) widgets for 'Quarto' dashboards using 'Crosstalk'. The package enables visualization of key metrics in a structured format, supporting interactive filtering and linking with other 'Crosstalk'-enabled components. Designed for use in 'Quarto' Dashboards.
Software for k-means clustering of partially 
    observed data from Chi, Chi, and Baraniuk (2016) <doi:10.1080/00031305.2015.1086685>.
A comprehensive R interface to access data from the Kraken cryptocurrency exchange REST API <https://docs.kraken.com/api/>.
  It allows users to retrieve various market data, such as asset information, trading pairs, and price data.
  The package is designed to facilitate efficient data access for analysis, strategy development, and monitoring of cryptocurrency market trends.
Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, <https://hbr.org/1983/09/purchasing-must-become-supply-management>)
    for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and
    uses this information to place products and services within the Kraljic Matrix.
Provides basic functions for Continuation-Passing Style development.
Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.
An implementation of a simple and highly optimized ordinary kriging algorithm to plot geographical data.
Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Provides tools for applying Krippendorff's Alpha methodology <DOI:10.1080/19312450709336664>. Both the customary methodology and Hughes' methodology <DOI:10.48550/arXiv.2210.13265> are supported, the former being preferred for larger datasets, the latter for smaller datasets. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval estimation can be done in parallel for either methodology.
Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) <DOI:10.1093/biostatistics/kxu056>.
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
The goal of 'kronos' is to provide an easy-to-use framework to analyse circadian or otherwise rhythmic data using the familiar R linear modelling syntax, while taking care of the trigonometry under the hood.
Kernel smoothers for univariate and multivariate data, with comprehensive visualisation and bandwidth selection capabilities, including for densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) <doi:10.1201/9780429485572>.
The Retained Component Criterion for Principal Component Analysis (RCC_PCA) is a tool to determine the optimal number of components to retain in PCA.
Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests 
	with different rank score criteria, Steel's multiple comparison test, and the 
        Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact 
        P-values, all valid under randomization, with or without ties, or conditionally 
        under random sampling from populations, given the observed tie pattern.  Except for 
        Steel's test and the JT test it also combines these tests across several blocks of 
	samples.  Also analyzed are 2 x t contingency tables and their blocked combinations 
	using the Kruskal-Wallis criterion.  Steel's test is inverted to provide simultaneous 
	confidence bounds for shift parameters.  A plotting function compares tail probabilities
	obtained under asymptotic approximation with those obtained via simulation or exact 
	calculations.
Implements the Lilliefors-corrected Kolmogorov-Smirnov test for use
    in goodness-of-fit tests, suitable when population parameters are unknown and
    must be estimated by sample statistics. P-values are estimated by simulation.
    Can be used with a variety of continuous distributions, including normal,
    lognormal, univariate mixtures of normals, uniform, loguniform, exponential,
    gamma, and Weibull distributions. Functions to generate random numbers and
    calculate density, distribution, and quantile functions are provided for use
    with the log uniform and mixture distributions.
An adaptation of Kernelized Stein Discrepancy, this package provides a goodness-of-fit test of whether a given i.i.d. sample is drawn from a given distribution. It works for any distribution once its score function (the derivative of log-density) can be provided. This method is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <arXiv:1602.03253>.
Infers relative kinase activity from phosphoproteomics data
    using the method described by Casado et al. (2013) <doi:10.1126/scisignal.2003573>.
Selection of k in k-means clustering based on Pham et al. paper
    ``Selection of k in k-means clustering''.
Contains functions to compute p-values for the one-sample and two-sample Kolmogorov-Smirnov (KS) tests and the two-sample Kuiper test for any fixed critical level and arbitrary (possibly very large) sample sizes. For the one-sample KS test, this package implements a novel, accurate and efficient method named Exact-KS-FFT, which allows the pre-specified cumulative distribution function under the null hypothesis to be continuous, purely discrete or mixed. In the two-sample case, it is assumed that both samples come from an unspecified (unknown) continuous, purely discrete or mixed distribution, i.e. ties (repeated observations) are allowed, and exact p-values of the KS and the Kuiper tests are computed. Note, the two-sample Kuiper test is often used when data samples are on the line or on the circle (circular data). To cite this package in publication: (for the use of the one-sample KS test) Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42.  <doi:10.18637/jss.v095.i10>. (for the use of the two-sample KS and Kuiper tests) Dimitrina S. Dimitrova, Yun Jia and Vladimir K. Kaishev (2024). The R functions KS2sample and Kuiper2sample: Efficient Exact Calculation of P-values of the Two-sample Kolmogorov-Smirnov and Kuiper Tests. submitted.
Clustering typically assigns data points into discrete groups, but the clusters can sometimes be indistinct. Cluster sharpening adjusts an existing clustering to create contrast between groups. This package provides a general interface for cluster sharpening along with several implementations based on different excision criteria.
Prediction with k* nearest neighbor algorithm 
    based on a publication by Anava and Levy (2016) <arXiv:1701.07266>.
To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Implements the Known Sub-Sequence Algorithm <doi:10.1016/j.aaf.2021.12.013>, which helps to automatically identify and validate the best method for missing data imputation in a time series. Supports the comparison of multiple state-of-the-art algorithms.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain. The 'kst' package provides 
   basic functionalities to generate, handle, and manipulate knowledge 
   structures and knowledge spaces.
Tools for estimate (joint) cumulants and (joint) products of cumulants of a random sample using (multivariate) k-statistics and (multivariate) polykays, unbiased estimators with minimum variance. Tools for generating univariate and multivariate Faa di Bruno's formula and related polynomials, such as Bell polynomials, generalized complete Bell polynomials, partition polynomials and generalized partition polynomials. For more details see Di Nardo E., Guarino G., Senato D. (2009) <arXiv:0807.5008>, <arXiv:1012.6008>.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain.  The 'kstIO' package 
   provides basic functionalities to read and write KST data from/to 
   files to be used together with the 'kst', 'kstMatrix', 'CDSS', 'pks', or 
   'DAKS' packages.
Knowledge space theory by Doignon and Falmagne (1999)
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize
   knowledge structures in a particular domain. The 'kstMatrix' package 
   provides basic functionalities to generate, handle, and manipulate 
   knowledge structures and knowledge spaces. Opposed to the 'kst'
   package, 'kstMatrix' uses matrix representations for knowledge
   structures. Furthermore, 'kstMatrix' contains several knowledge spaces
   developed by the research group around Cornelia Dowling through
   querying experts.
A clustering algorithm similar to K-Means is implemented, it has two main advantages, 
    namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when
    there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, 
    if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means).
    Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale.
    (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).
Provides a function called COTUCKER3() (Co-Inertia Analysis
    + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of
    matrices, as well as other functions called PCA() (Principal Component Analysis)
    and BGA() (Between-Groups Analysis), which perform analysis of one matrix,
    COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA()
    (Partial Triadic Analysis), STATIS(), STATISDUAL() and TUCKER3(), which perform analysis of a sequence
    of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO()
    (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS
    method), which also perform analysis of two sequences of matrices.
This is designed for use with an arbitrary set of equations with
 an arbitrary set of unknowns.
 The user selects "fixed" values for enough unknowns to leave as many variables as
 there are equations, which in most cases means the system is properly
 defined and a unique solution exists. The function, the fixed values
 and initial values for the remaining unknowns are fed to a nonlinear backsolver.  
 The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.
Kernel-based 'Tweedie' compound Poisson gamma model using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions. For more details, please see Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi & Robert William Platt (2023) <doi:10.1080/00401706.2022.2156615>.
This function performs the two-sample Kuiper test to assess the anomaly of continuous, one-dimensional probability distributions. References used for this method are (1). Kuiper, N. H. (1960). <DOI:10.1016/S1385-7258(60)50006-0> and (2). Paltani, S. (2004). <DOI:10.1051/0004-6361:20034220>.
Computes measures of multivariate kurtosis, matrices of fourth-order moments and cumulants, kurtosis-based projection pursuit.
             Franceschini, C. and Loperfido, N. (2018, ISBN:978-3-319-73905-2). "An Algorithm for Finding Projections with Extreme Kurtosis".
             Loperfido, N. (2017,ISSN:0024-3795). "A New Kurtosis Matrix, with Statistical Applications".
Tools for data importation, recoding, and inspection.
    There are functions to create new project
    folders, R code templates, create uniquely named output
    directories, and to quickly obtain a visual summary for each
    variable in a data frame.  The main feature here is the systematic
    implementation of the "variable key" framework for data
    importation and recoding.  We are eager to have community feedback
    about the variable key and the vignette about it. In version 1.7,
    the function 'semTable' is removed. It was deprecated since 1.67.
    That is provided in a separate package, 'semTable'.
The format KVH is a lightweight format that can be read/written both by humans and machines.
   It can be useful in situations where XML or alike formats seem to be an overkill.
   We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.
Access business registration data from the Dutch Chamber of
    Commerce (Kamer van Koophandel, KvK) through their official API
    <https://developers.kvk.nl/>. Search for companies by name, location,
    or registration number. Retrieve detailed business profiles,
    establishment information, and company name histories. Built on
    'httr2' for robust API interaction with automatic pagination, error
    handling, and usage tracking.
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection).
    The algorithms are based on coordinate descent and local combinatorial search.
    For more details, check the paper by Hazimeh and Mazumder (2020) <doi:10.1287/opre.2019.1919>.
Trend filtering is a widely used nonparametric method for knot detection. This package provides an efficient solution for L0 trend filtering, avoiding the traditional methods of using Lagrange duality or Alternating Direction Method of Multipliers  algorithms. It employ a splicing approach that minimizes L0-regularized sparse approximation by transforming the L0 trend filtering problem. The package excels in both efficiency and accuracy of trend estimation and changepoint detection in segmented functions. References: Wen et al. (2020) <doi:10.18637/jss.v094.i04>; Zhu et al. (2020)<doi:10.1073/pnas.2014241117>; Wen et al. (2023) <doi:10.1287/ijoc.2021.0313>.
Provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.
Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2024a) <doi:10.48550/arXiv.2404.13233> and Kang and Oh (2024b) <doi:10.48550/arXiv.2408.12078>.
LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.
L1 estimation for linear regression using Barrodale and Roberts' method
    <doi:10.1145/355616.361024> and the EM algorithm <doi:10.1023/A:1020759012226>.
    Estimation of mean and covariance matrix using the multivariate Laplace distribution,
    density, distribution function, quantile function and random number generation
    for univariate and multivariate Laplace distribution <doi:10.1080/03610929808832115>.
    Implementation of Naik and Plungpongpun <doi:10.1007/0-8176-4487-3_7> for the 
    Generalized spatial median estimator is included.
Simplify the loading matrix in factor models using the l1 criterion as proposed in Freyaldenhoven (2025) <doi:10.21799/frbp.wp.2020.25>. Given a data matrix, find the rotation of the loading matrix with the smallest l1-norm and/or test for the presence of local factors with main function local_factors().
Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022).
Efficient implementation of Friedman's boosting algorithm with
    l2-loss function and coordinate direction (design matrix columns) basis
    functions.
Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.
An implementation of a computational framework for performing robust structured regression with the L2 criterion 
             from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and 
             Lange (2022+) added in Version 2.0.
Provides a method for detecting multiple change points in high-dimensional time
    series, targeting dense or spatially clustered signals. See Li et al. (2023) 
    "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". 
    arXiv preprint <arXiv:2208.13074>.
Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.
Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm 'lab2clean' as an open-source R-package. 'lab2clean' package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Version 1.0 of the algorithm is described in detail in 'Zayed et al. (2024)' <doi:10.1186/s12911-024-02652-7>.
Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design.
A variety of ordination and community analyses
   useful in analysis of data sets in community ecology.  
   Includes many of the common ordination methods, with 
   graphical routines to facilitate their interpretation, 
   as well as several novel analyses.
The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
Create custom labels, badges, certificates
  and other documents. Automate the production of potentially large 
  numbers of herbarium and collection labels, accreditation badges, 
  attendance and participation certificates, etc, and deliver them automatically.
  Documents are generated in PDF format, which requires a working 
  installation of 'LaTeX', such as 'TinyTeX'.
Functions which provide a range of axis labeling algorithms.
Work with labelled data imported from 'SPSS'
    or 'Stata' with 'haven' or 'foreign'. This package
    provides useful functions to deal with "haven_labelled" and
    "haven_labelled_spss" classes introduced by 'haven' package.
Assign meaningful labels to data frame columns.
    'labelmachine' manages your label assignment rules in 'yaml' files
    and makes it easy to use the same labels in multiple projects.
Create and use data frame labels for data frame objects (frame labels), their columns (name labels), and individual values of a column (value labels). Value labels include one-to-one and many-to-one labels for nominal and ordinal variables, as well as numerical range-based value labels for continuous variables. Convert value-labeled variables so each value is replaced by its corresponding value label. Add values-converted-to-labels columns to a value-labeled data frame while preserving parent columns. Filter and subset a value-labeled data frame using labels, while returning results in terms of values. Overlay labels in place of values in common R commands to increase interpretability. Generate tables of value frequencies, with categories expressed as raw values or as labels. Access data frames that show value-to-label mappings for easy reference.
Labels are a common construct in statistical software providing a 
  human readable description of a variable. While variable names are succinct,
  quick to type, and follow a language's naming conventions, labels may 
  be more illustrative and may use plain text and spaces. R does not provide
  native support for labels. Some packages, however, have made this feature
  available.  Most notably, the 'Hmisc' package provides labelling methods
  for a number of different object. Due to design decisions, these methods
  are not all exported, and so are unavailable for use in package development.
  The 'labelVector' package supports labels for atomic vectors in a light-weight
  design that is suitable for use in other packages.
Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time.
Provides functions for normalizing standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" (Netta Mendelson Cohen, Omer Schwartzman, Ram Jaschek, Aviezer Lifshitz, Michael Hoichman, Ran Balicer, Liran I. Shlush, Gabi Barbash & Amos Tanay, <doi:10.1038/s41591-021-01468-6>). Allows users to easily obtain normalized values for standard lab results, and to visualize their distributions. See more at <https://tanaylab.weizmann.ac.il/labs/>.
Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.
Dati, scripts e funzioni per il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale" (Datasets, scripts and functions to support the book "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale").
Simplex optimization algorithms as firstly proposed by Spendley
    et al. (1962) <doi:10.1080/00401706.1962.10490033> and later modified by 
    Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> for laboratory and 
    manufacturing processes. The package also provides tools for graphical 
    representation of the simplexes and some example response surfaces that 
    are useful in illustrating the optimization process.
Insieme di funzioni di supporto al volume "Laboratorio di
        Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006.
        This package contains sets of functions defined in "Laboratorio
        di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia,
        2006. Function names and docs are in italian as well.
Contains data sets to accompany the book: Lazic SE
    (2016). "Experimental Design for Laboratory Biologists: Maximising Information
    and Improving Reproducibility". Cambridge University Press.
Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details.
Connect to the 'Less Annoying CRM' API with ease to get your crm data in a clean and tidy format. 'Less Annoying CRM' is a simple CRM built for small businesses, more information is available on their website <https://www.lessannoyingcrm.com/>.
Set of tools for analyzing lactate thresholds from a step incremental test to exhaustion. Easily analyze
    the methods Log-log, Onset of Blood Lactate Accumulation (OBLA), Baseline plus (Bsln+), Dmax, Lactate Turning Point (LTP),
    and Lactate / Intensity ratio (LTratio) in cycling, running, or swimming.
    Beaver WL, Wasserman K, Whipp BJ (1985) <doi:10.1152/jappl.1985.59.6.1936>.
    Heck H, Mader A, Hess G, Mücke S, Müller R, Hollmann W (1985) <doi:10.1055/s-2008-1025824>.
    Kindermann W, Simon G, Keul J (1979) <doi:10.1007/BF00421101>.
    Skinner JS, Mclellan TH (1980) <doi:10.1080/02701367.1980.10609285>.
    Berg A, Jakob E, Lehmann M, Dickhuth HH, Huber G, Keul J (1990) PMID 2408033.
    Zoladz JA, Rademaker AC, Sargeant AJ (1995) <doi:10.1113/jphysiol.1995.sp020959>.
    Cheng B, Kuipers H, Snyder A, Keizer H, Jeukendrup A, Hesselink M (1992) <doi:10.1055/s-2007-1021309>.
    Bishop D, Jenkins DG, Mackinnon LT (1998) <doi:10.1097/00005768-199808000-00014>.
    Hughson RL, Weisiger KH, Swanson GD (1987) <doi:10.1152/jappl.1987.62.5.1975>.
    Jamnick NA, Botella J, Pyne DB, Bishop DJ (2018) <doi:10.1371/journal.pone.0199794>.
    Hofmann P, Tschakert G (2017) <doi:10.3389/fphys.2017.00337>.
    Hofmann P, Pokan R, von Duvillard SP, Seibert FJ, Zweiker R, Schmid P (1997) <doi:10.1097/00005768-199706000-00005>.
    Pokan R, Hofmann P, Von Duvillard SP, et al. (1997) <doi:10.1097/00005768-199708000-00009>.
    Dickhuth H-H, Yin L, Niess A, et al. (1999) <doi:10.1055/s-2007-971105>.
Functions for estimating the gliding box lacunarity (GBL),
    covariance, and pair-correlation of a random closed set (RACS) in 2D
    from a binary coverage map (e.g. presence-absence land cover maps).
    Contains a number of newly-developed covariance-based estimators of
    GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and
    balanced estimators, proposed by Picka (2000)
    <http://www.jstor.org/stable/1428408>, for covariance, centred
    covariance, and pair-correlation.  Also contains methods for
    estimating contagion-like properties of RACS and simulating 2D Boolean
    models.  Binary coverage maps are usually represented as raster images
    with pixel values of TRUE, FALSE or NA, with NA representing
    unobserved pixels.  A demo for extracting such a binary map from a
    geospatial data format is provided.  Binary maps may also be
    represented using polygonal sets as the foreground, however for most
    computations such maps are converted into raster images.  The package
    is based on research conducted during the author's PhD studies.
Calculates 3D lacunarity from voxel data. It is designed for use
    with point clouds generated from Light Detection And Ranging (LiDAR) scans
    in order to measure the spatial heterogeneity of 3-dimensional structures
    such as forest stands. It provides fast 'C++' functions to efficiently bin
    point cloud data into voxels and calculate lacunarity using different
    variants of the gliding-box algorithm originated by Allain & Cloitre (1991)
    <doi:10.1103/PhysRevA.44.3552>.
Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) <doi:10.1101/2022.10.28.513998>.
Create tables from within R directly on Google Slides
    presentations.  Currently supports matrix, data.frame and 'flextable'
    objects.
Set of tools for analyzing vertical fuel continuity at the tree level using Airborne Laser Scanning data. The workflow consisted of: 1) calculating the vertical height profiles of each segmented tree; 2) identifying gaps and fuel layers; 3) estimating the distance between fuel layers; and 4) retrieving the fuel layers base height and depth. Additionally, other functions recalculate previous metrics after considering distances greater than certain threshold. Moreover, the package calculates: i) the percentage of Leaf Area Density comprised in each fuel layer, ii) remove fuel layers with Leaf Area Density (LAD) percentage less than 10, and iii) recalculate the distances among the reminder ones. On the other hand, it identifies the crown base height (CBH) based on different criteria: the fuel layer with the highest LAD percentage and the fuel layers located at the largest- and at the last-distance. When there is only one fuel layer, it also identifies the CBH performing a segmented linear regression (breaking points) on the cumulative sum of LAD as a function of height. Finally, a collection of plotting functions is developed to represent: i) the initial gaps and fuel layers; ii) the fuels base height, depths and gaps with distances greater than certain threshold and, iii) the CBH based on different criteria. The methods implemented in this package are original and have not been published elsewhere.
Estimation of indicators on social exclusion and poverty, as well
    as Pareto tail modeling for empirical income distributions.
Methods for fast access to large ASCII files.  Currently the
    following file formats are supported: comma separated format (CSV) and fixed
    width format. It is assumed that the files are too large to fit into memory,
    although the package can also be used to efficiently access files that do
    fit into memory. Methods are provided to access and process files blockwise.
    Furthermore, an opened file can be accessed as one would an ordinary
    data.frame. The LaF vignette gives an overview of the functionality
    provided.
Provides classes and methods for objects, whose indexing
      naturally starts from zero. Subsetting, indexing and mathematical
      operations are defined naturally between lagged objects and lagged
      and base R objects. Recycling is not used, except for singletons.
      The single bracket operator doesn't drop dimensions by default.
Client for programmatic access to the Lake
    Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions
    for accessing lake water quality and ecological context data for the US.
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided.  For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.
Model-based linear model trees adjusting for spatial correlation using a
             simultaneous autoregressive spatial lag, Wagner and Zeileis (2019)
	     <doi:10.1111/geer.12146>.
Lag-sequential analysis is a method of assessing of patterns (what tends to 
        follow what?) in sequences of codes. The codes are typically for discrete 
        behaviors or states. The functions in this package read a stream of codes,
        or a frequency transition matrix, and produce a variety of lag sequential  
        statistics, including transitional frequencies, expected transitional frequencies, 
        transitional probabilities, z values, adjusted residuals, Yule's Q values, 
        likelihood ratio tests of stationarity across time and homogeneity across groups 
        or segments, transformed kappas for unidirectional dependence, bidirectional 
        dependence, parallel and nonparallel dominance, and significance levels based on 
        both parametric and randomization tests. The methods are described in
  Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>,
  O'Connor (1999) <doi:10.3758/BF03200753>,
  Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and
  Wampold (1995, ISBN:0-89391-919-5).
Provides the tables from the 'Sean Lahman Baseball Database' as
    a set of R data.frames. It uses the data on pitching, hitting and fielding
    performance and other tables from 1871 through 2023, as recorded in the 2024
    version of the database. Documentation examples show how many baseball
    questions can be investigated.
Convert Leaf Area Index (LAI) from the Normalized Difference Vegetation Index (NDVI) using available equations from literature. 
    Detailed description of conversion equations in Bajocco et al. 2022 <doi:10.3390/rs14153554>.
A collection of tools for the calculation of freewater metabolism
    from in situ time series of dissolved oxygen, water temperature, and,
    optionally, additional environmental variables. LakeMetabolizer implements
    5 different metabolism models with diverse statistical underpinnings:
    bookkeeping, ordinary least squares, maximum likelihood, Kalman filter,
    and Bayesian. Each of these 5 metabolism models can be combined with
    1 of 7 models for computing the coefficient of gas exchange across the
    air–water interface (k). LakeMetabolizer also features a variety of
    supporting functions that compute conversions and implement calculations
    commonly applied to raw data prior to estimating metabolism (e.g., oxygen
    saturation and optical conversion models).
Lake morphometry metrics are used by limnologists to understand,
    among other things, the ecological processes in a lake. Traditionally, these
    metrics are calculated by hand, with planimeters, and increasingly with
    commercial GIS products. All of these methods work; however, they are either
    outdated, difficult to reproduce, or require expensive licenses to use. The
    'lakemorpho' package provides the tools to calculate a typical suite
    of these metrics from an input elevation model and lake polygon. The metrics
    currently supported are: fetch, major axis, minor axis, major/minor axis 
    ratio, maximum length, maximum width, mean width, maximum depth, mean depth, 
    shoreline development, shoreline length, surface area, and volume.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) <https://volweb.utk.edu/~scolli46/sceLakhesis.pdf>.
Includes some procedures for latent variable modeling with a 
    particular focus on multilevel data.
    The 'LAM' package contains mean and covariance structure modelling
    for multivariate normally distributed data (mlnormal(); Longford, 1987;
    <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm 
    (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and 
    penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 
    2014; <doi:10.1093/aje/kwt245>).
A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, 
  state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. 
  Applied researchers often need custom models that standard software does not easily support. 
  Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. 
  We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. 
  These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. 
  To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'.
Runtime for serving containers that can execute R code on the 
    'AWS Lambda' serverless compute service <https://aws.amazon.com/lambda/>.
    Provides the necessary functionality for handling the various endpoints
    required for accepting new input and sending responses.
Lambert W x F distributions are a generalized framework to analyze
    skewed, heavy-tailed data. It is based on an input/output system, where the
    output random variable (RV) Y is a non-linearly transformed version of an input
    RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
    The transformed RV Y has a Lambert W x F distribution. This package contains
    functions to model and analyze skewed, heavy-tailed data the Lambert Way:
    simulate random samples, estimate parameters, compute quantiles, and plot/
    print results nicely. The most useful function is 'Gaussianize',
    which works similarly to 'scale', but actually makes the data Gaussian.
    A do-it-yourself toolkit allows users to define their own Lambert W x
    'MyFavoriteDistribution' and use it in their analysis right away.
Interact with 'LaminDB'. 'LaminDB' is an open-source data
    framework for biology. This package allows you to query and download
    data from 'LaminDB' instances.
Approximate marginal maximum likelihood estimation of multidimensional
	latent variable models via adaptive quadrature or Laplace approximations to the 
	integrals in the likelihood function, as presented for confirmatory factor 
	analysis models in Jin, S., Noh, M., and Lee, Y. (2018) 
	<doi:10.1080/10705511.2017.1403287>, for item response theory models in 
	Andersson, B., and Xin, T. (2021) <doi:10.3102/1076998620945199>, and for 
	generalized linear latent variable models in Andersson, B., Jin, S., and 
	Zhang, M. (2023) <doi:10.1016/j.csda.2023.107710>. Models implemented include
	the generalized	partial credit model, the graded response model, and generalized 
	linear latent variable models for Poisson, negative-binomial and normal 
	distributions. Supports a combination of binary, ordinal, count and continuous 
	observed variables and multiple	group models.
Log-analytic methods intended for testing multiplicative effects.
Implements both real-valued branches of the Lambert-W function
    (Corless et al, 1996) <doi:10.1007/BF02124750> without the need for
    installing the entire GSL.
Implementation of the methods described in Holzmann, Klar (2024) 
    <doi:10.48550/arXiv.2303.17872>. Lancaster correlation is a correlation coefficient 
    which equals the absolute value of the Pearson correlation for the bivariate 
    normal distribution, and is equal to or slightly less than the maximum 
    correlation coefficient for a variety of bivariate distributions. Rank and 
    moment-based estimators and corresponding confidence intervals are 
    implemented, as well as independence tests based on these statistics.
Changes of landscape diversity and structure can be detected
    soon if relying on landscape class combinations and analysing patterns
    at multiple scales. 'LandComp' provides such an opportunity, based on
    Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983
    <doi:10.1007/BF00129432>). Functions can handle multilayered data.
    Requirements of the input: binary data contained by a regular square
    or hexagonal grid, and the grid should have projected coordinates.
Provides functions to estimate survival and a treatment effect using a landmark estimation approach.
Provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types.
The landmark approach allows survival predictions to be
	updated dynamically as new measurements from an individual are recorded.
	The idea is to set predefined time points, known as "landmark times",
	and form a model at each landmark time using only the individuals in the
	risk set. This package allows the longitudinal data to be modelled
	either using the last observation carried forward or linear mixed
	effects modelling. There is also the option to model competing risks,
	either through cause-specific Cox regression or Fine-Gray regression.
	To find out more about the methods in this package, please see 
	<https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.
Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.
Contains functions for a flexible varying-coefficient landmark model by incorporating multiple short-term events into the prediction of long-term survival probability. For more information about landmark prediction please see Li, W., Ning, J., Zhang, J., Li, Z., Savitz, S.I., Tahanan, A., Rahbar.M.H., (2023+). "Enhancing Long-term Survival Prediction with Multiple Short-term Events: Landmarking with A Flexible Varying Coefficient Model".
Provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.
Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. The original package description was published as Goslee (2011) <doi:10.18637/jss.v043.i04>, and details of the topographic corrections in Goslee (2012) <doi:10.14358/PERS.78.9.973>.
Calculates landscape metrics for categorical landscape patterns in 
    a tidy workflow. 'landscapemetrics' reimplements the most common metrics from
    'FRAGSTATS' (<https://www.fragstats.org/>) and new ones from the current 
    literature on landscape metrics. This package supports 'terra' SpatRaster objects 
    as input arguments. It further provides utility functions to visualize patches, 
    select metrics and building blocks to develop new metrics.
Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.
A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution 
    of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. 
    It is based on a spatial geometry for describing the landscape and allocation of different cultivars, 
    a dispersal kernel for the dissemination of the pathogen, and a SEIR 
    ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step.
    It provides a useful tool to assess the performance of a wide range of deployment options with 
    respect to their epidemiological, evolutionary and economic outcomes.
    Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, 
    Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
Estimate drift and diffusion functions from time series and
    generate synthetic time series from given drift and diffusion coefficients.
An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Points can be brushed, and the selection can be linked to other widgets using crosstalk. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained R Markdown or Quarto document or presentation, or used in a Shiny app.
Improve your text analysis with languagelayer
        <https://languagelayer.com>, a powerful language detection
        API.
Data sets exemplifying statistical methods, and some
        facilitatory utility functions used in ``Analyzing Linguistic
        Data: A practical introduction to statistics using R'',
        Cambridge University Press, 2008.
An implementation of the Language Server Protocol
    for R. The Language Server protocol is used by an editor client to
    integrate features like auto completion. See
    <https://microsoft.github.io/language-server-protocol/> for details.
Allows to install the R 'languageserver' with all dependencies
    into a separate library and use that independent installation
    automatically when R is instantiated as a language server process.
    Useful for making language server seamless to use without
    running into package version conflicts.
R interface for working with nanometer scale secondary ion mass 
    spectrometry (NanoSIMS) data exported from Look at NanoSIMS.
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Auxiliary package for better/faster analytics, visualization, data mining, and machine
    learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling,
    Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or 
    data scientist to get quick and robust results, without the need of repetitive coding or 
    advanced R programming skills.
Useful 'shiny' production-ready modules and helpers such as login window and visualization tools.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
Conducts a cointegration test for high-dimensional vector autoregressions (VARs) of order k based on the large N,T asymptotics of Bykhovskaya and Gorin, 2022 (<doi:10.48550/arXiv.2202.07150>). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy-1 point process. This package contains simulated quantiles of the first ten partial sums of the Airy-1 point process that are precise up to the first three digits.
Compute lifetime attributable risk of 
    radiation-induced cancer reveals that it can be helpful with 
    enhancement of the flexibility in research with fast calculation 
    and various options. Important reference papers include
    Berrington de Gonzalez et al. (2012) <doi:10.1088/0952-4746/32/3/205>,
    National Research Council (2006, ISBN:978-0-309-09156-5).
Efficient procedures for fitting an entire lasso
		sequence with the cost of a single least squares
		fit. Least angle regression and infinitesimal forward
		stagewise regression are related to the lasso, as
		described in the paper below.
Implementation of the algorithm introduced in Shah, R. D.
    (2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>.
    Data with thousands of predictors can be handled. The algorithm
    performs sequential Lasso fits on design matrices containing
    increasing sets of candidate interactions. Previous fits are used to greatly
    speed up subsequent fits, so the algorithm is very efficient.
Fits generalized estimating equations with L1 regularization to longitudinal data with high dimensional covariates. Use a efficient iterative composite gradient descent algorithm.
Contains functions to estimate a penalized regression model using 3CoSE algorithm, see Weber, Striaukas, Schumacher Binder (2018) <doi:10.2139/ssrn.3211163>.
Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values.
Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2019) <doi:10.1080/01621459.2018.1520115>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.
Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) <doi:10.31234/osf.io/dy79z>.
A graph
  proposed by Rosenbaum is useful
  for checking some properties of various
  sorts of latent scale, this program generates commands
  to obtain the graph using 'dot' from 'graphviz'.
Estimating causal parameters in the presence of treatment spillover is of great interest in statistics.
    This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form.
    The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect.
    The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.
Effectively simulates the discretization process inherent to
    Likert scales while minimizing distortion.  It converts continuous
    latent variables into ordinal categories to generate Likert scale item
    responses.  Particularly useful for accurately modeling and analyzing
    survey data that use Likert scales, especially when applying
    statistical techniques that require metric data.
Bayesian model averaging (BMA) algorithms for univariate link latent Gaussian models (ULLGMs). For detailed information, refer to Steel M.F.J. & Zens G. (2024) "Model Uncertainty in Latent Gaussian Models with Univariate Link Function" <doi:10.48550/arXiv.2406.17318>. The package supports various g-priors and a beta-binomial prior on the model space. It also includes auxiliary functions for visualizing and tabulating BMA results. Currently, it offers an out-of-the-box solution for model averaging of Poisson log-normal (PLN) and binomial logistic-normal (BiL) models. The codebase is designed to be easily extendable to other likelihoods, priors, and link functions.
The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated),
             comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation.
             The estimation is based on latent copula Gaussian models.
             For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017).
             For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>.
             For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>.
             For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.
Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Three methods are provided to estimate graphical models with latent variables: (1) Jin, Y., Ning, Y., and Tan, K. M. (2020) (preprint available); (2) Chandrasekaran, V., Parrilo, P. A. & Willsky, A. S. (2012) <doi:10.1214/11-AOS949>; (3) Tan, K. M., Ning, Y., Witten, D. M. & Liu, H. (2016) <doi:10.1093/biomet/asw050>.
Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.
Executes arbitrary R or C functions some time after the current
    time, after the R execution stack has emptied. The functions are scheduled
    in an event loop.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Implements the Linear Approach to Threshold with Ergodic Rate 
    (LATER) model, which predicts distributions of reaction times and 
    summarises them with as little as two free parameters. Allows for easy 
    visualisation and comparison of datasets, along with fitting of datasets 
    using the LATER model.
Parses and converts LaTeX math formulas to R's plotmath
    expressions, used to enter mathematical formulas and symbols to be rendered as
    text, axis labels, etc. throughout R's plotting system.
Produces a PDF diff of two 'rmarkdown', 'quarto', Sweave or TeX 
  files, using the external 'latexdiff' utility.
Converts table-like objects to stand-alone PDF or PNG.
 Can be used to embed tables and arbitrary content in PDF or Word
 documents. Provides a low-level R interface for creating 'LaTeX'
 code, e.g. command() and a high-level interface for creating PDF
 documents, e.g. as.pdf.data.frame(). Extensive customization is
 available via mid-level functions, e.g. as.tabular(). See also 
 'package?latexpdf'. Support for PNG is experimental; see
 'as.png.data.frame'. Adapted from 'metrumrg' 
 <https://r-forge.r-project.org/R/?group_id=1215>.
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
Build complex 'LaTeX' mathematical expressions using intuitive 'R' functions. Replace error-prone 'LaTeX' syntax with readable, modular functions that make mathematical typesetting straightforward and maintainable.
A framework for clustering longitudinal datasets in a standardized way. 
    The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. 
    Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. 
    The interface enables users to compare results between methods, and to implement and evaluate new methods with ease.
    The 'akmedoids' package is available from <https://github.com/MAnalytics/akmedoids>.
Back-end connections to 'LattE' (<https://www.math.ucdavis.edu/~latte/>) 
	for counting lattice points and integration inside convex polytopes and 
	'4ti2' (<http://www.4ti2.de/>) for algebraic, geometric, and combinatorial 
	problems on linear spaces and front-end tools facilitating their use in the 
	'R' ecosystem.
A powerful and elegant high-level data visualization
  system inspired by Trellis graphics, with an emphasis on
  multivariate data. Lattice is sufficient for typical graphics needs,
  and is also flexible enough to handle most nonstandard requirements.
  See ?Lattice for an introduction.
Lattice-based space-filling designs with fill or separation distance properties including 
	interleaved lattice-based minimax distance designs proposed in Xu He (2017) 
	<doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed in 
	Xu He (2018) <doi:10.1093/biomet/asy069>, interleaved lattice-based designs with low fill and 
	high separation distance properties proposed in Xu He (2024) <doi:10.1137/23M156940X>, 
	rotated sphere packing designs proposed in Xu He (2017) 
	<doi:10.1080/01621459.2016.1222289>, sliced rotated sphere packing designs proposed in 
	Xu He (2019) <doi:10.1080/00401706.2018.1458655>, 
	and densest packing-based maximum projections designs proposed in Xu He (2021) 
	<doi:10.1093/biomet/asaa057> and Xu He (2018) <doi:10.48550/arXiv.1709.02062>.
Building on the infrastructure provided by the lattice
	     package, this package provides several new high-level
	     functions and methods, as well as additional utilities
	     such as panel and axis annotation functions.
A general implementation of Structural Equation Models
	with latent variables (MLE, 2SLS, and composite likelihood
	estimators) with both continuous, censored, and ordinal
	outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
	Mixture latent variable models and non-linear latent variable models
	(Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>).
	The package also provides methods for graph exploration (d-separation,
	back-door criterion), simulation of general non-linear latent variable
	models, and estimation of influence functions for a broad range of
	statistical models.
Fit a variety of latent variable models, including confirmatory
   factor analysis, structural equation modeling and latent growth curve models.
The primary purpose of 'lavaan.mi' is to extend the functionality 
     of the R package 'lavaan', which implements structural equation modeling
     (SEM).  When incomplete data have been multiply imputed, the imputed data
     sets can be analyzed by 'lavaan' using complete-data estimation methods,
     but results must be pooled across imputations (Rubin, 1987, <doi:10.1002/9780470316696>).
     The 'lavaan.mi' package automates the pooling of point and standard-error
     estimates, as well as a variety of test statistics, using a familiar interface
     that allows users to fit an SEM to multiple imputations as they would to a
     single data set using the 'lavaan' package.
Helpers for customizing selected outputs from
  'lavaan' by Rosseel (2012) <doi:10.18637/jss.v048.i02>
  and print them. The functions are intended to be used
  by package developers in their packages and so are not
  designed to be user-friendly. They are designed to be
  let developers customize the tables by other
  functions. Currently the parameter estimates tables of a
  fitted object are supported.
Interactive shiny application for working with different kinds of
    latent variable analysis, with the 'lavaan' package. Graphical output for models
    are provided and different estimators are supported.
Affords an alternative, vector-based syntax to 'lavaan', as well as other 
             convenience functions such as naming paths and defining indirect
             links automatically, in addition to convenience formatting optimized
             for a publication and script sharing workflow.
Provides a graphical user interface with an 
    integrated diagrammer for latent variables from the 'lavaan' package. 
    It offers two core functions:  first, lavaangui() launches a web application
    that allows users to specify models by drawing path diagrams, fitting them, 
    assessing model fit, and more; second, plot_lavaan() creates 
    interactive path diagrams from models specified in 'lavaan'. 
    Karch (2024) <doi: 10.31234/osf.io/f4ary> contains a tutorial.
Plots path diagrams from models in 'lavaan' using the plotting
    functionality from the 'DiagrammeR' package. 'DiagrammeR' provides nice path diagrams 
    via 'Graphviz', and these functions make it easy to generate these diagrams from a
    'lavaan' path model without having to write the DOT language graph specification.
Estimation of a multi-group count regression models (i.e., Poisson, 
    negative binomial) with latent covariates. This packages provides two extensions
    compared to ordinary count regression models based on a generalized linear model:
    First, measurement models for the predictors can be specified allowing to account 
    for measurement error. Second, the count regression can be simultaneously estimated 
    in multiple groups with stochastic group weights. The marginal maximum likelihood 
    estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.
Tools for model specification in the latent variable framework
    (add-on to the 'lava' package). The package contains three main functionalities:
    Wald tests/F-tests with improved control of the type 1 error in small samples,
    adjustment for multiple comparisons when searching for local dependencies,
    and adjustment for multiple comparisons when doing inference for multiple latent variable models.
A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.
Performs adjusted inferences based on model objects fitted, using 
    maximum likelihood estimation, by the extreme value analysis packages
    'eva' <https://cran.r-project.org/package=eva>, 
    'evd' <https://cran.r-project.org/package=evd>, 
    'evir' <https://cran.r-project.org/package=evir>, 
    'extRemes' <https://cran.r-project.org/package=extRemes>, 
    'fExtremes' <https://cran.r-project.org/package=fExtremes>, 
    'ismev' <https://cran.r-project.org/package=ismev>, 
    'mev' <https://cran.r-project.org/package=mev>, 
    'POT' <https://cran.r-project.org/package=POT> and
    'texmex' <https://cran.r-project.org/package=texmex>. 
    Adjusted standard errors and an adjusted loglikelihood are provided, using    
    the 'chandwich' package <https://cran.r-project.org/package=chandwich>
    and the object-oriented features of the 'sandwich' package 
    <https://cran.r-project.org/package=sandwich>. The adjustment is based on a 
    robust sandwich estimator of the parameter covariance matrix, based on the 
    methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This 
    can be used for cluster correlated data when interest lies in the 
    parameters of the marginal distributions, or for performing inferences that 
    are robust to certain types of model misspecification.  Univariate extreme 
    value models, including regression models, are supported.
Creating efficiently new column(s) in a data frame (including tibble) by applying a function one row at a time.
Simplifies the whole process of creating stacked tilted maps,
    that are often used in scientific publications to show different
    environmental layers for a geographical region. Tilting maps and
    layering them allows to easily draw visual correlations between these
    environmental layers.
By combining constant, linear, and quadratic local models,
        lazy estimates the value of an unknown multivariate function on
        the basis of a set of possibly noisy samples of the function
        itself.  This implementation of lazy learning automatically
        adjusts the bandwidth on a query-by-query basis through a
        leave-one-out cross-validation.
Multi-threaded serialization of compressed array that 
    fully utilizes modern solid state drives. It allows 
    to store and load extremely large data on demand within seconds
    without occupying too much memories. With data stored on hard drive, 
    a lazy-array data can be loaded, shared across multiple R sessions.
    For arrays with partition mode on, multiple R sessions can write to 
    a same array simultaneously along the last dimension (partition). 
    The internal storage format is provided by 'fstcore' package geared by 
    'LZ4' and 'ZSTD' compressors.
A simple progress bar showing estimated remaining time. 
    Multiple forecast methods and user defined forecast method for 
    the remaining time are supported.
Supplies a LazyData facility for packages which have data
		sets but do not provide LazyData: true.  A single function is
		is included, requireData, which is a drop-in replacement for
		base::require, but carrying the additional
		functionality. By default, it suppresses package
		startup messages as well.  See argument 'reallyQuitely'.
An alternative approach to non-standard evaluation using
    formulas. Provides a full implementation of LISP style 'quasiquotation',
    making it easier to generate code with other code.
Lazy read for drawings. A 'dplyr' back end for data sources supported by 
    'GDAL' vector drivers, that allows working with local or remote sources as if they 
    are in-memory data frames. Basic features works with any drawing format ('GDAL vector 
    data source') supported by the 'sf' package.
Helper functions to build SQL statements
    for dbGetQuery or dbSendQuery under program control.
    They are intended to increase speed of coding and
    to reduce coding errors. Arguments are carefully checked,
    in particular SQL identifiers such as names of tables or columns.
    More patterns will be added as required.
Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading.
    Main goal is to process information within "Decision Support System" to come up with analysis or predictions.
    There are several utilities such as dynamic and adaptive risk management using reinforcement learning
    and even functions to generate predictions of price changes using pattern recognition deep regression learning.
    Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, 
    Market Type research of Van Tharp Institute: <https://vantharp.com/>,
    Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.
Provides the functionality to write LaTeX code from within R
    without having to learn LaTeX.  Functionality also exists to create HTML
    and Markdown code.  While the functionality still exists to write
    complete documents with lazyWeave, it is generally easier to do so with
    with markdown and knitr.  lazyWeave's main strength now is the ability
    to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.
A suite of tools for literature-based discovery in biomedical research. 
    Provides functions for retrieving scientific articles from 'PubMed' and 
    other NCBI databases, extracting biomedical entities (diseases, drugs, genes, etc.), 
    building co-occurrence networks, and applying various discovery models 
    including 'ABC', 'AnC', 'LSI', and 'BITOLA'. The package also includes 
    visualization tools for exploring discovered connections.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0
        (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>)
        limited memory BFGS minimizer with bounds on parameters.
        This is a fork of 'lbfgsb3'.
	This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same
	function types and optimization as the optim() function (see writing 'R' extensions
        and source for details).  This package also adds more stopping criteria as well
        as allowing the adjustment of more tolerances.
Maximum likelihood estimation and likelihood ratio test are essential for modern statistics. This package supports in calculating likelihood based inference.
             Reference: Pawitan Y. (2001, ISBN:0-19-850765-8).
Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of 
    LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on 
    the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize 
    the model without losing any information, and fits the model on the standard fitting algorithm 
    with no convergence issues.
Implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.
The Length-Biased Power Garima distribution for computes the probability density,
    the cumulative density distribution and the quantile function of the distribution, 
    and generates sample values with random variables based on Kittipong and Sirinapa(2021)<DOI: 10.14456/sjst-psu.2021.89>.
Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.
Simulate expected equilibrium length composition, yield-per-recruit, and
    the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR
    model to length data to estimate  selectivity, relative apical fishing mortality, and
    the spawning potential ratio for data-limited fisheries.
    See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the
    LBSPR assessment method.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
Latent Class Analysis of
        phenotypic measurements in pedigrees and model selection
        based on one of two methods: likelihood-based cross-validation
        and Bayesian Information Criterion. Computation of individual
        and triplet child-parents weights in a pedigree is performed using an
        upward-downward algorithm. The model takes into account the familial
        dependence defined by the pedigree structure by considering
        that a class of a child depends on his parents classes via
        triplet-transition probabilities of the classes. The package
        handles the case where measurements are available on all
        subjects and the case where measurements are available only on
        symptomatic (i.e. affected) subjects. Distributions for
        discrete (or ordinal) and continuous data are currently
        implemented. The package can deal with missing data.
Provides Shiny widgets and theme that support a 'Library Computer Access/Retrieval System' (LCARS) aesthetic for Shiny apps. 
    The package also includes functions for adding a minimal LCARS theme to static 'ggplot2' graphs. 
    More details about LCARS can be found at <https://en.wikipedia.org/wiki/LCARS>.
Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) <doi:10.1007/s13253-018-0321-1>.  In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation based on Fisher Z-transformation.
Estimation of latent class models with individual covariates for capture-recapture data. See Bartolucci, F. and Forcina, A. (2022), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80(2), ujae017.
Providing a method for Local Discrimination via Latent Class Models. The approach is described in <https://www.r-project.org/conferences/useR-2009/abstracts/pdf/Bucker.pdf>.
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.
A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.
Bayesian population size estimation using non parametric latent-class models.
Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
The goal of 'LCMSQA' is to make it easy to check the quality of
             liquid chromatograph/mass spectrometry (LC/MS) experiments using a
             'shiny' application. This package provides interactive data
             visualizations for quality control (QC) samples, including total
             ion current chromatogram (TIC), base peak chromatogram (BPC), mass
             spectrum, extracted ion chromatogram (XIC), and feature detection
             results from internal standards or known metabolites.
Provides functions to upload vectorial data and derive landscape
    connectivity metrics in habitat or matrix systems. Additionally, includes an 
    approach to assess individual patch contribution to the overall landscape 
    connectivity, enabling the prioritization of habitat patches. The computation
    of landscape connectivity and patch importance are very useful in Landscape 
    Ecology research. The metrics available are: number of components, number of 
    links, size of the largest component, mean size of components, class coincidence
    probability, landscape coincidence probability, characteristic path length, 
    expected cluster size, area-weighted flux and integral index of connectivity.
    Pascual-Hortal, L., and Saura, S. (2006) <doi:10.1007/s10980-006-0013-z>
    Urban, D., and Keitt, T. (2001) <doi:10.2307/2679983>
    Laita, A., Kotiaho, J., Monkkonen, M. (2011) <doi:10.1007/s10980-011-9620-4>.
Collections of functions allowing random number generations and
    estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) <doi:10.1016/j.jmva.2017.05.008>.
An implementation of the Log Cumulative Probability Model (LCPM) 
  and Proportional Probability Model (PPM) for which the Maximum Likelihood Estimates are determined using constrained optimization. 
  This implementation accounts for the implicit constraints on the parameter space. Other 
  features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization.
For fitting Bayesian joint latent class and regression models using
    Gibbs sampling. See the documentation for the model.
    The technical details of the model implemented here are described in Elliott,
    Michael R., Zhao, Zhangchen, Mukherjee, Bhramar, Kanaya, Alka, Needham,
    Belinda L., "Methods to account for uncertainty in latent class assignments when
    using latent classes as predictors in regression models, with application to
    acculturation strategy measures" (2020) In press at Epidemiology
    <doi:10.1097/EDE.0000000000001139>.
Helper functions to implement univariate and bivariate latent change score models in R using the 'lavaan' package.
  For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>.
  The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints.
  The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually.
  Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications.
  Estimated model parameters and fit statistics can be extracted as data frames.
  Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects.
  This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together.
Provides researchers and educators with easy-to-learn user friendly tools for calculating 
    key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. 
    These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, 
    Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation 
    (Global and Local Moran's I), several Geographically Weighted Regression techniques and other 
    Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for 
    measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Analysis of stock data ups and downs trend, the stock technical analysis indicators function have trend line, reversal pattern and market trend.
Implements latent Dirichlet allocation (LDA)
	     and related models.  This includes (but is not limited
	     to) sLDA, corrLDA, and the mixed-membership stochastic
	     blockmodel.  Inference for all of these models is
	     implemented via a fast collapsed Gibbs sampler written
	     in C.  Utility functions for reading/writing data
	     typically used in topic models, as well as tools for
	     examining posterior distributions are also included.
Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). LDA calculates the pairwise linkage disequilibrium of ancestry between single nucleotide polymorphisms (SNPs). LDAS calculates the LDA score of SNPs. The methods are described in Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.
Cellular cooperation compromises the established method of
	calculating clonogenic activity from limiting dilution assay (LDA) data.
	This tool provides functions that enable robust analysis in presence or
	absence of cellular cooperation. The implemented method incorporates the
	same cooperativity module to model the non-linearity associated with
	cellular cooperation as known from the colony formation assay (Brix et al.
	(2021) <doi:10.1038/s41596-021-00615-0>: "Analysis of clonogenic growth in
	vitro." Nature protocols).
Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.
Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.
A classification tree method that uses Uncorrelated Linear Discriminant Analysis (ULDA) for variable selection, split determination, and model fitting in terminal nodes.  It automatically handles missing values and offers visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2410.23147>.
Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.
Tools to create an interactive web-based visualization of a
    topic model that has been fit to a corpus of text data using
    Latent Dirichlet Allocation (LDA). Given the estimated parameters of
    the topic model, it computes various summary statistics as input to
    an interactive visualization built with D3.js that is accessed via
    a browser. The goal is to help users interpret the topics in their
    LDA topic model.
Flexible procedures to compute local density-based outlier scores for ranking outliers.
  Both exact and approximate nearest neighbor search can be implemented, while also accommodating
  multiple neighborhood sizes and four different local density-based methods. It allows for
  referencing a random subsample of the input data or a user specified reference data set
  to compute outlier scores against, so both unsupervised and semi-supervised outlier
  detection can be implemented.
Computations related to group sequential boundaries.
    Includes calculation of bounds using the Lan-DeMets
    alpha spending function approach. Based on FORTRAN
        program ld98 implemented by Reboussin, et al. (2000) <doi:10.1016/s0197-2456(00)00057-x>.
Four measures of linkage disequilibrium are provided: the usual r^2
        measure, the r^2_S measure (r^2 corrected by the structure
        sample), the r^2_V (r^2 corrected by the relatedness of
        genotyped individuals), the r^2_VS measure (r^2 corrected by
        both the relatedness of genotyped individuals and the structure
        of the sample).
Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.
Hidden Markov Model (HMM) based on symmetric lambda distribution
    framework is implemented for the study of return time-series in the financial
    market. Major features in the S&P500 index, such as regime identification,
    volatility clustering, and anti-correlation between return and volatility,
    can be extracted from HMM cleanly. Univariate symmetric lambda distribution
    is essentially a location-scale family of exponential power distribution.
    Such distribution is suitable for describing highly leptokurtic time series
    obtained from the financial market. It provides a theoretically solid foundation
    to explore such data where the normal distribution is not adequate. The HMM
    implementation follows closely the book: "Hidden Markov Models for Time Series",
    by Zucchini, MacDonald, Langrock (2016).
Provides access to the 'LDlink' API (<https://ldlink.nih.gov/?tab=apiaccess>)
    using the R console.  This programmatic access facilitates researchers who are 
    interested in performing batch queries in 1000 Genomes Project (2015) <doi:10.1038/nature15393> 
    data using 'LDlink'. 'LDlink' is an interactive and powerful suite of web-based tools for querying 
    germline variants in human population groups of interest. For more details, please see 
    Machiela et al. (2015) <doi:10.1093/bioinformatics/btv402>.
A single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. It accommodates both continuous and discrete covariates as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for sample correlations. It can be applied to transformed data, and an omnibus test can combine results from analyses conducted on different transformation scales. It can also be used for testing presence-absence associations based on infinite number of rarefaction replicates, testing mediation effects of the microbiome, analyzing censored time-to-event outcomes, and for compositional analysis by fitting linear models to centered-log-ratio taxa count data.
A suite of tools for estimating, assessing model fit, simulating from, and visualizing location dependent marked point processes characterized by regularity in the pattern.
    You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model.
    'ldmppr' estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools.
    Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results.
    Documentation for the package 'ldmppr' is available in the form of a vignette.
This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces <doi:10.1007/978-81-322-3643-6_7>.
Estimate haplotypic or composite pairwise linkage disequilibrium
    (LD) in polyploids, using either genotypes or genotype likelihoods. 
    Support is provided to estimate the popular measures of LD: the LD 
    coefficient D, the standardized LD coefficient D', and the Pearson 
    correlation coefficient r. All estimates are returned with corresponding 
    standard errors. These estimates and standard errors can then be used
    for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(),
    sldest(), plot.lddf(), format_lddf(), and ldshrink(). Details of the methods
    are available in Gerard (2021a) <doi:10.1111/1755-0998.13349>
    and Gerard (2021b) <doi:10.1038/s41437-021-00462-5>.
Streamflow (and climate) reconstruction using Linear Dynamical Systems. 
    The advantage of this method is the additional state trajectory which can reveal more information 
    about the catchment or climate system. For details of the method please refer to Nguyen and Galelli 
    (2018) <doi:10.1002/2017WR022114>.
Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others). 
             'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented.
             Evaluations are both in-sample and out-of-sample. 
             It is designed to be efficient in terms of CPU usage and memory consumption.
The leader clustering algorithm provides
 a means for clustering a set of data points. Unlike many other clustering
 algorithms it does not require the user to specify the number of clusters,
 but instead requires the approximate radius of a cluster as its primary
 tuning parameter. The package provides a fast implementation of this
 algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
 and infinity) as well as for spatial data using the Haversine
 formula, which takes latitude/longitude pairs as inputs and clusters
 based on great circle distances.
Extracts and creates an analysis pipeline for the JSON data files from Brain Sense sessions using Medtronic's Deep Brain Stimulation surgery electrode implants.
An interface for the image processing program 'ImageJ', which
    allows a rapid digital image analysis for particle sizes. This package includes
    function to write an 'ImageJ' macro which is optimized for a leaf area analysis by
    default.
Provides drill down functionality for 'leaflet' choropleths in 'shiny' apps.
Provides extensions for packages 'leaflet' & 'mapdeck', 
    many of which are used by package 'mapview'. 
    Focus is on functionality readily available in 
    Geographic Information Systems such as 'Quantum GIS'. Includes functions
    to display coordinates of mouse pointer position, query image values via 
    mouse pointer and zoom-to-layer buttons. Additionally, provides a feature 
    type agnostic function to add points, lines, polygons to a map.
Provides bindings to the 'Leaflet.glify' JavaScript library which extends the 'leaflet' JavaScript library to render large data in the browser using 'WebGl'.
Provides extensions to the 'leaflet' package to 
    customize legends with images, text styling, orientation, sizing,
    and symbology and functions to create symbols to plot on maps.
Create and customize interactive maps using the 'Leaflet'
    JavaScript library and the 'htmlwidgets' package. These maps can be
    used directly from the R console, from 'RStudio', in Shiny
    applications and R Markdown documents.
The 'leaflet' JavaScript library provides many plugins some of which
    are available in the core 'leaflet' package, but there are many more. It is not
    possible to support them all in the core 'leaflet' package. This package serves
    as an add-on to the 'leaflet' package by providing extra functionality via 'leaflet'
    plugins.
Several 'leaflet' plugins are integrated, which are available as extension to the 'leaflet' package.
Add and modify small charts on an interactive map created with 
    package 'leaflet'. These charts can be used to represent at same time multiple 
    variables on a single map.
Contains third-party map tile provider information from
    'Leaflet.js', <https://github.com/leaflet-extras/leaflet-providers>,
    to be used with the 'leaflet' R package. Additionally,
    'leaflet.providers' enables users to retrieve up-to-date provider
    information between package updates.
Provides 'sf' data for Chinese provinces and cities,
  methods for plotting shape maps of Chinese provinces and cities,
  Convert Coordinates Between Different Systems,
  and a layer for 'leaflet' with Gaode tiles.
  It is designed to facilitate geographical data visualization in China.
A collection of tools for interactive manipulation of (spatial) data 
    layers on leaflet web maps. Tools include editing of existing layers, creation 
    of new layers through drawing of shapes (points, lines, polygons), deletion 
    of shapes as well as cutting holes into existing shapes. Provides control over 
    options to e.g. prevent self-intersection of polygons and lines or to enable/disable 
    snapping to align shapes.
Creates 'HTML' strings to embed tables, images or graphs in pop-ups
  of interactive maps created with packages like 'leaflet' or 'mapview'. Handles
  local images located on the file system or via remote URL. Handles graphs created 
  with 'lattice' or 'ggplot2' as well as interactive plots created with 'htmlwidgets'.
A set of functions for analyzing the structure 
  of forests based on the leaf area density (LAD) and leaf area index (LAI) measures 
  calculated from Airborne Laser Scanning (ALS), i.e., scanning lidar (Light Detection 
  and Ranging) data. The methodology is discussed and described in 
  Almeida et al. (2019) <doi:10.3390/rs11010092> and 
  Stark et al. (2012) <doi:10.1111/j.1461-0248.2012.01864.x>.
Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.
Create small multiples of several leaflet web maps with (optional) 
    synchronised panning and zooming control. When syncing is enabled all maps 
    respond to mouse actions on one map. This allows side-by-side comparisons
    of different attributes of the same geometries. Syncing can be adjusted
    so that any combination of maps can be synchronised.
Use the 'leaflet-timeline' plugin with a leaflet widget to add an
    interactive slider with play, pause, and step buttons to explore temporal
    geographic spatial data changes.
Instrumental variables (IVs) are a popular and powerful tool for 
  estimating causal effects in the presence of unobserved confounding. However, 
  classical methods rely on strong assumptions such as the exclusion criterion,
  which states that instrumental effects must be entirely mediated by 
  treatments. In the so-called "leaky" IV setting, candidate instruments are 
  allowed to have some direct influence on outcomes, rendering the average 
  treatment effect (ATE) unidentifiable. But with limits on the amount of 
  information leakage, we may still recover sharp bounds on the ATE, providing 
  partial identification. This package implements methods for ATE bounding in 
  the leaky IV setting with linear structural equations. For details, see Watson
  et al. (2024) <doi:10.48550/arXiv.2404.04446>.
Provides access to the 'Leanpub' API
    <https://leanpub.com/help/api> for gathering information about 
    publications and submissions to the 'Leanpub' platform.
An emulator designed for rapid sequential emulation (e.g., Markov chain Monte Carlo applications). Works via extension of the 'laGP' approach by Gramacy and Apley (2015 <doi:10.1080/10618600.2014.914442>). Details are given in Rumsey et al. (2023 <doi:10.1002/sta4.576>).
These functions take a gene expression value matrix, a
        primary covariate vector, an additional known covariates
        matrix.  A two stage analysis is applied to counter the effects
        of latent variables on the rankings of hypotheses.  The
        estimation and adjustment of latent effects are proposed by
        Sun, Zhang and Owen (2011).  "leapp" is developed in the
        context of microarray experiments, but may be used as a general
        tool for high throughput data sets where dependence may be
        involved.
Regression subset selection, including exhaustive search.
Provides methods for estimating borders of uniform distribution on
  the interval (one-dimensional) and on the elliptical domain (two-dimensional)
  under measurement errors. For one-dimensional case, it also estimates the
  length of underlying uniform domain and tests the hypothesized length against
  two-sided or one-sided alternatives. For two-dimensional case, it estimates
  the area of underlying uniform domain. It works with numerical inputs as well
  as with pictures in JPG format.
A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step.
  It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. 
  The package explains non expert users how hierarchical clustering algorithms work.
Implements transfer learning methods for low-rank matrix 
    estimation. These methods leverage similarity in the latent row and column 
    spaces between the source and target populations to improve estimation in 
    the target population. The methods include the LatEnt spAce-based tRaNsfer 
    lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) 
    method described by McGrath et al. (2024) <doi:10.48550/arXiv.2412.20605>.
Contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.
Crabs in the English channel, deer skulls, English
    monarchs, half-caste Manga characters, Jamaican cities,
    Shakespeare's The Tempest, drugged up cyclists and sexually
    transmitted diseases.
Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equation and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use.
Provides tools to teach students elemental statistics. The main topics covered are descriptive statistics, probability models (discrete and continuous variables) and statistical inference (confidence intervals and hypothesis tests). One of the main advantages of this package is that allows the user to read quite a variety of types of data files with one unique command. Moreover it includes shortcuts to simple but up-to-now not in R descriptive features such a complete frequency table or an histogram with the optimal number of intervals. Related to model distributions (both discrete and continuous), the package allows the student to easy plot the mass/density function, distribution function and quantile function just detailing as input arguments the known population parameters. The inference related tools are basically confidence interval and hypothesis testing. Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared).
The Programme for International Student Assessment (PISA) is a global study conducted by the Organization for Economic Cooperation and Development (OECD) in member and non-member countries to assess educational systems by assessing 15-year-old school students academic performance in mathematics, science, and reading. This datasets contains information on their scores and other socioeconomic characteristics, information about their school and its infrastructure, as well as the countries that are taking part in the program.
Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with 'Rcpp' for efficiency and 'R6' for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests.
Principal component analysis (PCA) is one of the most widely used data analysis techniques.  This package provides a series of vignettes explaining PCA starting from basic concepts. The primary purpose is to serve as a self-study resource for anyone wishing to understand PCA better. A few convenience functions are provided as well.
Conducts various numerical analyses and simulations in population genetics and evolutionary theory, primarily for the purpose of teaching (and learning about) key concepts in population & quantitative genetics, and evolutionary theory.
Create interactive tutorials using R Markdown. Use a
    combination of narrative, figures, videos, exercises, and quizzes to
    create self-paced tutorials for learning about R and R packages.
Data, scripts and code from chunks used as examples in the book 
   "Learn R: As a Language" 1ed and 2ed by Pedro J. Aphalo.
   ISBN 9780367182533 (pbk 1ed); ISBN 9780367182557 (hbk 1ed); ISBN 9780429060342 (ebk 1ed).
Supervised classification methods, which (if asked) can provide
    step-by-step explanations of the algorithms used, as described in
    PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to
    test them on, which highlight the strengths and weaknesses of each technique.
Summarizes characteristics of linear mixed effects models without 
    data or a fitted model by converting code for fitting lmer() from 'lme4' 
    and lme() from 'nlme' into tables, equations, and visuals. Outputs can be 
    used to learn how to fit linear mixed effects models in 'R' and to 
    communicate about these models in presentations, manuscripts, and analysis 
    plans.
Calculates cost surfaces based on slope to be used when modelling pathways and movement potential within a landscape (Lewis, 2021) <doi:10.1007/s10816-021-09522-w>.
Utilities for querying plain text accounting files from 'Ledger', 'HLedger', and 'Beancount'.
An educational package for teaching statistics and mathematics in both primary and higher education. The objective is to assist in the teaching/learning process, both for student study planning and teacher teaching strategies. The leem package aims to provide, in a simple yet in-depth manner, knowledge of statistics and mathematics to anyone who wants to study these areas of knowledge.
Complete analytical environment for the construction and analysis
             of matrix population models and integral projection models.
             Includes the ability to construct historical matrices, which are
             2d matrices comprising 3 consecutive times of demographic
             information. Estimates both raw and function-based forms of
             historical and standard ahistorical matrices. It also estimates
             function-based age-by-stage matrices and raw and function-based
             Leslie matrices.
A 'ggplot2' extension that focusses on expanding the
    plotter's arsenal of guides. Guides in 'ggplot2' include axes and
    legends. 'legendry' offers new axes and annotation options, as well as
    new legends and colour displays.
Functions implementing multivariate state space models for purposes of time series analysis and forecasting.
             The focus of the package is on multivariate models, such as Vector Exponential Smoothing,
             Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential
             Smoothing (VES, de Silva et al., 2010, <doi:10.1177/1471082X0901000401>), Vector ETS (Svetunkov et al., 2023,
             <doi:10.1016/j.ejor.2022.04.040>) and simulation function for VES.
Facilitates access to the Comparative Legislators Database (CLD). The CLD includes political, sociodemographic, career, online presence, public attention, and visual information for over 67,000 contemporary and historical politicians from 16 countries.
Constructs genotype x environment interaction (GxE) models where
    G is a weighted sum of genetic variants (genetic score) and E is a weighted
    sum of environments (environmental score) using the alternating optimization algorithm 
    by Jolicoeur-Martineau et al. (2017) <arXiv:1703.08111>. This approach has greatly 
    enhanced predictive power over traditional GxE models which include only a single 
    genetic variant and a single environmental exposure. Although this approach was 
    originally made for GxE modelling, it is flexible and does not require the use of 
    genetic and environmental variables. It can also handle more than 2 latent variables 
    (rather than just G and E) and 3-way interactions or more. The LEGIT model produces 
    highly interpretable results and is very parameter-efficient thus it can even be 
    used with small sample sizes (n < 250). Tools to determine the type of interaction
    (vantage sensitivity, diathesis-stress or differential susceptibility), with any 
    number of genetic variants or environments, are available <arXiv:1712.04058>. The 
    software can now produce mixed-effects LEGIT models through the lme4 package.
Provides a dataset containing several color naming conventions established by multiple sources, along with associated color metadata.
    The package also provides related helper functions for mapping among the different Lego color naming conventions and between Lego colors, hex colors, and 'R' color names, 
    making it easy to convert any color palette to one based on existing Lego colors while keeping as close to the original color palette as possible.
    The functions use nearest color matching based on Euclidean distance in RGB space. 
    Naming conventions for color mapping include those from 'BrickLink' (<https://www.bricklink.com>), 'The Lego Group' (<https://www.lego.com>), 'LDraw' (<https://www.ldraw.org/>), and 'Peeron' (<http://www.peeron.com/>).
Designed to query Longitudinal Employer-Household Dynamics (LEHD) 
    workplace/residential association and origin-destination flat files and 
    optionally aggregate Census block-level data to block group, tract, county, 
    or state. Data comes from the LODES FTP server <https://lehd.ces.census.gov/data/lodes/LODES8/>.
Miscellaneous R functions (for graphics, data import, 
    data transformation, and general utilities) and templates (for exploratory 
    analysis, Bayesian modeling, and crafting scientific manuscripts).
Implements the 'Python leidenalg' module to be called in R.
    Enables clustering using the leiden algorithm for partition a graph into communities.
    See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg>
    Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: <https://github.com/vtraag/leidenalg>. The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) <doi:10.1038/s41598-019-41695-z>.
An R to C/C++ interface that runs the Leiden community
    detection algorithm to find a basic partition (). It runs the
    equivalent of the 'leidenalg' find_partition() function, which is
    given in the 'leidenalg' distribution file
    'leiden/src/functions.py'. This package includes the
    required source code files from the official 'leidenalg'
    distribution and functions from the R 'igraph'
    package.  The 'leidenalg' distribution is available from
    <https://github.com/vtraag/leidenalg/>
    and the R 'igraph' package is available from
    <https://igraph.org/r/>.
    The Leiden algorithm is described in the article by
    Traag et al. (2019) <doi:10.1038/s41598-019-41695-z>.
    Leidenbase includes code from the packages:
       igraph version 0.9.8 with license GPL (>= 2),
       leidenalg version 0.8.10 with license GPL 3.
Estimate the slope and intercept of a bivariate
	linear relationship by calculating a posterior density
	that is invariant to interchange and scaling of the
	coordinates.
Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.
The reference implementation of model equations and default
    parameters for the toxicokinetic-toxicodynamic (TKTD) model of the Lemna
    (duckweed) aquatic plant. Lemna is a standard test macrophyte used in ecotox
    effect studies. The model was described and published by the SETAC Europe
    Interest Group Effect Modeling. It is a refined description of the Lemna
    TKTD model published by Schmitt et al. (2013)
    <doi:10.1016/j.ecolmodel.2013.01.017>.
Functions for working with legends and axis lines of 'ggplot2',
    facets that repeat axis lines on all panels, and some 'knitr' extensions.
Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.
Provides tools for creating and using lenses to simplify data manipulation. Lenses are composable getter/setter pairs for working with data in a purely functional way. Inspired by the 'Haskell' library 'lens' (Kmett, 2012) <https://hackage.haskell.org/package/lens>. For a fairly comprehensive (and highly technical) history of lenses please see the 'lens' wiki <https://github.com/ekmett/lens/wiki/History-of-Lenses>.
An implementation of the Input-Output model developed by
    Wassily Leontief that represents the interdependencies between different 
    sectors of a national economy or different regional economies.
A collection of colour palettes inspired by some of our
    dearest butterfly species.  This package provides continuous and
    categorical palettes, including some colour blind friendly options.
Code generator for robust dependency-free 'Shiny' 
    applications in the form of packages. It includes numerous
    convenience functions to create modules, include utility 
    functions to create common 'Bootstrap' elements, setup a
    project from the ground-up, and much more.
A way to fit Parsimonious Finite Mixtures of Multivariate Elliptical Leptokurtic-Normals. Two methods of estimation are implemented.
Generates quotes from 'Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.
"Learning with Subset Stacking" is a supervised learning algorithm that is based on training many local estimators on subsets of a given dataset, and then passing their predictions to a global estimator. You can find the details about LESS in our manuscript at <arXiv:2112.06251>.
Each function replaces multiple standard R functions. For example, two function calls, Read() and CountAll(), generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate. Other functions provide for summary statistics via pivot tables, a comprehensive regression analysis, ANOVA and t-test, visualizations including the Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, time series with aggregation and forecasting, color themes, and Trellis (facet) graphics. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, generation and rendering of regression instructions for interpretative output, and interactive visualizations.
Provides regularized structural equation modeling 
  (regularized SEM) with non-smooth penalty functions (e.g., lasso) building 
  on 'lavaan'. The package is heavily inspired by the 
  ['regsem'](<https://github.com/Rjacobucci/regsem>) and 
  ['lslx'](<https://github.com/psyphh/lslx>) packages.
Functions for vectorised conditional recoding of
    variables. case_when() enables you to vectorise multiple if and else
    statements (like 'CASE WHEN' in 'SQL'). if_else() is a stricter and
    more predictable version of ifelse() in 'base' that preserves
    attributes. These functions are forked from 'dplyr' with all package
    dependencies removed and behave identically to the originals.
Some simple objects and functions to do 
             statistics using linear models and a Bayesian framework.
Provides tools to retrieve and summarize taxonomic information and synonymy data for reptile species using data scraped from The Reptile Database website (<https://reptile-database.reptarium.cz/>). Outputs include clean and structured data frames useful for ecological, evolutionary, and conservation research.
Handling, processing, and analyzing geographic
    data on species' distributions and environmental variables. 
    Read Vilela & Villalobos (2015) <doi:10.1111/2041-210X.12401> for details.
Letter Values for the course Exploratory Data Analysis at Federal University of Bahia (Brazil). The approach implemented in the package is presented in the textbook of Tukey  (1977) <ISBN: 978-0201076165>.
Provides string similarity calculations inspired by the
    Python 'thefuzz' package. Compare strings by edit distance, similarity
    ratio, best matching substring, ordered token matching and set-based
    token matching. A range of edit distance measures are available thanks
    to the 'stringdist' package.
Implements code to identify lexical competitors in a given list
  of words. We include many of the standard competitor types used in spoken word
  recognition research, such as functions to find cohorts, neighbors, and
  rhymes, amongst many others. The package includes documentation for using a
  variety of lexicon files, including those with form codes made up of multiple
  letters (i.e., phoneme codes) and also basic orthographies. Importantly, the
  code makes use of multiple CPU cores and vectorization when possible, making
  it extremely fast and able to handle large lexicons. Additionally, the package
  contains documentation for users to easily write new functions, allowing
  researchers to examine other relationships within a lexicon. 
  Preprint: <https://osf.io/preprints/psyarxiv/8dyru/>. Open access: <doi:10.3758/s13428-021-01667-6>. 
  Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). 
  <doi:10.3758/s13428-021-01667-6>.
A collection of lexical hash tables, dictionaries, and word lists.
Provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts.
    As of now, two Portuguese lexicons are available: 'SentiLex-PT02' and 'OpLexicon' (v2.1 and v3.0).
My PhD supervisor once told me that everyone doing newspaper
    analysis starts by writing code to read in files from the 'LexisNexis' newspaper
    archive (retrieved e.g., from <https://www.lexisnexis.com/> or any of the partner
    sites). However, while this is a nice exercise I do recommend, not everyone has
    the time. This package takes files downloaded from the newspaper archive of
    'LexisNexis', reads them into R and offers functions for further processing.
Plots empty Lexis grids, adds lifelines and highlights certain areas of the grid, like cohorts and age groups.
An R implementation of the LexRank algorithm described by G. Erkan and D. R. Radev (2004) <DOI:10.1613/jair.1523>.
Provides an extension to factors called 'lfactor' that are similar
    to factors but allows users to refer to 'lfactor' levels by either the level or
    the label.
Shiny apps for the quantitative analysis of images from lateral flow assays (LFAs). The images are segmented and background corrected and color intensities are extracted. The apps can be used to import and export intensity data and to calibrate LFAs by means of linear, loess, or gam models. The calibration models can further be saved and applied to intensity data from new images for determining concentrations.
Ratios of count data such as obtained from RNA-seq are modelled
    using Bayesian statistics to derive posteriors for effects sizes. This
    approach is described in Erhard & Zimmer (2015) <doi:10.1093/nar/gkv696> 
    and Erhard (2018) <doi:10.1093/bioinformatics/bty471>.
Functions for performing and visualizing Local Fisher Discriminant
    Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised
    Local Fisher Discriminant Analysis(SELF).
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Estimation of the local false discovery rate using the method of moments.
New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a  "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.
Transforms away factors with many levels prior to doing an OLS.
  Useful for estimating linear models with multiple group fixed effects, and for
  estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) <doi:10.1016/j.csda.2013.03.024>
  Includes support for instrumental variables, conditional F statistics for weak instruments,
  robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 <doi:10.1002/sta4.68>).
  Since version 3.0, it provides dedicated functions to estimate Poisson models.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
    rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), 
    and forecasting time-series using fuzzy rule-based ensemble (FRBE). The package also contains basic
    fuzzy-related algebraic functions capable of handling missing values in different styles (Bochvar,
    Sobocinski, Kleene etc.), computation of Sugeno integrals and fuzzy transform.
Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods and Farm Test methods to these models. Evaluates the efficacy of these methods within the context of Laplace factor models by scrutinizing parameter estimation accuracy, mean square error, and the degree of sparsity.
Fast and accurate inference of 
             gene-environment associations (GEA) in genome-wide studies 
             (Caye et al., 2019, <doi:10.1093/molbev/msz008>). 
             We developed a least-squares estimation approach for confounder and effect sizes 
             estimation that provides a unique framework for several categories of genomic data, 
             not restricted to genotypes. 
             The speed of the new algorithm is several times faster than the existing GEA approaches, 
             then our previous version of the 'LFMM' program present in the 'LEA' package 
             (Frichot and Francois, 2015, <doi:10.1111/2041-210X.12382>).
Label-free bottom-up proteomics expression data is often affected by data heterogeneity and missing values. Normalization and missing value imputation are commonly used techniques to address these issues and make the dataset suitable for further downstream analysis. This package provides an optimal combination of normalization and imputation methods for the dataset. The package utilizes three normalization methods and three imputation methods.The statistical evaluation measures named pooled co-efficient of variance, pooled estimate of variance and pooled median absolute deviation are used for selecting the best combination of normalization and imputation method for the given dataset. The user can also visualize the results by using various plots available in this package. The user can also perform the differential expression  analysis between two sample groups with the function included in this package. The chosen three normalization methods, three imputation methods and three evaluation measures were chosen for this study based on the research papers published by	Välikangas et al. (2016) <doi:10.1093/bib/bbw095>, Jin et al. (2021) <doi:10.1038/s41598-021-81279-4> and Srivastava et al. (2023) <doi:10.2174/1574893618666230223150253>.
The "Manual on Low-flow Estimation and Prediction" (Gustard & Demuth (2009, ISBN:978-92-63-11029-9)), published by the World Meteorological Organisation, gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.
An implementation of locally Gaussian distributions. It provides methods for
    implementing locally Gaussian multivariate density estimation, conditional density 
    estimation, various independence tests for iid and time series data, a test for conditional 
    independence and a test for financial contagion.
Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.
Spatial and spatio-temporal modelling of point patterns using the
    log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
    multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.
Implements Cumulative Sum (CUSUM) control charts specifically
 designed for monitoring processes following a Gamma distribution. Provides
 functions to estimate distribution parameters, simulate control limits, and
 apply cautious learning schemes for adaptive thresholding. It supports
 upward and downward monitoring with guaranteed performance evaluated via
 Monte Carlo simulations. It is useful for quality control applications in
 industries where data follows a Gamma distribution. Methods are based on
 Madrid-Alvarez et al. (2024) <doi:10.1002/qre.3464> and Madrid-Alvarez et
 al. (2024) <doi:10.1080/08982112.2024.2440368>.
The goal of this package is to cover the most common steps in Loss Given Default (LGD) rating model development.
             The main procedures available are those that refer to bivariate and multivariate analysis. In particular two statistical methods for 
             multivariate analysis are currently implemented – OLS regression and fractional logistic regression.
             Both methods are also available within different blockwise model designs and both have customized stepwise algorithms. 
             Descriptions of these customized designs are available in Siddiqi (2016) <doi:10.1002/9781119282396.ch10> and 
             Anderson, R.A. (2021) <doi:10.1093/oso/9780192844194.001.0001>. 
             Although they are explained for PD model, the same designs are applicable for LGD model with different underlying regression methods 
             (OLS and fractional logistic regression). To cover other important steps for LGD model development, it is recommended to use 
             'LGDtoolkit' package along with 'PDtoolkit', and 'monobin' (or 'monobinShiny') packages.
             Additionally, 'LGDtoolkit' provides set of procedures handy for initial and periodical model validation.
Functions for genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".
For high-dimensional correlated observations, this package carries out the L_1 penalized maximum likelihood 
            estimation of the precision matrix (network) and the correlation parameters. The correlated data can be 
            longitudinal data (may be irregularly spaced) with dampening correlation or clustered data with uniform correlation.  
            For the details of the algorithms, please see the paper Jie Zhou et al. Identifying Microbial Interaction Networks Based on Irregularly Spaced 
            Longitudinal 16S rRNA sequence data <doi:10.1101/2021.11.26.470159>.
Interpretable nonparametric modeling of longitudinal data
    using additive Gaussian process regression. Contains functionality
    for inferring covariate effects and assessing covariate relevances.
    Models are specified using a convenient formula syntax, and can include
    shared, group-specific, non-stationary, heterogeneous and temporally
    uncertain effects. Bayesian inference for model parameters is performed
    using 'Stan'. The modeling approach and methods are described in detail in
    Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.
A flexible, feature-rich yet light-weight logging
    framework based on 'R6' classes. It supports hierarchical loggers,
    custom log levels, arbitrary data fields in log events, logging to
    plaintext, 'JSON', (rotating) files, memory buffers. For extra
    appenders that support logging to databases, email and push
    notifications see the the package lgr.app.
A largish collection of example datasets, including several classics. Many of
    these datasets are well suited for regression, classification, and visualization.
Additional appenders for the logging package 'lgr' that
    support logging to databases, email and push notifications.
Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.
Implements Lagrangian multiplier smoothing splines for flexible
    nonparametric regression and function estimation. Provides tools for fitting,
    prediction, and inference using a constrained optimization approach to 
    enforce smoothness.
    Supports generalized linear models, Weibull accelerated failure time (AFT) models, 
    quadratic programming problems, and customizable arbitrary correlation structures. 
    Options for fitting in parallel are provided. The method builds upon the framework described 
    by Ezhov et al. (2018) <doi:10.1515/jag-2017-0029> using Lagrangian multipliers 
    to fit cubic splines. For more information on correlation structure estimation, 
    see Searle et al. (2009) <ISBN:978-0470009598>. For quadratic programming and 
    constrained optimization in general, see Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. 
    For a comprehensive background on smoothing splines, see Wahba (1990) 
    <doi:10.1137/1.9781611970128> and Wood (2006) <ISBN:978-1584884743> 
    "Generalized Additive Models: An Introduction with R".
A very simple implementation of a class for 
	     longitudinal data.
Contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.
Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.
Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.
Provides functions for validating and normalizing bibliographic
   codes such as ISBN, ISSN, and LCCN. Also includes functions to communicate
   with the WorldCat API, translate Call numbers (Library of Congress and
   Dewey Decimal) to their subject classifications or subclassifications,
   and provides various loadable data files such call number / subject
   crosswalks and code tables.
Basic infrastructure for linear test statistics and permutation
  inference in the framework of Strasser and Weber (1999) <https://epub.wu.ac.at/102/>.
  This package must not be used by end-users. CRAN package 'coin' implements all
  user interfaces and is ready to be used by anyone.
Whole-buffer DEFLATE-based compression and 
    decompression of raw vectors using the 'libdeflate' library 
    (see <https://github.com/ebiggers/libdeflate>). Provides the user with additional 
    control over the speed and the quality of DEFLATE compression 
    compared to the fixed level of compression offered in R's 
    'memCompress()' function. Also provides the 'libdeflate' static library and 
    'C' headers along with a 'CMake' target and 'package‑config' file that 
    ease linking of 'libdeflate' in packages that compile and statically link 
    bundled libraries using 'CMake'.
Provides the Open Source Geometry Engine ('GEOS') as a
  C API that can be used to write high-performance C and C++
  geometry operations using R as an interface. Headers are provided
  to make linking to and using these functions from C++ code as
  easy and as safe as possible. This package contains an internal
  copy of the 'GEOS' library to guarantee the best possible
  consistency on multiple platforms.
A wrapper around the LIBLINEAR C/C++ library for machine
        learning (available at
        <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is
        a simple library for solving large-scale regularized linear
        classification and regression. It currently supports
        L2-regularized classification (such as logistic regression,
        L2-loss linear SVM and L1-loss linear SVM) as well as
        L1-regularized classification (such as L2-loss linear SVM and
        logistic regression) and L2-regularized support vector
        regression (with L1- or L2-loss). The main features of
        LiblineaR include multi-class classification (one-vs-the rest,
        and Crammer & Singer method), cross validation for model
        selection, probability estimates (logistic regression only) or
        weights for unbalanced data. The estimation of the models is
        particularly fast as compared to other libraries.
Contains a set of functions to create data libraries,
    generate data dictionaries, and simulate a data step.
    The libname() function will load a directory of data into 
    a library in one line of code.  The dictionary() function
    will generate data dictionaries for individual
    data frames or an entire library.  And the datestep() function
    will perform row-by-row data processing.
Efficient procedures for fitting the regularization path
    for linear, binomial, multinomial, Ising and Potts models with lasso,
    group lasso or column lasso(only for multinomial) penalty.
    The package uses Linearized Bregman Algorithm to solve the
    regularization path through iterations. Bregman Inverse Scale Space Differential
    Inclusion solver is also provided for linear model with lasso penalty.
Automatically install, update, and load 'CRAN', 'GitHub', and 'Bioconductor' 
    packages in a single function call. By accepting bare unquoted names for packages, 
    it's easy to add or remove packages from the list.
Generate a local library copy with relevant packages. 
    All packages currently found within the search path - except base packages - 
    will be copied to the directory provided and can be used later on with the 
    .libPaths() function.
Tools for fast and accurate evaluation of skew stable distributions 
  (CDF, PDF and quantile functions), random number generation, and parameter 
  estimation.  This is 'libstableR' as per Royuela del Val, Simmross-Wattenberg, 
  and Alberola López (2017) <doi:10.18637/jss.v078.i01> under a new maintainer.
The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2022) <doi:10.1080/02664763.2022.2053949>.
Reads raw files from Li-COR gas analyzers and produces a dataframe that can directly be used with 'fluxible' <https://cran.r-project.org/package=fluxible>.
Provides functions for forest objects detection, structure metrics 
    computation, model calibration and mapping with airborne laser scanning:
    co-registration of field plots (Monnet and Mermin (2014) 
    <doi:10.3390/f5092307>); tree detection (method 1 in Eysn et al. (2015) 
    <doi:10.3390/f6051721>) and segmentation; forest parameters estimation with 
    the area-based approach: model calibration with ground reference, and maps 
    export (Aussenac et al. (2023) <doi:10.12688/openreseurope.15373.2>); 
    extraction of both physical (gaps, edges, trees) and statistical 
    features useful for e.g. habitat suitability modeling (Glad et al. (2020) 
    <doi:10.1002/rse2.117>) and forest maturity mapping (Fuhr et al. (2022) 
    <doi:10.1002/rse2.274>).
Airborne LiDAR (Light Detection and Ranging) interface for data
    manipulation and visualization. Read/write 'las' and 'laz' files, computation
    of metrics in area based approach, point filtering, artificial point reduction,
    classification from geographic data, normalization, individual tree segmentation
    and other manipulations.
Real-time quantitative polymerase chain reaction (qPCR) data
    sets by Lievens et al. (2012) <doi:10.1093/nar/gkr775>. Provides one
    single tabular tidy data set in long format, encompassing three
    dilution series, targeted against the soybean Lectin endogene. Each dilution
    series was assayed in one of the following PCR-efficiency-modifying
    conditions: no PCR inhibition, inhibition by isopropanol and inhibition by
    tannic acid. The inhibitors were co-diluted along with the dilution series.
    The co-dilution series consists of a five-point, five-fold serial dilution.
    For each concentration there are 18 replicates. Each amplification
    curve is 60 cycles long. Original raw data file is available at the 
    Supplementary Data section at Nucleic Acids Research Online <doi:10.1093/nar/gkr775>.
Classes and methods that allow the user to manage life table,
    actuarial tables (also multiple decrements tables). Moreover, functions to easily
    perform demographic, financial and actuarial mathematics on life contingencies
    insurances calculations are contained therein. See Spedicato (2013)	<doi:10.18637/jss.v055.i10>.
Provides in built datasets and three functions.
  These functions are mobility_index, nonStanTest and linkedLives.  The mobility_index
  function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the 
  linkedLives functions allow the user to determine the probability that the observed sequence data 
  was due to chance.  The linkedLives function acknowledges the fact that some individuals may
  have identical sequences.
  The datasets available provide sequence data on marital status(maritalData) 
  and mobility (mydata) for a selected group of individuals from the British Household Panel Study
  (BHPS).  In addition, personal and house ID's for 100 individuals are provided in a 
  third dataset (myHouseID) from the BHPS.
Manage the life cycle of your exported functions with shared
    conventions, documentation badges, and user-friendly deprecation
    warnings.
Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.
Use of this package is deprecated.  It has been renamed to 'LifeInsureR'.
R6 classes to model traditional life insurance
    contracts like annuities, whole life insurances or endowments. Such life
    insurance contracts provide a guaranteed interest and are not directly linked
    to the performance of a particular investment vehicle, but they typically
    provide (discretionary) profit participation. This package provides a framework
    to model such contracts in a very generic (cash-flow-based) way and includes
    modelling profit participation schemes, dynamic increases or more general
    contract layers, as well as contract changes (like sum increases or premium
    waivers). All relevant quantities like premium decomposition, reserves and 
    benefits over the whole contract period are calculated and potentially 
    exported to 'Excel'. Mortality rates are given using the 'MortalityTables' package.
Allow to visualise data on the NCBI phylogenetic tree as presented in Lifemap <https://lifemap.cnrs.fr/>. It takes as input a dataframe with at least a "taxid" column containing NCBI format TaxIds and allows to draw multiple layers with different visualisation tools.
Calculates insurance reserves and equivalence premiums using advanced numerical methods, including the Runge-Kutta algorithm and product integrals for transition probabilities. This package is useful for actuarial analyses and life insurance modeling, facilitating accurate financial projections.
A suite of tools to use the 'eBird' database 
    (<https://ebird.org/home/>) and APIs to compare users' species lists to 
    recent observations and create a report of the top sites to visit to see 
    new species.
Life and Fertility Tables are appropriate to study the dynamics of 
    arthropods populations. This package provides utilities for constructing
    Life Tables and Fertility Tables, related demographic parameters, and some
    simple graphs of interest. It also offers functions to transform the
    obtained data into a known format for better manipulation. This document is
    based on the article by Maia, Luiz, and Campanhola "Statistical Inference on
    Associated Fertility Life Table Parameters Using Jackknife Technique
    Computational Aspects" (April 2000, Journal of Economic Entomology, Volume
    93, Issue 2) <doi:10.1603/0022-0493-93.2.511>.
Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy of Knight et al (2017) <doi:10.1007/s11222-016-9698-2>.
Persistent reproducible reporting by containerization of R Markdown documents.
A bootstrap proportion test for Brand Lift Testing to quantify the effectiveness of online advertising. Methods of the bootstrap proportion test are presented in
    Liu, Yu, Mao, Wu, Dyer (2023) <doi:10.1145/3583780.3615021>.
Allows you to read and change the state of 'LIFX' smart light bulbs via the 'LIFX' developer api <https://api.developer.lifx.com/>.
    Covers most 'LIFX' api endpoints, including changing light color and brightness, selecting lights by id, group or location as well as activating effects.
Fast calculation of Area Under Curve (AUC) metric of a
             Receiver Operating Characteristic (ROC) curve, using the algorithm
             of Fawcett (2006) <doi:10.1016/j.patrec.2005.10.010>. Therefore it
             is appropriate for large-scale AUC metric calculations.
Tree based algorithms can be improved by introducing boosting frameworks.
    'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.
    This package offers an R interface to work with it.
    It is designed to be distributed and efficient with the following advantages:
        1. Faster training speed and higher efficiency.
        2. Lower memory usage.
        3. Better accuracy.
        4. Parallel learning supported.
        5. Capable of handling large-scale data.
    In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions.
    Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.
Import, processing, validation, and visualization of personal light exposure measurement data from wearable devices. The package implements features such as the import of data and metadata files, conversion of common file formats, validation of light logging data, verification of crucial metadata, calculation of common parameters, and semi-automated analysis and visualization.
Split your 'rmarkdown' or 'quarto' files by sections into a
    tibble: titles, text, chunks. Rebuild the file from the tibble.
Parse various reflectance/transmittance/absorbance spectra file
    formats to extract spectral data and metadata, as described in Gruson, White
    & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import
    files from 'Avantes' <https://www.avantes.com/>, 'CRAIC' 
    <https://www.microspectra.com/>, and 'OceanOptics'/'OceanInsight'
    <https://www.oceanoptics.com/> brands.
Lights Out is a puzzle game consisting of a grid of lights
    that are either on or off. Pressing any light will toggle it and its
    adjacent lights. The goal of the game is to switch all the lights off. This
    package provides an interface to play the game on different board sizes,
    both through the command line or with a visual application. Puzzles can
    also be solved using the automatic solver included. View a demo
    online at <https://daattali.com/shiny/lightsout/>.
Tools for maximum likelihood estimation of parameters 
  of scientific models. Based on Goffe et al (1994) <doi:10.1016/0304-4076(94)90038-8>.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Provides likelihood functions as defined by Fisher (1922)
    <doi:10.1098/rsta.1922.0009> and a function that creates likelihood 
    functions from density functions. The functions are meant to aid in 
    education of likelihood based methods.
A collection of functions that calculate the log likelihood 
    (support) for a range of statistical tests. Where possible the likelihood 
    function and likelihood interval for the observed data are displayed. The 
    evidential approach used here is based on the book "Likelihood" by A.W.F. 
    Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. 
    Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall 
    (2011) <doi:10.2105/AJPH.78.12.1568>, "Understanding 
    Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), 
    S. Glover & P. Dixon <doi:10.3758/BF03196706> 
    and others. This package accompanies "Evidence-Based Statistics" by 
    P. Cahusac (2020, ISBN-13 : 978-1119549802) 
    <doi:10.1002/9781119549833>.
Managing and exploring parameter estimation results derived from Maximum Likelihood Estimation (MLE) using the 'likelihood' package. It provides functions for organizing, visualizing, and summarizing MLE outcomes, streamlining statistical analysis workflows. By improving interpretation and facilitating model evaluation, it helps users gain deeper insights into parameter estimation and model fitting, making MLE result exploration more efficient and accessible. See Goffe et al. (1994) <doi:10.1016/0304-4076(94)90038-8> for details on MLE, and Canham and Uriarte (2006) <doi:10.1890/04-0657> for application of MLE using 'likelihood'.
An approach to analyzing Likert response items, with an emphasis on visualizations. 
    The stacked bar plot is the preferred method for presenting Likert results. Tabular results
    are also implemented along with density plots to assist researchers in determining whether 
    Likert responses can be used quantitatively instead of qualitatively. See the likert(), 
    summary.likert(), and plot.likert() functions to get started.
Provides functions for summarizing, visualizing, and analyzing 
    Likert-scale survey data. Includes support for computing descriptive statistics, 
    Relative Importance Index (RII), reliability analysis (Cronbach's Alpha), 
    and response distribution plots.
Generate and correlate synthetic Likert and rating-scale data
    with predefined means, standard deviations, Cronbach's Alpha, Factor
    Loading table, and other summary statistics.  Worked examples and
    documentation are available in the package vignettes, accessible via
    browseVignettes("LikertMakeR").
A comprehensive analysis tool for metabolomics data.  It consists a variety of functional modules, including several new modules: a pre-processing module for normalization and imputation, an exploratory data analysis module for dimension reduction and source of variation analysis, a classification module with the new deep-learning method and other machine-learning methods, a prognosis module with cox-PH and neural-network based Cox-nnet methods, and pathway analysis module to visualize the pathway and interpret metabolite-pathway relationships. References: H. Paul Benton <http://www.metabolomics-forum.com/index.php?topic=281.0> Jeff Xia <https://github.com/cangfengzhe/Metabo/blob/master/MetaboAnalyst/website/name_match.R> Travers Ching, Xun Zhu, Lana X. Garmire (2018) <doi:10.1371/journal.pcbi.1006076>.
Estimation of life expectancy and
    Life Years Lost (LYL, or lillies for short) for a given population, for 
    example those with a given disease or condition. In addition, the package 
    can be used to compare estimates from different populations, or to estimate 
    confidence intervals. Technical details of the method are available in
    Plana-Ripoll et al. (2020) <doi:10.1371/journal.pone.0228073> and Andersen
    (2017) <doi:10.1002/sim.7357>.
This is for code management functions, NLP tools, a Monty Hall simulator, and for implementing my own variable reduction technique called Feed Reduction. The Feed Reduction technique is not yet published, but is merely a tool for implementing a series of binary neural networks meant for reducing data into N dimensions, where N is the number of possible values of the response variable.
When building complex models, it is often difficult to explain why
    the model should be trusted. While global measures such as accuracy are
    useful, they cannot be used for explaining why a model made a specific
    prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for
    explaining the outcome of black box models by fitting a local model around
    the point in question an perturbations of this point. The approach is
    described in more detail in the article by Ribeiro et al. (2016) 
    <arXiv:1602.04938>.
Provides an interface to the financial data platform <https://datahub.limex.com/>., enabling users to retrieve real-time and historical financial data. Functions within the package allow access to instruments, candlestick charts, fundamentals, news, events, models, and trading signals. Authentication is managed through user-specific API tokens, which are securely handled via environment variables.
Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).
'LimeSurvey' is Free/Libre Open Source Software for
  the development and administrations of online studies, using
  sophisticated tailoring capabilities to support multiple study
  designs (see <https://www.limesurvey.org>). This package supports
  programmatic creation of surveys that can then be imported into
  'LimeSurvey', as well as user friendly import of responses from
  'LimeSurvey' studies.
A flexible approach, inspired by cosinor regression, for
  differential analysis of rhythmic transcriptome data. See Singer and Hughey
  (2018) <doi:10.1177/0748730418813785>.
Fit linear models based on periodic splines, moderate model
  coefficients using multivariate adaptive shrinkage, then compute properties of
  the moderated curves.
Functions that (1) find the minimum/maximum of a linear or quadratic function:
  min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i)
  subject to equality constraints Ex=f and/or inequality constraints Gx>=h,
  (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b,      
  (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems.
Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.
Short for 'linear binning', the linbin package provides functions
    for manipulating, binning, and plotting linearly referenced data. Although
    developed for data collected on river networks, it can be used with any interval
    or point data referenced to a 1-dimensional coordinate system. Flexible bin
    generation and batch processing makes it easy to compute and visualize variables
    at multiple scales, useful for identifying patterns within and between variables
    and investigating the influence of scale of observation on data interpretation.
Estimate and confidence/credible intervals for an unknown
    regressor x0 given an observed y0.
Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. 'MOSEK' solver is used and needs to be installed; an academic license for 'MOSEK' is free.
Sample truncated multivariate Normal distribution following Gessner, A., Kanjilal, O., & Hennig, P. (2019). Integrals over Gaussians under Linear Domain Constraints. 108. <arxiv:1910.09328>.
L-systems or Lindenmayer systems are parallel rewriting systems which can
    be used to simulate biological forms and certain kinds of fractals.
    Briefly, in an L-system a series of symbols in a string are replaced
    iteratively according to rules to give a more complex string. Eventually,
    the symbols are translated into turtle graphics for plotting. Wikipedia has
    a very good introduction: en.wikipedia.org/wiki/L-system This package
    provides basic functions for exploring L-systems.
Provides a set of streamlined functions that allow
    easy generation of linear regression diagnostic plots necessarily 
    for checking linear model assumptions.
    This package is meant for easy scheming of linear 
    regression diagnostics, while preserving merits of 
    "The Grammar of Graphics" as implemented in 'ggplot2'.
    See the 'ggplot2' website for more information regarding the
    specific capability of graphics.
Computes the probability density function, the cumulative distribution function, the hazard rate function, the quantile function and random generation for Lindley Power Series distributions, see Nadarajah and Si (2018) <doi:10.1007/s13171-018-0150-x>.
Functions to access and test results from a linear model.
Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Tests whether the linear hypothesis of a model
    is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation 
    Specification Error Test) test is implemented and Wald tests can be carried out. 
    Although RESET test is widely used to 
    test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel 
    approach that generalizes well known specification tests such as Ramsey's. This test 
    relies on wild-bootstrap; this package implements this approach to be 
    usable with any function that fits linear models and is compatible with 
    the update() function such as 'stats'::lm(), 'lfe'::felm() and 'forecast'::Arima(), 
    for ARMA (autoregressive–moving-average) models. 
    Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov 
    Smirnov, described by the authors, and custom distributions such as Mammen (discrete 
    and continuous) and Rademacher.
    Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.
Provides tools to help storing and handling case line list data. The 'linelist' class adds a tagging system to classical 'data.frame' objects to identify key epidemiological data such as dates of symptom onset, epidemiological case definition, age, gender or disease outcome. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines more robust and reliable.
Provides tools for performing Bayesian inference on epidemiological 
  data to estimate the time-varying reproductive number and other related metrics.
  These methods were published in Li and White (2021) <doi:10.1371/journal.pcbi.1009210>.
  This package supports analyses based on aggregated case count data and individual line list 
  data, facilitating enhanced surveillance and intervention planning for infectious 
  diseases like COVID-19.
Create maps made of lines. The package contains one function:
    linemap(). linemap() displays a map made of lines using a
    raster or gridded data.
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two tissues.
    Broman et al. (2015) <doi:10.1534/g3.115.019778>.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two
    tissues. This is a revised version of the 'lineup' package, to be
    more general and not tied to the 'qtl' package.
'LineUp' is an interactive technique designed to create, visualize and explore rankings of items based on a set of heterogeneous attributes.
  This is a 'htmlwidget' wrapper around the JavaScript library 'LineUp.js'.
  It is designed to be used in 'R Shiny' apps and 'R Markddown' files.
  Due to an outdated 'webkit' version of 'RStudio' it won't work in the integrated viewer.
Create interactive time series visualizations.
    'linevis' includes an extensive API to manipulate time series after creation,
    and supports getting data out of the visualization. Based on the
    'timevis' package and the 'vis.js' Timeline 'JavaScript' library <https://visjs.github.io/vis-timeline/docs/graph2d/>.
A fast generalized edit distance and string alignment computation mainly for linguistic aims. As a generalization to the classic edit distance algorithms, the package allows users to define custom cost for every symbol's insertion, deletion, and substitution. The package also allows character combinations in any length to be seen as a single symbol which is very useful for International Phonetic Alphabet (IPA) transcriptions with diacritics. In addition to edit distance result, users can get detailed alignment information such as all possible alignment scenarios between two strings which is useful for testing, illustration or any further usage. Either the distance matrix or its long table form can be obtained and tools to do such conversions are provided. All functions in the package are implemented in 'C++' and the distance matrix computation is parallelized leveraging the 'RcppThread' package.
Helps to render interlinear glossed linguistic examples in html 
    'rmarkdown' documents and then semi-automatically compiles the list of
    glosses at the end of the document. It also provides a database of linguistic
    glosses.
Measure similarity between texts. Offers a variety of processing
  tools and similarity metrics to facilitate flexible representation of texts and matching.
  Implements forms of Language Style Matching (Ireland & Pennebaker, 2010) <doi:10.1037/a0020386>
  and Latent Semantic Analysis (Landauer & Dumais, 1997) <doi:10.1037/0033-295X.104.2.211>.
Provides R with the Glottolog database <https://glottolog.org/> and some more abilities for purposes of linguistic mapping. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <https://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub pages <https://docs.ropensci.org/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.
Provides 'Shiny gadgets' to search, type, and insert IPA symbols
    into documents or scripts, requiring only knowledge about phonetics or 
    'X-SAMPA'. Also provides functions to facilitate the rendering of IPA
    symbols in 'LaTeX' and PDF format, making IPA symbols properly rendered
    in all output formats. A minimal R Markdown template for authoring 
    Linguistics related documents is also bundled with the package. Some
    helper functions to facilitate authoring with R Markdown is also provided.
Automatic detection of hyperlinks for packages and calls in the text
    of 'rmarkdown' or 'quarto' documents.
Functions and tools for using open GIS and remote sensing command-line interfaces in a reproducible environment.
It allows to cluster communication networks using the Stochastic
  Topic Block Model <doi:10.1007/s11222-016-9713-7> by posting jobs through 
  the API of the linkage.fr server, which implements the clustering method.
  The package also allows to visualize the clustering results returned by the server.
Produces high resolution, publication ready linkage maps
    and quantitative trait loci maps. Input can be output from 'R/qtl',
    simple text or comma delimited files. Output is currently
    a portable document file.
Link communities reveal the nested and overlapping
    structure in networks, and uncover the key nodes that form connections
    to multiple communities. linkcomm provides a set of tools for
    generating, visualizing, and analysing link communities in networks of
    arbitrary size and type. The linkcomm package also includes tools for
    generating, visualizing, and analysing Overlapping Cluster Generator
    (OCG) communities. Kalinka and Tomancak (2011) <doi:10.1093/bioinformatics/btr311>.
Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.
Collect  marketing data from 'LinkedIn' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
A class that links matrix-like objects (nodes) by rows or by
    columns while behaving similarly to a base R matrix. Very large matrices
    are supported if the nodes are file-backed matrices.
Implementations of most of the existing proximity-based methods of 
  link prediction in graphs. Among the 20 implemented methods are e.g.:
  Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>,
  Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>,
  Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and
  Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.
Compute and visualize using the 'visNetwork' package all the bivariate correlations of a dataframe.
  Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau,
  distance correlation, maximal information coefficient and 
  equal-freq discretization-based maximal normalized mutual information) are used according to 
  the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).
A 'LaTeX' Letter class for 'rmarkdown', using the
 'pandoc-letter' template adapted for use with 'markdown'.
Conveniently generate 'CSS' using R code.
Generate concentration-time profiles from linear pharmacokinetic
  (PK) systems, possibly with first-order absorption or zero-order infusion,
  possibly with one or more peripheral compartments, and possibly under
  steady-state conditions. Single or multiple doses may be specified. Secondary
  (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are
  computed.
Can be used to solve Linear Programming / Linear
   Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions 
    and marginal effects for different kinds of regression models. In this version 
    linear regression models, generalized linear models, generalized additive
    models and linear mixed-effects models are supported.  
    Major features are the interactive approach and the handling of the effects of categorical covariates: 
    if two or more factors are used as covariates every combination of the levels of each 
    factor is treated separately. The automatic calculation of 
    marginal effects and a number of possibilities to customize the graphical output 
    are useful features as well.
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators,
  following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>.
  In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso,
  elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), 
    Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution,  
    projecting a vector on the convex polytope described by a system of (in)equations, 
    simplify systems by removing spurious columns and rows and collapse implied equalities, 
    test if a matrix is totally unimodular, compute variable ranges implied by linear
    (in)equalities.
Checks adherence to a given style, syntax errors and possible
    semantic issues.  Supports on the fly checking of R code edited with
    'RStudio IDE', 'Emacs', 'Vim', 'Sublime Text', 'Atom' and 'Visual
    Studio Code'.
Extends the functionality of the 'tourr' package by an interactive
             graphical user interface. The interactivity allows users to
             effortlessly refine their 'tourr' results by manual intervention,
             which allows for integration of expert knowledge and aids the
             interpretation of results. For more information on 'tourr' see
             Wickham et. al (2011) <doi:10.18637/jss.v040.i02> or
             <https://github.com/ggobi/tourr>.
Lipid Maps Rest service. Researchers can access the Lipid Maps Rest
  service programmatically and conveniently integrate it into the current workflow
  or packages.
Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.
Create lipidome-wide heatmaps of statistics with the 'lipidomeR'.
    The 'lipidomeR' provides a streamlined pipeline for the systematic 
    interpretation of the lipidome through publication-ready visualizations 
    of regression models fitted on lipidomics data. 
    With 'lipidomeR', associations between covariates and the lipidome 
    can be interpreted systematically and intuitively through heatmaps, where 
    lipids are categorized by the lipid class and are presented on 
    two-dimensional maps organized by the lipid size and level of saturation. 
    This way, the 'lipidomeR' helps you gain an immediate understanding of 
    the multivariate patterns in the lipidome already at first glance.
    You can create lipidome-wide heatmaps of statistical associations, changes, 
    differences, variation, or other lipid-specific values.  
    The heatmaps are provided with publication-ready quality and the results 
    behind the visualizations are based on rigorous statistical models.
An elegant tool for processing and visualizing lipidomics data generated by mass spectrometry. 'LipidomicsR' simplifies channel and replicate handling while providing thorough lipid species annotation. Its visualization capabilities encompass principal components analysis plots, heatmaps, volcano plots, and radar plots, enabling concise data summarization and quality assessment. Additionally, it can generate bar plots and line plots to visualize the abundance of each lipid species.
This computes Lipinski Rule of Five parameters and offers visualization for drug discovery. It analyzes molecular properties like molecular weight, hydrogen bond donors, acceptors, and ALogP, providing histograms and pass/fail status plots for efficient compound evaluation, aiding in drug development.
Provides three classes: Queue, PriorityQueue and Stack. Queue is just a
    "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.
Performs Bayesian linear regression and forecasting in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables) and scatter correlation, time evolution of slopes, normalization, scatters, Malmquist and Eddington bias, upper limits and break of linearity. The posterior distribution of the regression parameters is sampled with a Gibbs method exploiting the JAGS library.
Contains 128 palettes from Color Lisa. All palettes are based on 
    masterpieces from the worlds greatest artists. For more information, see 
    <http://colorlisa.com/>.
This is an unofficial package aimed at automating the import of LISREL output in R. 
				This package or its maintainer is not in any way affiliated with the creators of LISREL and SSI, Inc.
Allows researchers to conduct multivariate
    statistical analyses of survey data with list experiments. This
    survey methodology is also known as the item count technique or
    the unmatched count technique and is an alternative to the commonly
    used randomized response method. The package implements the methods
    developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, 
    Blair and Imai (2012) <doi:10.1093/pan/mpr048>, 
    Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, 
    Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>,
    Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, 
    Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and 
    Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. 
    This includes a Bayesian MCMC implementation of regression for the 
    standard and multiple sensitive item list experiment designs and a 
    random effects setup, a Bayesian MCMC hierarchical regression model 
    with up to three hierarchical groups, the combined list experiment 
    and endorsement experiment regression model, a joint model of the 
    list experiment that enables the analysis of the list experiment as 
    a predictor in outcome regression models, a method for combining 
    list experiments with direct questions, and methods for diagnosing and
    adjusting for response error. In addition, the package implements the 
    statistical test that is designed to detect certain failures of list 
    experiments, and a placebo test for the list experiment using data from 
    direct questions.
The aim of the package is to create data objects which allow for accesses like x["test"] and x["test","test"].
A toolbox for R arrays. Flexibly split, bind, reshape, modify, 
    subset and name arrays.
An implementation of list comprehensions as purely syntactic
  sugar with a minor runtime overhead. It constructs nested for-loops and
  executes the byte-compiled loops to collect the results.
Syntactic shortcuts for creating synthetic lists, vectors, 
    data frames, and matrices using list comprehension.
Programmatically create R Markdown documents from lists.
List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a = 1, b = 2); for (i in seq_along(x)) x[[i]] <- x[[i]] ^ 2; y <- as.list(x)'.
Tools for common operations on lists. Provided are short-cuts to
    operations like selecting and merging data stored in lists. The functions in
    this package are designed to be used with pipes.
R lists, especially nested lists, can be very difficult to
    visualize or represent. Sometimes 'str()' is not enough, so this suite of
    htmlwidgets is designed to help see, understand, and maybe even modify your R
    lists.  The function 'reactjson()' requires a package
    'reactR' that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.
Provides a function that, as an alternative to base::list, allows
    default values to be inherited from another list.
Identifying latent genetic interactions in genome-wide association studies
    using the Latent Interaction Testing (LIT) framework.
    LIT is a flexible kernel-based approach that leverages information across
    multiple traits to detect latent genetic interactions without specifying or
    observing the interacting variable (e.g., environment). LIT accepts
    standard PLINK files as inputs to analyze large genome-wide association studies.
Performs likelihood-based inference for stationary time series 
    extremes.  The general approach follows Fawcett and Walshaw (2012)
    <doi:10.1002/env.2133>.  Marginal extreme value inferences are adjusted for 
    cluster dependence in the data using the methodology in Chandler and Bate 
    (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood 
    for the model parameters.  A log-likelihood for the extremal index is 
    produced using the K-gaps model of Suveges and Davison (2010) 
    <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make 
    inferences about extreme values. Both maximum likelihood and Bayesian 
    approaches are available.
Render R Markdown to Markdown (without using 'knitr'), and Markdown
    to lightweight HTML or 'LaTeX' documents with the 'commonmark' package (instead
    of 'Pandoc'). Some missing Markdown features in 'commonmark' are also
    supported, such as raw HTML or 'LaTeX' blocks, 'LaTeX' math, superscripts,
    subscripts, footnotes, element attributes, and appendices,
    but not all 'Pandoc' Markdown features are (or will be) supported. With
    additional JavaScript and CSS, you can also create HTML slides and articles.
    This package can be viewed as a trimmed-down version of R Markdown and
    'knitr'. It does not aim at rich Markdown features or a large variety of
    output formats (the primary formats are HTML and 'LaTeX'). Book and website
    projects of multiple input documents are also supported.
Temporary and permanent message queues for R. Built on top of
    'SQLite' databases. 'SQLite' provides locking, and makes it possible
    to detect crashed consumers. Crashed jobs can be automatically marked
    as "failed", or put in the queue again, potentially a limited number of times.
An updated implementation of R package 'ranger' by Wright et al,
    (2017) <doi:10.18637/jss.v077.i01> for training and predicting from random
    forests, particularly suited to high-dimensional data, and for embedding in
    'Multiple Imputation by Chained Equations' (MICE) by van Buuren (2007)
    <doi:10.1177/0962280206074463>. Ensembles of classification and regression
    trees are currently supported. Sparse data of class 'dgCMatrix' (R package
    'Matrix') can be directly analyzed. Conventional bagged predictions are
    available alongside an efficient prediction for MICE via the algorithm
    proposed by Doove et al (2014) <doi:10.1016/j.csda.2013.10.025>. Survival
    and probability forests are not supported in the update, nor is data of
    class 'gwaa.data' (R package 'GenABEL'); use the original 'ranger' package
    for these analyses.
Dataset and functions to explore quality of literary novels. The package is a part of the Riddle of Literary Quality project, and it contains the data of a reader survey about fiction in Dutch, a description of the novels the readers rated, and the results of stylistic measurements of the novels. The package also contains functions to combine, analyze, and visualize these data. For more details, see: Eder M, van Zundert J, Lensink S, van Dalen-Oskam K (2022). Replicating The Riddle of Literary Quality: The litRiddle package for R. In _Digital Humanities 2022: Conference Abstracts_, 636-637.
Data sets on various litter types like beach litter, riverain
    litter, floating litter, and seafloor litter are rapidly growing. This 
    package offers a simple user interface to analyse these litter data in
    a consistent and reproducible way. It also provides functions to 
    facilitate several kinds of litter analysis, e.g., trend analysis, 
    power analysis, and baseline analysis. Under the hood, these functions 
    are also used by the user interface. See Schulz et al. (2019)
    <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are
    advised to run 'litteR' in 'RStudio'. See our vignette: Installation manual 
    for 'RStudio' and 'litteR'.
There is a long tradition of studying the flux of carbon from the biosphere to the atmosphere by following a particular cohort of litter (wood, leaves, roots, or other organic material) through time.  The resulting data are mass remaining and time. A variety of functional forms may be used to fit the resulting data. Some work better empirically. Some are better connected to a process-based understanding.  Some have a small number of free parameters; others have more. This package matches decomposition data to a family of these curves using likelihood--based fitting. This package is based on published research by Cornwell & Weedon (2013) <doi:10.1111/2041-210X.12138>.
A scripting and command-line front-end
 is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
 the GNU R language and environment for statistical computing and graphics.
 While R can be used in batch mode, the r binary adds full support for
 both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
 expression as the first line in scripts) as well as command-line use in
 standard Unix pipelines. In other words, r provides the R language without
 the environment.
Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585>
  ii.  Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011>
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Liu (1993) <doi:10.1080/03610929308831027>
  v.   Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.
Interpretability of complex machine learning models is a growing concern.
    This package helps to understand key factors that drive the 
    decision made by complicated predictive model (so called black box model). 
    This is achieved through local approximations that are either based on 
    additive regression like model or CART like model that allows for 
    higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
    More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.
Provides a suite of helper functions and a collection of datasets used in the book <https://uncovering-data-science.netlify.app>. It is designed to make data science techniques accessible to individuals with minimal coding experience. Inspired by an ancient Persian idiom, the package likens this learning process to "eating the liver of data science," symbolizing deep and immersive engagement with the field.
Extracts 'zip' files, converts 'Word', 'Excel', and 'html'/'htm' files to 'pdf' format. 'Word' and 'Excel' conversion uses 'VBScript', while 'html'/'htm' conversion uses 'webshot' and 'PhantomJS'. Additionally, the package merges 'pdf' files into a single document. This package is only supported on 'Windows' due to 'VBScript' dependencies.
Converts video files to 'mp3', merges multiple audio files and trims audio files using 'FFmpeg', which is dynamically downloaded to avoid bundling any third-party binaries. Users must ensure compliance with the license terms of 'FFmpeg' when using the package. See <https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-win64-gpl.zip> for details.
Fits and tests logistic joinpoint models.
Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.
Provides functionality to train and evaluate algorithm selection models for portfolios.
R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazán, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, <https://www.imstat.org/wp-content/uploads/2018/05/BJPS397.pdf>.
Performs likelihood criterion analysis using the Laplace regression model to determine its optimal subset of variables. The methodology is based on Guo et al. (2023), LIC criterion for optimal subset selection in distributed interval estimation <doi:10.1080/02331888.2020.1823979>.
Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).
Provides modular, graph-based agents powered by large language models (LLMs) for intelligent task execution in R. 
      Supports structured workflows for tasks such as forecasting, data visualization, feature engineering, data wrangling, data cleaning, 'SQL', code generation, weather reporting, and research-driven question answering. 
      Each agent performs iterative reasoning: recommending steps, generating R code, executing, debugging, and explaining results. 
      Includes built-in support for packages such as 'tidymodels', 'modeltime', 'plotly', 'ggplot2', and 'prophet'. Designed for analysts, developers, and teams building intelligent, reproducible AI workflows in R. 
      Compatible with LLM providers such as 'OpenAI', 'Anthropic', 'Groq', and 'Ollama'. Inspired by the Python package 'langagent'.
Provides a unified interface to interact with multiple Large Language Model (LLM) APIs. The package supports text generation, embeddings, parallelization, as well as tidyverse integration.  Users can switch between different LLM providers seamlessly within R workflows, or call multiple models in parallel. The package enables creation of LLM agents for automated tasks and provides consistent error handling across all supported APIs. APIs include 'OpenAI' (see <https://platform.openai.com/docs/overview> for details), 'Anthropic' (see <https://docs.anthropic.com/en/api/getting-started> for details), 'Groq' (see <https://console.groq.com/docs/api-reference> for details), 'Together AI' (see <https://docs.together.ai/docs/quickstart> for details), 'DeepSeek' (see <https://api-docs.deepseek.com> for details), 'Gemini' (see <https://aistudio.google.com> for details), and 'Voyage AI' (see <https://docs.voyageai.com/docs/introduction> for details).
Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and phi. The parameter m is the median of the distribution.
Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research.
Adds standardized regression coefficients to objects created by 'lm'. Also extends the S3 methods 'print', 'summary' and 'coef' with additional boolean argument 'standardized' and provides 'xtable'-support.
Exact significance tests for a changepoint in linear or multiple linear regression.  
  Confidence regions with exact coverage probabilities for the changepoint.  Based on 
  Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.
Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu).
Various efficient and robust bootstrap methods are implemented for 
    linear models with least squares estimation.  Functions within this package 
    allow users to create bootstrap sampling distributions for model parameters, 
    test hypotheses about parameters, and visualize the bootstrap sampling or null 
    distributions.  Methods implemented for linear models include the wild bootstrap by 
    Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by
    Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by 
    Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by 
    Rubin (1981) <doi:10.1214/aos/1176345338>.
Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the 'Python' library 'PyLMD'.
Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in  Onofri et al (2020) <DOI:10.1007/s00122-020-03716-8>.
A fast dimensionality reduction method scaleable to large numbers of samples.
  Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS,
  but rather than calculating a complete distance matrix between all pairs of samples,
  only the distances between a set of landmarks and the samples are calculated.
Fit linear and generalized linear mixed-effects models.
    The models and their components are represented using S4 classes and
    methods.  The core computational algorithms are implemented using the
    'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the
    'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.
Flexible functions that use 'lme4' as computational engine for 
             fitting models used in Genomic Selection (GS). GS is a technology used for genetic 
             improvement, and it has many advantages over phenotype-based selection. There 
             are several statistical models that adequately approach the statistical 
             challenges in GS, such as in linear mixed models (LMMs). The 'lme4' is the 
             standard package for fitting linear and generalized LMMs in the R-package, 
             but its use for genetic analysis is limited because it does not allow the 
             correlation between individuals or groups of individuals to be defined. The 
             'lme4GS' package is focused on fitting LMMs with covariance structures defined 
             by the user, bandwidth selection, and genomic prediction. The new package is 
             focused on genomic prediction of the models used in GS and can fit LMMs using
             different variance-covariance matrices. Several examples of GS models are 
             presented using this package as well as the analysis using real data. For more
             details see Caamal-Pat et.al. (2021) <doi:10.3389/fgene.2021.680569>.
Provides analytic derivatives and information matrices for
    fitted linear mixed effects (lme) models and generalized least squares (gls) models
    estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively.
    The package includes functions for estimating the sampling variance-covariance of variance
    component parameters using the inverse Fisher information. The variance components include
    the parameters of the random effects structure (for lme models), the variance structure,
    and the correlation structure. The expected and average forms of the Fisher information matrix
    are used in the calculations, and models estimated by full maximum likelihood or
    restricted maximum likelihood are supported. The package also includes a function for estimating
    standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>)
    based on fitted lme or gls models.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Bootstrap routines for nested linear mixed effects models fit using
    either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the
    parametric, residual, cases, random effect block (REB), and wild bootstrap 
    procedures. An overview of these procedures can be found 
    in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, 
    Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>,
    and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.
We provide a solution for performing permutation tests on linear and mixed linear regression models. It
             allows users to obtain accurate p-values without making distributional assumptions about the data. By 
             generating a null distribution of the test statistics through repeated permutations of the response variable,
             permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) 
             <doi:10.1007/s10683-023-09799-6>). In this early version, we focus on the permutation tests over observed 
             t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null 
             distribution of the test statistic through repeated permutations of the response variable, each observed t 
             values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop 
             criterion (Anscombe (1953) <doi:10.1111/j.2517-6161.1953.tb00121.x>) is adopted to force permutation to stop 
             if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so,
             we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate 
             p-values.
Provides p-values in type I, II or III anova and summary tables
    for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A
    Kenward-Roger method is also available via the pbkrtest package. Model selection
    methods include step, drop1 and anova-like tables for random effects (ranova).
    Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed
    effects are also available.
Adds smoothing spline modelling capability to nlme. Fits
        smoothing spline terms in Gaussian linear and nonlinear
        mixed-effects models.
Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.
Provides methods for estimation and statistical
               inference on directional and fluctuating selection in age-structured
               populations.
We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.
Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.
Introduces in-sample, out-of-sample, pseudo out-of-sample, and
    benchmark model forecast tests and a new class for working with forecast data, Forecast.
A collection of helper functions for multiple
  regression models fitted by lm(). Most of them are simple
  functions for simple tasks which can be done with coding,
  but may not be easy for occasional users of R. Most of
  the tasks addressed are those sometimes needed when
  using the 'manymome' package (Cheung and Cheung, 2023,
  <doi:10.3758/s13428-023-02224-z>) and 'stdmod' package
  (Cheung, Cheung, Lau, Hui, and Vong, 2022,
  <doi:10.1037/hea0001188>). However, they can also be used
  in other scenarios.
The Gaussian location-scale regression model is a multi-predictor
    model with explanatory variables for the mean (= location) and the standard
    deviation (= scale) of a response variable. This package implements maximum
    likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms
    from Girolami and Calderhead (2011) <doi:10.1111/j.1467-9868.2010.00765.x>
    and Nesterov (2009) <doi:10.1007/s10107-007-0149-x>), a parametric
    bootstrap algorithm, and diagnostic plots for the model class.
It implements Expectation/Conditional Maximization Either (ECME)
             and rapidly converging algorithms as well as
             Bayesian inference for linear mixed models, 
             which is described in Schafer, J.L. (1998)
             "Some improved procedures for linear mixed models".
             Dept. of Statistics, The Pennsylvania State University.
In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) <doi:10.1027/1015-5759/a000624>. Hedeker, Mermelstein, and Demirtas (2008) <doi:10.1111/j.1541-0420.2007.00924.x>.
Fit right censored Multiple Ordinal Tobit (MOT) model.
Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.
An efficient and flexible system to solve sparse mixed model
    equations. Important applications are the use of splines to model spatial or temporal
    trends as described in Boer (2023). (<doi:10.1177/1471082X231178591>).
Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
	     taught by the section of Biostatistics of the University of Copenhagen.
	     It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...).
	     Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom.
	     Normalized residuals are provided to assess model misspecification.
	     Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients.
	     Predictions can be computed conditional to covariates only or also to outcome values.
Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model.  Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.
Computes model II simple linear regression using ordinary
 least squares (OLS), major axis (MA), standard major axis (SMA), and
 ranged major axis (RMA).
A complete framework for frequency analysis is provided by 'LMoFit'. It has functions related to the determination of sample L-moments as in Hosking, J.R.M. (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x>, the fitting of various distributions as in Zaghloul et al. (2020) <doi:10.1016/j.advwatres.2020.103720> and Hosking, J.R.M. (2019) <https://CRAN.R-project.org/package=lmom>, besides plotting and manipulating L-space diagrams as in Papalexiou, S.M. & Koutsoyiannis, D. (2016) <doi:10.1016/j.advwatres.2016.05.005> for two-shape parametric distributions on the L-moment ratio diagram. Additionally, the quantile, probability density, and cumulative probability functions of various distributions are provided in a user-friendly manner.
Functions related to L-moments: computation of L-moments
  and trimmed L-moments of distributions and data samples; parameter
  estimation; L-moment ratio diagram; plot vs. quantiles of an
  extreme-value distribution.
Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs),
  distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate
  Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and
  maximum product spacings estimation are available. Right-tail and left-tail LM censoring
  by threshold or indicator variable are available. LMs of residual (resid) and reversed
  (rev) residual life are implemented along with 13 quantile operators for reliability analyses.
  Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available.
  Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added
  (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L],
  Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp
  Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L],
  Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L],
  Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L],
  3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L],
  Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L],
  Truncated Exponential [L], Wakeby [L], and Weibull [L].
Contains functions to estimate
        L-moments and trimmed L-moments from the data. Also
        contains functions to estimate the parameters of the normal
        polynomial quantile mixture and the Cauchy polynomial quantile
        mixture from L-moments and trimmed L-moments.
It is an extension of 'lmom' R package: 'pel...()','cdf...()',qua...()' function
    families are lumped and called from one function per each family respectively in order to
    create robust automatic tools to fit data  with different probability
    distributions and then to estimate probability values and return periods.  The implemented functions are able to manage time series with constant and/or missing values without stopping
    the execution with error messages. The package also contains tools to  calculate several  indices based on variability (e.g. 'SPI' , Standardized
    Precipitation Index, see <https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi> and <http://spei.csic.es/>) for multiple time series or spatially gridded values.
Functions for regional frequency analysis
  using the methods of J. R. M. Hosking and J. R. Wallis (1997),
  "Regional frequency analysis: an approach based on L-moments".
European Commission's Labour Market Policy (LMP) database
  (<https://webgate.ec.europa.eu/empl/redisstat/databrowser/explore/all/lmp?lang=en&display=card&sort=category>)
  provides information on labour market interventions, which are government
  actions to help and support the unemployed and other disadvantaged groups in
  the transition from unemployment or inactivity to work. It covers the EU
  countries and Norway. This package provides functions for downloading and
  importing the LMP data and metadata (codelists).
Linear model functions using permutation tests.
Implementation based on Zhang, Jie & Huang, Kun (2014) <doi:10.4137/CIN.S14021> Normalized ImQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers. Cancer informatics, 13, CIN-S14021.
Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>,
  ii.  Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>,
  iii. Imdadullah, Aslam, and Saima (2017),
  iv.  Marquardt (1970) <doi:10.2307/1267205>.
Exact and approximation algorithms for variable-subset
  selection in ordinary linear regression models.  Either compute all
  submodels with the lowest residual sum of squares, or determine the
  single-best submodel according to a pre-determined statistical
  criterion.  Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.
A collection of tests, data sets, and examples
 for diagnostic checking in linear regression models. Furthermore,
 some generic tools for inference in parametric models are provided.
Robust test(s) for model diagnostics in regression. The current version contains a robust test for functional specification (linearity). The test is based on the robust bounded-influence test by Heritier and Ronchetti (1994) <doi:10.1080/01621459.1994.10476822>.
Non-parametric estimators for casual effects based on longitudinal modified treatment 
  policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, 
  and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are 
  censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure 
  irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects 
  can be calculated and relative risks and odds ratios may be calculated for binary outcomes. 
  Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).
Contains a suite of shiny applications
    meant to explore linear model inference feature through simulation
    and games.
Computes the implied weights of linear regression models for estimating
     average causal effects and provides diagnostics based on these weights. These
     diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023)
     <doi:10.1093/biomet/asac058> where
     several regression estimators are represented as weighting estimators, in connection
     to inverse probability weighting. 'lmw' provides tools to diagnose
     representativeness, balance, extrapolation, and influence for these models,
     clarifying the target population of inference. Tools are also available to
     simplify estimating treatment effects for specific target populations of interest.
We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>.
Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han S., et al. (2019) <doi:10.1093/bib/bby065>.
Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, <doi:10.18637/jss.v020.i07>.
An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.
Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.
Estimates a lognormal-Pareto mixture by means of the Expectation-Conditional-Maximization-Either algorithm and by maximizing the profile likelihood function. A likelihood ratio test for discriminating between lognormal and Pareto tail is also implemented. See Bee, M. (2022) <doi:10.1007/s11634-022-00497-4>.
Various plots and functions that make use of the lattice/trellis plotting framework. 
   The plots, which include loaPlot(), loaMapPlot() and trianglePlot(), and use panelPal(), a function that 
   extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel 
   and panel-to-key synchronization/management.
Provides a framework to load text and excel files through a 'shiny' graphical interface. It allows renaming, transforming, ordering and removing variables. It includes basic exploratory methods such as the mean, median, mode, normality test, histogram and correlation.
Computing statistical hypothesis testing for loading in principal component analysis (PCA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), orthogonal smoothed PCA (OS-PCA) (Yamamoto, H. et al. (2021) <doi:10.3390/metabo11030149>), one-sided kernel PCA (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>), partial least squares (PLS) and PLS discriminant analysis (PLS-DA) (Yamamoto, H. et al. (2009) <doi:10.1016/j.chemolab.2009.05.006>), PLS with rank order of groups (PLS-ROG) (Yamamoto, H. (2017) <doi:10.1002/cem.2883>), regularized canonical correlation analysis discriminant analysis (RCCA-DA) (Yamamoto, H. et al. (2008) <doi:10.1016/j.bej.2007.12.009>), multiset PLS and PLS-ROG (Yamamoto, H. (2022) <doi:10.1101/2022.08.30.505949>).
Modifying a load shape to match specific peak and 
  load factor is a fundamental component for various power system 
  planning and operation studies. This package is an efficient tool 
  to modify a reference load shape while matching the desired peak
  and load factor. The package offers both linear and non-linear method,
  described in <https://rpubs.com/riazakhan94/load_shape_match_peak_energy>. 
  The user can control the shape of the final load shape by regulating 
  certain parameters. The package provides validation metrics for 
  assessing the derived load shape in terms of preserving time series 
  properties. It also offers powerful graphics, that allows the user to
  visually assess the derived load shape.
Automated analysis and modeling of longitudinal 'omics' data (e.g. breath 'metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.
Simulate lobster catch process in a trap fishery. Factors such as lobster density on ocean floor, their movement, trap saturation and bait shrinkage rate can be modeled. Details of the methods for modeling those processes can be found in: Addison and Bell (1997) <doi:10.1071/MF97169>.
A set of tools for inspecting and understanding R data
    structures inspired by str(). Includes ast() for visualizing abstract
    syntax trees, ref() for showing shared references, cst() for showing
    call stack trees, and obj_size() for computing object sizes.
Network analysis usually requires estimating the uncertainty of
    graph statistics. Through this package, we provide tools to bootstrap 
    various networks via local bootstrap procedure. Additionally, it includes 
    functions for generating probability matrices, creating network adjacency 
    matrices from probability matrices, and plotting network structures. 
    The reference will be updated soon.
Implements novel nonparametric approaches to address
    biases and confounding when comparing treatments or exposures in
    observational studies of outcomes. While designed and appropriate for use
    in studies involving medicine and the life sciences, the package can be
    used in other situations involving outcomes with multiple confounders.
    The package implements a family of methods for non-parametric bias correction
    when comparing treatments in observational studies, including survival
    analysis settings, where competing risks and/or censoring may be present.
    The approach extends to bias-corrected personalized predictions of treatment
    outcome differences, and analysis of heterogeneity of treatment effect-sizes
    across patient subgroups. For further details, please see: 
    Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl:
    An R Package for Comparative Safety and Effectiveness Research.
    Journal of Statistical Software. 2020. p. 1–32. Available from <doi:10.18637/jss.v096.i04>.
Implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models.  Copula family and local likelihood bandwidth parameters are selected by leave-one-out cross-validation.  The models are implemented in 'TMB', meaning that the local score function is efficiently calculated via automated differentiation (AD), such that quasi-Newton algorithms may be used for parameter estimation.
Implementation of a theoretically supported alternative to k-nearest neighbors for functional data 
  to solve problems of estimating unobserved segments of a partially observed functional data sample, 
  functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. 
  Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. 
  The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.
Computational routines for estimating local Gaussian parameters. Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48 <DOI:10.1016/j.jeconom.2012.08.001>.
Local Individual Conditional Expectation ('localICE') is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI). localICE is a model-agnostic XAI approach which provides three-dimensional local explanations for particular data instances. The approach is proposed in the master thesis of Martin Walter as an extension to ICE (see Reference). The three dimensions are the two features at the horizontal and vertical axes as well as the target represented by different colors. The approach is applicable for classification and regression problems to explain interactions of two features towards the target. For classification models, the number of classes can be more than two and each class is added as a different color to the plot. The given instance is added to the plot as two dotted lines according to the feature values. The localICE-package can explain features of type factor and numeric of any machine learning model. Automatically supported machine learning packages are 'mlr', 'randomForest', 'caret' or all other with an S3 predict function. For further model types from other libraries, a predict function has to be provided as an argument in order to get access to the model. Reference to the ICE approach: Alex Goldstein, Adam Kapelner, Justin Bleich, Emil Pitkin (2013) <arXiv:1309.6392>.
In the generalized Roy model, the marginal treatment effect (MTE) can be used as
  a building block for constructing conventional causal parameters such as the average treatment
  effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection
  equation and an outcome equation, the function mte() estimates the MTE via the semiparametric
  local instrumental variables method or the normal selection model. The function mte_at() evaluates
  MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at()
  evaluates MTE projected onto the estimated propensity score. The function ace() estimates
  population-level average causal effects such as ATE, ATT, or the marginal policy relevant
  treatment effect.
Local explanations of machine learning models describe, how features contributed to a single prediction. 
    This package implements an explanation method based on LIME 
    (Local Interpretable Model-agnostic Explanations, 
    see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable
    inputs are created based on local rather than global behaviour of each original feature.
Functionalities for calculating the local score and calculating statistical relevance (p-value) to find a local Score in a sequence of given distribution (S. Mercier and J.-J. Daudin (2001) <https://hal.science/hal-00714174/>) ; S. Karlin and S. Altschul (1990) <https://pmc.ncbi.nlm.nih.gov/articles/PMC53667/>  ; S. Mercier, D. Cellier and F. Charlot (2003) <https://hal.science/hal-00937529v1/> ; A. Lagnoux, S. Mercier and P. Valois (2017) <doi:10.1093/bioinformatics/btw699> ).
The package converts R data onto input and data for LocalSolver,
    executes optimization and exposes optimization results as R data.
    LocalSolver (http://www.localsolver.com/) is an optimization engine
    developed by Innovation24 (http://www.innovation24.fr/). It is designed to
    solve large-scale mixed-variable non-convex optimization problems.  The
    localsolver package is developed and maintained by WLOG Solutions
    (http://www.wlogsolutions.com/en/) in collaboration with Decision Support
    and Analysis Division at Warsaw School of Economics
    (http://www.sgh.waw.pl/en/).
Implements a local indicator of stratified power to analyze local spatial stratified association and demonstrate how spatial stratified association changes spatially and in local regions, as outlined in Hu et al. (2024) <doi:10.1080/13658816.2024.2437811>.
A set of functions and tools to conduct acoustic source localization, as well as organize and check localization data and results. The localization functions implement the modified steered response power algorithm described by Cobos et al. (2010) <doi:10.1109/LSP.2010.2091502>.
Download Internet Protocol (IP) address location and more from the 'ip-api' application programming interface (API) <https://ip-api.com/>. The package makes it easy to get the latitude, longitude, country, region, and organisation associated to the provided IP address. The information is conveniently returned in a rectangular format.
A set of functions to locate some programs 
 available on the user machine. The package provides functions to locate 
 'Node.js', 'npm', 'LibreOffice', 'Microsoft Word', 'Microsoft PowerPoint', 
 'Microsoft Excel', 'Python', 'pip', 'Mozilla Firefox' and 'Google Chrome'.
 User can test the availability of a program with eventually a version 
 and call it with function system2() or system(). This allows the use of 
 a single function to retrieve the path to a program regardless of the 
 operating system and its configuration.
Identification of equilibrium locations in location games (Hotelling (1929) 
    <doi:10.2307/2224214>). In these games, two competing actors place
    customer-serving units in two locations simultaneously. Customers make the 
    decision to visit the location that is closest to them. The functions in 
    this package include Prim algorithm (Prim (1957) 
    <doi:10.1002/j.1538-7305.1957.tb01515.x>) to find the minimum spanning tree 
    connecting all network vertices, an implementation of Dijkstra algorithm 
    (Dijkstra (1959) <doi:10.1007/BF01386390>) to find the shortest distance and 
    path between any two vertices, a self-developed algorithm using elimination 
    of purely dominated strategies to find the equilibrium, and several plotting 
    functions.
A bioinformatics pipeline for performing taxonomic
    assignment of DNA metabarcoding sequence data while considering
    geographic location. A detailed tutorial is available at
    <https://urodelan.github.io/Local_Taxa_Tool_Tutorial/>.
    A manuscript describing these methods is in preparation.
Computation of local false discovery rates.
Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.
Provides test of second-order stationarity for time
	series (for dyadic and arbitrary-n length data). Provides
	localized autocovariance, with confidence intervals,
	for locally stationary (nonstationary) time series.
	See Nason, G P (2013) "A test for second-order stationarity and
	approximate confidence intervals for localized autocovariance
	for locally stationary time series." Journal of the Royal Statistical
	Society, Series B, 75, 879-904.  <doi:10.1111/rssb.12015>.
Datasets for the third edition of "Statistics: Unlocking the Power of Data" by Lock^5
    Includes version of datasets from earlier editions.
Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data by kernel-weighted estimating equation.
Computes local polynomial estimators for 
  the regression and also density. It comprises several 
  different utilities to handle kernel estimators.
Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) <doi:10.1002/gepi.22356>). Full support for sparse matrices from the R-package 'Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from 'bed'+'bim'+'fam' files.
To decompose symmetric matrices such as brain connectivity matrices so that one can extract sparse latent component matrices and also estimate mixing coefficients, a blind source separation (BSS) method named LOCUS was proposed in Wang and Guo (2023) <arXiv:2008.08915>. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings.
Publication-ready regional gene locus plots similar to those produced by the web interface 'LocusZoom' <https://my.locuszoom.org>, but running locally in R. Genetic or genomic data with gene annotation tracks are plotted via R base graphics, 'ggplot2' or 'plotly', allowing flexibility and easy customisation including laying out multiple locus plots on the same page. It uses the 'LDlink' API <https://ldlink.nih.gov/?tab=apiaccess> to query linkage disequilibrium data from the 1000 Genomes Project and can overlay this on plots <doi:10.1093/bioadv/vbaf006>.
Read and write access to PNG image files using the LodePNG
    library. The package has no external dependencies.
Genome-wide association (GWAS) analyses
  of a biomarker that account for the limit of detection.
Impute observed values below the limit of detection (LOD) via
    censored likelihood multiple imputation (CLMI) in single-pollutant
    models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>.
    CLMI handles exposure detection limits that may change throughout the course
    of exposure assessment. 'lodi' provides functions for imputing and
    pooling for this method.
Tools to fit linear regression model to data while taking into account covariates with lower limit of detection (LOD).
Data sets for Chirok Han (2022, ISBN:979-11-303-1497-6,
  "Lectures on Econometrics"). Students, teachers, and self-learners will
  find the data sets essential for replicating the results in the book.
Estimate model parameters to determine whether two compounds have synergy, antagonism, or Loewe's Additivity.
Alternate font rendering is useful when rendering text to novel
   graphics outputs where modern font rendering is not available or 
   where bespoke text positioning is required. Bitmap and vector fonts allow for 
   custom layout and rendering using pixel coordinates and line drawing.  
   Formatted text is created as a data.frame of pixel coordinates (for bitmap fonts)
   or stroke coordinates (for vector fonts).  All text can be easily 
   previewed as a matrix or raster image.  
   A selection of fonts is included with this package.
Logger to keep track of informational events and errors useful for debugging.
The log4r package is meant to provide a fast, lightweight,
  object-oriented approach to logging in R based on the widely-emulated
  'log4j' system and etymology.
Enables users to handle the dataset cleaning for conducting
    specific analyses with the log files from two international educational
    assessments: the Programme for International Student Assessment (PISA,
    <https://www.oecd.org/pisa/>) and the Programme for the International
    Assessment of Adult Competencies (PIAAC,
    <https://www.oecd.org/skills/piaac/>). An illustration of the analyses can be
    found on the LOGAN Shiny app (<https://loganpackage.shinyapps.io/shiny/>) on
    your browser.
Methods for fitting log-link GLMs and GAMs to binomial data,
    including EM-type algorithms with more stable convergence properties than standard methods.
Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach and Schuhmacher (2014) <doi:10.1214/14-EJS930>.
Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Inspired by the the 'futile.logger' R package and 'logging'
    Python module, this utility provides a flexible and extensible way of
    formatting and delivering log messages with low overhead.
Pure R implementation of the ubiquitous log4j package. It offers hierarchic 
  loggers, multiple handlers per logger, level based filtering, space handling in messages 
  and custom formatting.
An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary
    log-writing interfaces. It provides a set of wrappings for base R's
    message(), warning(), and stop() functions that maintain identical
    functionality, but also log the handler message to an 'ndjson' log file.
    'loggit' also exports its internal 'loggit()' function for powerful and
    configurable custom logging. No change in existing code is necessary to use
    this package, and should only require additions to fully leverage the power
    of the logging system. 'loggit' also provides a log reader for reading an
    'ndjson' log file into a data frame, log rotation, and live echo of the
    'ndjson' log messages to terminal 'stdout' for log capture by external
    systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines,
    modeling work flows, and more. Please see the vignettes for detailed example
    use cases.
An easy-to-use 'ndjson' (newline-delimited 'JSON') logger.
    It provides a set of wrappers for base R's message(), warning(), and
    stop() functions that maintain identical functionality, but also log
    the handler message to an 'ndjson' log file.
    No change in existing code is necessary to use this package, 
    and only a few additional adjustments are needed to fully 
    utilize its potential.
Implementation of the Swiss Confederation's standard analysis
    model for salary analyses
    <https://www.ebg.admin.ch/en/equal-pay-analysis-with-logib> in R. The
    analysis is run at company-level and the model is intended for
    medium-sized and large companies. It can technically be used with 50
    or more employees (apprentices, trainees/interns and expats are not
    included in the analysis). Employees with at least 100 employees are
    required by the Gender Equality Act to conduct an equal pay analysis.
    This package allows users to run the equal salary analysis in R,
    providing additional transparency with respect to the methodology and
    simple automation possibilities.
Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.
A statistical learning method that tries to find the best set
  of predictors and interactions between predictors for modeling binary or
  quantitative response data in a decision tree. Several search algorithms
  and ensembling techniques are implemented allowing for finetuning the
  method to the specific problem. Interactions with quantitative
  covariables can be properly taken into account by fitting local
  regression models. Moreover, a variable importance measure for assessing
  marginal and interaction effects is provided. Implements the
  procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).
Two classification ensemble methods based on logic regression models.  LogForest() uses a bagging approach to construct an ensemble of logic regression models.  LBoost() uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.
Routines for fitting Logic Regression models. Logic Regression is described
	in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte
        Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005)
        <DOI:10.1002/gepi.20042>.
Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.
Framework for adding authentication to 'shiny' applications.
    Provides flexibility as compared to other options for where user
    credentials are saved, allows users to create their own accounts, and
    password reset functionality. Bryer (2024)
    <doi:10.5281/zenodo.10987876>.
Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys 
	prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal
	solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary
	maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available
	in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.
Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
An implementation of a method of extending a logistic regression
    model beyond linear effects of the co-variates. The extension in is
    constructed by first equating the logistic regression model to a naive Bayes
    model where all the margins are specified to follow natural exponential 
    distributions conditional on Y, that is, a model for Y given X that is
    specified through the distribution of X given Y, where the columns of X are
    assumed to be mutually independent conditional on Y. Subsequently, the
    model is expanded by adding vine - copulas to relax the assumption of
    mutual independence, where pair-copulas are added in a stage-wise, forward
    selection manner. Some heuristics are employed during the process of
    selecting edges, as well as the families of pair-copula models. After each
    component is added, the parameters are updated by a (smaller) number of
    gradient steps to maximise the likelihood. When the algorithm has stopped
    adding edges, based the criterion that a new edge should improve the
    likelihood more than k times the number new parameters, the parameters are
    updated with a larger number of gradient steps, or until convergence.
A system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).
Automatically returns 36 logistic models including 23 individual models and 13 ensembles of models of logistic data. The package also returns 10 plots, 5 tables, and a summary report. The package automatically
    builds all 36 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data sets. The package includes medical data (the Pima Indians data set), and
    information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as
    True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver
    Operating Characteristic (ROC) curves for each of the 36 models.
Dimensionality reduction techniques for binary data including
    logistic PCA.
Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. <https://psyarxiv.com/gq7az/>. The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline.
Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.
Functions for fitting a functional principal components logit regression model
	in four different situations: ordinary and filtered functional principal components
	of functional predictors, included in the model according to their variability
	explanation power, and according to their prediction ability by stepwise methods. The
	proposed methods were developed in Escabias et al (2004) 
	<doi:10.1080/10485250310001624738> and Escabias et al (2005)
	<doi:10.1016/j.csda.2005.03.011>.
Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.
Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.
Functions to sample from the double log normal distribution and calculate the density, distribution and quantile functions.
Functions to fit log-multiplicative models using 'gnm', with
  support for convenient printing, plots, and jackknife/bootstrap
  standard errors. For complex survey data, models can be fitted from
  design objects from the 'survey' package. Currently supported models
  include UNIDIFF (Erikson & Goldthorpe, 1992),
  a.k.a. log-multiplicative layer effect model (Xie, 1992)
  <doi:10.2307/2096242>, and several association models:
  Goodman (1979) <doi:10.2307/2286971>
  row-column association models of the RC(M) and RC(M)-L families
  with one or several dimensions; two skew-symmetric association
  models proposed by Yamaguchi (1990) <doi:10.2307/271086>
  and by van der Heijden & Mooijaart (1995) <doi:10.1177/0049124195024001002>
  Functions allow computing the intrinsic association coefficient
  (see Bouchet-Valat (2022) <doi:10.1177/0049124119852389>)
  and the Altham (1970) index <doi:10.1111/j.2517-6161.1970.tb00816.x>,
  including via the Bayes shrinkage estimator proposed
  by Zhou (2015) <doi:10.1177/0081175015570097>;
  and the RAS/IPF/Deming-Stephan algorithm.
Estimation of a lognormal - Generalized Pareto mixture via the Expectation-Maximization algorithm. Computation of bootstrap standard errors is supported and performed via parallel computing. Functions for random number simulation and density evaluation are also available. For more details see Bee and Santi (2025) <doi:10.48550/arXiv.2505.22507>.
The lognormal distribution  
  (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>)
  can characterize uncertainty that is bounded by zero.
  This package provides estimation of distribution parameters, computation of
  moments and other basic statistics, and an approximation of the distribution
  of the sum of several correlated lognormally distributed variables 
  (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the 
  difference of two correlated lognormally distributed variables
  (Lo 2012 <doi:10.1155/2012/838397>).
Functions to fits simple linear regression models with log normal errors 
		and identity link, i.e. taking the responses on the original scale. See
		Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.
Uses approximations to compute the natural logarithm of the Gamma
    function for large values.
Access to the Greek New Testament (27 books) and the Old Testament (39 books) and allow users to do textual analysis on the data. The New and Old Testament have been provided in their original languages, Greek and Hebrew, respectively. Additionally, the Revised American Standard Bible is also provided for users who'd rather use a word–for–word modern English translation.
Contains functions to help create log files.  The 
    package aims to overcome the difficulty of the base R sink() command.  The
    log_print() function will print to both the console and the file log, 
    without interfering in other write operations.
Tools for assessing equivalence of similar Logistic Regression models.
A utility to facilitate the logging and review of R programs in clinical trial programming workflows.
This is an extension package to 'logrx', which is a log creation 
  program focused on Clinical Reporting within the Pharma Industry.  This package 
  enables a simple 'shiny-based' Add-in that provides a point and click interface 
  to produce a log for a single program.
Contains routines for logspline density estimation.
	The function oldlogspline() uses the same algorithm as the logspline package
	version 1.0.x; i.e. the Kooperberg and Stone (1992) 
	algorithm (with an improved interface).  The recommended routine logspline()
	uses an algorithm from Stone et al (1997)  <DOI:10.1214/aos/1031594728>.
Kernel regression smoothing with adaptive local or global plug-in
	     bandwidth selection.
Draw lolliplot using GRanges objects. this package was designed 
  only for drawing lolliplot. So, it's faster than 'trackViewer', but un-related 
  functions has been derived.
Estimation of Latent Order Logistic (LOLOG) Models for Networks.
    LOLOGs are a flexible and fully general class of statistical graph models. 
    This package provides functions for performing MOM, GMM and variational 
    inference. Visual diagnostics and goodness of fit metrics are provided. 
    See Fellows (2018) <arXiv:1804.04583> for a detailed description of the methods.
Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.
Read, register and compare point sets from single molecule localization microscopy.
Computes the Lomb-Scargle Periodogram and actogram for evenly or unevenly sampled time series. Includes a randomization procedure to obtain exact p-values. Partially based on C original by Press et al. (Numerical Recipes) and the Python module Astropy. For more information see Ruf, T. (1999). The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biological Rhythm Research, 30(2), 178-201.
An easy tool to transform 2D longitudinal data into 3D arrays suitable for 
  Long short-term memory neural networks training. The array output can be
  used by the 'keras' package. Long short-term memory neural networks are described
  in: Hochreiter, S., & Schmidhuber, J. (1997) <doi:10.1162/neco.1997.9.8.1735>.
Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the 'LongCART' and 'SurvCART' algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.
Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure. Details in McNicholas and Murphy (2010) <doi:10.1002/cjs.10047> and McNicholas and Subedi (2012) <doi:10.1016/j.jspi.2011.11.026>.
This tool takes longitudinal dataset as input and analyzes if there is significant 
             change of the features over time (a proxy for treatments), while detects and controls 
             for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input, 
             including count, proportion, binary, ordinal and continuous data. The output table contains 
              p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.
A collection of parametric and nonparametric methods for the analysis of survival data. Parametric families implemented include Gompertz-Makeham, exponential and generalized Pareto models and extended models. The package includes an implementation of the nonparametric maximum likelihood estimator for arbitrary truncation and censoring pattern based on Turnbull (1976) <doi:10.1111/j.2517-6161.1976.tb01597.x>, along with graphical goodness-of-fit diagnostics. Parametric models for positive random variables and peaks over threshold models based on extreme value theory are described in Rootzén and Zholud (2017) <doi:10.1007/s10687-017-0305-5>; Belzile et al. (2021) <doi:10.1098/rsos.202097> and Belzile et al. (2022) <doi:10.1146/annurev-statistics-040120-025426>.
High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). 
             Currently support mixed effect regression with or without missing observations by considering 
             covariance structures. It provides estimates by missing at random and missing not at random assumptions.
             In this R package, we present Bayesian approaches that statisticians and clinical 
             researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by 
             Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.
Contains general data structures and
  functions for longitudinal data with multiple variables, 
  repeated measurements, and irregularly spaced time points.
  Also implements a shrinkage estimator of dynamical correlation
  and dynamical covariance.
Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2023) <arXiv:2305.17715>  and  Liu, C. et al. (2023) <arXiv:2305.17662>.
Creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests.
Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).
Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data.  In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.
The proposed method aims at predicting the longitudinal mean response trajectory by a 
              kernel-based estimator. The kernel estimator is constructed by imposing weights based on 
              subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. 
              Users could also perform variable selections to derive functional predictors with predictive significance
              by the proposed multiplicative model with multivariate Gaussian kernels.
Datasets and Functionality from
  'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall.
  Estimation of Hurst (and more) parameters for fractional Gaussian noise,
  'fARIMA' and 'FEXP' models.
Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.
An adaption of the consensus clustering approach from
    'ConsensusClusterPlus' for longitudinal data. The longitudinal data is
    clustered with flexible mixture models from 'flexmix', while the consensus
    matrices are hierarchically clustered as in 'ConsensusClusterPlus'. By using
    the flexibility from 'flexmix' and 'FactoMineR', one can use mixed data
    types for the clustering.
Compute power and sample size for linear models of longitudinal
    data. Supported models include mixed-effects models and models fit by
    generalized least squares and generalized estimating equations. The package
    is described in Iddi and Donohue (2022) <DOI:10.32614/RJ-2022-022>. Relevant
    formulas are derived by Liu and Liang (1997) <DOI:10.2307/2533554>, 
    Diggle et al (2002) <ISBN:9780199676750>, and Lu, Luo, and Chen (2008)
    <DOI:10.2202/1557-4679.1098>.
Performs variety of viral quasispecies diversity analyses [see Pamornchainavakul et al. (2024) <doi:10.21203/rs.3.rs-4637890/v1>] based on long-read sequence alignment. Main functions include 1) sequencing error and other noise minimization and read sampling, 2) Single nucleotide variant (SNV) profiles comparison, and 3) viral quasispecies profiles comparison and visualization.
Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.
Performs recursive partitioning of linear and nonlinear mixed effects models,
    specifically for longitudinal data. The package is an
    extension of the original 'longRPart' package by 
    Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.
Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>.
Tools are provided to expand vectors of short URLs into long 'URLs'. 
    No 'API' services are used, which may mean that this operates more slowly than 
    'API' services do (since they usually cache results of expansions that every 
    user of the service requests). You can setup your own caching layer with the 
    'memoise' package if you wish to have a speedup during single sessions or add 
    larger dependencies, such as 'Redis', to gain a longer-term performance boost 
    at the expense of added complexity.
Efficient approximate leave-one-out cross-validation (LOO)
    for Bayesian models fit using Markov chain Monte Carlo, as 
    described in Vehtari, Gelman, and Gabry (2017) 
    <doi:10.1007/s11222-016-9696-4>. 
    The approximation uses Pareto smoothed importance sampling (PSIS), 
    a new procedure for regularizing importance weights. 
    As a byproduct of the calculations, we also obtain approximate 
    standard errors for estimated predictive errors and for the comparison 
    of predictive errors between models. The package also provides methods 
    for using stacking and other model weighting techniques to average 
    Bayesian predictive distributions.
Outlier detection using leave-one-out kernel density estimates and
    extreme value theory. The bandwidth for kernel density estimates is computed 
    using persistent homology, a technique in topological data analysis. Using 
    peak-over-threshold method, a generalized Pareto distribution is fitted to
    the log of leave-one-out kde values to identify outliers.
Simple functions to lookup items in key-value pairs. See  Mehta (2021) <doi:10.1007/978-1-4842-6613-7_6>.
Fits look-up tables by filling entries with the mean or median values of observations
            fall in partitions of the feature space. Partitions can be determined by user of the
            package using input argument feature.boundaries, and dimensions of the feature space
            can be any combination of continuous and categorical features provided by the data set.
            A Predict function directly fetches corresponding entry value, and a default value is
            defined as the mean or median of all available observations.
            The table and other components are represented using the S4 class lookupTable.
An extendable toolkit for interactive data visualization and exploration.
Data used as examples in the 'loon' package.
Provides a bridge between the 'loon' and  'ggplot2' packages.  Extends the grammar of ggplot to add clauses to create interactive 'loon' plots. Existing ggplot(s) can be turned into interactive 'loon' plots and 'loon' plots into static ggplot(s); the function 'loon.ggplot()' is the bridge from one plot structure to the other.
Package 'shiny' provides interactive web applications in R. Package 'loon' is an interactive toolkit engaged in open-ended, creative and unscripted data exploration. The 'loon.shiny' package can take 'loon' widgets and display a selfsame 'shiny' app.
Implement tour algorithms in interactive graphical system 'loon'.
Performs Levins' loop analysis of qualitatively-specified complex causal systems. Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means direct causal relationships and indirect causal effects are coded as sign only (i.e. increases, decreases, no change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as 'graphviz' (<https://graphviz.org>). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.
Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using 'numDeriv'. Graph-based algorithms from 'igraph' are employed for path detection.
Performs extreme value analysis at multiple locations using functions from the 'evd' package. Supports both point-based and gridded input data using the 'terra' package, enabling flexible looping across spatial datasets for batch processing of generalised extreme value, Gumbel fits.
Common coordinate-based workflows involving processed chromatin loop and genomic element data are considered and packaged into appropriate customizable functions. Includes methods for linking element sets via chromatin loops and creating consensus loop datasets.
Change-point detection algorithm with
 label constraints and a penalty for each change outside of labels.
 Read TD Hocking, A Srivastava (2023) <doi:10.1007/s00180-022-01238-z>
 for details.
Estimates marginal likelihood from a posterior sample using the method described in Wang et al. (2023) <doi:10.1093/sysbio/syad007>, which does not require evaluation of any additional points and requires only the log of the unnormalized posterior density for each sampled parameter vector.
Performs analysis of Differential Item Functioning (DIF) for
        dichotomous and polytomous items using an iterative hybrid of
        ordinal logistic regression and item response theory (IRT) according to 
        Choi, Gibbons, and Crane (2011) <doi:10.18637/jss.v039.i08>.
Estimate covariance matrices that contain low rank and sparse components
Quickly generate lorem ipsum placeholder text. Easy to
    integrate in RMarkdown documents. Includes an RStudio addin to insert
    lorem ipsum into the current document.
The Lorentz transform in special relativity; also the
    gyrogroup structure of three-velocities.  Performs active and
    passive transforms and has the ability to use units in which the
    speed of light is not unity.  Includes some experimental
    functionality for celerity and rapidity.  For general relativity,
    see the 'schwarzschild' package.
Provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census.
  These methods use different interpolation techniques to infer the distribution of incomes within income bins.  One method is an implementation of 
  Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB).  The other method is based on a new technique, Lorenz interpolation, 
  which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data.  These methods can be used to 
  estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index.  
  Jargowsky and Wheeler (2018) <doi:10.1177/0081175018782579>.
Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) <doi:10.1016/j.csda.2021.107347> and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) <doi:10.1214/23-EJS2200>.
Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <doi:10.1016/j.jmva.2019.04.004>.
Provides a robust collection of functions tailored for microbial ecology analysis, encompassing both data analysis and visualization. It introduces an encapsulation feature that streamlines the process into a summary object. With the initial configuration of this summary object, users can execute a wide range of analyses with a single line of code, requiring only two essential parameters for setup. The package delivers comprehensive outputs including analysis objects, statistical outcomes, and visualization-ready data, enhancing the efficiency of research workflows. Designed with user-friendliness in mind, it caters to both novices and seasoned researchers, offering an intuitive interface coupled with adaptable customization options to meet diverse analytical needs.
Functions for simulating missing morphometric
	data randomly, with taxonomic bias and with anatomical bias. LOST also 
	includes functions for estimating linear and geometric morphometric data.
Provides a simple mechanism to specify a symmetric block
    diagonal matrices (often used for covariance matrices).  This is based
    on the domain specific language implemented in 'nlmixr2' but expanded
    to create matrices in R generally instead of specifying parts of
    matrices to estimate. It has expanded to include some matrix manipulation
    functions that are generally useful for 'rxode2' and 'nlmixr2'.
A curated collection of Howard Phillips 
    Lovecraft's complete stories, collected for the purpose of text 
    analysis.
Performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.
Creates lowpass filters which are commonly used in ion channel recordings. It supports generation of random numbers that are filtered, i.e. follow a model for ion channel recordings, see <doi:10.1109/TNB.2018.2845126>. Furthermore, time continuous convolutions of piecewise constant signals with the kernel of lowpass filters can be computed.
Solves quadratic programming problems where the Hessian is represented as the product of two matrices. Thanks to Greg Hunt for helping getting this version back on CRAN. The methods in this package are described in: Ormerod, Wand and Koch (2008) "Penalised spline support vector classifiers: computational issues" <doi:10.1007/s00180-007-0102-8>.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Niederreiter-Xing sequence <DOI:10.1007/978-3-642-56046-0_30>.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Sobol sequence.
Provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.
Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.
Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering.  See Einbeck, Tutz and Evers (2005) <doi:10.1007/s11222-005-4073-8> and Ameijeiras-Alonso and Einbeck (2023) <doi:10.1007/s11634-023-00575-1>.
Classification method obtained through linear programming.
    It is advantageous with respect to the classical developments when the distribution of the variables
    involved is unknown or when the number of variables is much greater than the number of individuals.
    Mathematical details behind the method are published in Nueda, et al. (2022) "LPDA: A new classification method based on linear programming".
    <doi:10.1371/journal.pone.0270403>.
Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.
It uses 'phenological' and productivity-related variables derived from time series of vegetation 
    indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which 
    eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator 
    is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing 
    productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description 
    of the methods used in the package to calculate the indicator.
A nonparametric method to approximate Laplacian graph spectra of a network with 
    ordered vertices. This provides a computationally efficient algorithm for obtaining an 
	accurate and smooth estimate of the graph Laplacian basis. The approximation results can 
	then be used for tasks like change point detection, k-sample testing, and so on. The 
	primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).
Provides a bunch of algorithms based on linear programming for estimating, under 
    the homogeneity hypothesis, RxC ecological contingency tables (or vote transition matrices) 
    using mainly aggregate data (from voting units). 
    References: 
    Pavía and Romero (2024) <doi:10.1177/00491241221092725>.
    Pavía and Romero (2024) <doi:10.1093/jrsssa/qnae013>.
    Pavía (2023) <doi:10.1007/s43545-023-00658-y>.
    Pavía (2024) <doi:10.1080/0022250X.2024.2423943>.
    Pavía (2024) <doi:10.1177/07591063241277064>.
    Pavía and Penadés (2024). A bottom-up approach for ecological inference.
    Romero, Pavía, Martín and Romero (2020) <doi:10.1080/02664763.2020.1804842>.
    Acknowledgements:
    The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257, CIAICO/2023/031) and Ministerio de Economía e Innovación (grant PID2021-128228NB-I00) for supporting this research.
Functions to estimate the intensity function and its derivative of a given order of a multiplicative counting process using the local polynomial method.
Provides functions to estimate and visualize linear as well as nonlinear impulse 
             responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>.
             The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.
LP nonparametric high-dimensional K-sample comparison method that includes 
    (i) confirmatory test, (ii) exploratory analysis, and (iii) options to output a 
	data-driven LP-transformed matrix for classification. The primary reference is 
	Mukhopadhyay, S. and Wang, K. (2020, Biometrika); <arXiv:1810.01724>.
Local partial likelihood estimation by Fan, Lin and Zhou(2006)<doi:10.1214/009053605000000796> and simultaneous confidence band is a set of tools to test the covariates-biomarker interaction for survival data. Test for the covariates-biomarker interaction using the bootstrap method and the asymptotic method with simultaneous confidence band (Liu, Jiang and Chen (2015)<doi:10.1002/sim.6563>).
Apply Univariate Long Memory Models,
    Apply Multivariate Short Memory Models To Hydrological Dataset,
    Estimate Intensity Duration Frequency curve to rainfall series.
    NEW -- Calculate the monthly water requirement for herbaceous and arboreal 
    plants.
Creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.
Computes power, or sample size or the detectable difference for a repeated measures model with attrition. It requires the variance covariance matrix of the observations but can compute this matrix for several common random effects models. See Diggle, P, Liang, KY and Zeger, SL (1994, ISBN:9780198522843).
Provide methods to perform customized inference at individual level by taking 
	contextual covariates into account. Three main functions are provided 
	in this package: (i) LASER(): it generates specially-designed artificial relevant 
	samples for a given case; (ii) g2l.proc(): computes customized fdr(z|x); and (iii) 
	rEB.proc(): performs empirical Bayes inference based on LASERs. The details can be 
	found in Mukhopadhyay, S., and Wang, K (2021, <arXiv:2004.09588>).
Local Polynomial Regression with Ridging.
Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.
Lp_solve is freely available (under LGPL 2) software for
        solving linear, integer and mixed integer programs. In this
        implementation we supply a "wrapper" function in C and some R
        functions that solve general linear/integer problems,
        assignment problems, and transportation problems. This version
        calls lp_solve version 5.5.
The lpSolveAPI package provides an R interface to 'lp_solve',
    a Mixed Integer Linear Programming (MILP) solver with support for pure
    linear, (mixed) integer/binary, semi-continuous and special ordered sets
    (SOS) models.
Produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.<doi:10.1002/sim.9212>.
Estimate linear quantile mixtures based on Time-Constant (TC) and/or Time-Varying (TV), discrete, random coefficients.
Functions to fit quantile regression models for hierarchical
    data (2-level nested designs) as described in Geraci and
    Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>.
    A vignette is given in Geraci (2014, Journal of Statistical Software)
    <doi:10.18637/jss.v057.i13> and included in the package documents.
    The packages also provides functions to fit quantile models for
    independent	data and for count responses.
It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.
Lexical response data is a package that 
    can be used for processing cued-recall, free-recall, 
    and sentence responses from memory experiments.
The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.
In the fashion of 'node.js' <https://nodejs.org/>, requires a file,
  sourcing into the current environment only the variables explicitly specified
  in the module.exports or exports list variable. If the file was already sourced,
  the result of the earlier sourcing is returned to the caller.
Implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.
Provides S3 classes to represent low rank matrix
    decompositions.
Performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures.
A collection of hypothesis tests and confidence intervals based on the likelihood ratio
    <https://en.wikipedia.org/wiki/Likelihood-ratio_test>.
Estimates two-dimensional local wavelet spectra.
Wavelet-based methods for testing stationarity and quadtree segmenting of images, see Taylor et al (2014) <doi:10.1080/00401706.2013.823890>.
The basic idea of latent semantic analysis (LSA) is, 
  that text do have a higher order (=latent semantic) structure which, 
  however, is obscured by word usage (e.g. through the use of synonyms 
  or polysemy). By using conceptual indices that are derived statistically 
  via a truncated singular value decomposition (a two-mode factor analysis) 
  over a given document-term matrix, this variability problem can be overcome.
Provides functions that allow for convenient working with vector space models of semantics/distributional semantic models/word embeddings. 
	Originally built for LSA models (hence the name), but can be used for all such vector-based models. 
	For actually building a vector semantic space, use the package 'lsa' or other specialized software. 
	Downloadable semantic spaces can be found at <https://sites.google.com/site/fritzgntr/software-resources>.
Provides a lasso-based method for building mechanistic models using the SAMBA algorithm (Stochastic Approximation for Model Building Algorithm) (M Prague, M  Lavielle (2022) <doi:10.1002/psp4.12742>). The package extends the 'Rsmlx' package (version 2024.1.0) to better handle high-dimensional data. It relies on the 'Monolix' software (version 2024R1; see (<https://monolixsuite.slp-software.com/monolix/2024R1/>), which must be installed beforehand.
Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen
             und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, 
             C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen 
             der österreichischen Bildungsstandardüberprüfung." Wien: facultas. 
             (ISBN: 978-3-7089-1343-8, <https://www.iqs.gv.at/themen/bildungsforschung/publikationen/veroeffentlichte-publikationen>) zur 
             Verfügung.
Provides functions to simulate data from large-scale educational
  assessments, including background questionnaire data and cognitive item
  responses that adhere to a multiple-matrix sampled design. The theoretical
  foundation can be found on
  Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018)
  <doi:10.1186/s40536-018-0068-8>.
Bandwidth selection for kernel density estimators of 2-d
    level sets and highest density regions. It applies a plug-in
    strategy to estimate the asymptotic risk function and minimize to
    get the optimal bandwidth matrix. See Doss and Weng (2018)
    <arXiv:1806.00731> for more detail.
Create lots of colorful plots in a plethora of variations. Try the LSD demotour().
Implementation of Locally Scaled Density Based Clustering (LSDBC) algorithm proposed by Bicici and Yuret (2007) <doi:10.1007/978-3-540-71618-1_82>. This package also contains some supporting functions such as betaCV() function and get_spectral() function.
Interfaces R with LSD simulation models. Reads object-oriented data in results files (.res[.gz]) produced by LSD and creates appropriate multi-dimensional arrays in R. Supports multiple core parallel threads of multi-file data reading for increased performance. Also provides functions to extract basic information and statistics from data files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).
Performing impulse-response function (IRF) analysis of relevant variables of agent-based simulation models, in particular for models described in 'LSD' format. Based on the data produced by the simulation model, it performs both linear and state-dependent IRF analysis, providing the tools required by the Counterfactual Monte Carlo (CMC) methodology (Amendola and Pereira (2024) <doi:10.2139/ssrn.4740360>), including state identification and sensitivity. CMC proposes retrieving the causal effect of shocks by exploiting the opportunity to directly observe the counterfactual in a fully controlled experimental setup. 'LSD' (Laboratory for Simulation Development) is free software available at <https://www.labsimdev.org/>).
Tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).
The solution of equality constrained least squares problem (LSE) is
    given through four analytics methods (Generalized QR Factorization, Lagrange 
    Multipliers, Direct Elimination and Null Space method). We expose the 
    orthogonal decomposition called Generalized QR Factorization (GQR) and also RQ 
    factorization. Finally some codes for the solution of LSE applied in 
    quaternions.
Implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.
It contains functions that solve least squares linear
	     regression problems under linear equality/inequality
	     constraints. Functions for solving quadratic programming
	     problems are also available, which transform such problems
	     into least squares ones first. It is developed based on the
	     'Fortran' program of Lawson and Hanson (1974, 1995), which is
	     public domain and available at
	     <http://www.netlib.org/lawson-hanson/>.
Analysis of dichotomous and continuous response data using latent factor by both 1PL LSIRM and 2PL LSIRM as described in Jeon et al. (2021) <doi:10.1007/s11336-021-09762-5>. It includes original 1PL LSIRM and 2PL LSIRM provided for binary response data and its extension for continuous response data. Bayesian model selection with spike-and-slab prior and method for dealing data with missing value under missing at random, missing completely at random are also supported. Various diagnostic plots are available to inspect the latent space and summary of estimated parameters.
Fits structural equation modeling via penalized likelihood.
Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) <doi:10.18637/jss.v093.i07>.
When the values of the outcome variable Y are either 0 or 1,
      the function lsm() calculates the estimation of the log likelihood 
      in the saturated model. 
      This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 
      through the assumptions 1 and 2. 
      The function LogLik() works (almost perfectly) when the number of independent
      variables K is high, but for small K it calculates wrong values in some cases.
      For this reason, when Y is dichotomous and the data are grouped in J populations,
      it is recommended to use the function lsm() because it works very well for all K.
Obtain least-squares means for linear, generalized linear, 
    and mixed models. Compute contrasts or linear functions of 
    least-squares means, and comparisons of slopes. 
    Plots and compact letter displays. Least-squares means were proposed in
    Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers",
    Tech Report ARS-20-8, USDA National Agricultural Library, and discussed
    further in Searle, Speed, and Milliken (1980) "Population marginal means 
    in the linear model: An alternative to least squares means", 
    The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
    NOTE: lsmeans now relies primarily on code in the 'emmeans' package.
    'lsmeans' will be archived in the near future.
The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.
The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. 'LSMRealOptions' provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. 'LSMRealOptions' values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. 'LSMRealOptions' provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) <doi:10.1093/rfs/14.1.113>.
Tools facilitating access to the 'macro_data' service of the
    'La Societe Nouvelle' API. It ensures an easy and fully-disclosed
    access to all macro-level data used in the 'La Societe Nouvelle'
    systems and the related metadata.  Related API can be accessed from
    <https://api.lasocietenouvelle.org/>.
A 'C++' header library for using the 'libsoda-cxx' library with R. The 'C++' header reimplements the 'lsoda' function from the 'ODEPACK' library for solving initial value problems for first order ordinary differential equations (Hindmarsh, 1982; <https://computing.llnl.gov/sites/default/files/ODEPACK_pub1_u88007.pdf>). The 'C++' header can be used by other R packages by linking against this package. The 'C++' functions can be called inline using 'Rcpp'. Finally, the package provides an 'ode' function to call from R.
Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2020a, <doi:10.48550/arXiv.1804.04916>) and Cattaneo, Farrell and Feng (2020b, <doi:10.48550/arXiv.1906.00202>): lsprobust() for nonparametric point estimation of regression functions and their derivatives and for robust bias-corrected (pointwise and uniform) inference; lspkselect() for data-driven selection of the IMSE-optimal number of knots; lsprobust.plot() for regression plots with robust confidence intervals and confidence bands; lsplincom() for estimation and inference for linear combinations of regression functions from different groups.
Creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.
Linear splines with convenient parametrisations such that 
  (1) coefficients are slopes of consecutive segments or (2) coefficients are 
  slope changes at consecutive knots. Knots can be set manually or at break points
  of equal-frequency or equal-width intervals covering the range of 'x'.
  The implementation follows Greene (2003), chapter 7.2.5.
Implements the LS-PLS (least squares - partial least squares)
  method described in for instance Jørgensen, K., Segtnan, V. H., Thyholt, K.,
  Næs, T. (2004)  "A Comparison of Methods for Analysing Regression Models with
  Both Spectral and Designed Variables"
  Journal of Chemometrics, 18(10), 451--464, <doi:10.1002/cem.890>.
A collection of tools intended to make introductory 
    statistics easier to teach, including wrappers for common 
    hypothesis tests and basic data manipulation. It accompanies 
    Navarro, D. J. (2015). Learning Statistics with R: A Tutorial 
    for Psychology Students and Other Beginners, Version 0.6.
Rapid satellite data streams in operational applications
    have clear benefits for monitoring land cover, 
    especially when information can be delivered as fast as changing
    surface conditions. Over the past decade,
    remote sensing has become a key tool for monitoring and predicting
    environmental variables by using satellite data. 
    This package presents the main applications in remote sensing for 
    land surface monitoring and land cover mapping (soil, vegetation, water...).
     Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.
Due to lack of proper inference procedure and software,
        the ordinary linear regression model is seldom used in practice
        for the analysis of right censored data. This paper presents an
        S-Plus/R program that implements a recently developed inference
        procedure (Jin, Lin and Ying, 2006) <doi:10.1093/biomet/93.1.147> 
        for the accelerated failure time model based on the least-squares
        principle.
Calculates Land Surface Temperature from Landsat band 10 and 11.
  Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). <doi: 10.1109/TGRS.2008.2007125>.
  Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). <doi:10.1016/j.rse.2004.02.003>.
  Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). <doi: 10.1016/j.asr.2009.01.023>.
  Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). <doi:10.1016/0034-4257(96)00039-9>.
  On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). <doi:10.1080/01431169308904400>.
  Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). <doi:10.3390/rs6109829>.
  Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. Skoković D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014).
"Lessons in Statistical Thinking" D.T. Kaplan (2014) 
    <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> 
    is a textbook for a first or second course in statistics that embraces 
    data wrangling, causal reasoning, modeling, statistical adjustment, 
    and simulation. 'LSTbook' supports the student-centered, tidy, 
    pipeline-oriented computing style featured in the book.
A set of functions that allow stationary analysis and locally stationary time series analysis.
Implementations of estimation algorithm of low rank plus sparse structured VAR model by using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). It relates to the algorithm in Sumanta, Li, and Michailidis (2019) <doi:10.1109/TSP.2018.2887401>.
Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes.  The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.
A word embeddings-based semi-supervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>.
    LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).
    It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.
Constructs tables of counts and proportions out of data sets with possibility to insert tables to Excel, Word, HTML, and PDF documents. Transforms tables to data suitable for modelling. Features Gibbs sampling based log-linear (NB2) and power analyses (original by Oleksandr Ocheredko <doi:10.35566/isdsa2019c5>) for tabulated data.
A set of tools for forecasting the next step in a multidimensional setting using tensors.  In the examples, a forecast is made of sea surface temperatures of a geographic grid (i.e. lat/long).  Each observation is a matrix, the entries in the matrix and the sea surface temperature at a particular lattitude/longitude. Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021) "Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting" in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 461-466), IEEE <doi:10.1109/ICMLA52953.2021.00078>.
A suite of functions for reading in a rate file in XML format, 
    stratify a cohort, and calculate 'SMRs' from the stratified cohort and rate file.
Implementation of the three-step approach of latent transition cognitive diagnosis model (CDM) with covariates. This approach can be used to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) <doi:10.3102/10769986231163320> and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) <doi:10.17632/kpjp3gnwbt.1>.
Curated datasets from US Long Term Ecological Research sites.
Allows identification of palettes derived from LTER (Long Term Ecological Research) 
   photographs based on user criteria.
   Also facilitates extraction of palettes from users' photos directly.
Set of the data science tools created by various members of the Long Term 
    Ecological Research (LTER) community. These functions were initially written largely 
    as standalone operations and have later been aggregated into this package.
Implementation of LT-FH++, an extension of
    the liability threshold family history (LT-FH) model.
    LT-FH++ uses a Gibbs sampler for sampling from the truncated 
    multivariate normal distribution and allows for 
    flexible family structures. LT-FH++ was first described in 
    Pedersen, Emil M., et al. (2022) 
    <doi:10.1016/j.ajhg.2022.01.009> 
    as an extension to LT-FH with more flexible family structures, 
    and again as the age-dependent liability threshold (ADuLT) model
    Pedersen, Emil M., et al. (2023) 
    <https://www.nature.com/articles/s41467-023-41210-z> 
    as an alternative to traditional time-to-event genome-wide
    association studies, where family history was not considered.
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Mixture modelling of one-dimensional data using combinations of left-truncated Gamma, Weibull, and Lognormal Distributions. Blostein, Martin & Miljkovic, Tatjana. (2019) <doi:10.1016/j.insmatheco.2018.12.001>.
Targeted Maximum Likelihood Estimation ('TMLE') of
    treatment/censoring specific mean outcome or marginal structural model for
    point-treatment and longitudinal data.
Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.
Recursive partition algorithms designed for fitting survival trees with left-truncated and right-censored (LTRC) data, as well as interval-censored data.
    The LTRC trees can also be used to fit survival trees with time-varying covariates.
Methods of developing linear time series modelling.
 Methods are given for loglikelihood computation, forecasting
  and simulation.
Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.
Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <DOI:10.1080/00401706.2019.1671234>.
Sparklines are small plots (about one line of text high),
  made popular by Edward Tufte.  This package is the interface from R
  to the LaTeX package sparklines by Andreas Loeffer and Dan Luecking
  (<http://www.ctan.org/pkg/sparklines>).  It can work with Sweave or
  knitr or other engines that produce TeX.  The package can be used to
  plot vectors, matrices, data frames, time series (in ts or zoo format).
An interface to 'LuaJIT' <https://luajit.org>, a just-in-time 
    compiler for the 'Lua' scripting language <https://www.lua.org>. Allows 
    users to run 'Lua' code from 'R'.
Functions to work with date-times and time-spans: fast and
    user friendly parsing of date-time data, extraction and updating of
    components of a date-time (years, months, days, hours, minutes, and
    seconds), algebraic manipulation on date-time and time-span objects.
    The 'lubridate' package has a consistent and memorable syntax that
    makes working with dates easy and fun.
Provides a set of tools designed to enhance transparency and understanding of date-time manipulation functions from the 'lubridate' package. 
        It provides detailed feedback about the operations performed by 'lubridate' functions, allowing users to better comprehend and debug their code. 
        These insights serve as both a learning tool for newcomers and a debugging aid for programmers working with date-time data.
Reproduces the harmonized DB of the ESTAT survey of the same name. The survey data is served as separate spreadsheets with noticeable differences in the collected attributes. The tool here presented carries out a series of instructions that harmonize the attributes in terms of name, meaning, and occurrence, while also introducing a series of new variables, instrumental to adding value to the product. Outputs include one harmonized table with all the years, and three separate geometries, corresponding to the theoretical point, the gps location where the measurement was made and the 250m east-facing transect.
Print vectors (and data frames) of floating point numbers
    using a non-scientific format optimized for human readers.  Vectors of
    numbers are rounded using significant digits, aligned at the decimal
    point, and all zeros trailing the decimal point are dropped.  See:
    Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point
    Numbers. In JSM Proceedings, Statistical Computing Section.
    Alexandria, VA: American Statistical Association. 2270-2279.
An implementation of estimating the Latent Unknown Clusters By Integrating Multi-omics Data (LUCID) model (Peng (2019) <doi:10.1093/bioinformatics/btz667>). LUCID conducts integrated clustering using exposures, omics information (and outcome information as an option). This package implements three different integration strategies for multi-omics data analysis within the LUCID framework: LUCID early integration (the original LUCID model), LUCID in parallel (intermediate integration), and LUCID in serial (late integration). Automated model selection for each LUCID model is available to obtain the optimal number of latent clusters, and an integrated imputation approach is implemented to handle sporadic and list-wise missingness in multi-omics data. Lasso-type regularity for exposure and omics features were added. S3 methods for summary and plotting functions were fixed. Fixed minor bugs.
Probabilistic record linkage without direct identifiers using only 
    diagnosis codes. Method is detailed in: Hejblum, Weber, Liao, Palmer, 
    Churchill, Szolovits, Murphy, Kohane & Cai (2019) <doi: 10.1038/sdata.2018.298> ;
    Zhang, Hejblum, Weber, Palmer, Churchill, Szolovits, Murphy, Liao, Kohane 
    & Cai (2021) <doi: 10.1101/2021.05.02.21256490>.
Contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.
Miscellaneous functions commonly used by LuLab. This package aims to help more researchers on epidemiology to perform data management and visualization more efficiently.
Classes and methods for spatially explicit land use change
    modelling in R.
A framework that allows for easy logging of changes in data.
    Main features: start tracking changes by adding a single line of code to 
    an existing script. Track changes in multiple datasets, using multiple 
    loggers. Add custom-built loggers or use loggers offered by other 
    packages. <doi:10.18637/jss.v098.i01>.
A collection of various R functions for the purpose of Luminescence
    dating data analysis. This includes, amongst others, data import, export,
    application of age models, curve deconvolution, sequence analysis and
    plotting of equivalent dose distributions.
Provides functions to calculate lunar and other related
    environmental covariates.
Input latitude and longitude values or an 'sf/sfc' POINT 
    object and get back the time zone in which they exist. Two methods are implemented. 
    One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library
    (<https://github.com/darkskyapp/tz-lookup-oss/>). This method also works outside of countries' 
    borders and in international waters, however speed comes at the cost of accuracy - near time 
    zone borders away from populated centres there is a chance that it will return the incorrect
    time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect 
    points with a detailed map of time zones from here: 
    <https://github.com/evansiroky/timezone-boundary-builder/>. The package also 
    contains several utility functions for helping to understand and visualize 
    time zones, such as listing of world time zones, including information about 
    daylight savings times and their offsets from UTC. You can also plot a 
    time zone to visualize the UTC offset over a year and when daylight savings 
    times are in effect.
A high level interface for 'torch' providing utilities to reduce the
    the amount of code needed for common tasks, abstract away torch details and 
    make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to
    support expressing a large range of models. It's heavily inspired by 'fastai' by 
    Howard et al. (2020) <arXiv:2002.04688>, 'Keras' by Chollet et al. (2015) and 
    'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.
Provides flexible but lightweight logging facilities for R scripts.
    Supports priority levels for logs and messages, flagging messages, capturing
    script output, switching logs, and logging to files or connections.
Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D).
    The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
    The output variable of the datasets is a scalar (quantitative).
    The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM".
    The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors"
    by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>. The package is developed in IDEAL of Northwestern University.
Provides stochastic EM algorithms for latent variable models
    with a high-dimensional latent space. So far, we provide functions for confirmatory item
    factor analysis based on the multidimensional two parameter logistic (M2PL) model and the 
    generalized multidimensional partial credit model. These functions scale well for problems
    with many latent traits (e.g., thirty or even more) and are virtually tuning-free.
    The computation is facilitated by multiprocessing 'OpenMP' API.
    For more information, please refer to:
    Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale
    Full-information Item Factor Analysis. British Journal of Mathematical and Statistical
    Psychology. <doi:10.1111/bmsp.12153>.
Contains a collection of useful
    functions for basic data computation and manipulation,
    wrapper functions for generating 'ggplot2' graphics,
    including statistical model diagnostic plots, methods
    for computing statistical models quality measures (such
    as AIC, BIC, r squared, root mean squared error) and
    general utilities.
Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.
Implements the letter value 'boxplot' which extends the
    standard 'boxplot' to deal with both larger and smaller number of data
    points by dynamically selecting the appropriate number of letter
    values to display.
Generates the Langa-Weir classification of cognitive function for 
    the 2022 Health and Retirement Study (HRS) cognition data. It is 
    particularly useful for researchers studying cognitive aging who wish to 
    work with the most recent release of HRS data. The package provides 
    user-friendly functions for data preprocessing, scoring, and classification
    allowing users to easily apply the Langa-Weir classification system. 
    For details regarding the;
    HRS <https://hrsdata.isr.umich.edu/> and
    Langa-Weir classifications <https://hrsdata.isr.umich.edu/data-products/langa-weir-classification-cognitive-function-1995-2020>.
Provides a flexible and easy-to use interface for the soil vegetation 
    atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran.
    The model simulates daily transpiration, interception, soil and snow evaporation, 
    streamflow and soil water fluxes through a soil profile covered with vegetation, 
    as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) 
    <doi:10.1175/1525-7541(2003)004%3C1276:SOAETS%3E2.0.CO;2>. A set of high-level functions
    for model set up, execution and parallelization provides easy access to plot-level SVAT 
    simulations, as well as multi-run and large-scale applications.
Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/master/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.
Wrapper functions for the implementation of lagged weighted quantile sum regression, as per 'Gennings et al' (2020) <doi:10.1016/j.envres.2020.109529>.
An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.
Provide access to  the 'lz-string' 
    <http://pieroxy.net/blog/pages/lz-string/index.html> 'C++' library for 
    Lempel-Ziv (LZ) based compression and decompression of strings.
Prediction of behaviour from movement 
	characteristics using observation and random forest for the analyses of movement
	data in ecology.
	From movement information (speed, bearing...) the model predicts the
	observed behaviour (movement, foraging...) using random forest. The
	model can then extrapolate behavioural information to movement data
	without direct observation of behaviours.
	The specificity of this method relies on the derivation of multiple predictor variables from the
	movement data over a range of temporal windows. This procedure allows to capture
	as much information as possible on the changes and variations of movement and
	ensures the use of the random forest algorithm to its best capacity. The method
	is very generic, applicable to any set of data providing movement data together with
	observation of behaviour.
Persistent interface to 'Macaulay2' <https://www.macaulay2.com>
    and front-end tools facilitating its use in the 'R' ecosystem. For details see 
    Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.
A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.
A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMjF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.
Provides functions to read in and manipulate air quality model output from Models3-formatted files.  This format is used by the Community Multiscale Air Quality (CMAQ) model.
Multi modality data matrices are factorized conjointly into the multiplication of a shared sub-matrix and multiple modality specific sub-matrices, group sparse constraint is applied to the shared sub-matrix to capture the homogeneous and heterogeneous information, respectively. Then the samples are classified by clustering the shared sub-matrix with kmeanspp(), a new version of kmeans() developed here to obtain concordant results. The package also provides the cluster number estimation by rotation cost. Moreover, cluster specific features could be retrieved using hypergeometric tests.
Data manipulation in one package and in base R.
  Minimal. No dependencies.
  'dplyr' and 'tidyr'-like in one place.
  Nothing else than base R to build the package.
A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.
Provides an extension of the shadow-test approach to computerized adaptive
    testing (CAT) implemented in the 'TestDesign' package for the assessment framework
    involving multiple tests administered periodically throughout the year. This framework
    is referred to as the Multiple Administrations Adaptive Testing (MAAT) and supports
    multiple item pools vertically scaled and multiple phases (stages) of CAT within each test.
    Between phases and tests, transitioning from one item pool (and associated constraints)
    to another is allowed as deemed necessary to enhance the quality of measurement.
Easy implementation of the MABAC multi-criteria decision method, that was introduced by Pamučar and Ćirović in the work entitled: "The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC)" - <doi:10.1016/j.eswa.2014.11.057> - which aimed to choose implements for logistics centers. This package receives data, preferably in a spreadsheet, reads it and applies the mathematical algorithms inherent to the MABAC method to generate a ranking with the optimal solution according to the established criteria, weights and type of criteria. The data will be normalized, weighted by the weights, the border area will be determined, the distances to this border area will be calculated and finally a ranking with the optimal option will be generated.
Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>. For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data.
Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency.
Efficient way to design and conduct psychological experiments for testing the performance of large language models. It simplifies the process of setting up experiments and data collection via language models’ API, facilitating a smooth workflow for researchers in the field of machine behaviour.
To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. 'MACER' accepts a list of genera as a user input and uses NCBI-GenBank and BOLD as resources to download and assemble molecular sequence datasets. These datasets are then assembled by marker, aligned, trimmed, and cleaned. The use of this package allows the publication of specific parameters to ensure reproducibility. The 'MACER' package has four core functions and an example run through using all of these functions can be found in the associated repository <https://github.com/rgyoung6/MACER_example>.
Meta-package for statistical and machine learning with a unified
    interface for model fitting, prediction, performance assessment, and
    presentation of results.  Approaches for model fitting and prediction of
    numerical, categorical, or censored time-to-event outcomes include
    traditional regression models, regularization methods, tree-based methods,
    support vector machines, neural networks, ensembles, data preprocessing,
    filtering, and model tuning and selection.  Performance metrics are provided
    for model assessment and can be estimated with independent test sets, split
    sampling, cross-validation, or bootstrap resampling.  Resample estimation
    can be executed in parallel for faster processing and nested in cases of
    model tuning and selection.  Modeling results can be summarized with
    descriptive statistics; calibration curves; variable importance; partial
    dependence plots; confusion matrices; and ROC, lift, and other performance
    curves.
Download data from the Ada and Archibald MacLeish Field 
    Station in Whately, MA. The Ada 
    and Archibald MacLeish Field Station is a 260-acre patchwork of 
    forest and farmland located in West Whately, MA that provides opportunities 
    for faculty and students to pursue environmental research, outdoor 
    education, and low-impact recreation 
    (see <https://www.smith.edu/about-smith/sustainable-smith/macleish> for more information). 
    This package contains 
    weather data over several years, and spatial data on various man-made and 
    natural structures.
Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.
Procedures for simulating biomes by equilibrium vegetation 
  models, with a special focus on paleoenvironmental applications.
  Three widely used equilibrium biome models are currently implemented in 
  the package: the Holdridge Life Zone (HLZ) system (Holdridge 1947, 
  <doi:10.1126/science.105.2727.367>), the Köppen-Geiger classification 
  (KGC) system (Köppen 1936, 
  <https://koeppen-geiger.vu-wien.ac.at/pdf/Koppen_1936.pdf>) and the 
  BIOME model (Prentice et al. 1992, <doi:10.2307/2845499>). Three 
  climatic forest-steppe models are also implemented.
  An approach for estimating monthly time series of relative sunshine 
  duration from temperature and precipitation data (Yin 1999, 
  <doi:10.1007/s007040050111>) is also adapted, allowing 
  process-based biome models to be combined with high-resolution 
  paleoclimate simulation datasets (e.g., CHELSA-TraCE21k v1.0 dataset: 
  <https://chelsa-climate.org/chelsa-trace21k/>).
This repository aims to contribute to the econometric models' production
	with Colombian data, by providing a set of web-scrapping functions 
	of some of the main macro-financial indicators. All the sources are public and
	free, but the advantage of these functions is that they directly download 
	and harmonize the information in R's environment. No need to import or download
	additional files. You only need an internet connection!
Use standard genomics file format (BED) and a table of orthologs to 
 illustrate synteny conservation at the genome-wide scale. 
 Significantly conserved linkage groups are identified as described in Simakov et al. (2020) <doi:10.1038/s41559-020-1156-z> 
 and displayed on an Oxford Grid (Edwards (1991) <doi:10.1111/j.1469-1809.1991.tb00394.x>) or a chord diagram as in Simakov et al. (2022) <doi:10.1126/sciadv.abi5884>. 
 The package provides a function that uses a network-based greedy algorithm to find communities (Clauset et al. (2004) <doi:10.1103/PhysRevE.70.066111>) 
 and so automatically order the chromosomes on the plot to improve interpretability.
Includes functions  for calculating basic indices of macrozoobenthos for water quality and is designed to provide researchers and environmental professionals with a comprehensive tool for evaluating the ecological health of aquatic ecosystems.The package is based on the following references: Paisley, M. F., Trigg, D. J. and Walley, W. J. (2014)<doi:10.1002/rra.2686>. Arslan, N., Salur, A., Kalyoncu, H. et al.(2016)  <doi:10.1515/biolog-2016-0005>. Hilsenhoff W.L. (1987). Hilsenhoff. W.L. (1988)  Barbour, M.T., Gerritsen, J., Snyder, B.D., and Stribling, J.B. (1999).
Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models.  M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model.  M-activation can detect high-order interactions -- a traditionally non-trivial challenge.  Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."
Performs causal mediation analysis for count and zero-inflated
    count data without or with a post-treatment confounder; calculates power
    to detect prespecified causal mediation effects, direct effects, and 
    total effects; performs sensitivity analysis when there is a treatment-
    induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., 
    Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) 
    <doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV) 
    method to estimate the controlled (natural) direct and mediation effects, 
    and compute the bootstrap Confidence Intervals as described by Guo, Z., 
    Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This 
    software was made possible by Grant R03DE028410 from the National 
    Institute of Dental and Craniofacial Research, a component of the National 
    Institutes of Health.
A collection of functions for conducting a meta-analysis with mean differences data.  It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter & Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
This is an open-source software designed specifically for text mining in the Persian language. It allows users to examine word frequencies, download data for analysis, and generate word clouds. This tool is particularly useful for researchers and analysts working with Persian language data.
    This package mainly makes use of the 'PersianStemmer' (Safshekan, R., et al. (2019). <https://CRAN.R-project.org/package=PersianStemmer>),
    'udpipe' (Wijffels, J., et al. (2023). <https://CRAN.R-project.org/package=udpipe>),
    and 'shiny' (Chang, W., et al. (2023). <https://CRAN.R-project.org/package=shiny>) packages.
Provides an extension to 'MadanText' for creating and analyzing co-occurrence networks in Persian text data.
             This package mainly makes use of the 'PersianStemmer' (Safshekan, R., et al. (2019). <https://CRAN.R-project.org/package=PersianStemmer>),
             'udpipe' (Wijffels, J., et al. (2023). <https://CRAN.R-project.org/package=udpipe>),
             and 'shiny' (Chang, W., et al. (2023). <https://CRAN.R-project.org/package=shiny>) packages.
Contains the Maddison Project 2018 database, which provides 
    estimates of GDP per capita for all countries in the world between AD 1 and
    2016. See <https://www.rug.nl/ggdc/historicaldevelopment/maddison/> for more information.
A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic 
  Optimization algorithm. MADGRAD is a 'best-of-both-worlds' optimizer with the 
  generalization performance of stochastic gradient descent and at least as fast 
  convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation
  is provided based on Defazio et al (2020) <arxiv:2101.11075>.
Provides pipe-style interface for 'data.table'. Package preserves all 'data.table' features without
              significant impact on performance. 'let' and 'take' functions are simplified interfaces for most common data
              manipulation tasks. For example, you can write 'take(mtcars, mean(mpg), by = am)' for aggregation or 
              'let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification. Use 'take_if/let_if' for conditional
              aggregation/modification. Additionally there are some conveniences such as automatic 'data.frame' 
              conversion to 'data.table'.
This system allows one to model a multi-variate, multi-response
    problem with interaction effects. It combines the usual squared error loss
    for the multi-response problem with some penalty terms to encourage
    responses that correlate to form groups and also allow for modeling main and
    interaction effects that exit within the covariates.
    The optimization method employed is the Alternating Direction Method of
    Multipliers (ADMM). The implementation is based on the methodology
    presented on Quachie Asenso, T., & Zucknick, M. (2023)
    <doi:10.48550/arXiv.2303.11155>.
An object that supports automatic differentiation
    of matrix- and multidimensional-valued functions with 
    respect to multidimensional independent variables. 
    Automatic differentiation is via 'forward accumulation'.
Tools for the analysis of population differences
    using the Major Histocompatibility Complex (MHC) genotypes of samples
    having a variable number of alleles (1-4) recorded for each
    individual.  A hierarchical Dirichlet-Multinomial model on the
    genotype counts is used to pool small samples from multiple
    populations for pairwise tests of equality.  Bayesian inference is
    implemented via the 'rstan' package.  Bootstrapped and posterior
    p-values are provided for chi-squared and likelihood ratio tests of
    equal genotype probabilities.
Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.
Provides a framework which should improve reproducibility and
    transparency in data processing. It provides functionality such as
    automatic meta data creation and management, rudimentary quality
    management, data caching, work-flow management and data aggregation.
    * The title is a wish not a promise. By no means we expect this
    package to deliver everything what is needed to achieve full
    reproducibility and transparency, but we believe that it supports
    efforts in this direction.
Functions to support data cleaning, evaluation, and description, 
     developed for integration with Maelstrom Research software tools. 'madshapR' 
     provides functions primarily to evaluate and manipulate datasets and 
     data dictionaries in preparation for data harmonization with the package 
     'Rmonize' and to facilitate integration and transfer between RStudio servers 
     and secure Opal environments. 'madshapR' functions can be used independently
     but are optimized in conjunction with ‘Rmonize’ functions for streamlined 
     and coherent harmonization processing.
This function allows to generate two biological conditions synthetic 
             microarray dataset which has similar behavior to those currently 
             observed with common platforms. User provides a subset of parameters.
             Available default parameters settings can be modified.
Framework for creating and orchestrating data pipelines. Organize, orchestrate, and monitor multiple pipelines in a single project. Use tags to decorate functions with scheduling parameters and configuration.
A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.
Interfaces the Python library 'zuko' implementing Masked Autoregressive Flows. See Rozet, Divo and Schnake (2023) <doi:10.5281/zenodo.7625672> and Papamakarios, Pavlakou and Murray (2017) <doi:10.48550/arXiv.1705.07057>.
Data class for increased interoperability working with
    spatial-temporal data together with corresponding functions and
    methods (conversions, basic calculations and basic data manipulation).
    The class distinguishes between spatial, temporal and other dimensions
    to facilitate the development and interoperability of tools build for
    it. Additional features are name-based addressing of data and internal
    consistency checks (e.g. checking for the right data order in
    calculations).
Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) <DOI:10.1002/gepi.22351>.
Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential
    Equations, including the case when there are unobserved system components. Implements the MAGI method
    (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>.
    A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.
A collection of functions for the manipulation and
 analysis of arbitrarily dimensioned arrays.  The original motivation
 for the package was the development of efficient, vectorized
 algorithms for the creation and investigation of magic squares and
 high-dimensional magic hypercubes.
Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
Magic functions to obtain results from for loops.
Bindings to 'ImageMagick': the most comprehensive open-source image
    processing library available. Supports many common formats (png, jpeg, tiff,
    pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc).
    All operations are vectorized via the Magick++ STL meaning they operate either
    on a single frame or a series of frames for working with layers, collages,
    or animation. In RStudio images are automatically previewed when printed to
    the console, resulting in an interactive editing environment. The latest 
    version of the package includes a native graphics device for creating 
    in-memory graphics or drawing onto images using pixel coordinates.
Enables us to use the functions of the package 'magick' interactively.
Set of utility functions to interact with 'WeMo Switch', a smart 
    plug that can be remotely controlled via wifi. The provided functions make 
    it possible to turn one or more 'WeMo Switch' plugs on and off in a 
    scriptable fashion. More information about 'WeMo Switch' can be found at 
    <http://www.belkin.com/us/p/P-F7C027/>.
Balancing quasi-experimental field research for effects of covariates is fundamental for drawing causal inference. Propensity Score Matching deals with this issue but current
    techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The
    MAGMA R-package addresses these restrictions by offering nearest neighbor matching for two to four groups. It also includes the option to match data of a 2x2 design. In addition, 
    MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a tutorial on 
    MAGMA as vignette. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) <https://osf.io/p47nc/>.
An implementation for the multi-task Gaussian processes with common 
    mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', 
    are available to perform predictions for supervised learning problems, in
    particular for time series or any functional/continuous data applications.
    The corresponding articles has been respectively proposed by Arthur Leroy, 
    Pierre Latouche, Benjamin Guedj and Servane Gey (2022) 
    <doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche, 
    Benjamin Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>.
    Theses approaches leverage the learning of cluster-specific mean processes,
    which are common across similar tasks, to provide enhanced prediction
    performances (even far from data) at a linear computational cost (in
    the number of tasks).  'MagmaClust' is a generalisation of 'Magma'
    where the tasks are simultaneously clustered into groups, each being
    associated to a specific mean process.  User-oriented functions in the
    package are decomposed into training, prediction and plotting
    functions. Some basic features (classic kernels, training, prediction) of
    standard Gaussian processes are also implemented.
A client for interacting with 'magma', the data warehouse of the
    'UCSF Data Library'. 'magmaR' includes functions for querying and
    downloading data from 'magma', in order to enable working with such data in
    R, as well as for uploading local data to 'magma'.
Correlates variation within the meta-genome to target species
    phenotype variations in meta-genome with association studies. Follows
    the pipeline described in Chaston, J.M. et al. (2014) <doi:10.1128/mBio.01631-14>.
Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) <DOI:10.2307/2531148>. Implements Fortran 77 code for the methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>. Includes estimates of average agreement for each observer and average agreement for each subject.
Provides a mechanism for chaining commands with a new
    forward-pipe operator, %>%. This operator will forward a value, or the
    result of an expression, into the next function call/expression.
    There is flexible support for the type of right-hand side expressions.
    For more information, see package vignette.  To quote Rene Magritte,
    "Ceci n'est pas un pipe."
Provides a comprehensive graphical user interface for analysis of Affymetrix, Agilent, Illumina, Nimblegen and other microarray data. It can perform miscellaneous tasks such as gene set enrichment and test analyses, identifying gene symbols and building co-expression network. It can also estimate sample size for atleast two-fold expression change. The current version is its slenderized form for compatable and flexible implementation.
A generalised workflow for generation of subject weights to be 
    used in Matching-Adjusted Indirect Comparison (MAIC) per Signorovitch et 
    al. (2012) <doi:10.1016/j.jval.2012.05.004>, Signorovitch et al (2010) 
    <doi:10.2165/11538370-000000000-00000>. In MAIC, unbiased 
    comparison between outcomes of two trials is facilitated by weighting the
    subject-level outcomes of one trial with weights derived such that the 
    weighted aggregate measures of the prognostic or effect modifying variables 
    are equal to those of the sample in the comparator trial. The functions and
    classes included in this package wrap and abstract the process demonstrated
    in the UK National Institute for Health and Care Excellence Decision 
    Support Unit (NICE DSU)'s example (Phillippo et al, (2016) [see URL]),
    providing a repeatable and easily specifiable workflow for producing 
    multiple comparison variable sets against a variety of target studies, with
    preprocessing for a number of aggregate target forms (e.g. mean, median, 
    domain limits).
The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2025)). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2021) <doi:10.1002/pst.2210>.)
Facilitates performing matching adjusted indirect comparison
 (MAIC) analysis where the endpoint of interest is either time-to-event
 (e.g. overall survival) or binary (e.g. objective tumor response). The method
 is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.
A generalised workflow for Matching-Adjusted Indirect
    Comparison (MAIC) analysis, which supports both anchored and
    non-anchored MAIC methods.  In MAIC, unbiased trial outcome comparison
    is achieved by weighting the subject-level outcomes of the
    intervention trial so that the weighted aggregate measures of
    prognostic or effect-modifying variables match those of the comparator
    trial. Measurements supported include time-to-event (e.g., overall
    survival) and binary (e.g., objective tumor response). The method is
    described in Signorovitch et al. (2010)
    <doi:10.2165/11538370-000000000-00000> and Signorovitch et al. (2012)
    <doi:10.1016/j.jval.2012.05.004>.
Collect  your data on digital marketing campaigns from Mailchimp using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Perform a mail merge (mass email) using the message defined in 
    markdown, the recipients in a 'csv' file, and gmail as the mailing engine. 
    With this package you can parse markdown documents as the body of email, and 
    the 'yaml' header to specify the subject line of the email.  Any '{}' braces 
    in the email will be encoded with 'glue::glue()'. You can preview the email 
    in the RStudio viewer pane, and send (draft) email using 'gmailr'.
Interface to Apache Commons Email to send emails
    from R.
Allows the user to generate a friendly user interface for emails sending. 
    The user can choose from the most popular free email services ('Gmail', 'Outlook', 'Yahoo') and his default email application. 
    The package is a wrapper for the 'Mailtoui' 'JavaScript' library.  See <https://mailtoui.com/#menu> for more information.
Shiny for Open Science to visualize, share, and inventory the
    main existing human datasets for researchers.
Implements methodologies for modelling interval data by Normal
    and Skew-Normal distributions, considering appropriate parameterizations of
    the variance-covariance matrix that takes into account the intrinsic nature of
    interval data, and lead to four different possible configuration structures.
    The Skew-Normal parameters can be estimated by maximum likelihood, while Normal
    parameters may be estimated by maximum likelihood or robust trimmed maximum
    likelihood methods.
A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajKMeans' are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.
A hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package 'MajMinKmeans' are cluster_km() and cluster_MajKm(). Cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering. Another useful function is MajMinOptim(), which helps to find the optimum values of the Majorization-Minimization estimator.
Create dummy variables from categorical data.
    This package can convert categorical data (factor and ordered) into
    dummy variables and handle multiple columns simultaneously.
    This package enables to select whether a dummy variable for base group
    is included (for principal component analysis/factor analysis) or
    excluded (for regression analysis) by an option.
    'makedummies' function accepts 'data.frame', 'matrix', and
    'tbl' (tibble) class (by 'tibble' package).
    'matrix' class data is automatically converted to 'data.frame' class.
A user-friendly interface for the construction of
        'Makefiles'.
A user-friendly tool for visualizing categorical or group movement.
Automation tool to run R scripts if needed, based on last modified
  time. It comes with no package dependencies, organizational overhead, or
  structural requirements. In short: run an R script if underlying files have
  changed, otherwise do nothing.
Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, <doi:10.18637/jss.v110.i03>).
Functions that allow you to create your own color palette from an image, using mathematical algorithms.
A suite of tools for transforming an existing workflow into a
    self-documenting pipeline with very minimal upfront costs. Segments of
    the pipeline are specified in much the same way a 'Make' rule is, by
    declaring an executable recipe (which might be an R script), along
    with the corresponding targets and dependencies. When the entire
    pipeline is run through, only those recipes that need to be executed
    will be. Meanwhile, execution metadata is captured behind the scenes
    for later inspection.
Helper functions that interface with the system utilities to learn 
    about the local build environment. Lets you explore 'make' rules to test the
    local configuration, or query 'pkg-config' to find compiler flags and libs 
    needed for building packages with external dependencies. Also contains tools
    to analyze which libraries that a installed R package linked to by inspecting
    output from 'ldd' in combination with information from your distribution 
    package manager, e.g. 'rpm' or 'dpkg'.
Make all elements of a character vector unique. 
    Differs from 'make.unique' by starting at 1 and allowing users to customise suffix format.
R package associated with the Multiple Approximate Kernel Learning (MAKL) algorithm proposed in <doi:10.1093/bioinformatics/btac241>. The algorithm fits multiple approximate kernel learning (MAKL) models that are fast, scalable and interpretable.
MAle Lineage ANalysis by simulating 
    genealogies backwards and imposing short tandem repeats (STR) mutations forwards. 
    Intended for forensic Y chromosomal STR (Y-STR) haplotype analyses. 
    Numerous analyses are possible, e.g. number of matches and meiotic distance to matches. 
    Refer to papers mentioned in citation("malan") (DOI's: 
    <doi:10.1371/journal.pgen.1007028>, 
    <doi:10.21105/joss.00684> and 
    <doi:10.1016/j.fsigen.2018.10.004>).
Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks.
A suite of tools to allow you to download all 
  publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from 
  the 'Malaria Atlas Project' <https://malariaatlas.org/> servers as well as utility functions for plotting
  the downloaded data.
It is designed to work with text written in Bahasa Malaysia. We provide 
    functions and data sets that will make working with Bahasa Malaysia text much easier. 
    For word stemming in particular, we will look up the Malay words in a dictionary and
    then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, 
    Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, 
    Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi
    (2017) <https://ijrest.net/vol-4-issue-12.html> . This package includes
    a dictionary of Malay words that may be used to perform word stemming, 
    a dataset of Malay stop words, a dataset of sentiment words
    and a dataset of normalized words.
Convenient wrapper functions for the analysis of
    matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF)
    spectra data in order to select only representative spectra (also
    called cherry-pick). The package covers the preprocessing and
    dereplication steps (based on Strejcek, Smrhova, Junkova and Uhlik
    (2018) <doi:10.3389/fmicb.2018.01294>) needed to cluster MALDI-TOF
    spectra before the final cherry-picking step. It enables the easy
    exclusion of spectra and/or clusters to accommodate complex
    cherry-picking strategies. Alternatively, cherry-picking using
    taxonomic identification MALDI-TOF data is made easy with functions to
    import inconsistently formatted reports.
A complete analysis pipeline for matrix-assisted laser
        desorption/ionization-time-of-flight (MALDI-TOF) and other
        two-dimensional mass spectrometry data. In addition to commonly
        used plotting and processing methods it includes distinctive
        features, namely baseline subtraction methods such as
        morphological filters (TopHat) or the statistics-sensitive
        non-linear iterative peak-clipping algorithm (SNIP), peak
        alignment using warping functions, handling of replicated
        measurements as well as allowing spectra with different
        resolutions.
Functions for reading (tab, csv, Bruker fid, Ciphergen
        XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and
        writing (tab, csv, mMass MSD, mzML, imzML) different file
        formats of mass spectrometry data into/from 'MALDIquant'
        objects.
Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) <doi:10.1093/bioinformatics/btx628>).
Run multiple 'Large Language Model' predictions against a table. The
    predictions run row-wise over a specified column. It works using a 
    one-shot prompt, along with the current row's content. The prompt that is used
    will depend of the type of analysis needed.
An R interface for the Java Machine Learning for Language Toolkit (mallet)
  <http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such
  as Latent Dirichlet Allocation. We can use the R package to read textual 
  data into mallet from R objects, run the Java implementation of mallet 
  directly in R, and extract results as R objects. The Mallet toolkit 
  has many functions, this wrapper focuses on the topic modeling sub-package 
  written by David Mimno. The package uses the rJava package to connect to a 
  JVM.
Data sets related to the Islas Malvinas /// Sets de datos relacionados a las Islas 
  Malvinas - La Nación Argentina ratifica su legítima e imprescriptible soberanía sobre las islas 
  Malvinas, Georgias del Sur y Sándwich del Sur y los espacios marítimos e insulares 
  correspondientes, por ser parte integrante del territorio nacional. La recuperación de dichos 
  territorios y el ejercicio pleno de la soberanía, respetando el modo de vida de sus habitantes y 
  conforme a los principios del Derecho Internacional, constituyen un objetivo permanente e 
  irrenunciable del pueblo argentino.
Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.
Providing the kubernetes-like class 'ManagedCloudProvider' as a child class of 
  the 'CloudProvider' class in the 'DockerParallel' package. The class is able to
  manage the cloud instance made by the non-kubernetes cloud service.
  For creating a provider for the non-kubernetes cloud service, the developer
  needs to define a reference class inherited from 'ManagedCloudProvider' and
  define the method for the generics runDockerWorkerContainers(),
  getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see
  the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.
This will allow easier management of a CRAN-style repository on
    local networks (i.e. not on CRAN). This might be necessary where hosted
    packages contain intellectual property owned by a corporation.
High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.
Several functions can be used to analyze neuroimaging data using multivariate methods based on the 'msma' package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained.
Provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal.
Estimates membership for the Mandelbrot set.
Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted.
Methods for performing genetic risk
        prediction from genotype data.  You can use it to perform risk
        prediction for individuals, or for families with missing data.
This plot integrates annotation into a manhattan plot. The plot is implemented as a heatmap, which is binned using -log10(p-value) and chromosome position. Annotation currently supported is minor allele frequency and gene function high impact variants.
Provides access to coded election programmes from the Manifesto
    Corpus and to the Manifesto Project's Main Dataset and routines to analyse this
    data. The Manifesto Project <https://manifesto-project.wzb.eu> collects and
    analyses election programmes across time and space to measure the political
    preferences of parties. The Manifesto Corpus contains the collected and
    annotated election programmes in the Corpus format of the package 'tm' to enable
    easy use of text processing and text mining functionality. Specific functions
    for scaling of coded political texts are included.
Implements operations for Riemannian manifolds, e.g., geodesic distance, Riemannian metric, exponential and logarithm maps, etc. Also incorporates random object generator on the manifolds. See Dai, Lin, and Müller (2021) <doi:10.1111/biom.13385>.
An R interface to version 0.3 of the 'ROPTLIB' optimization library
    (see <https://www.math.fsu.edu/~whuang2/> for more information). Optimize real-
    valued functions over manifolds such as Stiefel, Grassmann, and Symmetric
    Positive Definite matrices. For details see Martin et. al. (2020) <doi:10.18637/jss.v093.i01>. 
    Note that the optional ldr package used in some of this package's examples can be obtained from either JSS 
    <https://www.jstatsoft.org/index.php/jss/article/view/v061i03/2886> or from the CRAN archives 
    <https://cran.r-project.org/src/contrib/Archive/ldr/ldr_1.3.3.tar.gz>.
Interactive plotting functions for use within RStudio.
  The manipulate function accepts a plotting expression and a set of
  controls (e.g. slider, picker, checkbox, or button) which are used
  to dynamically change values within the expression. When a value is
  changed using its corresponding control the expression is
  automatically re-executed and the plot is redrawn.
Like package 'manipulate' does for static graphics, this package
    helps to easily add controls like sliders, pickers, checkboxes, etc. that 
    can be used to modify the input data or the parameters of an interactive 
    chart created with package 'htmlwidgets'.
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.
Chromatin immunoprecipitation followed by high-throughput
    sequencing (ChIP-seq) is the premier technology for profiling genome-wide
    localization of chromatin-binding proteins, including transcription
    factors and histones with various modifications.
    This package provides a robust method for normalizing ChIP-seq
    signals across individual samples or groups of samples. It also designs
    a self-contained system of statistical models for calling differential
    ChIP-seq signals between two or more biological conditions as well as
    for calling hypervariable ChIP-seq signals across samples. Refer to
    Tu et al. (2021) <doi:10.1101/gr.262675.120> and
    Chen et al. (2022) <doi:10.1186/s13059-022-02627-9>
    for associated statistical details.
Implemented are various tests for semi-parametric repeated measures
    and general MANOVA designs that do neither assume multivariate normality nor
    covariance homogeneity, i.e., the procedures are applicable for a wide range
    of general multivariate factorial designs. In addition to asymptotic inference
    methods, novel bootstrap and permutation approaches are implemented as well. These provide more
    accurate results in case of small to moderate sample sizes. Furthermore, post-hoc 
    comparisons are provided for the multivariate analyses.
    Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.
The Multivariate Asymptotic Non-parametric Test of Association (MANTA) enables 
    non-parametric, asymptotic P-value computation for multivariate linear models. MANTA
    relies on the asymptotic null distribution of the PERMANOVA test statistic. P-values are
    computed using a highly accurate approximation of the corresponding cumulative distribution 
    function. Garrido-Martín et al. (2022) <doi:10.1101/2022.06.06.493041>.
The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.
This is the core package for the many packages universe.
    It includes functions to help researchers work with and contribute to 
    event datasets on global governance.
A comprehensive framework for calculating unbiased distances in datasets 
    containing mixed-type variables (numerical and categorical). The package implements 
    a general formulation that ensures multivariate additivity and commensurability, 
    meaning that variables contribute equally to the overall distance regardless of 
    their type, scale, or distribution. Supports multiple distance measures including 
    Gower's distance, Euclidean distance, Manhattan distance, and various categorical 
    variable distances such as simple matching, Eskin, occurrence frequency, and 
    association-based distances. Provides tools for variable scaling (standard 
    deviation, range, robust range, and principal component scaling), and handles 
    both independent and association-based category dissimilarities. Implements 
    methods to correct for biases that typically arise from different variable types, 
    distributions, and number of categories. Particularly useful for cluster analysis, 
    data visualization, and other distance-based methods when working with mixed data. 
    Methods based on van de Velden et al. (2024) <doi:10.48550/arXiv.2411.00429> 
    "Unbiased mixed variables distance".
Comprehensive toolkit for Environmental Phillips Curve analysis 
    featuring multidimensional instrumental variable creation, transfer entropy 
    causal discovery, network analysis, and state-of-the-art econometric methods.
    Implements geographic, technological, migration, geopolitical, financial, 
    and natural risk instruments with robust diagnostics and visualization.
    Provides 24 different instrumental variable approaches with empirical validation.
    Methods based on Phillips (1958) <doi:10.1111/j.1468-0335.1958.tb00003.x>,
    transfer entropy by Schreiber (2000) <doi:10.1103/PhysRevLett.85.461>, and 
    weak instrument tests by Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006>.
Frequently one needs a convenient way to build and tune
             several models in one go.The goal is to provide a number of machine learning convenience 
             functions. It provides the ability to build, tune and obtain predictions of 
             several models in one function. The models are built using functions from
             'caret' with easier to read syntax.
             Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.
Computes indirect effects, conditional effects, and conditional
  indirect effects in a structural equation model or path model after model
  fitting, with no need to define any user parameters or label any paths in
  the model syntax, using the approach presented in Cheung and Cheung
  (2024) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap
  confidence intervals by doing bootstrapping only once and reusing the
  bootstrap estimates in all subsequent computations. Supports bootstrap
  confidence intervals for standardized (partially or completely) indirect
  effects, conditional effects, and conditional indirect effects as described
  in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui,
  and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by
  structural equation modeling using lavaan() or regression using lm().
Converts results from the 'manymome' package,
  presented in Cheung and Cheung (2023)
  <doi:10.3758/s13428-023-02224-z>, to publication-ready
  tables.
Many tools for making, modifying, mapping, marking, measuring, 
   and motifs and memberships of many different types of 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.
   The package includes functions for importing and exporting, creating and generating networks,
   modifying networks and node and tie attributes,
   and describing and visualizing networks with sensible defaults.
Performs the multiple testing procedures of Cox (2011) <doi:10.5170/CERN-2011-006> and Wong and Cox (2007) <doi:10.1080/02664760701240014>.
A set of evolutionary algorithms to solve many-objective optimization. 
    Hybridization between the algorithms are also facilitated. Available algorithms are:
    'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008>
    'NSGA-III' <doi:10.1109/TEVC.2013.2281535>
    'MO-CMA-ES' <doi:10.1145/1830483.1830573>
    The following many-objective benchmark problems are also provided: 
    'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and
    'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.
Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).
Electronic health records (EHR) linked with biorepositories are 
    a powerful platform for translational studies. A major bottleneck exists 
    in the ability to phenotype patients accurately and efficiently. 
    Towards that end, we developed an automated high-throughput 
    phenotyping method integrating International 
    Classification of Diseases (ICD) codes and narrative data extracted 
    using natural language processing (NLP). Specifically, our proposed method, 
    called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent 
    mixture models on aggregated ICD and NLP counts along with healthcare 
    utilization. The MAP algorithm yields a predicted probability of phenotype 
    for each patient and a threshold for classifying subjects with phenotype 
    yes/no (See Katherine P. Liao, et al. (2019) <doi:10.1093/jamia/ocz066>.).
Allows the user to generate a list of features (gene, pseudo, RNA,
    CDS, and/or UTR) directly from NCBI database for any species with a current 
    build available. Option to save downloaded and formatted files is available, 
    and the user can prioritize the feature list based on type and assembly builds 
    present in the current build used. The user can then use the list of features 
    generated or provide a list to map a set of markers (designed for SNP markers 
    with a single base pair position available) to the closest feature based on 
    the map build. This function does require map positions of the markers to be 
    provided and the positions should be based on the build being queried through 
    NCBI.
Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.
Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman (2014) <doi:10.1080/01431161.2014.930207>.
Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in 'mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.
Includes support for 'Mapbox' Navigation APIs, including directions, 
  isochrones, and route optimization; the Search API for forward and reverse geocoding; 
  the Maps API for interacting with 'Mapbox' vector tilesets and visualizing 
  'Mapbox' maps in R; and 'Mapbox Tiling Service' and 'tippecanoe' for generating map tiles.
  See <https://docs.mapbox.com/api/> for more information about the 'Mapbox' APIs.
Makes 'Mapbox GL JS' <https://docs.mapbox.com/mapbox-gl-js/api/>,
  an open source JavaScript library that uses WebGL to render interactive maps,
  available within R via the 'htmlwidgets' package. Visualizations can be used from the R console,
  in R Markdown documents and in Shiny apps.
A variety of functions that make it easy to plot standard choropleth maps as well as choropleth alternatives in 'ggplot2'.
Geospatial shapefile data of China administrative divisions to the county/district-level.
Bayesian multivariate age-period-cohort (MAPC) models for analyzing health data, with support for model fitting, visualization, stratification, and model comparison. Inference focuses on identifiable cross-strata differences, as described by Riebler and Held (2010) <doi:10.1093/biostatistics/kxp037>. Methods for handling complex survey data via the 'survey' package are included, as described in Mercer et al. (2014) <doi:10.1016/j.spasta.2013.12.001>.
Supplement to maps package, providing some larger and/or
	higher-resolution databases. NOTE: this is a legacy package. The world map is out-dated.
Provides a mechanism to plot an interactive map using 'Mapbox GL' 
		(<https://docs.mapbox.com/mapbox-gl-js/api/>), a javascript library for interactive maps,
		and 'Deck.gl' (<https://deck.gl/>), a javascript library which uses 'WebGL' for 
		visualising large data sets.
Suite of interactive functions and helpers for selecting and editing
    geospatial data.
Estimation methods for phase-type
  distribution (PH) and Markovian arrival process (MAP) from
  empirical data (point and grouped data) and density function.
  The tool is based on the following researches:
  Okamura et al. (2009) <doi:10.1109/TNET.2008.2008750>,
  Okamura and Dohi (2009) <doi:10.1109/QEST.2009.28>,
  Okamura et al. (2011) <doi:10.1016/j.peva.2011.04.001>,
  Okamura et al. (2013) <doi:10.1002/asmb.1919>,
  Horvath and Okamura (2013) <doi:10.1007/978-3-642-40725-3_10>,
  Okamura and Dohi (2016) <doi:10.15807/jorsj.59.72>.
Contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020).  Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
Provides an interface to the 'Mapbox GL JS' (<https://docs.mapbox.com/mapbox-gl-js/guides>)
    and the 'MapLibre GL JS' (<https://maplibre.org/maplibre-gl-js/docs/>) interactive mapping libraries to help users
    create custom interactive maps in R.  Users can create interactive globe visualizations; layer 'sf' objects to create
    filled maps, circle maps, 'heatmaps', and three-dimensional graphics; and customize map styles and views.  The package
    also includes utilities to use 'Mapbox' and 'MapLibre' maps in 'Shiny' web applications.
Mapping Averaged Pairwise Information (MAPI) is an
    exploratory method providing graphical representations summarizing the
    spatial variation of pairwise metrics (eg. distance, similarity
    coefficient, ...) computed between georeferenced samples.
Get map data frames for the Indian subcontinent with different region
    levels (e.g., district, state). The package also offers convenience functions
    for plotting choropleths, visualizing spatial data, and handling state/district
    codes.
Provides a container for data used by the 'mapindia' package. 
    The data used by 'mapindia' has been extracted into this package so that the 
    file size of the 'mapindia' package can be reduced considerably. The data in
    this package will be updated when latest data is available.
Facilitates the incorporation of biological processes in biogeographical analyses. It offers conveniences in fitting, comparing and extrapolating models of biological processes such as physiology and phenology. These spatial extrapolations can be informative by themselves, but also complement traditional correlative species distribution models, by mixing environmental and process-based predictors. Caetano et al (2020) <doi:10.1111/oik.07123>.
Regularly spaced grids containing continuous data are transformed
    to contour polygons. A grid can be defined by a data.frame (x, y, value),
    an 'sf' object or a raster from 'terra'.
A genetic analysis tool and variance component 
    model for identifying marginal epistasis between pathways 
    and the rest of the genome. 'MAPITR' uses as input a matrix 
    of genotypes, a vector of phenotypes, and a list of 
    pathways. 'MAPITR' then iteratively tests each pathway for 
    epistasis between any variants within the pathway versus 
    any variants remaining in the rest of the genome. 'MAPITR'
    returns results in the form of p-values for every pathway 
    indicating whether the null model of there being no 
    epistatic interactions between a pathway and the rest of 
    the genome can be rejected.
Create legends for maps and other graphics. Thematic maps need to
    be accompanied by legible legends to be fully comprehensible. This package
    offers a wide range of legends useful for cartography, some of which may
    also be useful for other types of graphics.
Biodiversity areas, especially primary forest, serve a
    multitude of functions for local economy, regional functionality of
    the ecosystems as well as the global health of our planet. Recently,
    adverse changes in human land use practices and climatic responses to
    increased greenhouse gas emissions, put these biodiversity areas under
    a variety of different threats. The present package helps to analyse a
    number of biodiversity indicators based on freely available
    geographical datasets. It supports computational efficient routines
    that allow the analysis of potentially global biodiversity portfolios.
    The primary use case of the package is to support evidence based
    reporting of an organization's effort to protect biodiversity areas
    under threat and to identify regions were intervention is most duly
    needed.
Provides a minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections.  See Brown (2016) <doi:10.32614/RJ-2016-005>.
Visualise admixture as pie charts on a projected map, admixture as traditional structure barplots or facet barplots, and scatter plots from genotype principal components analysis. A 'shiny' app allows users to create admixture maps interactively. Jenkins TL (2024) <doi:10.1111/1755-0998.13943>.
Topological data analysis (TDA) is a method of data analysis that 
  uses techniques from topology to analyze high-dimensional data. Here we
  implement Mapper, an algorithm from this area developed by Singh, Mémoli and 
  Carlsson (2007) which generalizes the concept of a 
  Reeb graph <https://en.wikipedia.org/wiki/Reeb_graph>.
The Mapper algorithm from Topological Data Analysis, the steps are as follows 1. Define a filter (lens) function on the data. 2. Perform clustering within each level set. 3. Generate a complex from the clustering results.
Easily create functions to map between different sets of values,
  such as for re-labelling categorical variables.
Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.
Construction of genetic maps in autopolyploid full-sib populations. 
             Uses pairwise recombination fraction estimation as the first 
             source of information to sequentially position allelic variants 
             in specific homologous chromosomes. For situations where pairwise 
             analysis has limited power, the algorithm relies on the multilocus 
             likelihood obtained through a hidden Markov model (HMM). 
             For more detail, please see  Mollinari and Garcia (2019) 
             <doi:10.1534/g3.119.400378> and Mollinari et al. (2020) 
             <doi:10.1534/g3.119.400620>.
Provides one function, which is a wrapper around purrr::map() with some extras on top, including parallel computation, progress bars, error handling, and result caching.
Converts latitude/longitude into projected coordinates.
Display of maps.  Projection code and larger maps are in
             separate packages ('mapproj' and 'mapdata').
Interface to the 'Google Maps' APIs: (1) routing directions based on the 'Directions' API, returned as 'sf' objects, either as single feature per alternative route, or a single feature per segment per alternative route; (2) travel distance or time matrices based on the 'Distance Matrix' API; (3) geocoded locations based on the 'Geocode' API, returned as 'sf' objects, either points or bounds; (4) map images using the 'Maps Static' API, returned as 'stars' objects.
Enables preparation of maps to be printed and drawn on.
    Modified maps can then be scanned back in, and hand-drawn marks
    converted to spatial objects.
Create and integrate thematic maps in your workflow. This package
    helps to design various cartographic representations such as proportional
    symbols, choropleth or typology maps. It also offers several functions to
    display layout elements that improve the graphic presentation of maps
    (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on
    'base' graphics.
Administrative Boundaries of Spain at several levels
    (Autonomous Communities, Provinces, Municipalities) based on the
    'GISCO' 'Eurostat' database <https://ec.europa.eu/eurostat/web/gisco>
    and 'CartoBase SIANE' from 'Instituto Geografico Nacional'
    <https://www.ign.es/>.  It also provides a 'leaflet' plugin and the
    ability of downloading and processing static tiles.
Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.
Local adaptation and evaluation of maps of continuous attributes in raster format by use of point location data.
Automated calculation and visualization of survey data statistics on a color-coded (choropleth) map.
To create maps from tiles, 'maptiles' downloads, composes and
    displays tiles from a large number of providers (e.g. 'OpenStreetMap',
    'Stadia', 'Esri', 'CARTO', or 'Thunderforest').
Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis,
	as described in Taddy (2012) 'On estimation and selection for topic models'.  Previous versions of this code were included as part of the 'textir' package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Functions with example data for graphing, pruning, and
        mapping models from hierarchical clustering, and classification
        and regression trees.
Quickly and conveniently create interactive
    visualisations of spatial data with or without background maps.
    Attributes of displayed features are fully queryable via pop-up
    windows. Additional functionality includes methods to visualise true-
    and false-color raster images and bounding boxes.
Policy evaluation using generalized Qini curves: Evaluate data-driven treatment
            targeting rules for one or more treatment arms over different budget
            constraints in experimental or observational settings under unconfoundedness.
R functions for the estimation and eigen-decomposition of multivariate autoregressive models.
Multiplicative AR(1) with Seasonal is a stochastic
  process model built on top of AR(1). The package provides the
  following procedures for MAR(1)S processes: fit, compose, decompose,
  advanced simulate and predict.
Supports visual interpretation of hierarchical composite
    endpoints (HCEs). HCEs are complex constructs used as primary endpoints in
    clinical trials, combining outcomes of different types into ordinal endpoints,
    in which each patient contributes the most clinically important event (one and
    only one) to the analysis. See Karpefors M et al. (2022)
    <doi:10.1177/17407745221134949>.
Recently, multiple marginal variable selection methods have been developed and shown to be effective in Gene-Environment interactions studies. We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'.
Computation of various Markovian models for categorical data
    including homogeneous Markov chains of any order, MTD models, Hidden Markov
    models, and Double Chain Markov Models.
A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>.  Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment.  Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.
Estimation of marginal hazard ratios in clustered failure time data. It implements the weighted generalized estimating equation approach based on a semiparametric marginal proportional hazards model (See Niu, Y. Peng, Y.(2015). "A new estimating equation approach for marginal hazard ratio estimation"), accounting for within-cluster correlations. 5 different correlation structures are supported. The package is designed for researchers in biostatistics and epidemiology who require accurate and efficient estimation methods for survival analysis in clustered data settings.
Datasets, constants, conversion factors, and utilities for 'MArine', 'Riverine',
             'Estuarine', 'LAcustrine' and 'Coastal' science. 
             The package contains among others: (1) chemical and
             physical constants and datasets, e.g. atomic weights, gas
             constants, the earths bathymetry; (2) conversion factors
             (e.g. gram to mol to liter, barometric units,
             temperature, salinity); (3) physical functions, e.g. to
             estimate concentrations of conservative substances, gas
             transfer and diffusion coefficients, the Coriolis force
             and gravity; (4) thermophysical properties of the
             seawater, as from the UNESCO polynomial or from the more
             recent derivation based on a Gibbs function.
Local recombination rates are graphically estimated across a genome using Marey maps.
Implements likelihood inference based on higher order approximations for linear nonnormal regression models.
The target of 'margaret' is help to extract data from Minciencias to analyze scientific production in Colombia.
Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.
Estimates risk as a function of a marker by integrating over other covariates in a conditional risk model.
Test the marginal correlation between a scalar response variable with a vector of explanatory variables 
    using the max-type test with bootstrap.
    The test is based on the max-type statistic and its asymptotic distribution
    under the null hypothesis of no marginal correlation.
    The bootstrap procedure is used to approximate the null distribution of the test statistic.
    The package provides a function for performing the test. For more technical details, refer to Zhang and Laber (2014) <doi:10.1080/01621459.2015.1106403>.
Provides the ability to perform "Marginal Mediation"--mediation
   wherein the indirect and direct effects are in terms of the average marginal effects 
   (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). 
   The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package.
   This framework allows the use of categorical mediators and outcomes with little change in interpretation
   from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details 
   on the method.
An R port of the margins command from 'Stata', which can be used to
    calculate marginal (or partial) effects from model objects.
Multivariate ARIMA and ARIMA-X estimation using Spliid's 
    algorithm (marima()) and simulation (marima.sim()).
Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015) <doi:10.18637/jss.v067.i01>.
Miscellaneous functions and wrappers for development in other
    packages created, maintained by Jordan Mark Barbone.
Render Markdown to full and lightweight HTML/LaTeX documents with
    the 'commonmark' package. This package has been superseded by 'litedown'.
An R-Shiny module containing a "markdownInput". This input allows
    the user to write some markdown code and to preview the result. This input
    has been inspired by the "comment" window of <https://github.com/>.
Functions for fitting various models to capture-recapture data
    including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and
    the multi-variate state model structure for survival
    estimation and POPAN structured Jolly-Seber models for abundance estimation.
    There are also Hidden Markov model (HMM) implementations of CJS and multistate
    models with and without state uncertainty and a simulation capability for HMM
    models.
Implementation of the 'MarkerPen' algorithm, short for marker gene detection
    via penalized principal component analysis, described in the paper by Qiu, Wang, Lei,
    and Roeder (2020, <doi:10.1101/2020.11.07.373043>). 'MarkerPen' is a semi-supervised
    algorithm for detecting marker genes by combining prior marker information with bulk
    transcriptome data.
For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the 'dtw' package) to do the matching and the 'CausalImpact' package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).
Facilitates tidy calculation of popular quantitative marketing 
    metrics. It also includes functions for doing analysis that will help
    marketers and data analysts better understand the drivers and/or trends
    of these metrics. These metrics include Customer Experience Index 
    <https://go.forrester.com/analytics/cx-index/> and Net Promoter Score 
    <https://www.netpromoter.com/know/>.
Provides estimation methods for markets in equilibrium and
    disequilibrium. Supports the estimation of an equilibrium and
    four disequilibrium models with both correlated and independent shocks.
    Also provides post-estimation analysis tools, such as aggregation,
    marginal effect, and shortage calculations. See Karapanagiotis (2024)
    <doi:10.18637/jss.v108.i02> for an overview of the functionality 
    and examples. The estimation methods are based on full information
    maximum likelihood techniques given in
    Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented
    using the analytic derivative expressions calculated in
    Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard
    errors can be estimated by adjusting for heteroscedasticity or clustering.
    The equilibrium estimation constitutes a case of a system of linear,
    simultaneous equations. Instead, the disequilibrium models replace the
    market-clearing condition with a non-linear,
    short-side rule and allow for different specifications of price dynamics.
Automatic marking of R assignments for students and teachers based
    on 'testthat' test suites.
Allows for fitting of maximum likelihood models using Markov chains
    on phylogenetic trees for analysis of discrete character data. Examples of such
    discrete character data include restriction sites, gene family presence/absence,
    intron presence/absence, and gene family size data. Hypothesis-driven user-
    specified substitution rate matrices can be estimated. Allows for biologically
    realistic models combining constrained substitution rate matrices, site rate
    variation, site partitioning, branch-specific rates, allowing for non-stationary
    prior root probabilities, correcting for sampling bias, etc. See Dang and Golding 
    (2016) <doi:10.1093/bioinformatics/btv541> for more details.
Functions and S4 methods to create and manage discrete time Markov
    chains more easily. In addition functions to perform statistical (fitting
    and drawing random variates) and probabilistic (analysis of their structural
    proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>.
    Some functions for continuous times Markov chains depend on the suggested ctmcd package.
Functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.
Fit mixture of Markov chains of higher orders from multiple
    sequences. It is also compatible with ordinary 1-component, 1-order or
    single-sequence Markov chains. Various utility functions are provided
    to derive transition patterns, transition probabilities per component
    and component priors. In addition, print(), predict() and component
    extracting/replacing methods are also defined as a convention of
    mixture models.
The inference in multi-state models is traditionally performed under
             a Markov assumption that claims that past and future of the process
             are independent given the present state. In this package, we 
             consider tests of the Markov assumption that are applicable to 
             general multi-state models. Three approaches using existing 
             methodology are considered: a simple method based on including
             covariates depending on the history in Cox models for the transition
             intensities; methods based on measuring the discrepancy of the 
             non-Markov estimators of the transition probabilities to the 
             Markov Aalen-Johansen estimators; and, finally, methods that were
             developed by considering summaries from families of log-rank 
             statistics where patients are grouped by the state occupied of the
             process at a particular time point (see Soutinho G, Meira-Machado L
             (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H
             (2020) <doi:10.1093/biostatistics/kxaa030>).
The Markowitz criterion is a multicriteria decision-making method that stands out in risk and uncertainty analysis in contexts where probabilities are known. This approach represents an evolution of Pascal's criterion by incorporating the dimension of variability. In this framework, the expected value reflects the anticipated return, while the standard deviation serves as a measure of risk. The 'markowitz' package provides a practical and accessible tool for implementing this method, enabling researchers and professionals to perform analyses without complex calculations. Thus, the package facilitates the application of the Markowitz criterion. More details on the method can be found in Octave Jokung-Nguéna (2001, ISBN 2100055372).
A collection of tools for analyzing significance of 
    Markowitz portfolios, using the delta method on the second moment
    matrix, <arxiv:1312.0557>.
Functions of marginal mean and quantile regression models are used to analyze environmental exposure and biomonitoring data with repeated measurements and non-detects (i.e., values below the limit of detection (LOD)), as well as longitudinal exposure data that include non-detects and time-dependent covariates. For more details see Chen IC, Bertke SJ, Curwin BD (2021) <doi:10.1038/s41370-021-00345-1>, Chen IC, Bertke SJ, Estill CF (2024) <doi:10.1038/s41370-024-00640-7>, Chen IC, Bertke SJ, Dahm MM (2024) <doi:10.1093/annweh/wxae068>, and Chen IC (2025) <doi:10.1038/s41370-025-00752-8>.
Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <https://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <https://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
It contains the function to apply MARMoT balancing technique discussed in: Silan, Boccuzzo, Arpino (2021) <DOI:10.1002/sim.9192>, Silan, Belloni, Boccuzzo, (2023) <DOI:10.1007/s10260-023-00695-0>; 
             furthermore it contains a function for computing the Deloof's approximation of the average rank 
             (and also a parallelized version) and a function to compute the Absolute Standardized Bias.
This algorithm provides a numerical solution to the
        problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than
        the Gauss-Newton-like algorithm when starting from points very
        far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.
Provides the mean to parse and render markdown text with grid along
    with facilities to define the styling of the text.
Provides a single function plotting Marradi's trees: a graphical representation
 of a numerical variable for comparing the variable mean and standard deviation across subgroups. See A. Marradi "L'analisi monovariata" (1993, ISBN: 9788820496876).
Includes functions for conducting univariate and multivariate meta-analysis.
   This includes the estimation of the asymptotic variance-covariance matrix of effect sizes.
   For more details see Becker (1992) <doi:10.2307/1165128>, 
   Cooper, Hedges, and Valentine (2019) <doi:10.7758/9781610448864>, and
   Schmid, Stijnen, and White (2020) <doi:10.1201/9781315119403>.
Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.
It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and
             GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model
             is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response
             variable. For method details see, Friedman, J.H. (1991). <DOI:10.1214/aos/1176347963>.The GWR model is then used to predict the response variable at 
             testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model 
             can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in 
             cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations.
The MARSS package provides maximum-likelihood parameter
    estimation for constrained and unconstrained linear multivariate autoregressive
    state-space (MARSS) models, including partially deterministic models. MARSS models are a class 
    of dynamic linear model (DLM) and vector autoregressive model (VAR)
    model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB'
    (using the 'marssTMB' companion package). Functions are provided for parametric and 
    innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including
    bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all
    conditional residual types. See the user guide for examples of dynamic factor analysis, 
    dynamic linear models, outlier and shock detection, and multivariate AR-p models.  
    Online workshops (lectures, eBook, and computer labs) 
    at <https://atsa-es.github.io/>.
Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.
Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of software to investigate splicing dynamics at single-cell resolution. 'MARVEL' enables splicing analysis of single-cell RNA-sequencing data generated from plate- and droplet-based library preparation methods.
A way to estimate and test marginal mediation effects for 
             zero-inflated compositional mediators. Estimates of Natural Indirect Effect (NIE),
             Natural Direct Effect (NDE) of each taxon, as well as their standard errors and 
             confident intervals, were provided as outputs. Zeros will not be imputed during 
             analysis. See Wu et al. (2022) <doi:10.3390/genes13061049>.
Genome-wide association analysis that accommodate membership information, variance adjustment, and correlated responses.
An S4 implementation of the unbiased extension of
    the model- assisted synthetic-regression estimator proposed by
    Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013)
    <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014)
    <DOI:10.1139/cjfr-2013-0449>.  It yields smaller variances than the
    standard bias correction, the generalised regression estimator.
A set of model-assisted survey estimators and corresponding
    variance estimators for single stage, unequal probability, without replacement
    sampling designs.  All of the estimators can be written as a generalized 
    regression estimator with the Horvitz-Thompson, ratio, post-stratified, and 
    regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6).
    Two of the estimators employ a statistical learning model as the assisting model:
    the elastic net regression estimator, which is an extension of the lasso regression
    estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the 
    regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. 
    The variance estimators which approximate the joint inclusion probabilities can
    be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the
    bootstrap variance estimator is presented in Mashreghi et al. (2016) 
    <doi:10.1214/16-SS113>.
Implements the multivariate adaptive shrinkage (mash)
    method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for
    estimating and testing large numbers of effects in many conditions
    (or many outcomes). Mash takes an empirical Bayes approach to
    testing and effect estimation; it estimates patterns of similarity
    among conditions, then exploits these patterns to improve accuracy
    of the effect estimates. The core linear algebra is implemented in
    C++ for fast model fitting and posterior computation.
Create vectors with sticky flags for elements that should not be
    displayed. Numeric vectors have basic subset and arithmetic methods
    implemented.
Mask ranges based on expert 
    knowledge or remote sensing layers. These tools can 
    be combined to quantitatively and reproducibly 
    generate a new map or to update an existing map.
    Methods include expert opinion and data-driven 
    tools to generate thresholds for binary masks.
Functions and datasets to support Venables and Ripley,
  "Modern Applied Statistics with S" (4th edition, 2002).
Mass measurement corrections and uncertainties using calibration
    data, as recommended by EURAMET's guideline No. 18 (2015) 
    ISBN:978-3-942992-40-4 . The package provides classes, functions, and 
    methods for storing information contained in calibration certificates
    and converting balance readings to both conventional mass and real mass.
    For the latter, the Magnitude of the Air Buoyancy Correction factor employs
    models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and
    Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to estimate the local air
    density using measured environmental conditions.
Some enhancements, extensions and additions
    to the facilities of the recommended 'MASS' package 
    that are useful mainly for teaching purposes, with
    more convenient default settings and user interfaces.
    Key functions from 'MASS' are imported and re-exported
    to avoid masking conflicts.  In addition we provide
    some additional functions mainly used to illustrate
    coding paradigms and techniques, such as Gramm-Schmidt
    orthogonalisation and generalised eigenvalue problems.
Constructing matrices for quick prototyping can be a nuisance, 
    requiring the user to think about how to fill the matrix with values using 
    the matrix() function. The %<-% operator solves that issue by allowing the 
    user to construct matrices using code that shows the actual matrices.
Friendly implementation of the Mann-Whitney-Wilcoxon test for competitive gene set enrichment analysis.
Recursively calculates mass properties (mass, center of mass,  moments and products of inertia, and optionally, their uncertainties) for arbitrary decomposition trees. R. L. Zimmerman, J. H. Nakai. (2005) <https://www.sawe.org/product/paper-3360/>).
Estimation equations are from a variety of sources and associated error estimation.
Methods for quality control and exploratory analysis of surface 
  water quality data collected in Massachusetts, USA.  Functions are developed 
  to facilitate data formatting for the Water Quality Exchange Network 
  <https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting 
  of data quality objectives to state agencies. Quality control methods are 
  from Massachusetts Department of Environmental Protection (2020) 
  <https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.
Analyzes production and dispersal of seeds dispersed from trees and recovered in seed traps.  Motivated by long-term inventory plots where seed collections are used to infer seed production by each individual plant.
Simulates Multidimensional Adaptive Testing using the multidimensional three-parameter logistic model as described in Segall (1996) <doi:10.1007/BF02294343>, van der Linden (1999) <doi:10.3102/10769986024004398>, Reckase (2009) <doi:10.1007/978-0-387-89976-3>, and Mulder & van der Linden (2009) <doi:10.1007/s11336-008-9097-5>.
Calculates Model-Averaged Tail Area Wald
    (MATA-Wald) confidence intervals, and MATA-Wald confidence densities
    and distributions, which are constructed using single-model
    frequentist estimators and model weights. See Turek and Fletcher
    (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019)
    <doi:10.1007/s10651-019-00432-5> for details.
Conveniently log everything you type into the R console. Logs are
    are stored as tidy data frames which can then be analyzed using 'tidyverse'
    style tools.
Calculate multiple statistics with confidence intervals for matched
    case-control data including risk difference, risk ratio, relative
    difference, and the odds ratio. Results are equivalent to those from
    'Stata', and you can choose how to format your input data. Methods used are
    those described on page 56 the 'Stata' documentation for "Epitab - Tables
    for Epidemologists" <https://www.stata.com/manuals/repitab.pdf>.
Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The 'Gurobi' software and its 'R' interface package are required for one of the package functions (match.2x()) and can be obtained at <https://www.gurobi.com/> (free academic license). For more details, refer to Degras (2022) <doi:10.1080/10618600.2022.2074429> "Scalable feature matching for large data collections" and Bandelt, Maas, and Spieksma (2004) <doi:10.1057/palgrave.jors.2601723> "Local search heuristics for multi-index assignment problems with decomposable costs".
Two novel matching-based methods for estimating group average treatment effects (GATEs). The match_y1y0() and match_y1y0_bc() functions are used for imputing the potential outcomes based on matching and bias-corrected matching techniques, respectively. The EstGATE() function is employed to estimate the GATE after imputing the potential outcomes.
Provides functions for multivariate and propensity score matching 
             and for finding optimal balance based on a genetic search algorithm. 
             A variety of univariate and multivariate metrics to
             determine if balance has been obtained are also provided. For
             details, see the paper by Jasjeet Sekhon 
             (2007, <doi:10.18637/jss.v042.i07>).
Implements structural estimators to correct for
    the sample selection bias from observed outcomes in matching
    markets. This includes one-sided matching of agents into groups 
    (Klein, 2015) <https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1521.pdf> 
    as well as two-sided matching of students to schools 
    (Aue et al., 2020) <https://ftp.zew.de/pub/zew-docs/dp/dp20032.pdf>.
    The package also contains algorithms to find stable matchings
    in the three most common matching problems: the stable roommates
    problem (Irving, 1985) <doi:10.1016/0196-6774(85)90033-1>, 
    the college admissions problem (Gale and Shapley, 1962) <doi:10.2307/2312726>, 
    and the house allocation problem (Shapley and Scarf, 1974) <doi:10.1016/0304-4068(74)90033-0>.
Computes matching algorithms quickly using Rcpp.
    Implements the Gale-Shapley Algorithm to compute the stable
    matching for two-sided markets, such as the stable marriage
    problem and the college-admissions problem. Implements Irving's
    Algorithm for the stable roommate problem. Implements the top
    trading cycle algorithm for the indivisible goods trading problem.
Selects matched samples of the original treated and
    control groups with similar covariate distributions -- can be
    used to match exactly on covariates, to match on propensity
    scores, or perform a variety of other matching procedures.  The
    package also implements a series of recommendations offered in
    Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 
    'gurobi' package, which is not on CRAN, is optional and comes with 
    an installation of the Gurobi Optimizer, available at 
    <https://www.gurobi.com>.)
Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
Provides flexible dictionary-based
    cleaning that allows users to specify implicit and explicit missing data,
    regular expressions for both data and columns, and global matches, while
    respecting ordering of factors. This package is part of the 'RECON'
    (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Performs multilevel matches for data with cluster-
	level treatments and individual-level outcomes using a network 
	optimization algorithm.  Functions for checking balance at the 
	cluster and individual levels are also provided, as are methods 
	for permutation-inference-based outcome analysis.  Details in 
	Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>.  The optmatch 
	package, which is useful for running many of the provided 
	functions, may be downloaded from Github at 
	<https://github.com/markmfredrickson/optmatch> if not available on 
	CRAN.
Inspired by pattern matching and enum types in Rust
    and many functional programming languages, this package offers
    an updated version of the 'switch' function called 'Match' that
    accepts atomic values, functions, expressions, and enum variants.
    Conditions and return expressions are separated by '->' and 
    multiple conditions can be associated with the same return expression
    using '|'. 'Match' also includes support for 'fallthrough'. The 
    package also replicates the Result and Option enums from Rust.
Provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).
Transferring over a code base from Matlab to R is often a repetitive
    and inefficient use of time. This package provides a translator for Matlab /
    Octave code into R code. It does some syntax changes, but most of the heavy
    lifting is in the function changes since the languages are so similar.
    Options for different data structures and the functions that can be changed
    are given. The Matlab code should be mostly in adherence to the standard
    style guide but some effort has been made to accommodate different number of
    spaces and other small syntax issues. This will not make the code more R
    friendly and may not even run afterwards. However, the rudimentary syntax,
    base function and data structure conversion is done quickly so that the
    maintainer can focus on changes to the design structure.
Simulate, manage, visualize, and analyze spatially and temporally 
    explicit datasets of mating potential. Implements methods to calculate 
    synchrony, proximity, and compatibility.Synchrony calculations are based on 
    methods described in Augspurger (1983) <doi:10.2307/2387650>, 
    Kempenaers (1993) <doi:10.2307/3676415>, Ison et al. (2014) 
    <doi:10.3732/ajb.1300065>, and variations on these, as described.
You can apply image processing effects that modifies the perceived material properties of objects
    in photos, such as gloss, smoothness, and blemishes. This is an implementation of the algorithm proposed by
    Boyadzhiev et al. (2015) "Band-Sifting Decomposition for Image Based Material Editing".
    Documentation and practical tips of the package is available at <https://github.com/tsuda16k/materialmodifier>.
Provides 'MathJax' and macros to enable its use within Rd files for rendering equations in the HTML help files.
Translate R expressions to 'MathML' or 'MathJax'/'LaTeX' so that
    they can be rendered in R markdown documents and shiny apps. This package
    depends on R package 'rolog', which requires an installation of the
    'SWI'-'Prolog' runtime either from 'swi-prolog.org' or from R
    package 'rswipl'.
Given an image of a formula (typeset or handwritten) this package
    provides calls to the 'Mathpix' service to produce the 'LaTeX' code which should
    generate that image, and pastes it into a (e.g. an 'rmarkdown') document. 
    See <https://docs.mathpix.com/> for full details. 'Mathpix' is an external service 
    and use of the API is subject to their terms and conditions.
Query, extract, and plot genealogical data from The Mathematics
    Genealogy Project <https://mathgenealogy.org/>. Data is gathered from the
    'WebSocket' server run by the 'geneagrapher-core' project
    <https://github.com/davidalber/geneagrapher-core>.
Allows users familiar with MATLAB to use MATLAB-named functions in
    R. Several basic MATLAB functions are written in this package to mimic the
    behavior of their original counterparts, with more to come as this package
    grows.
Provides users to call MATLAB from using the "system" command.
    Allows users to submit lines of code or MATLAB m files.
    This is in comparison to 'R.matlab', which creates a MATLAB server.
A collection of matrix functions for teaching and learning matrix
    linear algebra as used in multivariate statistical methods. These functions are
    mainly for tutorial purposes in learning matrix algebra ideas using R. In some
    cases, functions are provided for concepts available elsewhere in R, but where
    the function call or name is not obvious. In other cases, functions are provided
    to show or demonstrate an algorithm. In addition, a collection of functions are
    provided for drawing vector diagrams in 2D and 3D.
A set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.
A general framework for computing powers of matrices.  A
   key feature is the capability for users to write callback functions,
   called after each iteration, thus enabling customization for specific
   applications.  Diverse types of matrix classes/matrix multiplication
   are accommodated.  If the multiplication type computes in parallel,
   then the package computation is also parallel.
Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) <https://jmlr.org/papers/v24/23-0030.html>.
Provides functions, which make matrix creation conciser 
             (such as the core package's function m() for rowwise matrix definition or 
             runifm() for random value matrices).
             Allows to set multiple matrix values at once, by using list of formulae. 
             Provides additional matrix operators and dedicated plotting function.
Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix).
    The graphical elements of this package are based on the 'DescTools' package.
    This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research.
The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.
A rich hierarchy of sparse and dense matrix classes,
	including general, symmetric, triangular, and diagonal matrices
	with numeric, logical, or pattern entries.  Efficient methods for
	operating on such matrices, often wrapping the 'BLAS', 'LAPACK',
	and 'SuiteSparse' libraries.
A collection of functions to support matrix calculations
        for probability, econometric and numerical analysis. There are
        additional functions that are comparable to APL functions which
        are useful for actuarial models such as pension mathematics.
        This package is used for teaching and research purposes at the
        Department of Finance and Risk Engineering, New York
        University, Polytechnic Institute, Brooklyn, NY 11201.
        Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326.
        Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507.
        Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563.
Computation and visualization of matrix correlation coefficients.
    The main method is the Similarity of Matrices Index, while various related
    measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's
    linear correlation and Coxhead's coefficient are included
    for comparison and flexibility.
The user must supply a matrix filled with similarity values. The software will search for significant differences between similarity values at different hierarchical levels. The algorithm will return a Loess-smoothed plot of the similarity values along with the inflection point, if there are any. There is the option to search for an inflection point within a specified range. The package also has a function that will return the matrix components at a specified cutoff. References: Mullner. <ArXiv:1109.2378>; Cserhati, Carter. (2020, Journal of Creation 34(3):41-50), <https://dl0.creation.com/articles/p137/c13759/j34-3_64-73.pdf>.
Tools for phase-type distributions including the following variants:
    continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. 
    Methods for functional evaluation, simulation and estimation using the 
    expectation-maximization (EM) algorithm are provided for all models.
    The methods of this package are based on the following references.
    Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm,
    Olsson, M. (1996). Estimation of phase-type distributions from censored data,
    Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>,
    Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>,
    Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>,
    Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>,
    Bladt, M. (2022) <doi:10.1017/asb.2021.40>,
    Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>,
    Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>,
    Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.
Matrix eQTL is designed for fast eQTL analysis on large datasets.
        Matrix eQTL can test for association between genotype 
        and gene expression using linear regression 
        with either additive or ANOVA genotype effects.
        The models can include covariates to account for factors 
        as population stratification, gender, and clinical variables. 
        It also supports models with heteroscedastic and/or correlated errors,
        false discovery rate estimation and 
        separate treatment of local (cis) and distant (trans) eQTLs.
        For more details see Shabalin (2012) <doi:10.1093/bioinformatics/bts163>.
Extends sparse matrix and vector classes from the 'Matrix' package by providing: 
  (a) Methods and operators that work natively on CSR formats (compressed sparse row, 
  a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), 
  mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); 
  (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, 
  including the 'float32' type from 'float'; 
  (c) Coercion methods between pairs of classes which are not present in 'Matrix', 
  such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; 
  (d) Utility functions for sparse matrices such as sorting the indices or removing 
  zero-valued entries; 
  (e) Fast transposes that work by outputting in the opposite storage format;
  (f) Faster replacements for many 'Matrix' methods for all sparse types, such as
  slicing and elementwise multiplication.
  (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.
Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models.
Constructs the normalized Laplacian matrix of a square matrix, returns the eigenvectors (singular vectors) and visualization of normalized Laplacian map.
Implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) <arXiv:2005.03861>. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.
Generalized Linear Modelling with sparse and dense 'Matrix' matrices, using
  modular prediction and response module classes.
Provides functions for row-reducing and inverting matrices with
    entries in many of the finite fields (those with a prime number of 
    elements). With this package, users will be able to find the reduced row 
    echelon form (RREF) of a matrix and calculate the inverse of a (square, 
    invertible) matrix.
Computes densities, probabilities, and random deviates of the Matrix Normal (Pocuca et al. (2019) <doi:10.48550/arXiv.1910.02859>). Also includes simple but useful matrix functions. See the vignette for more information.
A simple and the early stage package for matrix profile based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh (2016) <DOI:10.1109/ICDM.2016.0179>. This package calculates all-pairs-similarity for a given window size for time series data.
This is the core functions needed by the 'tsmp' package.  The
    low level and carefully checked mathematical functions are here.
    These are implementations of the Matrix Profile concept that was
    created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Provides samplers for various matrix variate distributions: Wishart, inverse-Wishart, normal, t, inverted-t, Beta type I, Beta type II, Gamma, confluent hypergeometric. Allows to simulate the noncentral Wishart distribution without the integer restriction on the degrees of freedom.
Creates an object that stores a matrix ensemble, matrices
  that share the same common properties, where rows and columns can be
  annotated. Matrices must have the same dimension and dimnames. Operators to
  manipulate these objects are provided as well as mechanisms to apply
  functions to these objects.
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.  There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
Tests for block-diagonal structure in symmetric matrices (e.g. correlation matrices) under the null hypothesis of exchangeable off-diagonal elements. As described in Segal et al. (2019), these tests can be useful for construct validation either by themselves or as a complement to confirmatory factor analysis. Monte Carlo methods are used to approximate the permutation p-value with Hubert's Gamma (Hubert, 1976) and a t-statistic. This package also implements the chi-squared statistic described by Steiger (1980). Please see Segal, et al. (2019) <doi:10.1007/s11336-018-9647-4> for more information.
Functions to perform fast statistical hypothesis tests on rows/columns of matrices.
  The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use,
  3) compatibility with tests implemented in R (like those available in the 'stats' package).
An easy-to-use workflow that provides tools to create, update and fill literature matrices commonly used in research, specifically epidemiology and health sciences research. The project is born out of need as an easy–to–use tool for my research methods classes.
An implementation of matrix mathematics wherein operations are performed "by name."
Provides functions to collapse a tidy data frame into matrices in a data frame
    and expand a data frame of matrices into a tidy data frame.
Performs matrix skew-t parameter estimation, Gallaugher and McNicholas (2017)  <doi: 10.1002/sta4.143>.
Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations. For more information, please check: Xuwen Zhu, Shuchismita Sarkar, and Volodymyr Melnykov (2021), "MatTransMix: an R package for matrix model-based clustering and parsimonious mixture modeling",  <doi:10.1007/s00357-021-09401-9>.
Identifying maturation stages across young athletes is paramount for talent identification. Furthermore, the concept of biobanding, or grouping of athletes based on their biological development, instead of their chronological age, has been widely researched. The goal of this package is to help professionals working in the field of strength & conditioning and talent ID obtain common maturation metrics and as well as to quickly visualize this information via several plotting options. For the methods behind the computed maturation metrics implemented in this package refer to Khamis, H. J., & Roche, A. F. (1994) <https://pubmed.ncbi.nlm.nih.gov/7936860/>, Mirwald, R.L et al., (2002) <https://pubmed.ncbi.nlm.nih.gov/11932580/> and Cumming, Sean P. et al., (2017) <doi:10.1519/SSC.0000000000000281>.
Provides functions for the creation, evaluation and test of decision models based in
    Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities
    for a set of indexes, compute utilities and weights for the whole decision tree defining the
    decision model and simulate weights employing Dirichlet distributions under addition constraints 
    in weights.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is commonly accompanied by 'BEAUti 2' (<https://www.beast2.org>),
    which, among others, allows one to install 'BEAST2' package. 
    This package allows to work with 'BEAST2' packages from 'R'.
Takes a .state file generated by IQ-TREE as an input and, for each ancestral node present in the file, generates a FASTA-formatted maximum likelihood (ML) sequence as well as an ‘AltAll’ sequence in which uncertain sites, determined by the two parameters thres_1 and thres_2, have the maximum likelihood state swapped with the next most likely state as described in Geeta N. Eick, Jamie T. Bridgham, Douglas P. Anderson, Michael J. Harms, and Joseph W. Thornton (2017), "Robustness of Reconstructed Ancestral Protein Functions to Statistical Uncertainty" <doi:10.1093/molbev/msw223>.
Implements state-of-the-art block bootstrap methods for extreme value 
  statistics based on block maxima. Includes disjoint blocks, sliding blocks, 
  relying on a circular transformation of blocks. 
  Fast C++ backends (via 'Rcpp') ensure scalability for large time series.
Functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) <doi:10.1177/0962280220931560>.  The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo.
Methods of selecting one from many numeric predictors
       for a regression model, to ensure that the additional
       predictor has the maximum effect size.
Fit Maximum Entropy Optimality Theory models to data sets,
  generate the predictions made by such models for novel data, and compare the 
  fit of different models using a variety of metrics. The package is described
  in Mayer, C., Tan, A., Zuraw, K. (in press) <https://sites.socsci.uci.edu/~cjmayer/papers/cmayer_et_al_maxent_ot_accepted.pdf>.
Complex niche models show low performance in identifying
    the most important range-limiting environmental variables and in
    transferring habitat suitability to novel environmental conditions
    (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al.,
    2014 <DOI:10.1111/ddi.12160>). This package helps to identify the
    most important set of uncorrelated variables and to fine-tune Maxent's
    regularization multiplier. In combination, this allows to constrain
    complexity and increase performance of Maxent niche models (assessed
    by information criteria, such as AICc (Akaike, 1974
    <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver
    operating characteristic (AUC) (Fielding and Bell, 1997
    <DOI:10.1017/S0376892997000088>). Users of this package should be
    familiar with Maxent niche modelling.
Constructs a space-filling design under the criterion of maximum-minimum distance. Both discrete and continuous searches are provided.
Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in <arXiv:2011.07568>. It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.
Functions for Maximum Likelihood (ML) estimation, non-linear
   optimization, and related tools.  It includes a unified way to call
   different optimizers, and classes and methods to handle the results from
   the Maximum Likelihood viewpoint.  It also includes a number of convenience
   tools for testing and developing your own models.
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
Computes the maximum matching for unweighted graph and maximum
    matching for (un)weighted bipartite graph efficiently.
An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.
Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options.  See the 'Maxent' website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.
Generate maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018) "Designing Computer Experiments with Multiple Types of Factors: The MaxPro Approach", Journal of Quality Technology, to appear.
It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.
Dimension reduction for multivariate data of extreme events with a PCA like procedure as described in Reinbott, Janßen, (2024), <doi:10.48550/arXiv.2408.10650>. Tools for necessary transformations of the data are provided.
Maximally selected rank statistics with
 several p-value approximations.
Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or 'MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023) <doi:10.1007/978-3-031-29168-5_18>.
The maybe type represents the possibility of some value or nothing. 
    It is often used instead of throwing an error or returning `NULL`.
    The advantage of using a maybe type over `NULL` is that it is both composable 
    and requires the developer to explicitly acknowledge the potential absence 
    of a value, helping to avoid the existence of unexpected behaviour.
A suite of utility functions providing functionality commonly
    needed for production level projects such as logging, error handling,
    cache management and date-time parsing. Functions for date-time parsing and 
    formatting require that time zones be specified explicitly, avoiding a common 
    source of error when working with environmental time series.
Utility functions for discovering and managing metadata 
    associated with spatially unique "known locations". Applications include
    all fields of environmental monitoring (e.g. air and water quality) where 
    data are collected at stationary sites.
A suite of compiled functions calculating rolling mins, means, 
    maxes and other statistics. This package is designed to meet the needs of
    data processing systems for environmental time series.
A suite of convenience functions for generating US state and county
    thematic maps using datasets from the MazamaSpatialUtils package.
A suite of conversion functions to create internally standardized
    spatial polygons data frames. Utility functions use these data sets to
    return values such as country, state, time zone, watershed, etc. associated
    with a set of longitude/latitude pairs. (They also make cool maps.)
Utility functions for working with environmental time series data from known 
    locations. The compact data model is structured as a list with two dataframes. A 
    'meta' dataframe contains spatial and measuring device metadata associated with 
    deployments at known locations. A 'data' dataframe contains a 'datetime' column 
    followed by columns of measurements associated with each "device-deployment".
    Ephemerides calculations are based on code originally found in NOAA's
    "Solar Calculator" <https://gml.noaa.gov/grad/solcalc/>.
A novel mediation analysis approach to address zero-inflated mediators containing true zeros and false zeros. See Jiang et al (2023) "A Novel Causal Mediation Analysis Approach for Zero-Inflated Mediators" <arXiv:2301.10064> for more details.
Supports the generation of parallelogram, equilateral
    triangle, regular hexagon, isosceles trapezoid, Koch snowflake,
    'hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski
    trapezoid mazes via 'TurtleGraphics'. Mazes are generated by the recursive method:
    the domain is divided into sub-domains in which mazes are generated,
    then dividing lines with holes are drawn between them, see
    J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.
Methods for calculating and testing the significance of
  pairwise monotonic association from and based on the work of
  Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one
  or multiple sets can be performed in parallel thanks to the
  packages 'foreach' and 'doMC'.
Functionality for generating and plotting random mazes. The mazes are based on matrices, so can only consist of vertical and horizontal lines along a regular grid. But there is no need to use every possible space, so they can take on many different shapes.
A new way to predict time series using the marginal distribution table in the absence of the significance of traditional models.
Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by Øyvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).
Exploratory and predictive methods for the analysis of several blocks of variables measured on the same individuals.
Uses several Nonlinear Mixed effect (NONMEM) models (as NONMEM control files)
    to perform bootstrap model averaging and Monte Carlo Simulation for Model Based 
    Bio-Equivalence (MBBE). Power is returned as the fraction of the simulations with 
    successful bioequivalence (BE) test, for maximum concentration (Cmax), Area under the 
    curve to the last observed value (AUClast) and Area under the curve extrapolate to 
    infinity (AUCinf). See Hooker, A. (2020) Improved bioequivalence assessment through 
    model-informed and model-based strategies <https://www.fda.gov/media/138035/download>.
Distributions that are typically used for exposure rating in
             general insurance, in particular to price reinsurance contracts.
             The vignette shows code snippets to fit the distribution to
             empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208>
             freely available on-line.
Provides probability mass, distribution, quantile, random variate
    generation, and method-of-moments parameter fitting for the MBBEFD family of
    distributions used in insurance modeling as described in Bernegger (1997)
    <doi:10.2143/AST.27.1.563208> without any external dependencies.
Calibrate and apply multivariate bias correction algorithms
    for climate model simulations of multiple climate variables. Three methods
    described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and 
    Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented —
    (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr),
    and (iii) MBC N-dimensional PDF transform (MBCn) — as is the Rank
    Resampling for Distributions and Dependences (R2D2) method.
The companion package provides all original data sets and functions that are used in the book "Model-Based Clustering and Classification for Data Science" by Charles Bouveyron, Gilles Celeux, T. Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181).
Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods.
   Jorjani, H., et al. (2003) <doi:10.3168/jds.S0022-0302(03)73646-7>.
   Schaeffer, L. R. (2014) <http://animalbiosciences.uoguelph.ca/~lrs/ELARES/PDforce.pdf>.
Implements methods that are useful in designing research studies and analyzing data, with 
	particular emphasis on methods that are developed for or used within the behavioral, 
	educational, and social sciences (broadly defined). That being said, many of the methods 
	implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a 
	suite of functions for a variety of related topics, such as effect sizes, confidence intervals 
	for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size
	planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and 
	minimum-risk point estimation perspectives), mediation analysis, various properties of 
	distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally 
	an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became
	more general and now contains methods applicable and used in a wide variety of fields and is an 
	orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has 
	greatly benefited from others, see <https://www3.nd.edu/~kkelley/site/MBESS.html> for a detailed 
	list of those that have contributed and other details.
Fast moment-based hierarchical model fitting. Implements
    methods from the papers
    "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017)
    and
    "Fitting a Deeply Nested Hierarchical Model to a Large Book Review
    Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry
    (2018).
Modern model-based geostatistics for point-referenced data. This package provides a
    simple interface to run spatial machine learning models and geostatistical models
    that estimate a continuous (raster) surface from point-referenced outcomes and,
    optionally, a set of raster covariates. The package also includes functions to
    summarize raster outcomes by (polygon) region while preserving uncertainty.
Provides spatially survey balanced designs using the quasi-random number method described Robinson et al. (2013) <doi:10.1111/biom.12059> and adjusted in Robinson et al. (2017) <doi:10.1016/j.spl.2017.05.004>. Designs using MBHdesign can: 1) accommodate, without substantial detrimental effects on spatial balance, legacy sites (Foster et al., 2017 <doi:10.1111/2041-210X.12782>); 2) be based on points or transects (foster et al. 2020 <doi:10.1111/2041-210X.13321> and produce clustered samples (Foster et al. (in press). Additional information about the package use itself is given in Foster (2021) <doi:10.1111/2041-210X.13535>.
Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.
Provides linear models based on Theil-Sen
        single median and Siegel repeated medians. They are very robust
        (29 or 50 percent breakdown point, respectively), and if no
        outliers are present, the estimators are very similar to OLS.
Lightweight utilities for nucleic acid melting curve analysis are 
    important in life sciences and diagnostics. This software can be used for 
    the analysis and presentation of melting curve data from microbead-based 
    assays (surface melting curve analysis) and reactions in solution (e.g., 
    quantitative PCR (qPCR), real-time isothermal Amplification). Further 
    information are described in detail in two publications in The R Journal [
    <https://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf>; 
    <https://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf>].
Utilizing a combination of machine learning models (Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machines, Extreme Gradient Boosting, and Linear Discriminant Analysis) and a deep Artificial Neural Network model, 'MBMethPred' can predict medulloblastoma subgroups, including wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 from DNA methylation beta values. See Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. <doi: 10.3389/fgene.2023.1233657> for more details.
Evaluate whether a microbiome sample is a mixture of two
    samples, by fitting a model for the number of read counts as a
    function of single nucleotide polymorphism (SNP) allele and the
    genotypes of two potential source samples.
    Lobo et al. (2021) <doi:10.1093/g3journal/jkab308>.
Fits Bayesian dose-response model-based network meta-analysis (MBNMA) 
    that incorporate multiple doses within an agent by modelling different dose-response functions, as
    described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>. 
    By modelling dose-response relationships this can connect networks of evidence that might
    otherwise be disconnected, and can improve precision on treatment estimates. Several common 
    dose-response functions are provided; others may be added by the user. Various characteristics
    and assumptions can be flexibly added to the models, such as shared class effects. The consistency 
    of direct and indirect evidence in the network can be assessed using unrelated mean effects models 
    and/or by node-splitting at the treatment level.
Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple
  time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions,
  following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. 
  The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple 
  treatment comparisons. Several general time-course functions are provided; others may be added 
  by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared 
  class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects 
  models and/or by node-splitting.
Functional gradient descent algorithm
  (boosting) for optimizing general risk functions utilizing
  component-wise (penalised) least squares estimates or regression
  trees as base-learners for fitting generalized linear, additive
  and interaction models to potentially high-dimensional data.
  Models and algorithms are described in <doi:10.1214/07-STS242>,
  a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>.
  The package allows user-specified loss functions and base-learners.
Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) <DOI:10.1002/essoar.10504791.1>.
Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses.  The response dimension reduction is theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.
Functions provide comprehensive treatments for estimating, inferring, testing and model selecting in linear regression models with structural breaks. The tests, estimation methods, inference and information criteria implemented are discussed in Bai and Perron (1998) "Estimating and Testing Linear Models with Multiple Structural Changes" <doi:10.2307/2998540>.
Summarize multiple biomarker responses of aquatic organisms to
    contaminants using Cliff’s delta, as described in Pham & Sokolova (2023)
    <doi:10.1002/ieam.4676>.
Fit generalized linear models with binomial responses using  a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).
An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.
Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <doi:10.1016/j.jmva.2018.04.010>.
Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models.  Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component.
Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi:10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).
Provides tools for cleaning, processing, and preparing microbiome sequencing data (e.g., 16S rRNA) for downstream analysis. Supports CSV, TXT, and 'Excel' file formats. The main function, ezclean(), automates microbiome data transformation, including format validation, transposition, numeric conversion, and metadata integration. Also ensures efficient handling of taxonomic levels, resolves duplicated taxa entries, and outputs a well-structured, analysis-ready dataset.
Implements a Monte Carlo Based Heterogeneity Test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (OR) in Meta-Analysis Studies. Depending on the presence of moderators, this Monte Carlo Based Test can be implemented in the random or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihood, so installation of R package 'metafor' is required. This approach refers to the studies of Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).
A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous).
Provides functions for carrying out nonparametric hypothesis tests of the MCAR hypothesis based on the theory of Frechet classes and compatibility. Also gives functions for computing halfspace representations of the marginal polytope and related geometric objects.
Distance between multivariate Cauchy distributions, as presented by N. Bouhlel and D. Rousseau (2022) <doi:10.3390/e24060838>. Manipulation of multivariate Cauchy distributions.
Provides two variants of multiple correspondence analysis (ca):
                     multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.
When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. <DOI:10.1111/biom.13024>. Besides, 'flare' is needed only you apply the SQRT or LAD method ('mcb' totally has 8 methods). Although 'flare' has been archived by CRAN, you can still get it in <https://CRAN.R-project.org/package=flare> and the latest version is useful for 'mcb'.
Monte Carlo simulation is a stochastic method computing trajectories of photons in media. Surface backscattering is performing calculations in semi-infinite media and summarizing photon flux leaving the surface. This simulation is modeling the optical measurement of diffuse reflectance using an incident light beam. The semi-infinite media is considered to have flat surface. Media, typically biological tissue, is described by four optical parameters: absorption coefficient, scattering coefficient, anisotropy factor, refractive index. The media is assumed to be homogeneous.
    Computational parameters of the simulation include: number of photons, radius of incident light beam, lowest photon energy threshold, intensity profile (halo) radius, spatial resolution of intensity profile.
    You can find more information and validation in the Open Access paper.
    Laszlo Baranyai (2020) <doi:10.1016/j.mex.2020.100958>.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'mcbette' allows to do a Bayesian model comparison over some
    site and clock models, 
    using 'babette' (<https://github.com/ropensci/babette/>).
Computes the prime implicants or a minimal disjunctive normal form for a
  logic expression presented by a truth table or a logic tree. Has been particularly 
  developed for logic expressions resulting from a logic regression analysis, i.e.
  logic expressions typically consisting of up to 16 literals, where the prime implicants 
  are typically composed of a maximum of 4 or 5 literals.
Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
    'Multi-Accuracy Boosting' (2019) <doi:10.48550/arXiv.1805.12317> for the multi-calibration of a machine learning model's prediction.
    'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups.
    Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are.
    This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
It contains six common multi-category classification accuracy evaluation measures.
 All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically,
 Hypervolume Under Manifold (HUM), described in
 Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>.
 Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in
 Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>.
 Polytomous Discrimination Index (PDI), described in
 Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>.
 Li et al. (2018) <doi:10.1177/0962280217692830>.
 We described all these above measures and our mcca package in
 Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.
Performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the 
  package refers to Mariko Takagishi and Michel van de Velden (2022)<doi:10.1080/10618600.2022.2035737>.
The MCC-F1 analysis is a method to evaluate the performance of binary classifications. 
    The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth.
    The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.
Implements methods for processing a sample of (hard)
        clusterings, e.g. the MCMC output of a Bayesian clustering
        model. Among them are methods that find a single best
        clustering to represent the sample, which are based on the
        posterior similarity matrix or a relabelling algorithm.
The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of Moments) methods are applied to estimate mixed correlation coefficient matrix (Pearson, Polyseries, Polychoric), which can be estimated in pairs or simultaneously. For more information see Peng Zhang and Ben Liu (2024) <doi:10.1080/10618600.2023.2257251>; Ben Liu and Peng Zhang (2024) <doi:10.48550/arXiv.2404.06781>.
Applying the methodology from Manuel et al. to estimate parameters using a matched 
    case control data with a mismeasured exposure variable that is accompanied by 
    instrumental variables (Submitted).
The Matthews correlation coefficient (MCC) score is calculated (Matthews BW  (1975) <DOI:10.1016/0005-2795(75)90109-9>).
Support for the analyst in a Multicriteria Decision Aiding (MCDA) process with algorithms, 
    preference elicitation and data visualisation functions. Sébastien Bigaret, Richard Hodgett, Patrick Meyer, 
    Tatyana Mironova, Alexandru Olteanu (2017) Supporting the multi-criteria decision aiding process : 
    R and the MCDA package, Euro Journal On Decision Processes, Volume 5, Issue 1 - 4, 
    pages 169 - 194 <doi:10.1007/s40070-017-0064-1>.
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
    For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005)
	<DOI:10.1111/j.1467-9868.2005.00499.x>.
Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.
Machine coded genetic algorithm (MCGA) is a fast tool for
    real-valued optimization problems. It uses the byte
    representation of variables rather than real-values. It
    performs the classical crossover operations (uniform) on these
    byte representations. Mutation operator is also similar to
    classical mutation operator, which is to say, it changes a
    randomly selected byte value of a chromosome by +1 or -1 with
    probability 1/2. In MCGAs there is no need for
    encoding-decoding process and the classical operators are
    directly applicable on real-values. It is fast and can handle a
    wide range of a search space with high precision. Using a
    256-unary alphabet is the main disadvantage of this algorithm
    but a moderate size population is convenient for many problems.
    Package also includes multi_mcga function for multi objective
    optimization problems. This function sorts the chromosomes
    using their ranks calculated from the non-dominated sorting
    algorithm.
Simulating and estimating (regime-switching) Markov chain Gaussian 
    fields with covariance functions of the Gneiting class (Gneiting 2002) 
    <doi:10.1198/016214502760047113>. It supports parameter estimation by 
    weighted least squares and maximum likelihood methods, and produces Kriging 
    forecasts and intervals for existing and new locations.
Implementation of Warnes & Raftery's MCGibbsit run-length and 
  convergence diagnostic for a set of (not-necessarily independent) 
  Markov Chain Monte Carlo (MCMC) samplers.  It combines the quantile 
  estimate error-bounding approach of the Raftery and Lewis MCMC run
  length diagnostic `gibbsit` with the between verses within chain 
  approach of the Gelman and Rubin MCMC convergence diagnostic.
Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.
Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package implements two market area models (Huff Model, Multiplicative Competitive Interaction Model) into R, while the emphases lie on 1.) fitting these models based on empirical data via OLS regression and nonlinear techniques and 2.) data preparation and processing (esp. interaction matrices and data preparation for the MCI Model).
Apply the marginal classification method to achieve the purpose of providing 
    the point and interval estimates for the minimal clinically important difference 
    based on the classical anchor-based method. For more details of the methodology, please 
    see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.
Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.
Read, inspect and process corpus files for quantitative corpus linguistics.
  Obtain concordances via regular expressions, tokenize texts,
  and compute frequencies and association measures. Useful for collocation analysis,
  keywords analysis and variationist studies (comparison of linguistic variants
  and of linguistic varieties).
Provides estimators for multinomial logit models in their
    conditional logit and baseline logit variants, with or without random effects,
    with or without overdispersion. 
    Random effects models are estimated using the PQL technique (based on a Laplace approximation)
    or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated
    with caution if the group sizes are small.
Gaussian finite mixture models fitted via EM algorithm for
  model-based clustering, classification, and density estimation, 
  including Bayesian regularization, dimension reduction for 
  visualisation, and resampling-based inference.
Extend the functionality of the 'mclust' package for Gaussian
    finite mixture modeling by including: density estimation for data with
    bounded support (Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal
    clustering using MEM (Modal EM) algorithm for Gaussian mixtures
    (Scrucca, 2021 <doi:10.1002/sam.11527>); entropy estimation via
    Gaussian mixture modeling (Robin & Scrucca, 2023
    <doi:10.1016/j.csda.2022.107582>); Gaussian mixtures modeling of 
    financial log-returns (Scrucca, 2024 <doi:10.3390/e26110907>).
Given a set of data points, a clustering is defined as a disjoint partition
    where each pair of sets in a partition has no overlapping elements. 
    This package provides 25 methods that play a role somewhat similar to 
    distance or metric that measures similarity of two clusterings - or partitions.
    For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.
Estimate and test inter-generational social mobility effect on an outcome with cross-sectional or longitudinal data.
Provides a series of numerical methods for extracting parameters of distributions for risks based on knowing the expected value and c-statistics (e.g., from a published report on the performance of a risk prediction model). This package implements the methodology described in Sadatsafavi et al (2024) <doi:10.48550/arXiv.2409.09178>. The core of the package is mcmap(), which takes a pair of (mean, c-statistic) and the distribution type requested. This function provides a generic interface to more customized functions (mcmap_beta(), mcmap_logitnorm(), mcmap_probitnorm()) for specific distributions.
Simulates continuous distributions of random vectors using
    Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
    R function that evaluates the log unnormalized density.  Algorithms
    are random walk Metropolis algorithm (function metrop), simulated
    tempering (function temper), and morphometric random walk Metropolis
    (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>,
    function morph.metrop),
    which achieves geometric ergodicity by change of variable.
Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.
Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.
Generates derived parameter(s) from Monte Carlo Markov Chain
    (MCMC) samples using R code. This allows Bayesian models to be fitted
    without the inclusion of derived parameters which add unnecessary
    clutter and slow model fitting. For more information on MCMC samples
    see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Provides ensemble samplers for
    affine-invariant Monte Carlo Markov Chain, which allow a faster
    convergence for badly scaled estimation problems. Two samplers are
    proposed: the 'differential.evolution' sampler from ter Braak and
    Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the 'stretch' sampler
    from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.
Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
Implements a class ('mcmcOutput') for efficiently storing and handling Markov chain Monte Carlo (MCMC) output, intended as an aid for those writing customized MCMC samplers. A range of constructor methods are provided covering common output formats. Functions are provided to generate summary and diagnostic statistics and to display histograms or density plots of posterior distributions, for the entire output, or subsets of draws, nodes, or parameters.
Contains functions to perform Bayesian
        inference using posterior simulation for a number of
        statistical models. Most simulation is done in compiled C++
        written in the Scythe Statistical Library Version 1.0.3. All
        models return 'coda' mcmc objects that can then be summarized
        using the 'coda' package. Some useful
        utility functions such as density functions,
	pseudo-random number generators for statistical
        distributions, a general purpose Metropolis sampling algorithm,
        and tools for visualization are provided.
Functions for convenient plotting and viewing of MCMC output.
Estimates the precision of transdimensional Markov chain Monte Carlo 
    (MCMC) output, which is often used for Bayesian analysis of models with different 
    dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible 
    jump MCMC) relies on sampling a discrete model-indicator variable to estimate 
    the posterior model probabilities. If only few switches occur between the models, 
    precision may be low and assessment based on the assumption of independent 
    samples misleading. Based on the observed transition matrix of the indicator 
    variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, 
    Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws 
    posterior samples of the stationary distribution to (a) assess the uncertainty 
    in the estimated posterior model probabilities and (b) estimate the effective 
    sample size of the MCMC output.
Functions and classes to store, manipulate and summarise
    Monte Carlo Markov Chain (MCMC) samples. For more information see
    Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
Fit multi-level models with possibly correlated
    random effects using Markov Chain Monte Carlo simulation.
    Such models allow smoothing over space and time and are useful in,
    for example, small area estimation.
Provides tools for computing Monte Carlo standard
        errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
        computation for expectation and quantile estimators is
        supported as well as multivariate estimations. The package also provides 
	functions for computing effective sample size and for plotting
	Monte Carlo estimates versus sample size.
Provides functions to prepare time priors for 'MCMCtree' analyses in the 'PAML' software from Yang (2007)<doi:10.1093/molbev/msm088> and plot time-scaled phylogenies from any Bayesian divergence time analysis. Most time-calibrated node prior distributions require user-specified parameters. The package provides functions to refine these parameters, so that the resulting prior distributions accurately reflect confidence in known, usually fossil, time information. These functions also enable users to visualise distributions and write 'MCMCtree' ready input files. Additionally, the package supplies flexible functions to visualise age uncertainty on a plotted tree with using node bars, using branch widths proportional to the age uncertainty, or by plotting the full posterior distributions on nodes. Time-scaled phylogenetic plots can be visualised with absolute and geological timescales . All plotting functions are applicable with output from any Bayesian software, not just 'MCMCtree'.
Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with 'Stan', 'NIMBLE', 'JAGS', and other software.
Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems.
Family Planning programs and initiatives typically use nationally representative surveys to estimate key indicators of a country’s family planning progress. However, in recent years, routinely collected family planning services data (Service Statistics) have been used as a supplementary data source to bridge gaps in the surveys. The use of service statistics comes with the caveat that adjustments need to be made for missing private sector contributions to the contraceptive method supply chain. Evaluating the supply source of modern contraceptives often relies on Demographic Health Surveys (DHS), where many countries do not have recent data beyond 2015/16. Fortunately, in the absence of recent surveys we can rely on statistical model-based estimates and projections to fill the knowledge gap. We present a Bayesian, hierarchical, penalized-spline model with multivariate-normal spline coefficients, to account for across method correlations, to produce country-specific,annual estimates for the proportion of modern contraceptive methods coming from the public and private sectors. This package provides a quick and convenient way for users to access the DHS modern contraceptive supply share data at national and subnational administration levels, estimate, evaluate and plot annual estimates with uncertainty for a sample of low- and middle-income countries. Methods for the estimation of method supply shares at the national level are described in Comiskey, Alkema, Cahill (2022) <arXiv:2212.03844>.
A collection of function to solve multiple criteria optimization problems 
  using genetic algorithms (NSGA-II). Also included is a collection of test functions.
Two functions for simulating the solution of initial value problems of the form g'(x) = G(x, g) with g(x0) = g0.  One is an acceptance-rejection method. The other is a method based on the Mean Value Theorem.
Basic Setup for Projects in R for Monterey County Office of Education. It contains functions often used in the analysis of education data in the county office including seeing if an item is not in a list, rounding in the manner the general public expects, including logos for districts, switching between district names and their county-district-school codes, accessing the local 'SQL' table and making thematically consistent graphs.
The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.
Provides a class for multi-companion matrices with methods for
    arithmetic and factorization.  A method for generation of
    multi-companion matrices with prespecified spectral properties is
    provided, as well as some utilities for periodically correlated and
    multivariate time series models. See Boshnakov (2002)
    <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>.
Implementation of marginalized models for zero-inflated count data. This package provides a tool to implement an estimation algorithm for the marginalized count models, which
  directly makes inference on the effect of each covariate on the marginal mean of the outcome. 
  The method involves the marginalized zero-inflated Poisson model described 
  in Long et al. (2014) <doi:10.1002/sim.6293>.
Flexible and informed regression with Multiple Change Points. 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindeløv (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.
Multiple contrast tests and simultaneous confidence
 intervals based on normal approximation. With implementations for
 binomial proportions in a 2xk setting (risk difference and odds ratio),
 poly-3-adjusted tumour rates, biodiversity indices (multinomial data) 
 and expected values under lognormal assumption. Approximative power 
 calculation for multiple contrast tests of binomial and Gaussian data.
Provides a function that wraps 
    mcparallel() and mccollect() from 'parallel' with temporary variables and a 
    task handler.  Wrapped in this way the results of an mcparallel() call 
    can be returned to the R session when the fork is complete 
    without explicitly issuing a specific mccollect() to retrieve the value.
    Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with 
    a single call to mcparallelDoCheck().
Implements a methodology for the design and analysis of dose-response studies that
             combines aspects of multiple comparison procedures  and modeling approaches
	     (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>).
             The package provides tools for the analysis of dose finding trials as well as a variety
             of tools necessary to plan a trial to be conducted with the MCP-Mod methodology.
             Please note: The 'MCPMod' package will not be further developed, all future development of 
             the MCP-Mod methodology will be done in the 'DoseFinding' R-package.
Implementation of Multiple Comparison Procedures with Modeling
	(MCP-Mod) procedure with bias-corrected estimators and second-order 
	covariance matrices as described in Diniz, Gallardo and Magalhaes (2023) 
	<doi:10.1002/pst.2303>.
Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available.
	Bretz, F., Pinheiro, J. C., and Branson, M. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>.
	Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) <doi:10.2307/2533961>.
	Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) <doi:10.1002/sim.6052>.
An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).
Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.
Tools for monitoring progress during parallel processing. Lightweight package which acts as a wrapper around mclapply() and adds a progress bar to it in 'RStudio' or 'Linux' environments. Simply replace your original call to mclapply() with pmclapply(). A progress bar can also be displayed during parallelisation via the 'foreach' package. Also included are functions to safely print messages (including error messages) from within parallelised code, which can be useful for debugging parallelised R code.
Performs the execution of the main procedures of multiple 
    comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, 
    Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations 
    and export to different extensions of its results. An additional 
    part of the package is the presence of the performance evaluation 
    of the tests (Type I error per experiment and the power). This will 
    assist the user in making the decision for the chosen test.
Regression methods to quantify the relation between two measurement
    methods are provided by this package. In particular it addresses regression
    problems with errors in both variables and without repeated measurements. It
    implements the CLSI recommendations (see J. A. Budd et al. 
    (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) 
    for analytical method comparison and bias estimation using patient samples.
    Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators 
    are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and 
    J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>).
    A comprehensive overview over the implemented methods and references can be found 
    in the manual pages "mcr-package" and "mcreg".
Provides methods and functions to analyze the quantitative or 
    qualitative performance for diagnostic assays, and outliers detection, 
    reader precision and reference range are discussed. Most of the methods 
    and algorithms refer to CLSI (Clinical & Laboratory Standards Institute) 
    recommendations and NMPA (National Medical Products Administration) 
    guidelines. In additional, relevant plots are constructed by 'ggplot2'.
Multi-core replication function to make it easier to do fast 
  Monte Carlo simulation. Based on the mcreplicate() function from the 
  'rethinking' package. The 'rethinking' package requires installing 'rstan', 
  which is onerous to install, while also not adding capabilities to this 
  function.
Regression methods to quantify the relation between two measurement
    methods are provided by this package. In particular it addresses regression
    problems with errors in both variables and without repeated measurements. It
    implements the Clinical Laboratory Standard International (CLSI) 
    recommendations (see J. A. Budd et al. 
    (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) 
    for analytical method comparison and bias estimation using patient samples.
    Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators 
    are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and 
    J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>).
    Further the robust M-Deming and MM-Deming (experimental) are available, see
    G. Pioda (2021, <arXiv:2105:04628>).
    A comprehensive overview over the implemented methods and references can be found 
    in the manual pages 'mcrPioda-package' and 'mcreg'.
Perform the model confidence set procedure of Hansen et al (2011) <doi:10.3982/ECTA5771>.
Identifies the optimal number of clusters by calculating the similarity between
             two clustering methods at the same number of clusters using the corrected indices of Rand and
             Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at
             which the index attain its maximum more frequently is a candidate for being the optimal
             number of clusters.
Tools that facilitate ordinary differential equation (ODE) modeling in 'R'. This package allows one to perform simulations for ODE models that are encoded in the GNU 'MCSim' model specification language (Bois, 2009) <doi:10.1093/bioinformatics/btp162> using ODE solvers from the 'R' package 'deSolve' (Soetaert et al., 2010) <doi:10.18637/jss.v033.i09>.
Provides functionality to produce graphs of sampling distributions of test 	statistics from a variety of common statistical tests. With only a few keystrokes, 	the user can conduct a hypothesis test and visualize the test statistic and 		corresponding p-value through the shading of its sampling distribution. Initially       	created for statistics at Middlebury College.
A lightweight package designed to facilitate statistical 
    simulations through functional programming. It centralizes the simulation process into a single 
    higher-order function, enhancing manageability and usability without adding overhead from external 
    dependencies. The package includes ready-to-use functions for common simulation targets. A detailed example can be found on <https://github.com/ielbadisy/mcstatsim>.
Package computes popular and widely used multicollinearity diagnostic measures <doi:10.17576/jsm-2019-4809-26> and <doi:10.32614/RJ-2016-062> . Package also indicates which regressors may be the reason of collinearity among regressors.
A complete toolkit to process the Munich ChronoType 
    Questionnaire (MCTQ) for its three versions (standard, micro, and shift). 
    MCTQ is a quantitative and validated tool to assess chronotypes using 
    peoples' sleep behavior, originally presented by Till Roenneberg, Anna 
    Wirz-Justice, and Martha Merrow (2003, <doi:10.1177/0748730402239679>).
Application of a test to rule out that trends detected in hydrological time series are explained exclusively by the randomness of the climate. Based on: Ricchetti, (2018) <https://repositorio.uchile.cl/handle/2250/168487>.
Unit testing for Monte Carlo methods, particularly Markov Chain Monte Carlo (MCMC) methods, are implemented as extensions of the 'testthat' package. The MCMC methods check whether the MCMC chain has the correct invariant distribution. They do not check other properties of successful samplers such as whether the chain can reach all points, i.e. whether is recurrent. The tests require the ability to sample from the prior and to run steps of the MCMC chain. The methodology is described in Gandy and Scott (2020) <arXiv:2001.06465>.
Visualize the relationship between linear regression variables and causes of multi-collinearity. 
    Implements the method in Lin et. al. (2020) <doi:10.1080/10618600.2020.1779729>.
In the context of multistate models, which are popular in sociology,
    demography, and epidemiology, Markov chain with rewards calculations can
    help to refine transition timings and so obtain more accurate estimates. The
    package code accommodates up to nine transient states and irregular age (time)
    intervals. Traditional demographic life tables result as a special case.
    Formulas and methods involved are explained in detail in the accompanying
    article: Schneider / Myrskyla / van Raalte (2021): Flexible Transition Timing
    in Discrete-Time Multistate Life Tables Using Markov Chains with Rewards,
    MPIDR Working Paper WP-2021-002.
Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.
Produces clean and neat Markdown log file
    and also provide an argument to include the function call inside the Markdown log.
Provides an R wrapper for the 'MD4C' (Markdown for 'C') library. 
    Functions exist for parsing markdown ('CommonMark' compliant) along with support for other common
    markdown extensions (e.g. GitHub flavored markdown, 'LaTeX' equation support, etc.). The 
    package also provides a number of higher level functions for exploring and manipulating markdown
    abstract syntax trees as well as translating and displaying the documents.
Mixture and flexible discriminant analysis, multivariate
        adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines.
	Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.
Learning a mixed directed acyclic graph based on both continuous and categorical data.
Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.
Use the open source 'MDB Tools' utilities
    <https://github.com/mdbtools/mdbtools/>. Primarily used for converting
    proprietary Microsoft Access files to simple text files and then
    reading those as data frames.
Methods for detecting signals related to (adverse event, medical
    product e.g. drugs, vaccines) pairs, a data generation function for 
    simulating pharmacovigilance datasets, and various utility functions.
    For more details please see Liu A., Mukhopadhyay R., and Markatou M. 
    <doi:10.48550/arXiv.2410.01168>.
Lightweight maps of mammals of the world. These maps are a 
    comprehensive collection of maps aligned with the Mammal Diversity Database
    taxonomy of the American Society of Mammalogists. They are generated at low
    resolution for easy access, consultation and manipulation in shapefile
    format. The package connects to a binary backup hosted in the Digital Ocean
    cloud service and allows individual or batch download of any mammal species
    in the mdd taxonomy by providing the scientific species name.
Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data
             lies and therefore find the most appropriate action to take.
             Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.
Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
Causal and statistical inference on an arbitrary treatment
    effect curve requires care in both estimation and inference.  This
    package, implements the Method of Direct Estimation and Inference as introduced in "Estimation and Inference on Nonlinear and Heterogeneous Effects" by Ratkovic and Tingley (2023) <doi:10.1086/723811>.  The method takes an outcome, variable of theoretical interest
    (treatment), and set of variables and then returns a partial
    derivative (marginal effect) of the treatment variable at each point
    along with uncertainty intervals. The approach offers two advances.
    First, a split-sample approach is used as a guard against over-fitting.
    Second, the method uses a data-driven interval derived from conformal
    inference, rather than relying on a normality assumption on the error
    terms.
A comprehensive collection of linkage methods for agglomerative
  hierarchical clustering on a matrix of proximity data (distances or
  similarities), returning a multifurcated dendrogram or multidendrogram.
  Multidendrograms can group more than two clusters when ties in proximity data
  occur, and therefore they do not depend on the order of the input data.
  Descriptive measures to analyze the resulting dendrogram are additionally
  provided.
Functions for MultiDimensional Feature Selection (MDFS):
 calculating multidimensional information gains, scoring variables,
 finding important variables, plotting selection results.
 This package includes an optional CUDA implementation that speeds up
 information gain calculation using NVIDIA GPGPUs.
 R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.
'MTrackJ' is an 'ImageJ' plugin for motion tracking and analysis (see 
    <https://imagescience.org/meijering/software/mtrackj/>). This package reads 
    and writes 'MTrackJ Data Files' ('.mdf', see 
    <https://imagescience.org/meijering/software/mtrackj/format/>). It supports
    2D data and read/writes cluster, point, and channel information. If desired, 
    generates track identifiers that are unique over the clusters.
    See the project page for more information and examples.
Provides functions to impute missing values using Gaussian 
    copulas for mixed data types as described by Christoffersen et al. 
    (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) 
    <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> 
    but differs by making a direct approximation of the log marginal likelihood 
    using an extended version of the Fortran code created by Genz and Bretz 
    (2002) <doi:10.1198/106186002394> in addition to also support multinomial 
    variables.
Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.
Normalize data to minimize the difference between sample plates 
    (batch effects). For given data in a matrix and grouping variable (or
	plate), the function 'normn_MA' normalizes the data on MA coordinates. 
	More details are in the citation. The primary method is 'Multi-MA'. Other 
	fitting functions on MA coordinates can also be employed e.g. loess.
Implemented are the one-sided and two-sided 
  multiple-direction logrank test for two-sample right 
  censored data. In addition to the statistics p-values are calculated: 
  1. For the one-sided testing problem one p-value based on a
   wild bootstrap approach is determined. 2. In the two-sided case
   one p-value based on a chi-squared approximation and 
   a second p-values based on a permutation approach are calculated.
 Ditzhaus, M. and Friedrich, S. (2018) <arXiv:1807.05504>.
 Ditzhaus, M. and Pauly, M. (2018) <arXiv:1808.05627>.
Provides a variety of functions useful for data analysis,
    selection, manipulation, and graphics.
Runs a Shiny web application that merges raw 'qPCR' fluorescence data with related 
    metadata to visualize species presence/absence detection patterns and assess data quality. 
    The application calculates threshold values from raw fluorescence data using a method based 
    on the second derivative method, Luu-The et al (2005) <doi:10.2144/05382RR05>,  and utilizes 
    the ‘chipPCR’ package by Rödiger, Burdukiewicz, & Schierack (2015) <doi:10.1093/bioinformatics/btv205> 
    to calculate Cq values. The application has the ability to connect to a custom developed MySQL 
    database to populate the applications interface. The application allows users to interact with 
    visualizations such as a dynamic map, amplification curves and standard curves, that allow for 
    zooming and/or filtering. It also enables the generation of customized exportable reports based
    on filtered mapping data.
Contains model-based treatment of missing data for regression 
    models with missing values in covariates or the dependent 
    variable using maximum likelihood or Bayesian estimation 
    (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>;
    Luedtke, Robitzsch, & West, 2020a, 2020b;
    <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>).
    The regression model can be nonlinear (e.g., interaction 
    effects, quadratic effects or B-spline functions). 
    Multilevel models with missing data in predictors are
    available for Bayesian estimation. Substantive-model compatible 
    multiple imputation can be also conducted.
Allows users to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size. For details on the method, see McArtor, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.
Statistical inference for quadratic functional of the moderate-dimensional linear model in Guo and Cheng (2021) <DOI:10.1080/01621459.2021.1893177>.
Provides graph-constrained regression methods in which
    regularization parameters are selected automatically via estimation of
    equivalent Linear Mixed Model formulation. 'riPEER' (ridgified Partially
    Empirical Eigenvectors for Regression) method employs a penalty term being
    a linear combination of graph-originated and ridge-originated penalty terms,
    whose two regularization parameters are ML estimators from corresponding
    Linear Mixed Model solution; a graph-originated penalty term allows imposing
    similarity between coefficients based on graph information given whereas
    additional ridge-originated penalty term facilitates parameters estimation:
    it reduces computational issues arising from singularity in a graph-originated
    penalty matrix and yields plausible results in situations when graph information
    is not informative. 'riPEERc' (ridgified Partially Empirical Eigenvectors
    for Regression with constant) method utilizes addition of a diagonal matrix
    multiplied by a predefined (small) scalar to handle the non-invertibility of
    a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs
    variable-reduction procedure to handle the non-invertibility of a graph
    Laplacian matrix.
Provides comprehensive tools to scrape and analyze data from the MDPI journals. It allows users to extract metrics such as submission-to-acceptance times, article types, and whether articles are part of special issues. The package can also visualize this information through plots. Additionally, 'MDPIexploreR' offers tools to explore patterns of self-citations within articles and provides insights into guest-edited special issues.
The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.
A set of core functions for handling medical device event data in
    the context of post-market surveillance, pharmacovigilance, signal detection
    and trending, and regulatory reporting. Primary inputs are data on events by
    device and data on exposures by device. Outputs include: standardized
    device-event and exposure datasets, defined analyses, and time series.
A set of functions to obtain modified score test for generalized linear models.
A graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.
Estimate genetic linkage maps for markers on a single chromosome (or in a single linkage group) from pairwise recombination fractions or intermarker distances using weighted metric multidimensional scaling. The methods are suitable for autotetraploid as well as diploid populations. Options for assessing the fit to a known map are also provided. Methods are discussed in detail in Preedy and Hackett (2016) <doi:10.1007/s00122-016-2761-8>.
Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued data via metric MDS (ratio, interval, mspline).Selecting the optimal multidimensional scaling procedure for interval-valued data by varying all combinations of normalization and optimization methods.Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties.
 (Borg, I., Groenen, P.J.F., Mair, P. (2013) <doi:10.1007/978-3-642-31848-1>, 
 Walesiak, M. (2016) <doi:10.15611/ekt.2016.2.01>, 
 Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).
An interactive document on  the topic of multidimensional scaling and principal component analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/MDS_PCAShiny/>.
A complement to all editions of *Modern Data
    Science with R* 
     (ISBN: 978-0367191498, publisher URL: 
    <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>).
    This package contains data and code to complete exercises and 
    reproduce examples from the text. It also facilitates connections 
    to the SQL database server used in the book. All editions of the book are 
    supported by this package.
Modeling microstructures of
	human tooth dentin and
	horizontal serial-sectioning of the dentin.
	Corresponding age range of dentin serial sections,
	that is used in stable isotope analyses,
	can be calculated by using this package.
Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <DOI:10.1002/sim.8212>.
Analyse and visualise multi electrode array data at the single electrode and whole well level, downstream of 'AxIS Navigator 3.6.2 Software' processing. 
             Compare bursting parameters between time intervals and recordings using the bar chart visualisation functions. Compatible with 12- and 24- well plates.
Sentiment analysis is a popular technique in text mining that
    attempts to determine the emotional state of some text. We provide a new
    implementation of a common method for computing sentiment, whereby words are
    scored as positive or negative according to a dictionary lookup. Then the
    sum of those scores is returned for the document. We use the 'Hu' and 'Liu'
    sentiment dictionary ('Hu' and 'Liu', 2004) <doi:10.1145/1014052.1014073>
    for determining sentiment. The scoring function is 'vectorized' by document,
    and scores for multiple documents are computed in parallel via 'OpenMP'.
Performs mean shift classification using linear and 
  k-d tree based nearest neighbor implementations for the Gaussian,
  Epanechnikov, and biweight product kernels.
Quickly make tables of descriptive statistics (i.e., counts, 
    means, confidence intervals) for continuous variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to 'Microsoft Word' ® with minimal pain.
Estimate diagnostic classification models (also called cognitive
    diagnostic models) with 'Stan'. Diagnostic classification models are 
    confirmatory latent class models, as described by Rupp et al.
    (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the
    general loglinear cognitive diagnostic diagnostic model proposed by
    Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that
    introduce additional model constraints. Using the generated 'Stan' code,
    estimate the model evaluate the model's performance using model fit indices,
    information criteria, and reliability metrics.
Send server-side tracking data from R.
  The Measurement Protocol version 2 
  <https://developers.google.com/analytics/devguides/collection/protocol/ga4>
  allows sending HTTP tracking events from R code.
Collection of tools to make working with physical measurements
		easier. Convert between metric and imperial units, or calculate a dimension's
		unknown value from other dimensions' measurements.
Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.
Identification of ring borders on scanned image sections from dendrochronological samples. Processing of image reflectances to produce gray matrices and time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders or control of automatic detection. Routines to visually include/exclude ring borders on the R graphical devices, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to positive/negative extreme values in the smoothed time-series of gray values. Most of the in-package routines can be recursively implemented using the multiDetect() function.
Maximum entropy density based dependent data bootstrap. 
  An algorithm is provided to create a population of time series (ensemble) 
  without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains
  how the algorithm satisfies the ergodic theorem and the central limit theorem.
Solve scalar-on-function linear models,
    including generalized linear mixed effect model and quantile linear regression model,
    and bias correction estimation methods due to measurement error. 
    Details about the measurement error bias correction methods, see
    Luan  et al. (2023) <doi:10.48550/arXiv.2305.12624>, 
    Tekwe et al. (2022) <doi:10.1093/biostatistics/kxac017>, 
    Zhang et al. (2023) <doi:10.5705/ss.202021.0246>, 
    Tekwe et al. (2019) <doi:10.1002/sim.8179>.
Functions to facilitate model-based clustering of nodes in a network in a mixture of experts setting, which incorporates covariate information on the nodes in the modelling process. Isobel Claire Gormley and Thomas Brendan Murphy (2010) <doi:10.1016/j.stamet.2010.01.002>.
Compare microbial co-occurrence networks created from 'trans_network' class of 'microeco' package <https://github.com/ChiLiubio/microeco>.
    This package is the extension of 'trans_network' class of 'microeco' package and especially useful when different networks are constructed and analyzed simultaneously.
Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).
Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The 'betaturn' class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of 'taxaturn' class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.
Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.
Provides an extensive collection of datasets related to medicine, 
    diseases, treatments, drugs, and public health. This package covers topics 
    such as drug effectiveness, vaccine trials, survival rates, 
    infectious disease outbreaks, and medical treatments. The included datasets 
    span various health conditions, including AIDS, cancer, bacterial infections, 
    and COVID-19, along with information on pharmaceuticals and vaccines. These 
    datasets are sourced from the R ecosystem and other R packages, remaining 
    unaltered to ensure data integrity. This package serves as a valuable 
    resource for researchers, analysts, and healthcare professionals interested 
    in conducting medical and public health data analysis in R.
Multi Calculator of different scores to measure adherence to Mediterranean Diet, to compute them in nutriepidemiological data. Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies.
'MedDRA' data is used for defining adverse events in clinical studies. You
    can load and merge the data for use in categorizing the adverse events
    using this package. The package requires the data licensed from 'MedDRA'
    <https://www.meddra.org/>.
Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.
In breeding experiments, mating environmental (ME) designs are very popular as mating designs are directly implemented in the field environment using block or row-column designs. Here, three functions are given related to three new methods which will generate mating diallel cross designs (Hinkelmann and Kempthorne, 1963<doi:10.2307/2333899>) or mating environmental (ME) designs along with design parameters, C matrix, eigenvalues (EVs), degree of fractionations (DF) and canonical efficiency factor (CEF). Another one function is added to check the properties of a given ME diallel cross design.
Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) <doi: 10.1093/jamia/ocz207>. The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition.
Simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>; De Caceres et al. (2021) <doi:10.1016/j.agrformet.2020.108233>].
Simulate forest hydrology, forest function and dynamics over landscapes [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>]. Parallelization is allowed in several simulation functions and simulations may be conducted including spatial processes such as lateral water transfer and seed dispersal.
Run flexible mediation analyses using natural effect models as described in 
  Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, 
  Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> 
  and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.
API wrapper to gather news stories, media information and tags from the 'mediacloud.org' API, based on a multilevel query <https://mediacloud.org/>. A personal API key is required.
Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
Provides a general framework for clinical trial simulations based
    on the Clinical Scenario Evaluation (CSE) approach. The package supports a
    broad class of data models (including clinical trials with continuous, binary,
    survival-type and count-type endpoints as well as multivariate outcomes that are
    based on combinations of different endpoints), analysis strategies and commonly
    used evaluation criteria.
Efficient simulation-based power and sample size calculations are supported for a
  broad class of late-stage clinical trials. The following modules are included in
  the package:
  Adaptive designs with data-driven sample size or event count re-estimation,
  Adaptive designs with data-driven treatment selection,
  Adaptive designs with data-driven population selection,
  Optimal selection of a futility stopping rule,
  Event prediction in event-driven trials,
  Adaptive trials with response-adaptive randomization (experimental module),
  Traditional trials with multiple objectives (experimental module).
  Traditional trials with cluster-randomized designs (experimental module).
Extract textual data from different media channels 
    through its source based on users choice of keywords. 
    These data can be used to perform text analysis to 
    identify patterns in respective media reporting.
    The media channels used in this package are print media.
    The data (or news) used are publicly available to consumers.
Functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach. We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method.
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, and Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results 
 to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. 
 First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as 
 sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the 
 strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured 
 pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in 
 different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based 
 methods. It is applicable to both randomized experiments and observational studies.
Provides access to well-documented medical datasets for teaching.
    Featuring several from the Teaching of Statistics in the Health Sciences 
    website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical
    research, some reformatted and extended from existing R packages, 
    and some data donations.
Publicly available data from Medicare frequently requires extensive
    initial effort to extract desired variables and merge them; this package
    formalizes the techniques I've found work best. More information on the 
    Medicare program, as well as guidance for the publicly available data this package 
    targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.
Prints a random quote from Marcus Aurelius' book Meditations.
Contains a dataset of morphological and structural features of 'Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (<http://www.instituteofayurveda.org/plants/>). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka.
Parses information from text files with specific utility aimed at pulling information from Med Associate's (MPC) files. These functions allow for further analysis of MPC files.
A collection of methods for large scale single mediator hypothesis
    testing. The six included methods for testing the mediation effect are Sobel's
    test, Max P test, joint significance test under the composite null hypothesis,
    high dimensional mediation testing, divide-aggregate composite null test,
    and Sobel's test under the composite null hypothesis. Du et al (2023) 
    <doi:10.1002/gepi.22510>.
Implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.
Allows management of 'Meetup' groups via the <https:www.meetup.com/meetup_api/>.
    Provided are a set of functions that enable fetching information of joined meetups, attendance,
    and members. This package requires the use of an API key.
A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
An S4 update of the 'mefa' package
  using sparse matrices for enhanced efficiency.
  Sparse array-like objects are supported via
  lists of sparse matrices.
To perform main effect matrix factor model (MEFM) estimation for a given matrix time series as described in Lam and Cen (2024) <doi:10.48550/arXiv.2406.00128>. Estimation of traditional matrix factor models is also supported. Supplementary functions for testing MEFM over factor models are included.
Gradient boosting is a powerful statistical learning method known for its ability to model 
    complex relationships between predictors and outcomes while performing inherent variable selection. 
    However, traditional gradient boosting methods lack flexibility in handling longitudinal data where 
    within-subject correlations play a critical role. In this package, we propose a novel approach 
    Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data. 
    'MEGB' incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting 
    framework, allowing it to account for within-individual covariance over time. Additionally, the method 
    efficiently handles scenarios where the number of predictors greatly exceeds the number of observations
    (p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies.
Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.
Exploratory model analysis with <http://ggobi.org>.  
    Fit and graphical explore ensembles of linear models.
Create variable width bar charts i.e.
    "bar mekko" charts to include important quantitative context.
    Closely related to mosaic, spine (or spinogram), matrix,
    submarine, olympic, Mondrian or product plots and tree maps.
Performs multiple empirical likelihood tests. It offers an
    easy-to-use interface and flexibility in specifying hypotheses and
    calibration methods, extending the framework to simultaneous
    inferences.  The core computational routines are implemented using the
    'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for
    parallel computation.  Details of the testing procedures are provided
    in Kim, MacEachern, and Peruggia (2023)
    <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim,
    MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is
    available for further information. This work was supported by the U.S.
    National Science Foundation under Grants No. SES-1921523 and
    DMS-2015552.
The goal of 'meltr' is to provide a fast and friendly way to
    read non-rectangular data, such as ragged forms of csv (comma-separated
    values), tsv (tab-separated values), and fwf (fixed-width format) files.
Framework for merging and disambiguating event data based on spatiotemporal co-occurrence and secondary event characteristics. It can account for intrinsic "fuzziness" in the coding of events, varying event taxonomies and different geo-precision codes.
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. 'memapp' is a web application created in the Shiny framework for the 'mem' R package.
The word 'Meme' was originated from the book, 'The Selfish Gene', authored by Richard Dawkins (1976).
             It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity.
             The internet memes are captioned photos that are intended to be funny, ridiculous.
             Memes behave like infectious viruses and travel from person to person quickly through social media.
             The 'meme' package allows users to make custom memes.
Generates internet memes that optionally include a superimposed inset plot and other atypical features, 
    combining the visual impact of an attention-grabbing meme with graphic results of data analysis.
    The package differs from related packages that focus on imitating and reproducing standard memes.
    Some packages do this by interfacing with online meme generators whereas others achieve this natively.
    This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites.
    It reads images directly from local files or via URL and meme generation is done by the package.
    While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on 
    allowing for non-standard meme layouts and hybrids of memes mixed with graphs.
    While this package can be used to make basic memes like an online meme generator would produce, 
    it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting 
    foreground graphs of data analysis results.
    The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes 
    rather than restricting to the standard top and/or bottom text placement. 
    This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels.
    The package offers a selection of templates for graph placement and appearance with respect to the underlying meme.
    Graph templates also permit additional template-specific customization.
    Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 
    'magick' is not required unless gif functionality is desired.
Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation.  Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.
A simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation.
An infrastructure for the management of survey data including
        value labels, definable missing values, recoding of variables,
        production of code books, and import of (subsets of) 'SPSS' and
        'Stata' files is provided. Further, the package allows to produce
        tables and data frames of arbitrary descriptive statistics and
        (almost) publication-ready tables of regression model
        estimates, which can be exported to 'LaTeX' and HTML.
A simple in-memory, LRU cache that can be wrapped
    around any function to memoize it. The cache is keyed on a hash of the input data (using 'digest') or on pointer equivalence.
A simple way to memoize function results to improve performance by eliminating unnecessary computation or data retrieval activities.
Producing high-quality documents suitable for publication directly from R is made possible by the R Markdown ecosystem.
  'memoiR' makes it easy.
  It provides templates to knit memoirs, articles and slideshows with helpers to publish the documents on GitHub Pages and activate continuous integration.
Cache the results of a function so that when you
    call it again with the same arguments it returns the previously computed
    value.
A 'rmarkdown' template that supports company logo, contact info, 
    watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar 
    'HTML' theme will be added in the future.
Tools to quantify ecological memory in long time-series with Random Forest models (Breiman 2001 <doi:10.1023/A:1010933404324>) fitted with the 'ranger' library (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>). Particularly oriented to palaeoecological datasets and simulated pollen curves produced by the 'virtualPollen' package, but also applicable to other long time-series involving a set of environmental drivers and a biotic response.
Data sets and sample analyses from Pinheiro and Bates,
  "Mixed-effects Models in S and S-PLUS" (Springer, 2000).
How much ram do you need to store a 100,000 by 100,000 matrix?
    How much ram is your current R session using? How much ram do you even have?
    Learn the scintillating answer to these and many more such questions with
    the 'memuse' package.
Encodes several methods for performing Mendelian randomization
    analyses with summarized data. Summarized data on genetic associations with the
    exposure and with the outcome can be obtained from large consortia. These data
    can be used for obtaining causal estimates using instrumental variable methods.
Based on the input data an n-dimensional cube with sub cells of user specified side length is created.
    The number of sample points which fall in each sub cube is counted, and with the cell volume and overall sample
    size an empirical probability can be computed. A number of cubes of higher resolution can be superimposed. The
    basic method stems from J.L. Bentley in "Multidimensional Divide and Conquer".
    J. L. Bentley (1980) <doi:10.1145/358841.358850>.
    Furthermore a simple kernel density estimation method is made available, as well as an expansion of Bentleys
    method, which offers a kernel approach for the grid method.
Defines the classes used to explore, cluster and
  visualize distance matrices, especially those arising from binary
  data. See Abrams and colleagues, 2021, <doi:10.1093/bioinformatics/btab037>.
Compute case-wise and cluster-wise derivative for mixed effects models with respect to fixed effects parameter, random effect (co)variances, and residual variance. This material is partially based on work supported by the National Science Foundation under Grant Number 1460719.
Package to carry out merged block randomization (Van der Pas (2019), <doi:10.1177/1740774519827957>), a restricted randomization method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicentre trials. It can be used for more than two groups or unequal randomization ratios.
Employing artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. The self-correction feature ensures the generated code is optimized for performance and accuracy. 'mergen' features a user-friendly chat interface, enabling users to interact with the AI agent and extract valuable insights from their data effortlessly.
An 'RStudio' Addin wrapper for the 'mergen' package. This package employs artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. This package makes it easier to use Large Language Models in your development environment by providing a chat-like interface, while also allowing you to inspect and execute the returned code.
Aggregates a set of trees with the same leaves to create a consensus tree. The trees are typically 
  obtained via hierarchical clustering, hence the hclust format is used to encode both the aggregated trees and the final 
  consensus tree. The method is exact and proven to be O(nqlog(n)), n being the individuals and q being the number of trees to aggregate.
Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, <doi:10.1109/TCAD.2020.3013051>. For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery. <doi:10.1145/3412841.3441931>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).
Provides a novel framework to estimate mixed models via gradient 
    boosting. The implemented functions are based on the 'mboost' and 'lme4' packages, 
    and the family range is therefore determined by 'lme4'. A correction mechanism 
    for cluster-constant covariates is implemented, as well as estimation of the 
    covariance of random effects. These methods are described in 
    the accompanying publication; see <doi:10.1007/s11222-025-10612-y> for details.
Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation.
N>=3 methods are used to measure each of n items. 
 The data are used to estimate simultaneously systematic error (bias)
 and random error (imprecision). Observed measurements for each method
 or device are assumed to be linear functions of the unknown true values
 and the errors are assumed normally distributed. Pairwise calibration
 curves and plots can be easily generated. Unlike the 'ncb.od' function,
 the 'omx' function builds a one-factor measurement error model using 'OpenMx'
 and allows missing values, uses full information maximum likelihood to 
 estimate parameters, and provides both likelihood-based and bootstrapped 
 confidence intervals for all parameters, in addition to Wald-type intervals.
Provides methods for extracting results from mixed-effect model
    objects fit with the 'lme4' package. Allows construction of prediction intervals
    efficiently from large scale linear and generalized linear mixed-effects models.
    This method draws from the simulation framework used in the Gelman and Hill (2007) textbook:
    Data Analysis Using Regression and Multilevel/Hierarchical Models.
Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2022) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.
A mixed collection of useful and semi-useful diverse
    statistical functions, some of which may even be referenced in
    The R Primer book.
Provides tools for creating and issuing nicely-formatted
    text within R diagnostic messages and those messages given during
    warnings and errors. The formatting of the messages can be
    customized using templating features. Issues with singular and
    plural forms can be handled through specialized syntax.
Fits the MESSI, hard constraint, and unconstrained models in Boss 
    et al. (2023) <doi:10.48550/arXiv.2306.17347> for mediation analyses with external 
    summary-level information on the total effect.
For the purposes of teaching, it is often desirable to show 
    examples of working with messy data and how to clean it. This R package 
    creates messy data from clean, tidy data frames so that students have a 
    clean example to work towards.
Matching with string distance has never been easier! 'messy.cats' contains various functions that employ string distance tools in order to make data management easier for users working with categorical data. Categorical data, especially user inputted categorical data that often tends to be plagued by typos, can be difficult to work with. 'messy.cats' aims to provide functions that make cleaning categorical data simple and easy.
Contains a set of tools for constructing and coercing
  into and from the "mdate" class. 
  This date class implements ISO 8601-2:2019(E) and
  allows regular dates to be annotated 
  to express unspecified date components,
  approximate or uncertain date components, 
  date ranges, and sets of dates. 
  This is useful for describing and analysing temporal information,
  whether historical or recent, where date precision may vary.
Provides a flexible framework for estimating the variance-covariance matrix of estimated parameters. Estimation relies on unbiased estimating functions to compute the empirical sandwich variance. (i.e., M-estimation in the vein of Tsiatis et al. (2019) <doi:10.1201/9780429192692>.
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015):
 - common effect and random effects meta-analysis;
 - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
 - three-level meta-analysis model;
 - generalised linear mixed model;
 - logistic regression with penalised likelihood for rare events;
 - Hartung-Knapp method for random effects model;
 - Kenward-Roger method for random effects model;
 - prediction interval;
 - statistical tests  for funnel plot asymmetry;
 - trim-and-fill method to evaluate bias in meta-analysis;
 - meta-regression;
 - cumulative meta-analysis and leave-one-out meta-analysis;
 - import data from 'RevMan 5';
 - produce forest plot summarising several (subgroup) meta-analyses.
Implement meta-analyses for simultaneously estimating individual means with shrinkage,
 isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm.
 For the pretest estimator, the confidence interval for individual means are provided.
 Methodologies were published in 
 Taketomi et al. (2021) <doi:10.3390/axioms10040267>, Taketomi et al. (2022) <doi:10.3390/a15010026>, 
 Taketomi et al. (2023-) (under review).
Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <https://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).
An interactive application to visualise meta-analysis data as a
    physical weighing machine. The interface is based on the Shiny web application
    framework, though can be run locally and with the user's own data.
Provides common components (classes, methods, documentation) for
  packages that conduct meta-analytic corrections and sensitivity analyses
  for within-study and/or across-study biases in meta-analysis. See the packages
  'PublicationBias', 'phacking', and 'multibiasmeta'. These package implement
  methods described in, respectively: Mathur & VanderWeele (2020)
  <doi:10.31219/osf.io/s9dp6>; Mathur (2022) <doi:10.31219/osf.io/ezjsx>;
  Mathur (2022) <doi:10.31219/osf.io/u7vcb>.
The sample mean and standard deviation are two commonly used statistics in meta-analyses, 
    but some trials use other summary statistics such as the median and quartiles to report the results. 
    Therefore, researchers need to transform those information back to the sample mean and 
    standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.
Computes the posterior model probabilities for standard meta-analysis models 
    (null model vs. alternative model assuming either fixed- or random-effects, respectively).
    These posterior probabilities are used to estimate the overall mean effect size 
    as the weighted average of the mean effect size estimates of the random- and 
    fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & 
    Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define 
    a wide range of non-informative or informative priors for the mean effect size 
    and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, 
    meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) 
    priors can be fitted and tested. This allows to compute Bayes factors and 
    perform Bayesian model averaging across random- and fixed-effects meta-analysis 
    with and without moderators. For a primer on Bayesian model-averaged meta-analysis, 
    see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, <doi:10.1177/25152459211031256>).
Data sets from a variety of biological sample matrices, 
    analysed using a number of mass spectrometry based metabolomic analytical techniques.
    The example data sets are stored remotely using GitHub releases 
    <https://github.com/aberHRML/metaboData/releases> which can be accessed from R using the package.
    The package also includes the 'abr1' FIE-MS data set from the 'FIEmspro' package <https://users.aber.ac.uk/jhd/> <doi:10.1038/nprot.2007.511>.
A framework for deconvolution, alignment and postprocessing
    of 1-dimensional (1d) nuclear magnetic resonance (NMR) spectra, resulting in
    a data matrix of aligned signal integrals. The deconvolution part uses the
    algorithm described in Koh et al. (2009) <doi:10.1016/j.jmr.2009.09.003>.
    The alignment part is based on functions from the 'speaq' package, described
    in Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018> and Vu et al.
    (2011) <doi:10.1186/1471-2105-12-405>. A detailed description and evaluation
    of an early version of the package, 'MetaboDecon1D v0.2.2', can be found in
    Haeckl et al. (2021) <doi:10.3390/metabo11070452>.
Fits probabilistic principal components analysis,
        probabilistic principal components and covariates analysis and
        mixtures of probabilistic principal components models to
        metabolomic spectral data.
Dataset and functions from the meta-analysis published in Medicine & Science in Sports & Exercise. 
    It contains all the data and functions to reproduce the analysis.
    "Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis".
    Felipe Mattioni Maturana, Peter Martus, Stephan Zipfel, Andreas M Nieß (2020) <doi:10.1249/MSS.0000000000002506>.
An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. 
 Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. 
 Several methods were implemented to reduce the metabolomics matrix such as the  principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9> , 
 the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, the 
 elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>. 
 Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. 
 Large scale cross validation can be performed in  order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
The modified Adult Treatment Panel -III guidelines (ATP-III) proposed by American Heart Association (AHA) and National Heart, Lung and Blood Institute (NHLBI) are used widely for the clinical diagnosis of Metabolic Syndrome. The AHA-NHLBI criteria advise using parameters such as waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), triglycerides (TG) and high-density lipoprotein cholesterol (HDLC) for diagnosis of metabolic syndrome. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the modified ATP-III guidelines, and it will aid in the easy and quick diagnosis of metabolic syndrome in busy healthcare settings and also for research purposes. The modified ATP-III-AHA-NHLBI criteria for the diagnosis is described by Grundy et al ., (2005) <doi:10.1161/CIRCULATIONAHA.105.169404>.
A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification. A detailed description of best practice usage may be found in the publication <doi:10.1007/978-1-4939-7819-9_20>.
Provides a framework to perform soft clustering using 
        simplex-structured matrix factorisation (SSMF). The package contains a set of functions
        for determining the optimal number of prototypes, the optimal algorithmic
        parameters, the estimation confidence intervals and the diversity of clusters.
        Abdolali, Maryam & Gillis, Nicolas (2020) <doi:10.1137/20M1354982>.
Takes QC signal for each day and normalize metabolomic
    data that has been acquired in a certain period of time. At least
    three QC per day are required.
Contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances.   Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can  calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022)  <doi:10.1007/s42519-018-0027-2>, Evans and Sedransk (2001) <doi:10.1093/biomet/88.3.643>, and Malec and Sedransk (1992) <doi:10.1093/biomet/79.3.593>.
Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) <doi:10.1037/hea0000018> and Li et al. (2017) <doi:10.1111/bmsp.12088>.
Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.
Reads, plots, and manipulates large
    taxonomic data sets, like those generated from modern high-throughput
    sequencing, such as metabarcoding (i.e. amplification metagenomics, 16S
    metagenomics, etc). It provides a tree-based visualization called "heat
    trees" used to depict statistics for every taxon in a taxonomy using
    color and size. It also provides various functions to do common tasks in
    microbiome bioinformatics on data in the 'taxmap' format defined by the
    'taxa' package. The 'metacoder' package is described in the publication
    by Foster et al. (2017) <doi:10.1371/journal.pcbi.1005404>.
Functions to analyze coherence, boundary clumping, and turnover
    following the pattern-based metacommunity analysis of Leibold and Mikkelson
    2002  <doi:10.1034/j.1600-0706.2002.970210.x>. The package also includes 
		functions to visualize ecological networks, and to calculate modularity 
		as a replacement to boundary clumping.
Implements routines for metagenome sample taxonomy assignments collection, 
    aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2, 
    BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots 
    comparing taxa abundances across projects.
Visualize 'confounder' control in meta-analysis.
    'metaconfoundr' is an approach to evaluating bias in studies used in
    meta-analyses based on the causal inference framework. Study groups
    create a causal diagram displaying their assumptions about the
    scientific question. From this, they develop a list of important
    'confounders'. Then, they evaluate whether studies controlled for
    these variables well. 'metaconfoundr' is a toolkit to facilitate this
    process and visualize the results as heat maps, traffic light plots,
    and more.
Automatically estimate 11 effect size measures from a well-formatted dataset. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets.
    This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.
Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.
There are two functions-meta2d and meta3d for
    detecting rhythmic signals from time-series datasets. For analyzing
    time-series datasets without individual information, 'meta2d' is 
    suggested, which could incorporates multiple methods from ARSER, 
    JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For 
    analyzing time-series datasets with individual information, 'meta3d' is 
    suggested, which takes use of any one of these three methods to analyze 
	time-series data individual by individual and gives out integrated values 
    based on analysis result of each individual.
A collection of meta-analysis datasets for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.
Using non-parametric tests, naive associations between omics 
    features and metadata in cross-sectional data-sets are detected. In a second 
    step, confounding effects between metadata associated to the same omics 
    feature are detected and labeled using nested post-hoc model comparison
    tests, as first described in 
    Forslund, Chakaroun, Zimmermann-Kogadeeva, et al. (2021) <doi:10.1038/s41586-021-04177-9>. 
    The generated output can be graphically summarized using the built-in plotting function.
High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms.
Metadynamics is a state of the art biomolecular simulation technique.
  'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes
  it possible to perform metadynamics using various simulation codes. The results of
  metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package
  'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package.
  It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037>
  to calculate a free energy surface from hills. Minima can be located and plotted on
  the free energy surface. Transition states can be analyzed by Nudged Elastic Band
  method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy
  surfaces, minima and transition paths can be plotted to produce publication quality
  images.
Metadynamics is a state of the art biomolecular simulation technique.
  'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes
  it possible to perform metadynamics using various simulation codes. The results of
  metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package
  'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package.
  As an addendum, 'metadynaminer3d' is used to visualize 3D hills. It uses
  a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037>
  to calculate a free energy surface from hills. Minima can be located and plotted on
  the free energy surface. Free energy surfaces and minima can be plotted to produce
  publication quality images.
Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
A tool to simulate salmon metapopulations and apply financial
    portfolio optimization concepts. The package accompanies the paper 
    Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
Fused lasso method to cluster and estimate regression coefficients
    of the same covariate across different data sets when a large number of
    independent data sets are combined. Package supports Gaussian, binomial,
    Poisson and Cox PH models.
Meta-analysis of generalized additive
    models and generalized additive mixed models. A typical use case is
    when data cannot be shared across locations, and an overall meta-analytic
    fit is sought. 'metagam' provides functionality for removing individual
    participant data from models computed using the 'mgcv' and 'gamm4' packages such
    that the model objects can be shared without exposing individual data.
    Furthermore, methods for meta-analysing these fits are provided. The implemented
    methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>,
    extending previous works by Schwartz and Zanobetti (2000)
    and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.
Provides functions to perform all steps of genome-wide
    association meta-analysis for studying Genotype x Environment
    interactions, from collecting the data to the manhattan plot.  The
    procedure accounts for the potential correlation between studies. In
    addition to the Fixed and Random models, one can investigate the
    relationship between QTL effects and some qualitative or quantitative
    covariate via the test of contrast and the meta-regression,
    respectively.  The methodology is available from: (De Walsche, A., et
    al. (2025) \doi{10.1371/journal.pgen.1011553}).
Functionalities for facilitating systematic reviews, data
    extractions, and meta-analyses. It includes a GUI (graphical user interface)
    to help screen the abstracts and titles of bibliographic data; tools to assign
    screening effort across multiple collaborators/reviewers and to assess inter-
    reviewer reliability; tools to help automate the download and retrieval of
    journal PDF articles from online databases; figure and image extractions 
    from PDFs; web scraping of citations; automated and manual data extraction 
    from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for
    Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools
    to fill gaps in incomplete or missing study parameters; generation of random
    effects sizes for Hedges' d, log response ratio, odds ratio, and correlation
    coefficients for Monte Carlo experiments; covariance equations for modelling
    dependencies among multiple effect sizes (e.g., effect sizes with a common
    control); and finally summaries that replicate analyses and outputs from 
    widely used but no longer updated meta-analysis software (i.e., metawin).
	Funding for this package was supported by National Science Foundation (NSF) 
	grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) 
	Facilitating systematic reviews, data extraction and meta-analysis with the 
	metagear package for R. Methods in Ecology and Evolution 7, 323-330 
	<doi:10.1111/2041-210X.12472>.
According to a phenomenon known as "the wisdom of the crowds," 
    combining point estimates from multiple judges often provides a 
    more accurate aggregate estimate than using a point estimate from
    a single judge. However, if the judges use shared information in 
    their estimates, the simple average will over-emphasize this common
    component at the expense of the judges’ private information. 
    Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds
    Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions"
    <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes
    a procedure for calculating a weighted average of the judges’ individual
    estimates such that resulting aggregate estimate appropriately combines
    the judges' collective information within a single estimation problem. 
    The authors use both simulation and data from six experimental studies
    to illustrate that the weighting procedure outperforms existing averaging-like
    methods, such as the equally weighted average, trimmed average, and median.
    This aggregate estimate -- know as "the knowledge-weighted estimate" --
    inputs a) judges' estimates of a continuous outcome (E) and 
    b) predictions of others' average estimate of this outcome (P).  
    In this R-package, the function knowledge_weighted_estimate(E,P) 
    implements the knowledge-weighted estimate. Its use is illustrated
    with a simple stylized example and on real-world experimental data.
Performs multivariate meta-analysis for high-dimensional metabolomics data for integrating and collectively analysing individual-level data generated from multiple studies as well as for combining summary estimates. This approach accounts for correlation between outcomes, considers variability within and between studies, handles missing values and uses shrinkage estimation to allow for high dimensionality. A detailed vignette with example datasets and code to prepare data and analyses are available on <https://bookdown.org/a2delivera/MetaHD/>.
Helps calculate statistical values commonly used in meta-analysis. It provides several methods to compute different forms of standardized mean differences, as well as other values such as standard errors and standard deviations.
  The methods used in this package are described in the following references:
  Altman D G, Bland J M. (2011) <doi:10.1136/bmj.d2090>
  Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) <doi:10.1002/9780470743386.ch4>
  Chinn S. (2000) <doi:10.1002/1097-0258(20001130)19:22%3C3127::aid-sim784%3E3.0.co;2-m>
  Cochrane Handbook (2011) <https://handbook-5-1.cochrane.org/front_page.htm>
  Cooper, H., Hedges, L. V., & Valentine, J. C. (2009) <https://psycnet.apa.org/record/2009-05060-000>
  Cohen, J. (1977) <https://psycnet.apa.org/record/1987-98267-000>
  Ellis, P.D. (2009) <https://www.psychometrica.de/effect_size.html>
  Goulet-Pelletier, J.-C., & Cousineau, D. (2018) <doi:10.20982/tqmp.14.4.p242>
  Hedges, L. V. (1981) <doi:10.2307/1164588>
  Hedges L. V., Olkin I. (1985) <doi:10.1016/C2009-0-03396-0>
  Murad M H, Wang Z, Zhu Y, Saadi S, Chu H, Lin L et al. (2023) <doi:10.1136/bmj-2022-073141>
  Mayer M (2023) <https://search.r-project.org/CRAN/refmans/confintr/html/ci_proportion.html>
  Stackoverflow (2014) <https://stats.stackexchange.com/questions/82720/confidence-interval-around-binomial-estimate-of-0-or-1>
  Stackoverflow (2018) <https://stats.stackexchange.com/q/338043>.
An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>),  whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).
Assessment of inconsistency in meta-analysis by calculating the Decision Inconsistency index (DI) and the Across-Studies Inconsistency (ASI) index. These indices quantify inconsistency taking into account outcome-level decision thresholds.
An ensemble meta-prediction framework to integrate multiple regression 
    models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) 
    <arXiv:2010.09971>.
    A meta-analysis framework along with two weighted estimators as the ensemble 
    of empirical Bayes estimators, which combines the estimates from the different 
    external models. The proposed framework is flexible and robust in the ways 
    that (i) it is capable of incorporating external models that use a slightly 
    different set of covariates; (ii) it is able to identify the most relevant 
    external information and diminish the influence of information that is less 
    compatible with the internal data; and (iii) it nicely balances the bias-variance 
    trade-off while preserving the most efficiency gain. The proposed estimators 
    are more efficient than the naive analysis of the internal data and other 
    naive combinations of external estimators.
A set of tools to foster the development of reproducible analytical workflow by simplifying the download of data and 
    metadata from 'DataONE' (<https://www.dataone.org>) and easily importing this information into R.
Tools to generate random landscape graphs, evaluate species
    occurrence in dynamic landscapes, simulate future landscape occupation and
    evaluate range expansion when new empty patches are available (e.g. as a
    result of climate change). References: Mestre, F., Canovas, F., Pita, R., 
    Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., 
    Risk, B., Mira, A., Beja, P., Pita, R. (2017) 
    <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja,
    P. (2020) <doi:10.1186/s12898-019-0273-5>.
First- and higher-order likelihood inference in
        meta-analysis and meta-regression models.
A metadata structure for clinical data analysis
    and reporting based on Analysis Data Model (ADaM) datasets.
    The package simplifies clinical analysis and reporting tool development
    by defining standardized inputs, outputs, and workflow.
    The package can be used to create analysis and reporting planning grid,
    mock table, and validated analysis and reporting results based on
    consistent inputs.
Analyzes adverse events in clinical trials using the 'metalite'
    data structure. The package simplifies the workflow to create
    production-ready tables, listings, and figures discussed in the
    adverse events analysis chapters of
    "R for Clinical Study Reports and Submission"
    by Zhang et al. (2022) <https://r4csr.org/>.
Analyzes subject-level data in clinical trials using the 'metalite'
    data structure. The package simplifies the workflow to create
    production-ready tables, listings, and figures discussed in the
    subject-level analysis chapters of
    "R for Clinical Study Reports and Submission"
    by Zhang et al. (2022) <https://r4csr.org/>.
Create an interactive table of descriptive statistics in HTML. 
  This table is typically used for exploratory analysis in a clinical study (referred to as 'Table 1').
The 'MetAlyzer' S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app 'MetaboExtract' (<https://www.metaboextract.shiny.dkfz.de/MetaboExtract/>).
Combination of either p-values or modified effect sizes from different
    studies to find differentially expressed genes.
Implements several methods to meta-analyze studies that report the 
    sample median of the outcome. The methods described by 
    McGrath et al. (2019) <doi:10.1002/sim.8013>, 
    Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738>, and 
    McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be 
    applied to directly meta-analyze the median or difference of medians between 
    groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) 
    <doi:10.1177/0962280219889080>, Cai et al. (2021) 
    <doi:10.1177/09622802211047348>, and McGrath et al. (2023) 
    <doi:10.1177/09622802221139233>) are implemented to estimate 
    study-specific (difference of) means and their standard errors in order to 
    estimate the pooled (difference of) means. Methods for meta-analyzing median 
    survival times (McGrath et al. (2025) <doi:10.48550/arXiv.2503.03065>) are 
    also implemented. See McGrath et al. (2024) <doi:10.1002/jrsm.1686> for a 
    detailed guide on using the package.
Creates data with identical statistics (metamers) using an iterative 
   algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.
Generalized Additive Model for Location, Scale and Shape (GAMLSS) 
    with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data 
    (with various options for data transformation/normalization to address compositional effects) and 
    random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies 
    are implemented. 
    Random Forest model to predict microbiome age based on relative abundances of  
    shared bacterial genera with the Bangladesh data (Subramanian et al 2014), 
    comparison of multiple diversity indexes using linear/linear mixed effect models 
    and some data display/visualization are also implemented.
    The reference paper is published by 
    Ho NT, Li F, Wang S, Kuhn L (2019) <doi:10.1186/s12859-019-2744-2> .
Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to  summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.
Performs stability analysis of multi-environment trial data
    using parametric and non-parametric methods. Parametric methods
    includes Additive Main Effects and Multiplicative Interaction (AMMI)
    analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence
    by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot
    analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric
    adaptability index by Mohammadi & Amri (2008)
    <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart
    & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>,
    genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's
    (2004) method, power law residuals (POLAR) statistics by Doring et al.
    (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of
    variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>,
    stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>,
    weighted average of absolute scores by Olivoto et al. (2019a)
    <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by
    Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>.
    Non-parametric methods includes superiority index by Lin & Binns
    (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic
    stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third
    statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for
    computing biometrical analysis such as path analysis, canonical
    correlation, partial correlation, clustering analysis, and tools for
    inspecting, manipulating, summarizing and plotting typical
    multi-environment trial data are also provided.
Comprehensive network analysis package.
    Calculate correlation network fastly, accelerate lots of analysis by parallel computing.
    Support for multi-omics data, search sub-nets fluently.
    Handle bigger data, more than 10,000 nodes in each omics.
    Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize.
    Provide comprehensive topology indexes calculation, including ecological network stability.
A toolbox to handle and represent trophic networks in space or time across aggregation levels. This package contains a layout algorithm specifically designed for trophic networks, using dimension reduction on a diffusion graph kernel and trophic levels. Importantly, this package provides a layout method applicable for large trophic networks.
Given a CSV file with titles and abstracts, the package creates a
    document-term matrix that is lemmatized and stemmed and can directly be used to
    train machine learning methods for automatic title-abstract screening in the
    preparation of a meta analysis.
Novel method to unbiasedly include studies with Non-statistically Significant Unreported Effects (NSUEs) in a meta-analysis. First, the function calculates the interval where the unreported effects (e.g., t-values) should be according to the threshold of statistical significance used in each study. Afterward, the method uses maximum likelihood techniques to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance, and statistical significance bounds. Finally, it conducts a restricted-maximum likelihood random-effects meta-analysis separately for each set of imputations, and it performs estimations from these meta-analyses. Please read the reference in 'metansue' for details of the procedure.
The canonical way to perform meta-analysis involves using effect sizes.
   When they are not available this package provides a number of methods for
   meta-analysis of significance values including the methods of Edgington, Fisher,
   Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results;
   and routines for graphical display.
Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.
Designs plots in terms of core structure.  See 'example(metaplot)'.
 Primary arguments are (unquoted) column names; order and type (numeric or not)
 dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
 and any conditioning variables to metaplot() to generate density plots, boxplots, 
 mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() 
 to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' 
 are honored, producing fully annotated plots with minimal effort. Attribute 'guide' 
 is typically units, but may be encoded() to provide interpretations of categorical 
 values (see '?encode').  Utility unpack() transforms scalar column attributes to row 
 values and pack() does the reverse, supporting tool-neutral storage of metadata along 
 with primary data. The package supports customizable aesthetics such as such as reference 
 lines, unity lines, smooths, log transformation, and linear fits. The user may choose
 between trellis and ggplot output. Compact syntax and integrated metadata promote workflow 
 scalability.
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).
Provides an interface to 'MetaPost' (Hobby, 1998) 
    <http://www.tug.org/docs/metapost/mpman.pdf>.
    There are functions to generate an R description of a 'MetaPost'
    curve, functions to generate 'MetaPost' code from an R description,
    functions to process 'MetaPost' code, and functions to read
    solved 'MetaPost' paths back into R.
A simple and effective tool for computing and visualizing statistical power for meta-analysis,
    including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, 
    test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, 
    and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.
The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new method named weighted Fisher’s method we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method is useful for detecting partially associated features. Thus, the users can choose the function based on their purpose. Yoon et al. (2021) "Powerful p-value combination methods to detect incomplete association" <doi:10.1038/s41598-021-86465-y>.
Set of tools for descriptive analysis of metaproteomics data generated from high-throughput 
    mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, 
    expressed as spectral counts, and to manipulate them in groups of metaproteins. This information 
    can be represented using multiple visualization functions to portray the global metaproteome 
    landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, 
    taxonomic levels and/or functional annotation. The provided tools allow to implement flexible 
    analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.
Implements a novel density-based approach for estimating unknown means, visualizing distributions, 
	and meta-analyses of quantiles. A detailed vignettes with example datasets and code to prepare data and analyses is available at <https://bookdown.org/a2delivera/metaquant/>.
	The methods are described in the pre-print by De Livera, Prendergast and Kumaranathunga (2024, <doi:10.48550/arXiv.2411.10971>).
Build spatially and temporally explicit
    process-based species distribution models, that can include an arbitrary
    number of environmental factors, species and processes including metabolic
    constraints and species interactions. The focus of the package is simulating
    populations of one or multiple species in a grid-based landscape and studying
    the meta-population dynamics and emergent patterns that arise from the
    interaction of species under complex environmental conditions. It
    provides functions for common ecological processes such as
    negative exponential, kernel-based dispersal (see
    Nathan et al. (2012) <doi:10.1093/acprof:oso/9780199608898.003.0015>),
    calculation of the environmental suitability based on cardinal values (
    Yin et al. (1995) <doi:10.1016/0168-1923(95)02236-Q>, simplified by
    Yan and Hunt (1999) <doi:10.1006/anbo.1999.0955> see eq: 4), reproduction in
    form of an Ricker model (see Ricker (1954) <doi:10.1139/f54-039> and
    Cabral and Schurr (2010) <doi:10.1111/j.1466-8238.2009.00492.x>),
    as well as metabolic scaling based on the metabolic theory of ecology
    (see Brown et al. (2004) <doi:10.1890/03-9000> and
    Brown, Sibly and Kodric-Brown (2012)
    <doi:10.1002/9781119968535.ch>).
User-friendly package for reporting replicability-analysis methods, affixed to meta-analyses summary. The replicability-analysis output provides an assessment of the investigated intervention, where it offers quantification of effect replicability and assessment of the consistency of findings.
 - Replicability-analysis for fixed-effects and random-effect meta analysis: 
 - r(u)-value;
 - lower bounds on the number of studies with replicated positive and\or negative effect;
 - Allows detecting inconsistency of signals;
 - forest plots with the summary of replicability analysis results;
 - Allows Replicability-analysis with or without the common-effect assumption.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
Regression methods for the meta-SDT model. The package implements methods for cognitive experiments of metacognition as described in Kristensen, S. B., Sandberg, K., & Bibby, B. M. (2020). Regression methods for metacognitive sensitivity. Journal of Mathematical Psychology, 94. <doi:10.1016/j.jmp.2019.102297>.
A collection of functions for conducting meta-analysis using a
             structural equation modeling (SEM) approach via the 'OpenMx' and
             'lavaan' packages. It also implements various procedures to
             perform meta-analytic structural equation modeling on the
             correlation and covariance matrices, see Cheung (2015)
             <doi:10.3389/fpsyg.2014.01521>.
The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 'Small-Study Effects in Meta-Analysis':
 - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>;
 - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>;
 - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>;
 - imputation methods for missing binary data by Gamble & Hollis (2005) <DOI:10.1016/j.jclinepi.2004.09.013> and Higgins et al. (2008) <DOI:10.1177/1740774508091600>;
 - LFK index test and Doi plot by Furuya-Kanamori et al. (2018) <DOI:10.1097/XEB.0000000000000141>.
Functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by  Lee et al. (2013) <doi:10.1016/j.ajhg.2013.05.010>. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.
Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.
Performs Bayesian meta-analysis, meta-regression and model-based meta-analysis 
             using 'Stan'. Includes binomial-normal hierarchical models and option to use 
             weakly informative priors for the heterogeneity parameter and the treatment effect 
             parameter which are described in Guenhan, Roever, and Friede (2020) <doi:10.1002/jrsm.1370>.
If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. 
      The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 
      'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. 
      It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. 
      It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. 
      See for application: Nolte I.M. et al. (2017); <doi: 10.1038/ejhg.2017.50>.
To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity.
Fits and tests meta regression models and generates a
 number of useful test statistics: next to t- and z-tests, the likelihood ratio,
 bartlett corrected likelihood ratio and permutation tests are performed on
 the model coefficients.
Create meta tags for 'R Markdown' HTML documents and 'Shiny'
    apps for customized social media cards, for accessibility, and quality
    search engine indexing. 'metathis' currently supports HTML documents
    created with 'rmarkdown', 'shiny', 'xaringan', 'pagedown', 'bookdown',
    and 'flexdashboard'.
Uses the metadata information stored in 'metacore' objects to check and build metadata associated columns.
A comprehensive range of facilities to perform umbrella reviews with stratification of the evidence in R. The package accomplishes this aim by building on three core functions that: (i) automatically perform all required calculations in an umbrella review (including but not limited to meta-analyses), (ii) stratify evidence according to various classification criteria, and (iii) generate a visual representation of the results. Note that if you are not familiar with R, the core features of this package are available from a web browser (<https://www.metaumbrella.org/>).
Contains functions to estimate the proportion of effects stronger than a threshold
    of scientific importance (function prop_stronger), to nonparametrically characterize the distribution of effects in a meta-analysis (calib_ests, pct_pval),
    to make effect size conversions (r_to_d, r_to_z, z_to_r, d_to_logRR), to compute and format inference in a meta-analysis (format_CI, format_stat, tau_CI), to scrape results from 
    existing meta-analyses for re-analysis (scrape_meta, parse_CI_string, ci_to_var).
Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).
A compilation of functions to create visually appealing and information-rich 
    plots of meta-analytic data using 'ggplot2'. Currently allows to create forest plots, 
    funnel plots, and many of their variants, such as rainforest plots, thick forest plots, 
    additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities 
    for visual inference with the funnel plot in the context of meta-analysis are provided.
A tool for implementing so called 'deft' approach
    (see Fisher, David J., et al. (2017) <DOI:10.1136/bmj.j573>) and model
    visualization.
Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains over 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using the '{colorblindcheck} package by Jakub Nowosad'<https://jakubnowosad.com/colorblindcheck/>.
A new pipeline to explore chemical structural similarity across metabolites. It allows the metabolite classification in structurally-related modules and identifies common shared functional groups. The KODAMA algorithm is used to highlight structural similarity between metabolites. See Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705>, Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>, and Abdel-Shafy EA, Melak T, MacIntyre DA, Zadra G, Zerbini LF, Piazza S, Cacciatore S. (2023) Bioinformatics Advances <doi:10.1093/bioadv/vbad053>.
Random Forest Spatial Interpolation (RFSI, Sekulić et al. (2020) <doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) <doi:10.1002/2013JD020803>, Sekulić et al. (2020) <doi:10.1007/s00704-019-03077-3>) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data.
Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in the atmosphere by combining conserved quantities from hydrodynamics and thermodynamics. The package 'meteoEVT' provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, <doi:10.1111/j.1600-0870.2007.00272.x>], for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter [Hewson, 1998, <doi:10.1017/S1350482798000553>], the F diagnostic [Parfitt et al., 2017, <doi:10.1002/2017GL073662>] and the DSI [Mack et al., 2022, <arXiv:2208.11438>].
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS <https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast>, MeteoGalicia <https://www.meteogalicia.gal/web/modelos/threddsIndex.action>, NAM <https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale>, and RAP <https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update>.
Functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].
A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modeling, particularly crop and crop disease modeling.
Provides a unified mixture-of-experts (ME) modeling and 
    estimation framework with several original and flexible ME models to 
    model, cluster and classify heterogeneous data in many complex 
    situations where the data are distributed according to non-normal, 
    possibly skewed distributions, and when they might be corrupted by 
    atypical observations. Mixtures-of-Experts models for complex and 
    non-normal distributions ('meteorits') are originally introduced and 
    written in 'Matlab' by Faicel Chamroukhi. The references are mainly the 
    following ones. The references are mainly the following ones.
    Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>.
    Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>.
    Chamroukhi F. (2015) <arXiv:1506.06707>.
    Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>.
    Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>.
    Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>.
    Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.
Access to different Spanish meteorological stations data services and APIs (AEMET, SMC, MG, 
  Meteoclimatic...).
Fit and plot macroecological patterns predicted by the Maximum
    Entropy Theory of Ecology (METE).
In the omics data association studies, it is common to conduct the p-value corrections to control the false significance. Beyond the P-value corrections, E-value is recently studied to facilitate multiple testing correction based on V. Vovk and R. Wang (2021) <doi:10.1214/20-AOS2020>. This package provides E-value calculation for DNA methylation data and RNA-seq data. Currently, five data formats are supported: DNA methylation levels using DMR detection tools (BiSeq, DMRfinder, MethylKit, Metilene and other DNA methylation tools) and RNA-seq data. The relevant references are listed below: Katja Hebestreit and Hans-Ulrich Klein (2022) <doi:10.18129/B9.bioc.BiSeq>; Altuna Akalin et.al (2012) <doi:10.18129/B9.bioc.methylKit>.
Methods (standard and advanced) for analysis of agreement between measurement methods. These cover Bland-Altman plots, Deming regression, Lin's Total deviation index, and difference-on-average regression. See Carstensen B. (2010) "Comparing Clinical Measurement Methods: A Practical Guide (Statistics in Practice)" <doi:10.1002/9780470683019> for more information.
Identify and rank CpG DNA methylation conservation along the human 
    genome. Specifically it includes bootstrapping methods to provide ranking 
    which should adjust for the differences in length as without it short 
    regions tend to get higher conservation scores.
DNA methylation is an epigenetic modification involved in genomic stability,
      gene regulation, development and disease.
      DNA methylation occurs mainly through the addition of a methyl group to cytosines, 
      for example to cytosines in a CpG dinucleotide context (CpG stands for a cytosine followed by a guanine).
      Tissue-specific methylation patterns lead to genomic regions with different characteristic
      methylation levels.
      E.g. in vertebrates CpG islands (regions with high CpG content) that are associated to promoter regions of
      expressed genes tend to be unmethylated.
      'MethEvolSIM' is a model-based simulation software for the generation and modification
      of cytosine methylation patterns along a given tree, which can be a genealogy of
      cells within an organism, a coalescent tree of DNA sequences sampled from a population, or a species tree.
      The simulations are based on an extension of the model of
      Grosser & Metzler (2020) <doi:10.1186/s12859-020-3438-5> and allows for changes of
      the methylation states at single cytosine positions as well as simultaneous changes of methylation
      frequencies in genomic structures like CpG islands.
Evaluate bias and precision in method comparison 
    studies. One provides measurements for each method and it takes care of 
    the estimates. Multiple plots to evaluate bias, precision and compare
    methods.
A graphical user interface to apply an advanced method optimization algorithm to various sampling and analysis instruments. This includes generating experimental designs, uploading and viewing data, and performing various analyses to determine the optimal method. Details of the techniques used in this package are published in Gamble, Granger, & Mannion (2024) <doi:10.1021/acs.analchem.3c05763>.
Meteorological Tools following the FAO56 irrigation paper of Allen et al. (1998) [1].
    Functions for calculating:
    reference evapotranspiration (ETref),
    extraterrestrial radiation (Ra),
    net radiation (Rn),
    saturation vapor pressure (satVP),
    global radiation (Rs),
    soil heat flux (G),
    daylight hours,
    and more.
    [1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9).
Provides a number of functions to facilitate the handling and production of reports using time series data.
    The package was developed to be understandable for beginners, so some functions aim to transform processes that would be
    complex into functions with a few lines. The main advantage of using the 'metools' package is the ease of producing reports and
    working with time series using a few lines of code, so the code is clean and easy to understand/maintain. 
    Learn more about the 'metools' at <https://metoolsr.wordpress.com>.
Split an untargeted metabolomics data set into a set of likely true 
    metabolites and a set of likely measurement artifacts. This process involves 
    comparing missing rates of pooled plasma samples and biological samples. The 
    functions assume a fixed injection order of samples where biological samples are 
    randomized and processed between intermittent pooled plasma samples. By comparing 
    patterns of missing data across injection order, metabolites that appear in blocks
    and are likely artifacts can be separated from metabolites that seem to have 
    random dispersion of missing data. The two main metrics used are: 1. the number of 
    consecutive blocks of samples with present data and 2. the correlation of missing rates 
    between biological samples and flanking pooled plasma samples.
Many useful functions and extensions for dealing
    with meteorological data in the tidy data framework. Extends 'ggplot2'
    for better plotting of scalar and vector fields and provides commonly
    used analysis methods in the atmospheric sciences.
A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.
Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) <doi:10.3150/23-BEJ1647>, Bolin, Kovacs, Kumar and Simas (2023) <doi:10.1090/mcom/3929> and Bolin, Simas and Wallin (2023) <doi:10.48550/arXiv.2304.03190> and <doi:10.48550/arXiv.2304.10372>.
Mine metrics on common places on the web through the power of their APIs (application programming interfaces). 
    It also helps make the data in a format that is easily used for a dashboard or other purposes.
    There is an associated dashboard template and tutorials that are underdevelopment that help you  
    fully utilize 'metricminer'.
An implementation of evaluation metrics in R that are commonly
             used in supervised machine learning. It implements metrics for
             regression, time series, binary classification, classification,
             and information retrieval problems. It has zero dependencies and
             a consistent, simple interface for all functions.
Provides weighted versions of several metrics and performance
    measures used in machine learning, including average unit deviances of
    the Bernoulli, Tweedie, Poisson, and Gamma distributions, see
    Jorgensen B. (1997, ISBN: 978-0412997112).  The package also contains
    a weighted version of generalized R-squared, see e.g. Cohen, J. et al.
    (2002, ISBN: 978-0805822236).  Furthermore, 'dplyr' chains are
    supported.
Calculate different metrics based on aquatic
    macroinvertebrate density data (individuals per square meter) to
    assess water quality (Prat N et al. 2009).
The Washington Metropolitan Area Transit Authority is a
    government agency operating light rail and passenger buses in the
    Washington D.C. area. With a free developer account, access their
    'Metro Transparent Data Sets API' <https://developer.wmata.com/> to
    return data frames of transit data for easy analysis.
Provides classes and calculation and plotting functions 
 for metrology applications, including measurement uncertainty estimation
 and inter-laboratory metrology comparison studies.
Learning and using the Metropolis algorithm for
    Bayesian fitting of a generalized linear model. The package vignette
    includes examples of hand-coding a logistic model using several
    variants of the Metropolis algorithm. The package also contains R
    functions for simulating posterior distributions of Bayesian
    generalized linear model parameters using guided, adaptive,
    guided-adaptive and random walk Metropolis algorithms. The random walk
    Metropolis algorithm was originally described in Metropolis et al
    (1953); <doi:10.1063/1.1699114>.
Implementation of various statistical models for multivariate
    event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate
    cumulative incidence models <doi:10.1002/sim.6016>, and  bivariate random
    effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
    Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, 
    Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Provides a Shiny application to estimate the sample size required 
    for a metabolomic experiment to achieve a desired statistical power. 
    Estimation is possible with or without available data from a pilot study.
Provides an interface with the Meteo France Synop data API 
    (see <https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32> 
    for more information). 
    The Meteo France Synop data are made of meteorological data recorded 
    every three hours on 62 French meteorological stations.
Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.
Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.
Compute the average of a sequence of random vectors
  in a moving expanding window using a fixed amount of memory.
Color palettes inspired by the works of 
  Mexican painters and muralists. The package includes functions that
  return vectors of colors and also functions to use color and fill
  scales in 'ggplot2' visualizations.
Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.
A color palette generator inspired by Mexican politics, with 
    colors ranging from red on the left to gray in the middle and green on the 
    right. Palette options range from only a few colors to several colors, but with
    discrete and continuous options to offer greatest flexibility to the user. 
    This package allows for a range of applications, from mapping brief discrete scales 
    (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays 
    including dozens of shades graded from red to green.
The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.
Provide simple functions to (i) compute a class of 
  multi-functionality measures for a single ecosystem for given 
  function weights, (ii) decompose gamma multi-functionality 
  for pairs of ecosystems and K ecosystems (K can be greater than 2) 
  into a within-ecosystem component (alpha multi-functionality) 
  and an among-ecosystem component (beta multi-functionality). 
  In each case, the correlation between functions can be corrected for. 
  Based on biodiversity and ecosystem function data, this software 
  also facilitates graphics for assessing biodiversity-ecosystem 
  functioning relationships across scales.
Multivariate functional principal component analysis via fast covariance estimation for
             multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.
Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
Computing functional traits-based distances between pairs of 
    species for species gathered in assemblages allowing to build several
    functional spaces. The package allows to compute functional diversity
    indices assessing the distribution of species (and of their dominance) in a
    given functional space for each assemblage and the overlap between
    assemblages in a given functional space, see: Chao et al. (2018)
    <doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>,
    Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al.
    (2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009)
    <doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included.
    Visit the 'mFD' website for more information, documentation and examples.
Creates and manages a PostgreSQL database suitable for storing fisheries data
             and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model.
             See <https://mareframe.github.io/mfdb/> for more information.
Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), 
  MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale
  Multifractal Analysis (MMA). The MFDFA() function proposed in this package was 
  used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). 
  See references for more information. Interested users can find a parallel version of
  the MFDFA() function on GitHub.
Methods for controlling the median of the false discovery proportion (mFDP).
	Depending on the method, simultaneous or non-simultaneous inference is provided. 
	The methods take a vector of p-values or test statistics as input.
Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
The mFilter package implements several time series filters useful
        for smoothing and extracting trend and cyclical components of a
        time series. The routines are commonly used in economics and
        finance, however they should also be interest to other areas.
        Currently, Christiano-Fitzgerald, Baxter-King,
        Hodrick-Prescott, Butterworth, and trigonometric regression
        filters are included in the package.
A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.
Calculate the maximal fat oxidation, the exercise intensity that elicits the 
              maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. 
              Three variables can be obtained from the SIN model: dilatation, symmetry and translation. 
              Examples of these methods can be found in Montes de Oca et al (2021) <doi:10.1080/17461391.2020.1788650>
              and Chenevière et al. (2009) <doi:10.1249/MSS.0b013e31819e2f91>.
Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Multivariable fractional polynomial algorithm simultaneously selects variables and functional forms in both generalized linear models and Cox proportional hazard models. Key references are Royston and Altman (1994) <doi:10.2307/2986270> and Royston and Sauerbrei (2008, ISBN:978-0-470-02842-1). In addition, it can model a sigmoid relationship between variable x and an outcome variable y using the approximate cumulative distribution transformation proposed by Royston (2014) <doi:10.1177/1536867X1401400206>. This feature distinguishes it from a standard fractional polynomial function, which lacks the ability to achieve such modeling.
Calculate a multivariate functional principal component analysis
    for data observed on different dimensional domains. The estimation algorithm
    relies on univariate basis expansions for each element of the multivariate
    functional data  (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. 
    Multivariate and univariate functional data objects are
    represented by S4 classes for this type of data implemented in the package
    'funData'. For more details on the general concepts of both packages and a case 
    study, see Happ-Kurz (2020) <doi:10.18637/jss.v093.i05>.
Matrix-Based Flexible Project Planning. This package models, plans, and schedules flexible, such as agile, extreme, and hybrid project plans. The package contains project planning, scheduling, and risk assessment functions. Kosztyan (2022) <doi:10.1016/j.softx.2022.100973>.
Provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance <doi:10.1214/14-AOAS782>. Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points <doi:10.1111/jtsa.12254>. Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies <doi:10.1007/s10827-016-0635-3>. Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series <doi:10.1007/s00184-018-0672-1>. Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series.
Convenience functions for multivariate MCMC using univariate samplers including:
  slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>),
  adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>),
  adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and
  univariate Metropolis with Gaussian proposal.
Estimates probit, logit, Poisson, negative binomial, 
  and beta regression models, returning their marginal effects, odds ratios, 
  or incidence rate ratios as an output.
  Greene (2008, pp. 780-7) provides a textbook introduction to this topic.
The idea of a computational algorithm described in the article by Andronov M. et al. (2022) <https://link.springer.com/chapter/10.1007/978-3-030-92507-9_13>.
    The purpose of this package is to automate computations for a Markov-Modulated M/G/1 queuing system with alternating Poisson flow of arrivals. It offers a set of functions to calculate various mean indices of the system, including mean flow intensity, mean service busy and idle times, and the system's stationary probability.
Procedures to simulate, estimate and diagnose MGARCH
    processes of BEKK and multivariate GJR (bivariate asymmetric GARCH
    model) specification.
Compute the multiple Grubbs-Beck low-outlier test on positively distributed
 data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow
 data processing discussed in Cohn et al. (2013) <doi:10.1002/wrcr.20392> and
 England et al. (2017) <doi:10.3133/tm4B5>.
Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.
Generalized additive (mixed) models, some of their extensions and 
             other generalized ridge regression with multiple smoothing 
             parameter estimation by (Restricted) Marginal Likelihood, 
             Generalized Cross Validation and similar, or using iterated 
             nested Laplace approximation for fully Bayesian inference. See 
             Wood (2017) <doi:10.1201/9781315370279> for an overview. 
             Includes a gam() function, a wide variety of smoothers, 'JAGS' 
             support and distributions beyond the exponential family.
Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
A simulation modeling framework which significantly extends capabilities from the
    'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets.
    For more information about 'MGDrivE', see our publication: Sánchez et al. (2019) <doi:10.1111/2041-210X.13318>
    Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations,
    epidemiological dynamics, time-varying parameters, and a continuous-time simulation
    framework with various sampling algorithms for both deterministic and stochastic interpretations.
    'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we
    suggest installing that package initially.
Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. 
 The naive analysis which is solely based on the observed data without adjustment may lead to bias.
 The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. 
 The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given
 validation set. This package is an implementation 
 of Chen (2013) <doi:10.1002/bimj.201200195>.
Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>. Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal, Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).
Provides readers for easy and consistent importing of
    Mouse Genome Informatics (MGI) report files:
    <https://www.informatics.jax.org/downloads/reports/index.html>. These data
    are provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ,
    Mouse Genome Informatics Group (2024) <doi:10.1093/genetics/iyae031>.
An aggressive dimensionality reduction and network estimation
    technique for a high-dimensional Gaussian graphical model (GGM). Please
    refer to: Efficient Dimensionality Reduction for High-Dimensional Network
    Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of
    The 31st International Conference on Machine Learning, 2014, p. 1953--1961.
Inference of Multiscale graphical models with neighborhood
    selection approach.  The method is based on solving a convex
    optimization problem combining a Lasso and fused-group Lasso
    penalties.  This allows to infer simultaneously a conditional
    independence graph and a clustering partition. The optimization is
    based on the Continuation with Nesterov smoothing in a
    Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018)
    <doi:10.1109/TMI.2018.2829802> implemented in python.
Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.
Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.
A glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. 'MGMS2' (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) <doi:10.1021/acs.analchem.9b03340> "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) <doi:10.1093/bioinformatics/bts447> "MALDIquant: a versatile R package for the analysis of mass spectrometry data."
Provides an interface to the Maxar Geospatial Platform (MGP) Application Programming Interface. <https://www.maxar.com/maxar-geospatial-platform>
    It facilitates imagery searches using the MGP Streaming Application Programming Interface via the Web Feature Service (WFS) method, and supports image downloads through Web Map Service (WMS) and Web Map Tile Service (WMTS)
    Open Geospatial Consortium (OGC) methods. 
    Additionally, it integrates with the Maxar Geospatial Platform Basemaps Application Programming Interface for accessing Maxar basemaps imagery and seamlines. 
    The package also offers seamless integration with the Maxar Geospatial Platform Discovery Application Programming Interface, allowing users to search, filter, and sort Maxar content, 
    while retrieving detailed metadata in formats like SpatioTemporal Asset Catalog (STAC) and GeoJSON.
This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image.
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.
Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021).
Designed to enable simultaneous substitution in strings in a safe fashion.
    Safe means it does not rely on placeholders (which can cause errors in same length matches).
Fits a geographically weighted regression model with different scales
	for each covariate. Uses the negative binomial distribution as default,
	but also accepts the normal, Poisson, or logistic distributions.
	Can fit the global versions of each regression and also the geographically
	weighted alternatives with only one scale, since they are all particular
	cases of the multiscale approach.
	Hanchen Yu (2024). "Exploring Multiscale Geographically Weighted Negative Binomial Regression", Annals of the American Association of Geographers <doi:10.1080/24694452.2023.2289986>.
	Fotheringham AS, Yang W, Kang W (2017). "Multiscale Geographically Weighted Regression (MGWR)", Annals of the American Association of Geographers <doi:10.1080/24694452.2017.1352480>.
	Da Silva AR, Rodrigues TCV (2014). "Geographically Weighted Negative Binomial Regression - incorporating overdispersion", Statistics and Computing <doi:10.1007/s11222-013-9401-9>.
Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to 
    Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196.
    <https://www.researchgate.net/profile/Hasbi-Yasin-2/publication/289689583_Mixed_geographically_weighted_regression_model_case_study_The_percentage_of_poor_households_in_Mojokerto_2008/links/58e46aa40f7e9bbe9c94d641/Mixed-geographically-weighted-regression-model-case-study-The-percentage-of-poor-households-in-Mojokerto-2008.pdf>.
Functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
Nonparametric survival function estimates and semiparametric
    regression for the multivariate failure time data with right-censoring.
    For nonparametric survival function estimates, the Volterra, Dabrowska,
    and Prentice-Cai estimates for bivariate failure time data may be
    computed as well as the Dabrowska estimate for the trivariate failure
    time data. Bivariate marginal hazard rate regression can be fitted for
    the bivariate failure time data. Functions are also provided to compute
    (bootstrap) confidence intervals and plot the estimates of the bivariate
    survival function. For details, see "The Statistical Analysis of
    Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice,
    R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.
MHCnuggets (<https://github.com/KarchinLab/mhcnuggets>) is a Python
    tool to predict MHC class I and MHC class II epitopes.
    This package allows one to call MHCnuggets from R.
Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) <doi:10.1080/01621459.2021.2011298>.
Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
An implementation of the multilevel (also known as mixed or random 
    effects) hidden Markov model using Bayesian estimation in R. The multilevel 
    hidden Markov model (HMM) is a generalization of the well-known hidden
    Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The 
    multilevel HMM is tailored to accommodate (intense) longitudinal data of 
    multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. 
    <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow 
    for heterogeneity in the model parameters (transition probability matrix and 
    conditional distribution), while estimating one overall HMM. The model can 
    be fitted on multivariate data with either a categorical, normal, or Poisson 
    distribution, and include individual level covariates (allowing for e.g., 
    group comparisons on model parameters). Parameters are estimated using 
    Bayesian estimation utilizing the forward-backward recursion within a hybrid 
    Metropolis within Gibbs sampler. Missing data (NA) in the dependent 
    variables is accommodated assuming MAR. The package also includes various 
    visualization options, a function to simulate data, and a function to obtain 
    the most likely hidden state sequence for each individual using the Viterbi 
    algorithm.
Provides exact and approximate algorithms for the horseshoe prior
    in linear regression models, which were proposed by Johndrow et al. (2020)
    <https://www.jmlr.org/papers/v21/19-536.html>.
Transforms, calculates, and presents results from the Mental Health Quality of Life Questionnaire (MHQoL), a measure of health-related quality of life for individuals with mental health conditions. Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) <doi:10.1007/s11136-021-02935-w>.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
A framework for multiple hypothesis testing based on distribution
    of p values. It is well known that the p values come from different
    distribution for null and alternatives, in this package we provide
    functions to detect that change. We provide a method for using the change
    in distribution of p values as a way to detect the true signals in the
    data.
A comprehensive tool for almost all existing multiple testing
    methods for discrete data. The package also provides some novel multiple testing
    procedures controlling FWER/FDR for discrete data. Given discrete p-values
    and their domains, the [method].p.adjust function returns adjusted p-values,
    which can be used to compare with the nominal significant level alpha and make
    decisions. For users' convenience, the functions also provide the output option 
    for printing decision rules.
A Comprehensive tool for almost all existing multiple testing
    methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.
Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
Estimation of models with dependent variable left-censored at zero.
             Null values may be caused by a selection process
	     Cragg (1971) <doi:10.2307/1909582>, insufficient resources
	     Tobin (1958) <doi:10.2307/1907382>, or infrequency of purchase
	     Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <doi:10.1371/journal.pcbi.1010420>. It is dedicated to dealing with multiple imputation for proteomics.
Tools for training, selecting, and evaluating maximum entropy
    (and standard logistic regression) distribution models. This package 
    provides tools for user-controlled transformation of explanatory variables, 
    selection of variables by nested model comparison, and flexible model 
    evaluation and projection. It follows principles based on the maximum-
    likelihood interpretation of maximum entropy modeling, and uses infinitely-
    weighted logistic regression for model fitting. The package is described in 
    Vollering et al. (2019; <doi:10.1002/ece3.5654>).
Analyse, plot, and tabulate antimicrobial minimum inhibitory concentration (MIC) data.
    Validate the results of an MIC experiment by comparing observed MIC values to
    a gold standard assay, in line with standards from the International Organization for
    Standardization (2021) <https://www.iso.org/standard/79377.html>. Perform MIC prediction from
    whole genome sequence data stored in the Pathosystems Resource Integration Center (2013)
    <doi:10.1093/nar/gkt1099> database or locally.
'Mica' is a server application used to create data web portals for 
    large-scale epidemiological studies or multiple-study consortia. 'Mica' helps
    studies to provide scientifically robust data visibility and web presence 
    without significant information technology effort. 'Mica' provides a 
    structured description of consortia, studies, annotated and searchable data
    dictionaries, and data access request management. This 'Mica' client allows
    to perform data extraction for reporting purposes.
Modified functions of the package 'pcalg' and some additional functions
            to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based 
            causal discovery in incomplete and multiply imputed datasets.
            Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>;
            Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.
Multiple imputation using Fully Conditional Specification (FCS)
    implemented by the MICE algorithm as described in Van Buuren and
    Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
    its own imputation model. Built-in imputation models are provided for
    continuous data (predictive mean matching, normal), binary data (logistic
    regression), unordered categorical data (polytomous logistic regression)
    and ordered categorical data (proportional odds). MICE can also impute
    continuous two-level data (normal model, pan, second-level variables).
    Passive imputation can be used to maintain consistency between variables.
    Various diagnostic plots are available to inspect the quality of the
    imputations.
Contains functions for multiple imputation which
    complements existing functionality in R.
    In particular, several imputation methods for the
    mice package (van Buuren & Groothuis-Oudshoorn, 2011,
    <doi:10.18637/jss.v045.i03>) are implemented.
    Main features of the miceadds package include
    plausible value imputation (Mislevy, 1991,
    <doi:10.1007/BF02294457>), multilevel imputation for
    variables at any level or with any number of hierarchical
    and non-hierarchical levels (Grund, Luedtke & Robitzsch,
    2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, 
    Ch.7, <doi:10.1201/9780429492259>), imputation using 
    partial least squares (PLS) for high dimensional 
    predictors (Robitzsch, Pham & Yanagida, 2016), 
    nested multiple imputation (Rubin, 2003, 
    <doi:10.1111/1467-9574.00217>), substantive model
    compatible imputation (Bartlett et al., 2015,
    <doi:10.1177/0962280214521348>), and features
    for the generation of synthetic datasets
    (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
    Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
Statistical Analyses and Pooling after Multiple Imputation. A large variety 
   of repeated statistical analysis can be performed and finally pooled. Statistical analysis 
   that are available are, among others, Levene's test, Odds and Risk Ratios, One sample 
   proportions, difference between proportions and linear and logistic regression models. 
   Functions can also be used in combination with the Pipe operator. 
   More and more statistical analyses and pooling functions will be added over time.
   Heymans (2007) <doi:10.1186/1471-2288-7-33>.
   Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	 Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	 Marshall (2009) <doi:10.1186/1471-2288-9-57>.
	 Sidi (2021) <doi:10.1080/00031305.2021.1898468>.
	 Lott (2018) <doi:10.1080/00031305.2018.1473796>.
	 Grund (2021) <doi:10.31234/osf.io/d459g>.
Various tools for microeconomic analysis and microeconomic modelling,
   e.g. estimating quadratic, Cobb-Douglas and Translog functions,
   calculating partial derivatives and elasticities of these functions,
   and calculating Hessian matrices, checking curvature
   and preparing restrictions for imposing monotonicity of Translog functions.
Functions and tools
   for analysing consumer demand
   with the Almost Ideal Demand System (AIDS)
   suggested by Deaton and Muellbauer (1980).
Tools for econometric analysis and economic modelling
   with the traditional two-input Constant Elasticity of Substitution (CES) function
   and with nested CES functions with three and four inputs.
   The econometric estimation can be done by the Kmenta approximation,
   or non-linear least-squares
   using various gradient-based or global optimisation algorithms.
   Some of these algorithms can constrain the parameters to certain ranges,
   e.g. economically meaningful values.
   Furthermore, the non-linear least-squares estimation
   can be combined with a grid-search for the rho-parameter(s).
   The estimation methods are described in Henningsen et al. (2021)
   <doi:10.4337/9781788976480.00030>.
Econometric analysis of multiple-input-multiple-output
  production technologies with ray-based input distance functions as suggested
  by Price and Henningsen (2022): "A Ray-Based Input Distance Function to Model
  Zero-Valued Output Quantities: Derivation and an Empirical Application",
  <https://ideas.repec.org/p/foi/wpaper/2022_03.html>.
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'.
Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021)<doi:10.1109/ACCESS.2021.3116526> and Tang et al. (2024)<doi:10.1109/TGRS.2024.3446950>). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results.
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.
Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) <doi:10.1128/aem.56.6.1875-1881.1990>), Baranyi model (Baranyi and Roberts (1994) <doi:10.1016/0168-1605%2894%2990157-0>), Rosso model (Rosso et al. (1993) <doi:10.1006/jtbi.1993.1099>) and linear model (Dantigny (2005) <doi:10.1016/j.ijfoodmicro.2004.10.013>).
Perform calculations for the WHO International Reference Reagents for the microbiome. Using strain, species or genera abundance tables generated through analysis of 16S ribosomal RNA sequencing or shotgun sequencing which included a reference reagent. This package will calculate measures of sensitivity, False positive relative abundance, diversity, and similarity based on mean average abundances with respect to the reference reagent.
A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects (Zhou et al. (2022)<doi:10.1186/s13059-022-02655-5>).  The methods can be applied to the analysis of other (high-dimensional) compositional data arising from sequencing experiments.
An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups.
 Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. 
 Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, Elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised partial least squares analysis by Inge S. Helland <https://www.jstor.org/stable/4616159>.
 Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation.
 During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
Stand-alone HTTP capable R-package repository,
    that fully supports R's install.packages() and available.packages().
    It also contains API endpoints for end-users to add/update packages.
    This package can supplement 'miniCRAN', which has functions for maintaining
    a local (partial) copy of 'CRAN'.
    Current version is bare-minimum without any access-control or much security.
Provides utilities for reading and processing microdata from Spanish official statistics with R.
A framework for analyzing broth microdilution assays in various
    96-well plate designs, visualizing results and providing descriptive and (simple)
    inferential statistics (i.e. summary statistics and sign test). The functions are 
    designed to add metadata to 8 x 12 tables of absorption values, creating a tidy
    data frame. Users can choose between clean-up procedures via function 
    parameters (which covers most cases) or user prompts (in cases with complex 
    experimental designs). Users can also choose between two validation methods,
    i.e. exclusion of absorbance values above a certain threshold or manual exclusion 
    of samples. A function for visual inspection of samples with their absorption values
    over time for certain group combinations helps with the decision. In addition,
    the package includes functions to subtract the background absorption (usually
    at time T0) and to calculate the growth performance compared to a baseline.
    Samples can be visually inspected with their absorption values displayed across
    time points for specific group combinations. Core functions of this package (i.e.
    background subtraction, sample validation and statistics) were inspired by the
    manual calculations that were applied in Tewes and Muller (2020)
    <doi:10.1038/s41598-020-67600-7>.
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.
Collect and normalize local microinverter energy and power production
    data through off-cloud API requests. Currently supports 'APSystems', 'Enphase',
    and 'Fronius' microinverters.
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 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.
Enables you to create accessible modal dialogs, with confidence and with minimal configuration.
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.
Vitamin and mineral deficiencies continue to be a significant 
    public health problem. This is particularly critical in developing countries
    where deficiencies to vitamin A, iron, iodine, and other micronutrients
    lead to adverse health consequences. Cross-sectional surveys are helpful in
    answering questions related to the magnitude and distribution of
    deficiencies of selected vitamins and minerals. This package provides tools
    for calculating and determining select vitamin and mineral deficiencies
    based on World Health Organization (WHO) guidelines found at
    <https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system>.
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.
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.
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.
A generalization of the 'Synth' package that is
    designed for data at a more granular level (e.g., micro-level).
    Provides functions to construct weights (including propensity
    score-type weights) and run analyses for synthetic control methods
    with micro- and meso-level data; see Robbins, Saunders, and Kilmer
    (2017) <doi:10.1080/01621459.2016.1213634> and Robbins and Davenport
    (2021) <doi:10.18637/jss.v097.i02>.
This 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.
Functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series <doi:10.1201/9781003100263>. The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included.
Companion package of Carrion-i-Silvestre & Sansó (2023): 
  "Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant 
  Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified 
  Iterative Cumulative Sum of Squares Algorithm, which is an extension of 
  the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the 
  unconditional variance of a time series controlling for the tail index of 
  the underlying distribution. The fourth order moment is estimated non-parametrically
  to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). 
  Critical values and p-values are generated using a Generalized Extreme Value distribution approach.
  References
  Carrion-i-Silvestre J.J & Sansó A (2023) <https://www.ub.edu/irea/working_papers/2023/202309.pdf>.
  Inclan C & Tiao G.C (1994) <doi:10.1080/01621459.1994.10476824>,
  Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) <https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.
Contains functions for converting existing HTML/JavaScript
   source into equivalent 'shiny' functions. Bootstraps the process of making
   new 'shiny' functions by allowing us to turn HTML snippets directly into 
   R functions.
The rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging, which necessitates innovative, integrated, and efficient trial designs(Yuan, Y., et al. (2016) <doi:10.1002/sim.6971>). MIDAS-2 package enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We used a regression model to characterize the efficacy pattern in subgroups. Information borrowing was applied through Bayesian hierarchical model to improve trial efficiency considering the limited sample size in subgroups(Cunanan, K. M., et al. (2019) <doi:10.1177/1740774518812779>). MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion(Wathen, J. K., & Thall, P. F. (2017) <doi: 10.1177/1740774517692302>).
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.
Provides tools and demonstrates methods for working with individual 
  undergraduate student-level records (registrar's data) in 'R'. Tools include 
  filters for program codes, data sufficiency, and timely completion. Methods 
  include gathering blocs of records, computing quantitative metrics such as 
  graduation rate, and creating charts to visualize comparisons. 'midfieldr' 
  interacts with practice data provided in 'midfielddata', an R data package 
  available at <https://midfieldr.github.io/midfielddata/>. 
  'midfieldr' also interacts with the full MIDFIELD database for users who have 
  access. This work is supported by the US National Science Foundation through 
  grant numbers 1545667 and 2142087.
An implementation of a taxonomy of models of restricted diffusion
    in biological tissues parametrized by the tissue geometry (axis, diameter,
    density, etc.). This is primarily used in the context of diffusion magnetic
    resonance (MR) imaging to model the MR signal attenuation in the presence of
    diffusion gradients. The goal is to provide tools to simulate the MR signal
    attenuation predicted by these models under different experimental
    conditions. The package feeds a companion 'shiny' app available at
    <https://midi-pastrami.apps.math.cnrs.fr> that serves as a graphical
    interface to the models and tools provided by the package. Models currently
    available are the ones in Neuman (1974) <doi:10.1063/1.1680931>, Van
    Gelderen et al. (1994) <doi:10.1006/jmrb.1994.1038>, Stanisz et al. (1997)
    <doi:10.1002/mrm.1910370115>, Soderman & Jonsson (1995)
    <doi:10.1006/jmra.1995.0014> and Callaghan (1995)
    <doi:10.1006/jmra.1995.1055>.
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.
A guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. 'midoc' will advise which analysis approaches can be used, and how best to perform them. 'midoc' follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). <doi:10.1016/j.jclinepi.2021.01.008>.
The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) <doi:10.48550/arXiv.2506.08338>.
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 were published by Batista and Ferreira (2023) <doi:10.28951/bjb.v41i4.640>.
Provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) <doi:10.1007/s40617-025-01058-9>.
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>.
Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) <doi:10.48550/arXiv.2209.04757> for smoothing multivariate data on half-spaces.
Allows the user to estimate transition probabilities for
    migratory animals between any two phases of the annual cycle, using a
    variety of different data types. Also quantifies the strength of
    migratory connectivity (MC), a standardized metric to quantify the
    extent to which populations co-occur between two phases of the annual
    cycle. Includes functions to estimate MC and the more traditional
    metric of migratory connectivity strength (Mantel correlation)
    incorporating uncertainty from multiple sources of sampling error. For
    cross-species comparisons, methods are provided to estimate
    differences in migratory connectivity strength, incorporating
    uncertainty. See Cohen et al. (2018) <doi:10.1111/2041-210X.12916>,
    Cohen et al. (2019) <doi:10.1111/ecog.03974>, and Roberts et al.
    (2023) <doi:10.1002/eap.2788> for details on some of these methods.
Implements methods for estimating generalized estimating equations (GEE) with advanced options for flexible modeling and handling missing data. This package provides tools to fit and analyze GEE models for longitudinal data, allowing users to address missingness using a variety of imputation techniques. It supports both univariate and multivariate modeling, visualization of missing data patterns, and facilitates the transformation of data for efficient statistical analysis. Designed for researchers working with complex datasets, it ensures robust estimation and inference in longitudinal and clustered data settings.
Tools for estimating, measuring and working with migration data.
A set of tools for testing networks.
   It includes functions for univariate and multivariate 
   conditional uniform graph and quadratic assignment procedure testing,
   and network regression.
   The package is a complement to 
   'Multimodal Political Networks' (2021, ISBN:9781108985000),
   and includes various datasets used in the book.
   Built on the 'manynet' package, all functions operate with matrices, 
   edge lists, and 'igraph', 'network', and 'tidygraph' objects,
   and on one-mode and two-mode (bipartite) networks.
Tools to help convert credit risk data at two timepoints
    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) <doi:10.48550/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.
Detection of migration events and segments of continuous residence 
    based on irregular time series of location data
    as published in Chi et al. (2020) <doi:10.1371/journal.pone.0239408>.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
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>.
Data-driven approach for Exploratory Factor Analysis (EFA)
    that uses Model Implied Instrumental Variables (MIIVs). The method
    starts with a one factor model and arrives at a suggested model with
    enhanced interpretability that allows cross-loadings and correlated
    errors.
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.
Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. 
  This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions.
  It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022.
  Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301–307 <doi:10.1111/2041-210X.14269>.
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).
  Two computationnally efficient methods are proposed for obtaining effect size estimates (beta) in 
  Mixed Logistic Regression in GWAS: the Approximate Maximum Likelihood Estimate (AMLE), and the Offset
  method. The wald test obtained with AMLE is identical to the score test. Data can be genotype matrices
  in plink format, or dosage (VCF files). The methods are described in details in 
  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.
Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and Tubo, B.F. (2024) <doi:10.24412/1932-2321-2024-278-115-123>].
Guesses the MIME type from a filename extension using the data
    derived from /etc/mime.types in UNIX-type systems.
Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, 
  a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers
  a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes,
  particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing
  researchers to focus on their primary inquiries without being bogged down by wrangling complexities.
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.
Our approach uses a  mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9).
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), Garcia and Gordon (1992), Stull (1947), 
	and Hamme and Emerson (2004) 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.
Count data is prevalent and informative, with widespread
    application in many fields such as social psychology, personality, and
    public health. Classical statistical methods for the analysis of count
    outcomes are commonly variants of the log-linear model, including
    Poisson regression and Negative Binomial regression. However, a
    typical problem with count data modeling is inflation, in the sense
    that the counts are evidently accumulated on some integers. Such an
    inflation problem could distort the distribution of the observed
    counts, further bias estimation and increase error, making the classic
    methods infeasible. Traditional inflated value selection methods based
    on histogram inspection are easy to neglect true points and
    computationally expensive in addition. Therefore, we propose a
    multiple-inflated negative binomial model to handle count data
    modeling with multiple inflated values, achieving data-driven inflated
    value selection. The proposed approach provides simultaneous
    identification of important regression predictors on the target count
    response as well. More details about the proposed method are described in 
    Li, Y., Wu, M., Wu, M., & Ma, S. (2023) <arXiv:2309.15585>.
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().
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>.
Play and record games of minesweeper using a graphics device that supports event handling.
    Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by
    Crow P. (1997) <https://minesweepergame.com/math/a-mathematical-introduction-to-the-game-of-minesweeper-1997.pdf>.
This is the very popular mine sweeper game! The game requires you to find out tiles that contain mines through clues from unmasking neighboring tiles. Each tile that does not contain a mine shows the number of mines in its adjacent tiles. If you unmask all tiles that do not contain mines, you win the game; if you unmask any tile that contains a mine, you lose the game. For further game instructions, please run `help(run_game)` and check details. This game runs in X11-compatible devices with `grDevices::x11()`.
In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum  number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017)<doi:10.1080/03610926.2016.1152490>.This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign().
Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), <doi:10.1007/s12532-023-00238-4>).
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.
Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" <doi:10.1111/biom.12079> and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" <doi:10.1146/annurev-statistics-033121-110134>.
Normally building a GODB is fairly complicated, involving downloading 
 multiple database files and using these to build e.g. a 'mySQL' database. 
 Accessing this database is also complicated, involving an intimate knowledge
 of the database in order to construct reliable queries.
 Here we have a more modest goal, generating GOGOA3, which is a stripped down
 version of the GODB that 
 was originally restricted to human genes as designated by the HUGO Gene Nomenclature
 Committee (HGNC) (see <https://geneontology.org/>). I have now added about two dozen
 additional species, namely all species represented on the Gene Ontology
 download page <https://current.geneontology.org/products/pages/downloads.html>.
 This covers most of the model organisms that are commonly used in bio-medical
 and basic research (assuming that anyone still has a grant to do such research).
 This can be built in a matter of seconds from 2 easily
 downloaded files (see <https://current.geneontology.org/products/pages/downloads.html>
 and <https://geneontology.org/docs/download-ontology/>), and it can be queried by e.g.
 w<-which(GOGOA3[,"HGNC"] %in% hgncList) where GOGOA3
 is a matrix representing the minimalist GODB and hgncList is a list of
 gene identifiers. This database will be used in my upcoming package 'GoMiner'
 which is based on my previous publication (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003)<doi:10.1186/gb-2003-4-4-r28>).
 Relevant .RData files are available from GitHub (<https://github.com/barryzee/GO/tree/main/databases>).
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.
Wrapper for 'Minimap2'. 'Minimap2' is a very valuable long read aligner for the Pacbio and Oxford Nanopore Technologies sequencing platforms. 'minimapR' is an R wrapper for 'minimap2' which was developed by Heng Li <me@liheng.org>. 
	*SPECIAL NOTES 
	1. Examples can only be run from 'GitHub' installation.
	2. 'conda' or 'mamba' must be used to install 'minimapR' on your system.
    3. For Windows users, 'minimap2' and 'samtools' can be installed via MSYS2, instructions are provided when 'minimap2_installation()' is run.
	Li, Heng (2018) <doi:10.1093/bioinformatics/bty191> "Minimap2: pairwise alignment for nucleotide sequences".
The minimax family of distributions is a two-parameter
        family like the beta family, but computationally a lot more
        tractible.
Implements the algorithm of Remez (1962) for polynomial minimax
    approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for
    rational minimax approximation.
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.
An R interface to the 'MinIO' Client. The 'MinIO' Client ('mc')
  provides a modern alternative to UNIX commands like 'ls', 'cat', 'cp',
  'mirror', 'diff', 'find' etc. It supports 'filesystems'  and Amazon "S3"
  compatible cloud storage service ("AWS" Signature v2 and v4).
  This package provides convenience functions for installing the 'MinIO'
  client and running any operations, as described in the official 
  documentation, <https://min.io/docs/minio/linux/reference/minio-mc.html?ref=docs-redirect>.
  This package provides a flexible and high-performance alternative to 'aws.s3'.
Density, distribution function, ... hazard function, cumulative
    hazard function, survival function for survival distributions with
    piece-wise constant hazards and multiple states and methods to plot and
    summarise those distributions. 
    A derivation of the used algorithms can be found in my masters thesis 
    <doi:10.25365/thesis.76098>.
Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.
This package is deprecated. Please use 'redatamx' instead.
  Provides an API to work with 'Redatam' (see <https://redatam.org>) 
  databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 
  'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' 
  core and provides functions to open/close a database (redatam_open()/redatam_close()), 
  list entities and variables from the database (redatam_entities(), redatam_variables()) 
  and execute a 'SPC' program and gets the results as data frames 
  (redatam_query(), redatam_run()).
Provides UI widget and layout functions for writing Shiny apps
    that work well on small screens.
Performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) <DOI:10419/161931>.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Derivative-free optimization by quadratic approximation
        based on an interface to Fortran implementations by M. J. D.
        Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
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>.
Provides an extension to the 'lolog' package by introducing the minTriadicClosure() statistic to capture higher-order interactions among triplets of nodes. This function facilitates improved modelling of group formations and 'triadic' closure in networks. A smoothing parameter has been incorporated to avoid numerical errors.
Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom').
A toolkit for genomic selection in animal breeding with
    emphasis on multi-breed and multi-trait nested grouping operations.
    Streamlines iterative analysis workflows when working with 'ASReml-R'
    package.  Includes utility functions for phenotypic data processing
    commonly used by animal breeders.
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.
Designed for simplicity, a 'mirai' evaluates an R expression
    asynchronously in a parallel process, locally or distributed over the
    network. Modern networking and concurrency, built on 'nanonext' and
    'NNG', ensures reliable scheduling over fast inter-process
    communications or TCP/IP secured by TLS.  Launch remote resources via
    SSH or cluster managers for distributed computing. The queued
    architecture scales efficiently to millions of tasks over thousands of
    connections, requiring no storage on the file system.  Innovative
    features include event-driven promises, asynchronous parallel map, and
    seamless serialization of otherwise non-exportable reference objects.
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.
Testing CRAN and Bioconductor mirror speed by recording download time of 'src/base/COPYING' (for CRAN) and 'packages/release/bioc/html/ggtree.html' (for Bioconductor).
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 discrete response data using
    unidimensional and multidimensional item analysis models under the Item
    Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). 
    Exploratory and confirmatory item factor analysis models
	are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
    bi-factor and two-tier models are available for modeling item testlets using
	dimension reduction EM algorithms, while multiple group analyses and 
	mixed effects designs are included for detecting differential item, bundle, 
	and test functioning, and for modeling item and person covariates. 
	Finally, latent class models such as the DINA, DINO, multidimensional latent class, 
	mixture IRT models, and zero-inflated response models are supported, as well 
	as a wide family of probabilistic unfolding models.
Provides tools to generate HTML interfaces for adaptive
    and non-adaptive 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 that contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying test item 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>.