PCIC's implementation of Climdex routines for computation of
    extreme climate indices.
A suite of routines for downscaling coarse scale global
    climate model (GCM) output to a fine spatial resolution. Includes
    Bias-Corrected Spatial Downscaling (BCDS), Constructed Analogues
    (CA), Climate Imprint (CI), and Bias Correction/Constructed
    Analogues with Quantile mapping reordering (BCCAQ). Developed by
    the the Pacific Climate Impacts Consortium (PCIC), Victoria,
    British Columbia, Canada.
A robust constrained L1 minimization method for estimating
        a large sparse inverse covariance matrix (aka precision
        matrix), and recovering its support for building graphical
        models.  The computation uses linear programming.
Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from 'extRemes', including for fitting the
    statistical models. Details are given in Paciorek, Stone, and Wehner (2018)
    <doi:10.1016/j.wace.2018.01.002>.
Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).
Toolkit for the 'ClimMob' platform in R. 'ClimMob' is an open source software for crowdsourcing citizen science in agriculture <https://climmob.net/climmob3/>. Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the research paradigm on its head; instead of a few researchers designing complicated trials to compare several technologies in search of the best solutions, it enables many farmers to carry out reasonably simple experiments that taken together can offer even more information. 'ClimMobTools' enables project managers to deep explore and analyse their 'ClimMob' data in R.
Set of tools to compute metrics and indices for climate analysis.
    The package provides functions to compute extreme indices, evaluate the
    agreement between models and combine theses models into an ensemble. Multi-model
    time series of climate indices can be computed either after averaging the 2-D
    fields from different models provided they share a common grid or by combining
    time series computed on the model native grid. Indices can be assigned weights
    and/or combined to construct new indices.
Contains functions to detect and visualise periods of climate
    sensitivity (climate windows) for a given biological response. 
    Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> 
    and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details.
Bayesian and ML Emax model fitting, graphics and simulation for clinical dose
    response.  The summary data from the dose response meta-analyses in  
    Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and
    Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> 
    Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> 
    are included 
    in the package.  The prior distributions for the Bayesian analyses default to
    the posterior predictive distributions derived from these references.
Utilities to make your clinical collaborations easier if not
          fun. It contains functions for designing studies such as Simon
          2-stage and group sequential designs and for data analysis such
          as Jonckheere-Terpstra test and estimating survival quantiles.
Provides estimates of several summary measures for clinical trials including
    the average hazard ratio, the weighted average hazard ratio, the restricted 
    superiority probability ratio, the restricted mean survival difference and 
    the ratio of restricted mean times lost, based on the short-term and 
    long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) 
    which accommodates various non-proportional hazards scenarios. The inference
    procedures and the asymptotic results for the summary measures are discussed 
    in Yang (2018, <doi:10.1002/sim.7676>).
Calculates equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.
Export to 'Microsoft Word' or R markdown documents nicely formatted tables of descriptive or inferential statistics. Can be used within the scope of clinical trials, or for any scientific publications. Descriptive tables for quantitative statistics (mean, median, max etc..) and/or qualitative statistics (frequencies and percentages) are available along with formatted tables of Least Square Means of Linear Models, Linear Mixed Models, Cox Models and Generalized Linear Mixed Models. It works mainly with 'emmeans' 'officer' and 'flextable' packages.
Functions for calculating clinical significance.
This package provides the estimation of an index measuring
        the clinical utility of DNA testing in the context of
        gene-environment interactions on a disease. The corresponding
        gene-environment interaction effect on the additive scale can
        also be obtained. The estimation is based on case-control or
        cohort data. The method was developed by Nguyen et al. 2013.
Simple utility functions to read from and write to
    the Windows, OS X, and X11 clipboards.
Clique percolation community detection for weighted and 
    unweighted networks as well as threshold and plotting functions.
    For more information see Farkas et al. (2007) <doi:10.1088/1367-2630/9/6/180>
    and Palla et al. (2005) <doi:10.1038/nature03607>.
A small subset of Unicode symbols, that are useful
    when building command line applications. They fall back to
    alternatives on terminals that do not support Unicode.
    Many symbols were taken from the 'figures' 'npm' package
    (see <https://github.com/sindresorhus/figures>).
Estimation and inference for linear models where some or all of the
    fixed-effects coefficients are subject to order restrictions. This package uses
    the robust residual bootstrap methodology for inference, and can handle some
    structure in the residual variance matrix.
A set of functions to fit a boosting conditional logit model.
Tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.
Fit a sequence of conditional logistic regression models with lasso, for small to large sized samples. Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015) <doi:10.1186/1471-2105-16-S6-S1>.
Defines the classes and functions used to simulate and
  to analyze data sets describing copy number variants and,
  optionally, sequencing mutations in order to detect clonal subsets.
  See Zucker et al. (2019) <doi:10.1093/bioinformatics/btz057>.
Analyze data from next-generation sequencing experiments on genomic samples. 'CLONETv2' offers a set of functions to compute allele specific copy number and clonality from segmented data and SNPs position pileup. The package has also calculated the clonality of single nucleotide variants given read counts at mutated positions. The package has been developed at the laboratory of Computational and Functional Oncology, Department of CIBIO, University of Trento (Italy), under the supervision of prof Francesca Demichelis. References: Prandi et al. (2014) <doi:10.1186/s13059-014-0439-6>; Carreira et al. (2014) <doi:10.1126/scitranslmed.3009448>.
Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.
Interface to the Google Cloud Machine Learning Platform
  <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine
  learning models.
Simple helper functions to fetch and read data from various formats stored on Amazon AWS S3 Buckets <https://aws.amazon.com/s3/> . Most functions are essentially wrapping over cloudyR <https://cloudyr.github.io/> which is a project and group of packages which provide easier cloud computing solutions for R.
Provides means of plots for comparing utilization data of compute systems.
R Interface to C API of COIN-OR Clp, depends on COIN-OR Clp Version >= 1.12.0.
A new methodology for linear regression with both curve response 
    and curve regressors, which is described in Cho, Goude, Brossat and Yao 
    (2013) <doi:10.1080/01621459.2012.722900> and (2015) 
    <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is 
    dimension reduction based on a singular value decomposition in a Hilbert 
    space, which reduces the curve regression problem to several scalar linear 
    regression problems.
Provides MLEdag() for constrained maximum likelihood estimation and likelihood ratio test of a large directed acyclic graph. The algorithms are described in the paper by Li, Shen, and Pan (2019) <doi:10.1080/01621459.2019.1623042>.
This package provides and implementation of a one step
        smoothing newton method for the solution of second order cone
        programming problems, originally described by Xiaoni Chi and
        Sanyang Liu.
Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.
Provides several cluster-robust variance estimators (i.e.,
    sandwich estimators) for ordinary and weighted least squares linear regression
    models, including the bias-reduced linearization estimator introduced by Bell
    and McCaffrey (2002) 
    <http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf> and 
    developed further by Pustejovsky and Tipton (2017) 
    <DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating
    the variance- covariance matrix and for testing single- and multiple-
    contrast hypotheses based on Wald test statistics. Tests of single regression
    coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-
    contrast hypotheses use an approximation to Hotelling's T-squared distribution.
    Methods are provided for a variety of fitted models, including lm() and mlm
    objects, glm(), ivreg() (from package 'AER'), plm() (from package 'plm'), gls()
    and lme() (from 'nlme'), lmer() (from `lme4`), robu() (from 'robumeta'), and 
    rma.uni() and rma.mv() (from 'metafor').
CLUster Evaluation (CLUE) is a computational method for identifying 
              optimal number of clusters in a given time-course dataset clustered by 
              cmeans or kmeans algorithms and subsequently identify key kinases or 
              pathways from each cluster. Its implementation in R is called ClueR. 
              See README on <https://github.com/PengyiYang/ClueR> for more details.
              P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>.
We developed the clues R package to provide functions 
        for automatically estimating the number of clusters and 
        getting the final cluster partition without any input 
        parameter except the stopping rule for convergence. 
        The package also provides functions to
        evaluate and compare the performances of partitions of a data
        set both numerically and graphically.
Clustering algorithms are designed to cluster objects into a number of clusters. Any clustering algorithm provides the 'best'
             grouping of objects according to some criterion.
             This does not guarantee a 'good' clustering solution in the sense that some allocations were not simply the result of chance.
             This package allows the user to apply any clustering algorithm to a data set. The cluster allocations are subjected to a 
             bootstrap analysis 
             to determine the extent to which the clustering structure is stable and fundamental to the data set. For more information 
             see <https://slubbe.wixsite.com/academic-cv/conference-presentations>.
Clustered covariate regression enables estimation and inference in 
 both linear and non-linear models with linear predictor functions even when the
 design matrix is column rank deficient. Routines in this package implement algorithms
 in Soale and Tsyawo (2019) <doi:10.13140/RG.2.2.32355.81441>.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data.
Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).
	Simultaneously perform clustering analysis and signature 	gene selection on high-dimensional transcriptome data sets. 	To do so, 'ClussCluster' incorporates a Lasso-type 	regularization penalty term to the objective function of K-	means so that cell-type-specific signature genes can be 	identified while clustering the cells.
Tests, utilities, and case studies for analyzing significance in
  clustered binary matched-pair data. The central function clust.bin.pair uses
  one of several tests to calculate a Chi-square statistic. Implemented are the
  tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998)
  <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>,
  Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010)
  <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996>
  included for comparison. The utility functions nested.to.contingency and
  paired.to.contingency convert data between various useful formats. Thyroids
  and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989)
  <doi:10.1016/0165-1781(89)90196-0> respectively.
Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>)  and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package.
A method for determining groups in multiple survival
    curves with an automatic selection of their number based on k-means or 
    k-medians algorithms. The selection of the optimal number is provided by 
    bootstrap methods.
     Implemented methods are:
    Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Functions to identify the pattern of a dose-response
		curve. Then fit a set of appropriate models to it according to the identified pattern, 
		followed by model averaging to estimate the effective dose.
Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2017) <https://iwsm2017.webhosting.rug.nl/IWSM_2017_V2.pdf>.
Methods for Cluster analysis.  Much extended the original from
	Peter Rousseeuw, Anja Struyf and Mia Hubert,
	based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
A collection of data sets for teaching cluster analysis.
Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' <http://openbudgets.eu/> data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface.
Test for cluster tendency (clusterability) of a data set.
    The methods implemented - reducing the data set to a single dimension using principal component analysis or computing
    pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - 
    are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms
    are appropriate for a data set.
Provides functionality for the analysis of clustered data using the cluster bootstrap.
Estimating causal effects from observational studies assuming 
    clustered (or partial) interference. These inverse probability-weighted
    estimators target new estimands arising from population-level treatment 
    policies. The estimands and estimators are introduced in Barkley et al. 
    (2017) <arXiv:1711.04834>.
Identification and visualization of groups of closely spaced mutations in the DNA sequence of cancer genome. The extremely mutated zones are searched in the symmetric dissimilarity matrix using the anti-Robinson matrix properties. Different data sets are obtained to describe and plot the clustered mutations information.
Visualise clustering algorithms with GGobi.  Contains both
    general code for visualising clustering results and specific
    visualisations for model-based, hierarchical and SOM clustering.
We developed the clusterGeneration package to provide functions 
        for generating random clusters, generating random 
        covariance/correlation matrices,
        calculating a separation index (data and population version)
        for pairs of clusters or cluster distributions, and 1-D and 2-D
        projection plots to visualize clusters.  The package also
        contains a function to generate random clusters based on
        factorial designs with factors such as degree of separation,
        number of clusters, number of variables, number of noisy
        variables.
The Partitioning Algorithm based on Recursive Thresholding
        (PART) is used to recursively uncover clusters and subclusters
        in the data. Functionality is also available for visualization
        of the clustering.
One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
  clusterhap groups together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, clusterhap groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, clusterhap calculates such
  probability from relative frequencies.
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used.
Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created 'clusterlab'. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. Through using vector rotations and scalar multiplication clusterlab can generate complex patterns of Gaussian clusters and outliers.
Evaluate arbitrary function calls using workers on HPC schedulers
    in single line of code. All processing is done on the network without
    accessing the file system. Remote schedulers are supported via SSH.
Integrative context-dependent clustering for heterogeneous
    biomedical datasets. Identifies local clustering structures in related
    datasets, and a global clusters that exist across the datasets.
The clustering 'NUMA' Optimized Routines package or 'clusternor' is a highly optimized package for performing clustering in parallel with accelerations specifically targeting multi-core Non-Uniform Memory Access ('NUMA') hardware architectures. Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein, Randal Burns (2019) <arXiv:1902.09527>.
Calculate power for cluster randomized trials (CRTs) that compare two means, two proportions, or two counts using closed-form solutions. In addition, calculate power for cluster randomized  crossover trials using Monte Carlo methods. For more information, see Reich et al. (2012) <doi:10.1371/journal.pone.0035564>.
Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.
Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.
This is a function for validating microarray clusters via reproducibility,
 based on the paper referenced below.
Calculate p-values and confidence intervals using cluster-adjusted
    t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.
Distance measures (GDM1, GDM2,	Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap,	Davies-Bouldin),	data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). 
 (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, 
 HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, 
 RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, 
 JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, 
 MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, 
 JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, 
 DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, 
 CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>,
 HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, 
 TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, 
 BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, 
 WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).
Allows one to assess the stability of individual objects, clusters 
    and whole clustering solutions based on repeated runs of the K-means and K-medoids 
    partitioning algorithms.
Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins' statistic is implemented.
An R package that provides a suite of tools to evaluate
        clustering algorithms, clusterings, and individual clusters.
Implements a Ward-like hierarchical clustering
    algorithm including spatial/geographical constraints.
This clustering algorithm deals with missing data via weights that are imposed on missings and succesively increased. See the vignette for details.
Model-based clustering of mixed data (i.e. data which consist of
    continuous, binary, ordinal or nominal variables) using a parsimonious
    mixture of latent Gaussian variable models.
Functions to perform k-prototypes partitioning clustering for
    mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means
    Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining
    and Knowledge Discovery 2, 283-304, <DOI:10.1023/A:1009769707641>.
An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.
Provides an interface to perform cluster analysis on a corpus of
    text. Interfaces to Quanteda to assemble text corpuses easily. Deviationalizes
    text vectors prior to clustering using technique described by Sherin (Sherin,
    B. [2013]. A computational study of commonsense science: An exploration in the
    automated analysis of clinical interview data. Journal of the Learning Sciences,
    22(4), 600-638. Chicago. <doi:10.1080/10508406.2013.836654>). Uses
    cosine similarity as distance metric for two stage clustering process, involving
    Ward's algorithm hierarchical agglomerative clustering, and k-means clustering.
    Selects optimal number of clusters to maximize "variance explained" by clusters,
    adjusted by the number of clusters. Provides plotted output of clustering
    results as well as printed output. Assesses "model fit" of clustering solution
    to a set of preexisting groups in dataset.
A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <DOI:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <DOI:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.
Deciding what resolution to use can be a difficult question when
    approaching a clustering analysis. One way to approach this problem is to
    look at how samples move as the number of clusters increases. This package
    allows you to produce clustering trees, a visualisation for interrogating
    clusterings as resolution increases.
Returns an edit-distance based clusterization of an input vector of strings.
    Each cluster will contain a set of strings w/ small mutual edit-distance
    (e.g., Levenshtein, optimum-sequence-alignment, Damerau-Levenshtein), as computed by
    stringdist::stringdist(). The set of all mutual edit-distances is then used by
    graph algorithms (from package 'igraph') to single out subsets of high connectivity.
An interactive document on  the topic of cluster analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/ClusterAnalysis/>.
A complimentary package for use with hclust; simprof tests
        to see which (if any) clusters are statistically different. The
        null hypothesis is that there is no a priori group structure.
        See Clarke, K.R., Somerfield, P.J., and Gorley R.N. 2008.
        Testing of null hypothesis in exploratory community analyses:
        similarity profiles and biota-environment linkage. J. Exp. Mar.
        Biol. Ecol. 366, 56-69.
Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.
Variable selection for Gaussian model-based clustering as implemented in the 'mclust' package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting 'mclust' models. By default the algorithm uses a sequential search, but parallelisation is also available.
Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification.
Package contains most of the popular internal and external
        cluster validation methods ready to use for the most of the
        outputs produced by functions coming from package "cluster".
        Package contains also functions and examples of usage for
        cluster stability approach that might be applied to algorithms
        implemented in "cluster" package as well as user defined
        clustering algorithms.
Statistical and biological validation of clustering results.
Single objective optimization using a CMA-ES.
Pure R implementation of the Covariance Matrix Adaptation -
    Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).
Automatically displays graphical visualization for exported data table (permutated results) from Connectivity Map (CMap) (2006) <doi:10.1126/science.1132939>. 
  It allows the representation of the statistics (p-value and enrichment) according to each cell lines in the form of a bubble plot.
Provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) <doi:10.1002/sim.6880>. Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors.
Calculation and plot of the stepwise Cronbach-Mesbah Curve
Implements the Bayesian calibration model described
    in Pratola and Chkrebtii (2018) <DOI:10.5705/ss.202016.0403> for stochastic 
    and deterministic simulators.  Additive and multiplicative discrepancy models 
    are currently supported. See <http://www.matthewpratola.com/software> for 
    more information and examples.
Provides functions for implementing cmenet - a bi-level variable selection method for conditional main effects (see Mak and Wu (2018) <doi:10.1080/01621459.2018.1448828>). CMEs are reparametrized interaction effects which capture the conditional impact of a factor at a fixed level of another factor. Compared to traditional two-factor interactions, CMEs can quantify more interpretable interaction effects in many problems. The current implementation performs variable selection on only binary CMEs; we are working on an extension for the continuous setting.
Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix.
Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm.
Quite extensive package for maximum likelihood estimation and
    weighted least squares estimation of categorical marginal models (CMMs;
    e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?;
    Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.
CEU (CEU San Pablo University) Mass Mediator is an on-line tool for aiding researchers in 
  performing metabolite annotation. 'cmmr' (CEU Mass Mediator RESTful API) allows 
  for programmatic access in R: batch search, batch advanced search, MS/MS (tandem mass spectrometry) search, etc.
  For more information about the API Endpoint please go to <https://github.com/lzyacht/cmmr>.
Provides the source and examples for James P. Howard, II, 
             "Computational Methods for Numerical Analysis with R," 
             <http://howardjp.github.io/cmna/>, a book on numerical
             methods in R.
Perceptually uniform palettes for commonly used
	variables in oceanography as functions taking an integer
	and producing character vectors	of colours.
	See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M.
	and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for
	the guidelines adhered to when creating the palettes.
The main purpose of this package is to juxtapose the different control limits obtained by modelling a data set through the COM-Poisson distribution vs. the classical Poisson distribution. Accordingly, this package offers the ability to compute the COM-Poisson parameter estimates and plot associated Shewhart control charts for a given data set.
Manhattan plot, a type of scatter plot, was widely used to display the association results. However, it is usually time-consuming and laborious for a non-specialist user to write scripts and adjust parameters of an elaborate plot. Moreover, the ever-growing traits measured have necessitated the integration of results from different Genome-wide association study researches. Circle Manhattan Plot is the first open R package that can lay out. Genome-wide association study P-value results in both traditional rectangular patterns, QQ-plot and novel circular ones. United in only one bull's eye style plot, association results from multiple traits can be compared interactively, thereby to reveal both similarities and differences between signals. Additional functions include: hightlight signals, a group of SNPs, chromosome visualization and candidate genes around SNPs.
A toolkit for flexible modeling of count processes where data (over- or under-) dispersion exists.
    Estimations can be obtained under two data constructs where one has:
    (1) data on number of events in an s-unit time interval, or (2) only wait-time data.
    This package is supplementary to the work set forth in Zhu et al. (2016) <doi:10.1080/00031305.2016.1234976>.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks, as described in Gray
 (1988), A class of K-sample tests for comparing the cumulative
 incidence of a competing risk, Ann. Stat. 16:1141-1154
 <DOI:10.1214/aos/1176350951>, and Fine JP and
 Gray RJ (1999), A proportional hazards model for the subdistribution
 of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>.
Estimation, testing and regression modeling of
 subdistribution functions in competing risks using quantile regressions,
 as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.
A collection of useful helper routines developed by
  students of the Center for Mathematical Research, Stankin,
  Moscow.
The Satellite Application Facility on Climate Monitoring (CM SAF) 
    is a ground segment of the European Organization for the Exploitation of 
    Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application 
    Facilities. The CM SAF contributes to the sustainable monitoring of the climate 
    system by providing essential climate variables related to the energy and water 
    cycle of the atmosphere (<http://www.cmsaf.eu>). It is a joint cooperation of eight 
    National Meteorological and Hydrological Services.
    The 'cmsaf' R-package provides a collection of R-operators for the analysis and 
    manipulation of CM SAF NetCDF formatted data. The 'cmsaf' R-package is tested 
    for CM SAF NetCDF data. Other CF conform NetCDF data should be applicable, but there
    is no guarantee for an error-free application. 
    The 'cmsaf' R-package includes a 'shiny' based interface for an easy application of 
    the 'cmsaf' package operators and the preparation and visualization of CM SAF NetCDF 
    data. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu>).
    Detailed information and test data are provided on the CM SAF webpage 
    (<http://www.cmsaf.eu/R_toolbox>).
An interactive document on  the topic of confusion matrix analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ConfusionMatrixShiny/>.
Various utilities for the complex multivariate Gaussian distribution.
Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is related to Qualitative Comparative Analysis (QCA), but contrary to the latter, it is custom-built for uncovering causal structures with multiple outcomes and it builds causal models from the bottom up by gradually combining single factors to complex dependency structures until the requested thresholds of model fit are met. The new functionalities provided by this package version include functions for evaluating and benchmarking the correctness of CNA's output, a function determining whether a solution is an INUS model, a function bringing non-INUS expressions into INUS form, and a function for identifying cyclic models. The package vignette has been updated accordingly.
This is an add-on to the 'cna' package <https://CRAN.R-project.org/package=cna> comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see <https://people.uib.no/mba110/docs/ConCovOpt.pdf>.
Provided R functions for working with the Conditional Negative Binomial distribution.
A GUI with which users can construct and interact
        with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.
Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.
Implementations of recent complex-valued wavelet spectral procedures for analysis of irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.
Contains functions for computing the nonparametric maximum likelihood estimate of a log-concave density function from univariate observations. This log-density estimate is always a piecewise linear function.
Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.
Conventional methods for producing standard scores in psychometrics or biometrics 
    are often plagued with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low 
    confidence for assessing extreme scores. The continuous norming method introduced by A. 
    Lenhard et al. (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>) and generates continuous test norm 
    scores on the basis of the raw data from standardization samples, without requiring 
    assumptions about the distribution of the raw data: Norm scores are directly established 
    from raw data by modeling the latter ones as a function of both percentile scores and an 
    explanatory variable (e.g., age). The method minimizes bias arising from sampling and 
    measurement error, while handling marked deviations from normality, addressing bottom 
    or ceiling effects and capturing almost all of the variance in the original norm data 
    sample.
This package evaluates DNA copy number data, using both their initial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm.
Efficient computations for null models that require shuffling columns on big matrix data.
             This package provides functions for faster computation of diversity measure statistics
             when independent random shuffling is applied to the columns of a given matrix. 
             Given a diversity measure f and a matrix M, the provided functions can generate random samples 
             (shuffled matrix rows of M), the mean and variance of f, and the p-values of this measure 
             for two different null models that involve independent random shuffling of the columns of M.
             The package supports computations of alpha and beta diversity measures.
Provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps.
Coalescent simulators can rapidly simulate biological sequences
    evolving according to a given model of evolution.
    You can use this package to specify such models, to conduct the simulations
    and to calculate additional statistics from the results.
    It relies on existing simulators for doing the simulation, and currently
    supports the programs 'ms', 'msms' and 'scrm'. It also supports finite-sites
    mutation models by combining the simulators with the program 'seq-gen'.
Flexible framework for coalescent analyses in R. It includes a main function running the  MCMC algorithm, auxiliary functions for tree rearrangement, and some functions to compute population genetic parameters.
An implementation of a Bayesian framework for the opinion poll
    based estimation of event probabilities in multi-party electoral systems
    (Bender and Bauer (2018) <doi:10.21105/joss.00606>).
Functions to analyze coarse data.
    Specifically, it contains functions to (1) fit parametric accelerated
    failure time models to interval-censored survival time data, and (2)
    estimate the case-fatality ratio in scenarios with under-reporting.
    This package's development was motivated by applications to infectious
    disease: in particular, problems with estimating the incubation period and
    the case fatality ratio of a given disease.  Sample data files are included
    in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>, 
    Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and 
    Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.
Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with longitudinal treatments.
Implementation of a probabilistic method for biclustering
    adapted to overdispersed count data. It is a Gamma-Poisson Latent Block Model.
    It also implements two selection criteria in order to select the number of
    biclusters.
This package performs prediction for regression-oriented problems, aggregating in a nonlinear scheme any basic regression machines suggested by the context and provided by the user. If the user has no valuable knowledge on the data, four defaults machines wrappers are implemented so as to cover a minimal spectrum of prediction methods. If necessary, the computations may be parallelized. The method is described in Biau, Fischer, Guedj and Malley (2013), "COBRA: A Nonlinear Aggregation Strategy".
Qualitatively Constrained (Regression) Smoothing Splines via
        Linear Programming and Sparse Matrices.
A 'shiny' app to discover cocktails. The
    app allows one to search for cocktails by ingredient,
    filter on rating, and number of ingredients. The
    package also contains data with the ingredients of
    nearly 26 thousand cocktails scraped from the web.
A copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2016). "Clustering dependent observations with copula functions". Statistical Papers, p.1-17. <doi:10.1007/s00362-016-0822-3>.
Allows for pooled analysis of microarray data by batch-correcting control samples, and then applying the derived correction parameters to non-control samples to obtain bias-free, inter-dataset corrected data.
Statistical tests for the comparison between two correlations
    based on either independent or dependent groups. Dependent correlations can
    either be overlapping or nonoverlapping. A web interface is available on the
    website http://comparingcorrelations.org. A plugin for the R GUI and IDE RKWard
    is included. Please install RKWard from https://rkward.kde.org to use this
    feature. The respective R package 'rkward' cannot be installed directly from a
    repository, as it is a part of RKWard.
The algorithm extracts shared variation from a collection of data sets using regression models.
Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.
Statistical tests for the comparison between two or more alpha
    coefficients based on either dependent or independent groups of individuals.
    A web interface is available at http://comparingcronbachalphas.org. A plugin
    for the R GUI and IDE RKWard is included. Please install RKWard from https://
    rkward.kde.org to use this feature. The respective R package 'rkward' cannot be
    installed directly from a repository, as it is a part of RKWard.
Provides functions for summarizing and plotting the
	output from Markov Chain Monte Carlo (MCMC) simulations, as
	well as diagnostic tests of convergence to the equilibrium
	distribution of the Markov chain.
A minimum set of functions to perform compositional data analysis
    using the log-ratio approach introduced by John Aitchison (1982) <http://www.jstor.org/stable/2345821>. Main functions
    have been implemented in c++ for better performance.
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.
Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis.
Tools for analyzing R expressions
  or blocks of code and determining the dependencies between them.
  It focuses on R scripts, but can be used on the bodies of functions.
  There are many facilities including the ability to summarize  or get a high-level
  view of code, determining dependencies between variables,  code improvement
  suggestions.
The 'Codemeta' Project defines a 'JSON-LD' format for describing
  software metadata, as detailed at <https://codemeta.github.io>. This package
  provides utilities to generate, parse, and modify 'codemeta.json' files 
  automatically for R packages, as well as tools and examples for working with
  'codemeta.json' 'JSON-LD' more generally.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
Augment clinical data with metadata to create
    output used in conventional publications and reports.
Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.
A collection of coding functions as alternatives to the standard
    functions in the stats package, which have names starting with 'contr.'.  Their
    main advantage is that they provide a consistent method for defining marginal
    effects in factorial models.  In a simple one-way ANOVA model the
    intercept term is always the simple average of the class means.
Univariate and multivariate temporal and spatial diversity indices, 
    rank abundance curves, and community stability measures. The functions 
    implement measures that are either explicitly temporal and include the 
    option to calculate them over multiple replicates, or spatial and include 
    the option to calculate them over multiple time points. Functions fall into 
    five categories: static diversity indices, temporal diversity indices, 
    spatial diversity indices, rank abundance curves, and community stability 
    measures. The diversity indices are temporal and spatial analogs to 
    traditional diversity indices. Specifically, the package includes functions 
    to calculate community richness, evenness and diversity at a given point in 
    space and time. In addition, it contains functions to calculate species 
    turnover, mean rank shifts, and lags in community similarity between two 
    time points.
Cronbach's alpha and McDonald's omega are widely used reliability or internal consistency measures in social, behavioral and education sciences. Alpha is reported in nearly every study that involves measuring a construct through multiple test items. The package 'coefficientalpha' calculates coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Version 0.5 added the bootstrap confidence intervals.
Plots the coefficients from model objects.  This very quickly shows the user the point estimates and confidence intervals for fitted models.
Simulate species occurrence and abundances (counts) along
    gradients.
Simulates the composition of samples of vegetation
        according to gradient-based vegetation theory.  Features a
        flexible algorithm incorporating competition and complex
        multi-gradient interaction.
species coexistence modeling under asymmetric dispersal
        and fluctuating source-sink dynamics;testing the proportion of
        coexistence scenarios driven by neutral and niche processes
Contrast analysis for factorial designs is an alternative to the classical ANOVA approach with the advantage of testing focused hypothesis. The method is mainly based on Rosenthal, Rosnow and Rubin (2000, ISBN:978-0521659802) and Sedlmeier and Renkewitz (2018, ISBN:978-3868943214).
Generate cofeature (feature by sample) matrices. The package 
  utilizes ggplot2::geom_tile() to generate the matrix allowing for easy
  additions from the base matrix.
Calculates complete functional regulation analysis and visualize
    the results in a single heatmap. The provided example data is for biological
    data but the methodology can be used for large data sets to compare quantitative
    entities that can be grouped. For example, a store might divide entities into
    cloth, food, car products etc and want to see how sales changes in the groups
    after some event. The theoretical background for the calculations are provided
    in New insights into functional regulation in MS-based drug profiling, Ana Sofia
    Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports <doi:10.1038/srep18826>.
Evaluation for density and distribution function of convolution of gamma
    distributions in R. Two related exact methods and one approximate method are
    implemented with efficient algorithm and C++ code. A quick guide for choosing
    correct method and usage of this package is given in package vignette. For the
    detail of methods used in this package, we refer the user to
    Mathai(1982)<doi:10.1007/BF02481056>,
    Moschopoulos(1984)<doi:10.1007/BF02481123>,
    Hu et al.(2019)<doi:10.1007/s00180-019-00924-9>.
Functions for building cognitive maps based on qualitative data. Inputs are textual sources (articles, transcription of qualitative interviews of agents,...). These sources have been coded using relations and are linked to (i) a table describing the variables (or concepts) used for the coding and (ii) a table describing the sources (typology of agents, ...). Main outputs are Individual Cognitive Maps (ICM), Social Cognitive Maps (all sources or group of sources) and a list of quotes linked to relations. This package is linked to the work done during the PhD of Frederic M. Vanwindekens (CRA-W / UCL) hold the 13 of May 2014 at University of Louvain in collaboration with the Walloon Agricultural Research Centre (project MIMOSA, MOERMAN fund).
Functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes 'Epi' package 'Lexis' data.
Designed for the cleaning, contextualization and assessment of cytochrome c oxidase I DNA barcode data (COI-5P, or the five prime portion of COI). 
    It contains functions for placing COI-5P barcode sequences into a common reading frame, translating DNA sequences to amino acids and for assessing 
    the likelihood that a given barcode sequence includes an insertion or deletion error. The error assessment relies on the comparison of input sequences
    against nucleotide and amino acid profile hidden Markov models (for details see Durbin et al. 1998, ISBN: 9780521629713) trained on a taxonomically 
    diverse set of reference sequences. The functions are provided as a complete pipeline and are also available individually for efficient and targeted
    analysis of barcode data.
Copula based imputation method. A semiparametric imputation procedure for missing multivariate data based on conditional copula specifications.
Conditional inference procedures for the general independence
  problem including two-sample, K-sample (non-parametric ANOVA), correlation,
  censored, ordered and multivariate problems.
Extract real-time Bitcoin price details by accessing 'CoinDesk' Bitcoin price Index API <https://www.coindesk.com/api/>.
Extract and monitor price and market cap of 'Cryptocurrencies' from 'Coin Market Cap' <https://coinmarketcap.com/api/>.
Methods for obtaining simultaneous confidence interval for
        multinomial proportion have been proposed by many authors and
        the present study include a variety of widely applicable
        procedures. Seven classical methods (Wilson, Quesenberry and
        Hurst, Goodman, Wald with and without continuity correction,
        Fitzpatrick and Scott, Sison and Glaz) and Bayesian Dirichlet
        models are included in the package. The advantage of MCMC pack
        has been exploited to derive the Dirichlet posterior directly
        and this also helps in handling the Dirichlet prior parameters.
        This package is prepared to have equal and unequal values for
        the Dirichlet prior distribution that will provide better scope
        for data analysis and associated sensitivity analysis.
Builds the 
  coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016) 
  <doi:10.1016/j.spl.2015.11.008>.
  This methodology studies the relationship between a couple of
  time series based on the the set of turning points of each
  time series. The coincident profile establishes if two time
  series are coincident, or one of them leads the second.
We propose a consistent monitoring procedure to detect a
    structural change from a cointegrating relationship to a spurious
    relationship. The procedure is based on residuals from modified least
    squares estimation, using either Fully Modified, Dynamic or Integrated
    Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in
    that it is based on parameter estimation on a pre-break "calibration" period
    only, rather than being based on sequential estimation over the full sample.
    See the discussion paper <DOI:10.2139/ssrn.2624657> for further information.
    This package provides the monitoring procedures for both the cointegration
    and the stationarity case (while the latter is just a special case of the
    former one) as well as printing and plotting methods for a clear
    presentation of the results.
Cointegration methods are widely used in empirical macroeconomics
    and empirical finance. It is well known that in a cointegrating
    regression the ordinary least squares (OLS) estimator of the
    parameters is super-consistent, i.e. converges at rate equal to the
    sample size T. When the regressors are endogenous, the limiting
    distribution of the OLS estimator is contaminated by so-called second
    order bias terms, see e.g. Phillips and Hansen (1990) <DOI:10.2307/2297545>.
    The presence of these bias terms renders inference difficult. Consequently,
    several modifications to OLS that lead to zero mean Gaussian mixture
    limiting distributions have been proposed, which in turn make
    standard asymptotic inference feasible. These methods include
    the fully modified OLS (FM-OLS) approach of Phillips and Hansen
    (1990) <DOI:10.2307/2297545>, the dynamic OLS (D-OLS) approach of Phillips
    and Loretan (1991) <DOI:10.2307/2298004>, Saikkonen (1991)
    <DOI:10.1017/S0266466600004217> and Stock and Watson (1993)
    <DOI:10.2307/2951763> and the new estimation approach called integrated
    modified OLS (IM-OLS) of Vogelsang and Wagner (2014)
    <DOI:10.1016/j.jeconom.2013.10.015>. The latter is based on an augmented
    partial sum (integration) transformation of the regression model. IM-OLS is
    similar in spirit to the FM- and D-OLS approaches, with the key difference
    that it does not require estimation of long run variance matrices and avoids
    the need to choose tuning parameters (kernels, bandwidths, lags). However,
    inference does require that a long run variance be scaled out.
    This package provides functions for the parameter estimation and inference
    with all three modified OLS approaches. That includes the automatic
    bandwidth selection approaches of Andrews (1991) <DOI:10.2307/2938229> and
    of Newey and West (1994) <DOI:10.2307/2297912> as well as the calculation of
    the long run variance.
Performs regression analysis for longitudinal count data,  
   allowing for serial dependence among observations from a given 
   individual and two dimensional random effects on the linear predictor. 
   Estimation is via maximization of the exact likelihood of a suitably 
   defined model. Missing values and unbalanced data are allowed; 
   M. Helena Goncalves et al.(2007) <doi:10.1016/j.csda.2007.03.002>.
Performs least squares constrained optimization on a linear objective function. It contains
    a number of algorithms to choose from and offers a formula syntax similar to lm().
Implements a generalized version of the CDH test (<DOI:10.1371/journal.pone.0028145> and <DOI:10.1186/s12859-016-1006-9>)
     for detecting compound heterozygosity on a
    genome-wide level, due to usage of generalized linear models it allows flexible
    analysis of binary and continuous traits with covariates.
An update to the previous version ( 0.1.0 ) of the package.
    Functions in the previous version required the binary outcome variable of the data set to be strictly a factor.In version 0.2.0  the binary outcome variable may be both a factor (with two levels) or an integer (or numeric ).
    All functions of the previous version have been retained.No new functions have been added.
    Like the previous version,provides functions to collapse the levels of an attribute based on response rates.
    It also provides functions to compute and display information value, and weight of evidence (WoE) for the attributes, and to convert numeric variables to categorical ones by binning.
    These functions only work for binary classification problems.
Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it.
    Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame.
    See 'collapsibleTree' website for more information and examples.
The purrr package allows you to capture the side effects (errors, warning, messages and other output) of functions using safely() and quietly(). Using collateral, you can quickly see which elements of a list (or list-column) returned results, which threw errors and which returned warnings or other output.
We often want to take all (or most) of the objects in one environment (such as the parameter values of a function) and pass them to another. This might be calling a second function, or iterating over a list, calling the same function. These functions wrap often repeated code.
    Current stable version (committed on October 14, 2017).
Provides high performance container data types such as Queue,
    Stack, Deque, Dict and OrderedDict. Benchmarks
    <https://randy3k.github.io/collections/articles/benchmark.html> have
    shown that these containers are asymptotically more efficient than
    those offered by other packages.
Computation of Colless-Like, Sackin and cophenetic balance indices of a phylogenetic tree and study of the distribution of these balance indices under the alpha-gamma model. For more details see A. Mir, F. Rossello, L. Rotger (2013) <doi:10.1016/j.mbs.2012.10.005> and (2018) <doi:10.1371/journal.pone.0203401>, M. J. Sackin (1972) <doi:10.1093/sysbio/21.2.225>, D. H. Colless (1982) <doi:10.2307/2413420>.
Check for namespace collisions between a string input (your function or package name) and a quarter of a million packages and functions on CRAN.
These functions implement collocation-inference
    for continuous-time and discrete-time stochastic processes.
    They provide model-based smoothing, gradient-matching,
    generalized profiling and forwards prediction error methods.
Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.
Provides some low level functions for processing PLINK input and output files.
Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21.
Performs the colocalisation tests described in
    Plagnol et al (2009) <doi:10.1093/biostatistics/kxn039>,
    Wallace et al (2013) <doi:10.1002/gepi.21765> and
    Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>.
Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearson’s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.
Also abbreviates to "CCSeq". Finds clusters of colocalized sequences in .bed annotation files
    up to a specified cut-off distance. Two sequences are colocalized if they
    are within the cut-off distance of each other, and clusters are sets of
    sequences where each sequence is colocalized to at least one other sequence
    in the cluster. For a set of .bed annotation tables provided in a list 
    along with a cut-off distance, the program will output a file containing
    the locations of each cluster. Annotated .bed files are from 
    the 'pwmscan' application at <https://ccg.epfl.ch/pwmtools/pwmscan.php>. 
    Personal machines might crash or take excessively long depending 
    on the number of annotated sequences in each file and whether chromsearch() 
    or gensearch() is used.
Automate the co-localization analysis of fluorescence microscopy 
  images. Selecting regions of interest, extract pixel intensities from 
  the image channels and calculate different co-localization statistics. The
  methods implemented in this package are based on Dunn et al. (2011) 
  <doi:10.1152/ajpcell.00462.2010>.
Loads and displays images, selectively masks specified background
    colors, bins pixels by color using either data-dependent or
    automatically generated color bins, quantitatively measures color
    similarity among images using one of several distance metrics for
    comparing pixel color clusters, and clusters images by object color
    similarity. Uses CIELAB, RGB, or HSV color spaces. Originally written
    for use with organism coloration (reef fish color diversity, butterfly
    mimicry, etc), but easily applicable for any image set.
It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively.
Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one.
    This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation).  The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.
Extracts colors from various image types, returns customized reports and plots treemaps 
    and 3D scatterplots of image compositions. Color palettes can also be created.
Given 2-,3- or 4-dimensional data, plots a Venn diagram, i.e. 'crossing circles'. The user can specify values, labels for each circle-group and unique colors for each plotted part. Here is what it would look like for a 3-dimensional plot: http://elliotnoma.files.wordpress.com/2011/02/venndiagram.png. To see what the 4-dimensional plot looks like, go to http://elliotnoma.files.wordpress.com/2013/03/4dplot.png.
Build dendrograms with sample groups highlighted by different colors. Visualize results of hierarchical clustering analyses as dendrograms whose leaves and labels are colored according to sample grouping. Assess whether data point grouping aligns to naturally occurring clusters.
Allows to generate colors from palettes defined in the colormap module of 'Node.js'. (see <https://github.com/bpostlethwaite/colormap> for more information). In total it provides 44 distinct palettes made from sequential and/or diverging colors. In addition to the pre defined palettes you can also specify your own set of colors. There are also scale functions that can be used with 'ggplot2'.
Different methods to generate a color palette based on a specified base color and a number of colors that should be created.
Shows color patches for encoding fold changes (e.g. log ratios) together with confidence values 
    within a single diagram. This is especially useful for rendering gene expression data as well as
    other types of differential experiments. In addition to different rendering methods (ggplot extensions)
    functionality for perceptually optimizing color palettes are provided.
    Furthermore the package provides extension methods of the colorspace color-class in order to
    simplify the work with palettes (a.o. length, as.list, and append are supported).
Color palettes for EPL, MLB, NBA, NHL, and NFL teams.
Methods and data for color science - color conversions by observer,
        illuminant, and gamma. Color matching functions and chromaticity diagrams.
        Color indices, color differences, and spectral data conversion/analysis.
Carries out mapping between assorted color spaces including RGB, HSV, HLS,
             CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB.
	     Qualitative, sequential, and diverging color palettes based on HCL colors
	     are provided along with corresponding ggplot2 color scales.
	     Color palette choice is aided by an interactive app (with either a Tcl/Tk
	     or a shiny GUI) and shiny apps with an HCL color picker and a
	     color vision deficiency emulator. Plotting functions for displaying
	     and assessing palettes include color swatches, visualizations of the
	     HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation
	     functions include: desaturation, lightening/darkening, mixing, and
	     simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly).
Calculate with spectral properties of light sources, materials, cameras, eyes, and scanners.
    Build complex systems from simpler parts using a spectral product algebra. For light sources,
    compute CCT, CRI, and SSI.  For object colors, compute optimal colors and Logvinenko coordinates.
    Work with the standard CIE illuminants and color matching functions, and read spectra from 
    text files, including CGATS files.  Estimate a spectrum from its response. A user guide and 9 vignettes are included.
R package with handy functions to help users select and play with
    color schemes in an HSV color model
Provides access to the COLOURlovers <http://www.colourlovers.com/>
    API, which offers color inspiration and color palettes.
A colour picker that can be used as an input in Shiny apps
    or Rmarkdown documents. The colour picker supports alpha opacity, custom
    colour palettes, and many more options. A Plot Colour Helper tool is
    available as an RStudio Addin, which helps you pick colours to use in your
    plots. A more generic Colour Picker RStudio Addin is also provided to let 
    you select colours to use in your R code.
Maps one of the viridis colour palettes, or a user-specified palette to values. 
    Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith. 
    They were set as the default palette for the 'Python' 'Matplotlib' library, 
    introduced at SciPy 2015 conference <http://scipy2015.scipy.org/ehome/index.php?eventid=115969&>. 
    Other palettes available in this library have been derived from 
    'RColorBrewer' <https://CRAN.R-project.org/package=RColorBrewer> and 
    'colorspace' <https://CRAN.R-project.org/package=colorspace> packages.
Colour vision models, colour spaces and colour thresholds. Provides flexibility to build user-defined colour vision models for n number of photoreceptor types. Also includes Vorobyev & Osorio (1998) Receptor Noise Limited models, Chittka (1992) colour hexagon, and Endler & Mielke (2005) model. Models have been extended to accept any number of photoreceptor types.
Powerful functions to select and rename columns in dataframes, lists and numeric types 
  by 'Perl' regular expression. Regular expression ('regex') are a very powerful grammar to match 
  strings, such as column names.
A collection of command-line color styles based on the 'crayon'
  package. 'Colt' styles are defined in themes that can easily be switched, to
  ensure command line output looks nice on dark as well as light consoles.
Builds co-occurrence matrices based on spatial raster data.
    It includes creation of weighted co-occurrence matrices (wecoma) and 
    integrated co-occurrence matrices 
    (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).
Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps).
Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal.  Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>.
A comprehensive synergy/antagonism analyses of drug combinations with 
		quality graphics and data. The analyses can be performed by Bliss 
		independence and Loewe additivity models. 'COMBIA' provides improved 
		statistical analysis and makes only very weak assumption of data 
		variability while calculating bootstrap intervals (BIs). It is based
		on heteroscedasticity controlled resampling (bootstrapping) and includes
		a global (omnibus) test. Finally, package shows analyzed data, 2D and 3D
	 	plots ready to use in research publications. 'COMBIA' does not require manual
		data entry. Data can be directly input from wet lab experimental platforms for
	 	example fluostar, automated robots etc. One only needs to call a single function, 
		analyzeCOMBO(), to perform all analysis (examples are provided with sample data).
Estimation of optimal portfolio weights as combination of simple portfolio strategies, like the tangency, global minimum variance (GMV) or naive (1/N) portfolio. It is based on a utility maximizing 8-fund rule. Popular special cases like the Kan-Zhou(2007) 2-fund and 3-fund rule or the Tu-Zhou(2011) estimator are nested.
We offer two statistical tests to combine p-values: selfcontained.test vs competitive.test. The goal is to test whether a vector of pvalues are jointly significant when we combine them together.
Series of partially balanced incomplete block designs (PBIB) based on the combinatory method (S) introduced in (Imane Rezgui et al, 2014) <doi:10.3844/jmssp.2014.45.48>; and it gives their associated U-type design.
Provides iterators for combinations, permutations, subsets, and
    Cartesian product, which allow one to go through all elements without creating a
    huge set of all possible values.
Functions for computing optimal convex combinations of
        model selection criteria based on ranks, along with utility
        functions for constructing model lists, MSCs, and priors on
        model lists.
Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.
An algorithm for identifying combinations of mutually exclusive alterations in cancer genomes. CoMEt represents the mutations in a set M of k genes with a 2^k dimensional contingency table, and then computes the tail probability of observing T(M) exclusive alterations using an exact statistical test.
Functions to calculate various common and less common thermal comfort indices, convert physical variables, and evaluate the performance of thermal comfort indices.
Provided are Computational methods for Immune Cell-type Subsets, including:(1) DCQ (Digital Cell Quantifier) to infer global dynamic changes in immune cell quantities within a complex tissue; and (2) VoCAL (Variation of Cell-type Abundance Loci) a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci.
An S4 representation of the Command design pattern. The
  Operation class is a simple implementation using closures and supports
  forward and reverse (undo) evaluation. The more complicated Protocol
  framework represents each type of command (or analytical protocol) by
  a formal S4 class. Commands may be grouped and consecutively executed
  using the Pipeline class. Example use cases include logging, do/undo,
  analysis pipelines, GUI actions, parallel processing, etc.
Autosimilarity curves, standardization of spatial extent, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. This should be a complement to available packages, particularly 'vegan'.
Functions to
    produce nicely formatted comments to use in R-scripts (or
    Latex/HTML/markdown etc). A comment with formatting is printed to the
    console and can then be copied to a script.
Useful libraries for building a Java based GUI under R are provided.
The CommonMark specification defines a rationalized version of markdown
    syntax. This package uses the 'cmark' reference implementation for converting
    markdown text into various formats including html, latex and groff man. In
    addition it exposes the markdown parse tree in xml format. Also includes opt-in
    support for GFM extensions including tables, autolinks, and strikethrough text.
Estimate bivariate common mean vector under copula models with known correlation. In the current version, available copulas include the Clayton, Farlie-Gumbel-Morgenstern (FGM), and Gaussian copulas. See Shih et al. (2019) <doi:10.1080/02331888.2019.1581782> for details under the FGM copula.
Java JAR files for the Apache Commons Mathematics Library for use by users and other packages.
Directly extract and plot stochastic common trends from
        a cointegration system using different approaches, currently
        including Kasa (1992) and Gonzalo and Granger (1995). 
	The approach proposed by Gonzalo and Granger, also known as
        Permanent-Transitory Decomposition, is widely used in
        macroeconomics and market microstructure literature. 
	Kasa's approach, on the other hand, has a nice property that it only
        uses the super consistent estimator: the cointegration vector
        'beta'. 
	This package also provides functions calculate P-value
        from Johansen Statistics according to the approximation method
        proposed by Doornik (1998).
	Update:
	0.7-1: Fix bugs in calculation alpha. Add formulas and more explanations.
        0.6-1: Rewrite the description file.
        0.5-1: Add functions to calculate P-value from Johansen statistic, and vice versa.
Provides functions to measure the difference between constrained and unconstrained gene tree distributions using various tree distance metrics. Constraints are enforced prior to this analysis via the estimation of a tree under the community tree model.
The CommunityCorrelogram package is designed for the geostatistical analysis of ecological community datasets with either a spatial or temporal distance component.
Computing comorbidity scores such as the weighted Charlson score
  (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser
  comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>)
  using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>).
Comparison of two ROC curves through the methodology proposed by Ana C. Braga.
Creates empty plots with compact axis notation to which users can
    add whatever they like (points, lines, text, etc.) The notation is more
    compact in the sense that the axis-labels and tick-labels are closer to the
    axis and the tick-marks are shorter. Also, if the plot appears as part of a
    matrix, the x-axis notation is suppressed unless the plot appears along the
    bottom row and the y-axis notation is suppress unless the plot appears
    along the left column.
Functions to compare a model object to a comparison object.
  If the objects are not identical, the functions can be instructed to
  explore various modifications of the objects (e.g., sorting rows,
  dropping names) to see if the modified versions are identical.
Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.
Unified interface for the estimation of causal networks, including
    the methods 'backShift' (from package 'backShift'), 'bivariateANM' (bivariate
    additive noise model), 'bivariateCAM' (bivariate causal additive model),
    'CAM' (causal additive model) (from package 'CAM'), 'hiddenICP' (invariant
    causal prediction with hidden variables), 'ICP' (invariant causal prediction)
    (from package 'InvariantCausalPrediction'), 'GES' (greedy equivalence
    search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC
    Algorithm), 'FCI' (fast causal inference), 
    'RFCI' (really fast causal inference) (all from package 'pcalg') and
    regression.
Compares two dataframes which have the same column
    structure to show the rows that have changed. Also gives a git style diff format
    to quickly see what has changes in addition to summary statistics.
Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
Input: 2 ODM files (ODM version 1.3) Output: list of
        identical, matching, similar and differing data items
Quickly run experiments to compare the run time and output of
    code blocks. The function mbc() can make fast comparisons of code,
    and will calculate statistics comparing the resulting outputs.
    It can be used to compare model fits to the same data or
    see which function runs faster.
    The function ffexp() runs a function using all possible combinations
    of selected inputs. This is useful for comparing the effect of
    different parameter values. It can also run in parallel and
    automatically save intermediate results, which is very useful
    for long computations.
A standard test is observed on all specimens.  We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens.  Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control.  We treat the total sample as stratified two-phase sampling and use inverse probability weighting.  We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)).  See: Katki HA, Li Y, Edelstein DW, Castle PE.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations
Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) <doi:10.1371/journal.pone.0116774>, fixed-point tests in Klein et al. (2007) <doi:10.1002/sim.2864>, short-term tests, and long-term tests in Logan et al. (2008) <doi:10.1111/j.1541-0420.2007.00975.x>. Some commonly used descriptive statistics and plots are also included.
Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.
C++ implementation of component-wise boosting implementation of component-wise 
  boosting written in C++ to obtain high runtime performance and full memory control. The main 
  idea is to provide a modular class system which can be extended without editing the source code. 
  Therefore, it is possible to use R functions as well as C++ functions for custom base-learners, 
  losses, logging mechanisms or stopping criteria.
Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user.  Also fits the user specified distribution to a given data set.  More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017)  CompDist: Multisection composite distributions.
The package enables to compute event-specific incidence
        rates for competing risks data, to compute rate ratios,
        event-specific incidence proportions and cumulative incidence
        functions from these, and to plot these in a comprehensive
        multi-state type graphic.
Package for the systematic retrieval and storage of
    functional genomics data via a MySQL database.
Compute ranking and rating based on competition results. Methods of
    different nature are implemented: with fixed Head-to-Head structure, with
    variable Head-to-Head structure and with iterative nature. All algorithms
    are taken from the book 'Who’s #1?: The science of rating and ranking' by
    Amy N. Langville and Carl D. Meyer (2012, ISBN:978-0-691-15422-0).
Tools for storing and managing competition results.
    Competition is understood as a set of games in which players gain some
    abstract scores.  There are two ways for storing results: in long (one
    row per game-player) and wide (one row per game with fixed amount of
    players) formats. This package provides functions for creation and
    conversion between them. Also there are functions for computing their
    summary and Head-to-Head values for players. They leverage grammar of
    data manipulation from 'dplyr'.
A graphical user interface for simulating the effects of mergers, tariffs, and quotas under an
             assortment of different economic models. The interface is powered by the 'Shiny' web application framework from 
             'RStudio'.
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
Contains functions to enhance approaches to the Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps.
Retail shopping transactions for 2,469 households over one year.
    Originates from the 84.51° Complete Journey 2.0 source files 
    <https://www.8451.com/area51> which also includes useful metadata on 
    products, coupons, campaigns, and promotions.
Allows for the easy computation of complexity: the proportion of the parameter space in line with the hypothesis by chance. The package comes with a Shiny application in which the calculations can be conducted as well.
Extension of several functions to the complex domain, including the matrix exponential and logarithm, and the determinant.
Robust regression methods for compositional data.
    The distribution of the estimates can be approximated with various bootstrap
    methods. These bootstrap methods are available for the compositional as well
    as for standard robust regression estimates. This allows for direct
    comparison between them.
Computes the probability density function, cumulative distribution function, quantile function, random numbers of any composite model based on the lognormal distribution.
Provides routines for density and moments of the
        Conway-Maxwell-Poisson distribution as well as functions for
        fitting the COM-Poisson model for over/under-dispersed count
        data.
Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models
    to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The
    package provides functions for model estimation, dispersion testing, and
    diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016)
    <doi:10.1016/j.csda.2016.01.007> is also supported.
Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include a) Tsagris M.T., Preston S. and Wood A.T.A. (2011) A data-based power transformation for compositional data. Fourth International International Workshop on Compositional Data Analysis. b) Tsagris M. (2014). The k-NN algorithm for compositional data: a revised approach with and without zero values present. Journal of Data Science, 12(3):519--534. c) Tsagris M. (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. d) Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2):47--57. e) Tsagris M., Preston S. and Wood A.T.A. (2016). Improved supervised classification for compositional data using the alpha-transformation. Journal of Classification, 33(2):243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554> g) Tsagris M. and Stewart C. (2018). A Dirichlet regression model for compositional data with zeros. Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). Regression for compositional data with compositional data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. Tsagris M. and Stewart C. (2019). A folded model for compositional data analysis. <arXiv:1802.07330>. Furher, we include functions for percentages (or proportions).
Provides functions for the consistent analysis of compositional 
  data (e.g. portions of substances) and positive numbers (e.g. concentrations) 
  in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.
Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions).
 Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>,
 statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>,
 Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al. (2019)<DOI:10.1016/j.cmpb.2018.10.020>.
 Algorithms for generating correlated gene expressions are also available.
Computing Continuous Distributions Obtained by Compounding
        a Continuous and a Discrete Distribution
Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.
Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful properties similar to those of the standard likelihood.
Provides 'Python'-style list comprehensions.
    List comprehension expressions use usual loops (for(), while() and repeat()) and
    usual if() as list producers. In many cases it gives more concise notation than
    standard "*apply + filter" strategy.
Provides a set of tools for creating yearly data sets of St. Louis 
    Metropolitan Police Department (SLMPD) crime data, which are available from
    January 2008 onward as monthly CSV releases on their website 
    (<http:www.slmpd.org/Crimereports.shtml>). Once data are validated and created 
    (monthly data releases have varying numbers of columns 
    as well as different column names and formats), 'compstatr' also provides 
    functions for categorizing and mapping crimes in St. Louis. The categorization 
    tools that are provided will also work with any police department that uses 5 
    and 6 digit numeric codes to identify specific crimes. These data provide researchers
    and policy makers detailed data for St. Louis, which in the last several years 
    has had some of the highest or the highest violent crime rates in the United States.
This package contains several functions for calculating the most
    widely used effect sizes (ES), along with their variances, confidence
    intervals and p-values.  The output includes ES's of d (mean difference), g
    (unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and
    OR (odds ratio and log odds ratio). In addition, NNT (number needed to
    treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are
    computed. This package uses recommended formulas as described in The
    Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, &
    Valentine, 2009).
Interface with and extract data from the United Nations Comtrade 
  API <https://comtrade.un.org/data/>. Comtrade provides country level shipping 
  data for a variety of commodities, these functions allow for easy API query 
  and data returned as a tidy data frame.
To calculate the AQI (Air Quality Index) from pollutant concentration data.
    O3, PM2.5, PM10, CO, SO2, and NO2 are available currently.
    The method can be referenced at Environmental Protection Agency, United States as follows:
    EPA (2016) <https://www3.epa.gov/airnow/aqi-technical-assistance-document-may2016.pdf>.
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Simple functions for joining strings. Construct human-friendly messages whose elements aren't known in advance, like in stop, warning, or message, from clean code.
The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.
There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
The four functions svdcp (cp for column partitioned),
        svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for
        a simultaneous optimization of one set of r solutions),
        correspond to a "SVD by blocks" notion, by supposing each block
        depending on relative subspaces, rather than on two whole
        spaces as usual SVD does. The other functions, based on this
        notion, are relative to two column partitioned data matrices x
        and y defining two sets of subsets xi and yj of variables and
        amount to estimate a link between xi and yj for the pair (xi,
        yj) relatively to the links associated to all the other pairs.
A set of utilities for matching products in different classification codes used in international trade research. It supports concordance between HS (Combined), ISIC Rev. 2,3, and SITC1,2,3,4 product classification codes, as well as BEC, NAICS, and SIC classifications. It also provides code nomenclature / descriptions look-up, Rauch classification look-up (via concordance to SITC2) and trade elasticity look-up (via concordance to SITC2/3 or HS3.ss).
Implements concordance regression which can be used to estimate generalized odds of concordance.
	Can be used for non- and semi-parametric survival analysis with non-proportional hazards, for binary and 
    for continuous outcome data.
Allows one to compute consonance (confidence)
    intervals for various statistical tests along with their corresponding
    P-values, S-values, and likelihoods. The intervals can be plotted to
    create consonance, surprisal, and likelihood functions allowing one to
    see what effect sizes are compatible with the test model at various
    consonance levels rather than being limited to one interval estimate
    such as 95%. These methods are discussed by Poole C. (1987)
    <doi:10.2105/AJPH.77.2.195>, Schweder T, Hjort NL. (2002)
    <doi:10.1111/1467-9469.00285>, Singh K, Xie M, Strawderman WE. (2007)
    <arXiv:0708.0976>, Rothman KJ, Greenland S, Lash TL. (2008,
    ISBN:9781451190052), Amrhein V, Trafimow D, Greenland S. (2019)
    <doi:10.1080/00031305.2018.1543137>, Greenland S. (2019)
    <doi:10.1080/00031305.2018.1529625>, Chow ZR, Greenland S. (2019)
    <arXiv:1909.08579>, and Greenland S, Chow ZR. (2019)
    <arXiv:1909.08583>.
Higher order likelihood-based inference for logistic and 
  loglinear models.
Apply and visualize conditional formatting to data frames in R.
    It renders a data frame with cells formatted according to
    criteria defined by rules, using a tidy evaluation syntax. The table is
    printed either opening a web browser or within the 'RStudio' viewer if
    available. The conditional formatting rules allow to highlight cells
    matching a condition or add a gradient background to a given column. This
    package supports both 'HTML' and 'LaTeX' outputs in 'knitr' reports, and
    exporting to an 'xlsx' file.
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.
Code for a variety of nonlinear conditional independence tests: 
  Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>),
  Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>),
  Invariant environment prediction,
  Invariant target prediction,
  Invariant residual distribution test,
  Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).
Set of functions for the easy analyses of conditioning data.
Provides ability to control how many times in function
    calls conditions are thrown (shown to the user). Includes control of
    warnings and messages.
Computes conditional multivariate normal probabilities, random deviates and densities.
An implementation of the computational strategy for the
    comprehensive analysis of condition-dependent operon maps in prokaryotes
    proposed by Fortino et al. (2014) <DOI:10.1186/1471-2105-15-145>. 
    It uses RNA-seq transcriptome profiles to improve prokaryotic operon map inference.
Method to implement some newly developed methods for the
    estimation of the conditional survival function.
Runs a function iteratively over each row of either a dataframe 
    or the results of a query.  Use the 'BigQuery' and 'DBI' wrappers to 
    iteratively pass each row of query results to a function.  If a field 
    contains a 'JSON' string, it will be converted to an object.  This is 
    helpful for queries that return 'JSON' strings that represent objects.  
    These fields can then be treated as objects by the pipeline.
Exploring fitted models by interactively taking 2-D and 3-D
  sections in data space.
Constructs a shiny app function with interactive displays for conditional visualization of models, 
     data and density functions. An extended version of package 'condvis'. 
     Mark O'Connell, Catherine B. Hurley, Katarina Domijan (2017) <doi:10.18637/jss.v081.i05>.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.
Enables: (1) plotting two-dimensional confidence regions, (2) coverage analysis
  of confidence region simulations and (3) calculating confidence intervals and the associated 
  actual coverage for binomial proportions. Each is given in greater detail next. 
  (1) Plots the two-dimensional confidence region for probability distribution parameters 
  (supported distribution suffixes: cauchy, gamma, invgauss, logis, llogis, lnorm, norm, unif, 
  weibull) corresponding to a user-given complete or right-censored dataset and level of 
  significance.  The crplot() algorithm plots more points in areas of greater curvature to 
  ensure a smooth appearance throughout the confidence region boundary.  An alternative 
  heuristic plots a specified number of points at roughly uniform intervals along its boundary. 
  Both heuristics build upon the radial profile log-likelihood ratio technique for plotting 
  confidence regions given by Jaeger (2016) <doi:10.1080/00031305.2016.1182946>, and
  are detailed in a publication by Weld (2019) <doi:10.1080/00031305.2018.1564696>. 
  (2) Performs confidence region coverage simulations for a random sample drawn from a user-
  specified parametric population distribution, or for a user-specified dataset and point of 
  interest with coversim(). (3) Calculates confidence interval bounds for a binomial 
  proportion with binomTest(), calculates the actual coverage with binomTestCoverage(), and 
  plots the actual coverage with binomTestCoveragePlot(). Calculates confidence interval 
  bounds for the binomial proportion using an ensemble of constituent confidence intervals 
  with binomTestEnsemble().
This small library contains a series of simple tools for
        constructing and manipulating confounded and fractional
        factorial designs.
Functions for estimating and reporting multi-year averages and
    corresponding confidence intervals and distributions. A potential use case
    is reporting the chemical and ecological status of surface waters according
    to the European Water Framework Directive.
Manage configuration values across multiple environments (e.g.
  development, test, production). Read values using a function that determines
  the current environment and returns the appropriate value.
Enhances the 'ini' package by adding the ability to interpolate variables.
	     The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module)
	     and the keys can be read, where '%(....)s' instances are interpolated by other included options or outside variables.
Implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package 'config'.
R functions for criterion profile analysis, Davison and Davenport (2002) <doi:10.1037/1082-989X.7.4.468> and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2019). Sensitivity analyses to aid in interpreting criterion profile analysis results are also included.
Produces descriptive interpretations of confidence intervals.
    Includes (extensible) support for various test types, specified as sets
    of interpretations dependent on where the lower and upper confidence limits
    sit. Provides plotting functions for graphical display of interpretations.
Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.
R's default conflict management system gives the most recently
    loaded package precedence. This can make it hard to detect conflicts, 
    particularly when they arise because a package update creates ambiguity
    that did not previously exist. 'conflicted' takes a different approach, 
    making every conflict an error and forcing you to choose which function 
    to use.
Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.
Conducts sensitivity analyses for unmeasured confounding in
    random-effects meta-analysis per Mathur & VanderWeele (in preparation).
    Given output from a random-effects meta-analysis with a relative risk
    outcome, computes point estimates and inference for: (1) the proportion
    of studies with true causal effect sizes more extreme than a specified threshold
    of scientific significance; and (2) the minimum bias factor and confounding
    strength required to reduce to less than a specified threshold the proportion
    of studies with true effect sizes of scientifically significant size.
    Creates plots and tables for visualizing these metrics across a range of bias values.
    Provides tools to easily scrape study-level data from a published forest plot or 
    summary table to obtain the needed estimates when these are not reported.
Implements three covariate-balance diagnostics for time-varying confounding and selection-bias in complex longitudinal data, as described in Jackson (2016) <doi:10.1097/EDE.0000000000000547> and Jackson (2019) <doi:10.1093/aje/kwz136>. Diagnostic 1 assesses measured confounding/selection-bias, diagnostic 2 assesses exposure-covariate feedback, and diagnostic 3 assesses residual confounding/selection-bias after inverse probability weighting or propensity score stratification. All diagnostics appropriately account for exposure history, can be adapted to assess a particular depth of covariate history, and can be implemented in right-censored data. Balance assessments can be obtained for all times, selected-times, or averaged across person-time. The balance measures are reported as tables or plots. These diagnostics can be applied to the study of multivariate exposures including time-varying exposures, direct effects, interaction, and censoring.
Offers several functions for Configural Frequencies
    Analysis (CFA), which is a useful statistical tool for the analysis of
    multiway contingency tables. CFA was introduced by G. A. Lienert as
    'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). 
    Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. 
    Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
For multiple testing.
    Computes estimates and confidence bounds for the
    False Discovery Proportion (FDP), the fraction of false positives among
    all rejected hypotheses.
    The methods in the package use permutations of the data. Doing so, they
    take into account the dependence structure in the data.
Downloads and tidies data from the Brazilian Federal Senate and
    Chamber of Deputies Application Programming Interfaces available at <http://
    legis.senado.gov.br/dadosabertos/> and <https://dadosabertos.camara.leg.br/>
    respectively.
Includes the 100 datasets simulated by Congreve and Lamsdell (2016) 
  <doi:10.1111/pala.12236>, and analyses of the partition and quartet distance of
  reconstructed trees from the generative tree, as analysed by Smith (2019) 
  <doi:10.1098/rsbl.2018.0632>.
Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.
plot conics (ellipses, hyperbolas, parabolas)
Provides a set of tools that can be used across 'data.frame' and
    'imputationList' objects.
This is a simple R package that allows to measure the stated preferences using traditional conjoint analysis method.
Implementation of a procedure (Domingue, 2012; see also
        Karabatsos, 2001 and Kyngdon, 2011) to test the single and
        double cancellation axioms of conjoint measure in data that is
        dichotomously coded and measured with error.
Enables 'DBI' compliant packages to integrate with the 'RStudio' connections 
  pane, and the 'pins' package. It automates the display of schemata, tables, views, as well 
  as the preview of the table's top 1000 records.
Collects several different methods for analyzing and
    working with connectivity data in R.  Though primarily oriented towards
    marine larval dispersal, many of the methods are general and useful for
    terrestrial systems as well.
Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.
Extends 'ACER ConQuest' by allowing R users to call 'ACER ConQuest' from within R. 
    The user can also access 'ACER ConQuest' data objects by reading 'ACER ConQuest' System Files (generated by the 'ACER ConQuest' command 'put').
    This is of particular use to those who are parsing text file output (e.g., 'show' files) as this is not consistent across releases of 'ACER ConQuest'.
    Requires 'ACER ConQuest' version 4.29.3 or later. A demonstration version can be downloaded from <https://shop.acer.edu.au/acer-conquest-4>.
Multi-species estimation of geographical range parameters
	for preliminary assessment of conservation status following Criterion B of the 
	International Union for Conservation of Nature (IUCN, 
	see <http://www.iucnredlist.org>).
Consonance Analysis is a useful numerical and graphical approach
    for evaluating the consistency of the measurements and the panel of people
    involved in sensory evaluation. It makes use of several uni and multivariate
    techniques either graphical or analytical. It shows the implementation of this
    procedure in a graphical interface.
Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline.  Function outputs fits and various types of inference.
Compute the median ranking according to the Kemeny's axiomatic approach. 
    Rankings can or cannot contain ties, rankings can be both complete or incomplete. 
    The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. 
    The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. 
    The package also provide some useful utilities for deal with preference rankings.
    This release declare as deprecated some functions that are still in the package for compatibility. Next release will not contains these functions.
    Please type '?ConsRank-deprecated'
    Essential references:
    Emond, E.J., and Mason, D.W. (2002) <doi:10.1002/mcda.313>; 
    D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) <doi:10.1285/i20705948v8n2p198>; 
    Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) <doi:10.1016/j.ejor.2015.08.048>; 
    D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) <doi:10.1016/j.cor.2017.01.017>.
CODATA internationally recommended values of the fundamental physical 
    constants, provided as symbols for direct use within the R language. Optionally, 
    the values with errors and/or the values with units are also provided if the 
    'errors' and/or the 'units' packages are installed. The Committee on Data
    for Science and Technology (CODATA) is an interdisciplinary committee of the
    International Council for Science which periodically provides the internationally 
    accepted set of values of the fundamental physical constants. This package 
    contains the "2014 CODATA" version, published on 25 June 2015:
    Mohr, P. J., Newell, D. B. and Taylor, B. N. (2016)
    <DOI:10.1103/RevModPhys.88.035009>, <DOI:10.1063/1.4954402>.
Examine any number of time series data frames to identify 
    instances in which various criteria are met within specified time
    frames. In clinical medicine, these types of events are often
    called "constellations of signs and symptoms", because a single 
    condition depends on a series of events occurring within a certain 
    amount of time of each other. This package was written to work with
    any number of time series data frames and is optimized for speed 
    to work well with data frames with millions of rows.
Provides functions for
        efficient computations of nonlinear spatial predictions with
        local change of support. This package supplies functions for
        tow-dimensional spatial interpolation by constrained,
        covariance-matching constrained and universal (external drift)
        kriging for points or block of any shape for data with a
        nonstationary mean function and an isotropic weakly stationary
        variogram. The linear spatial interpolation methods,
        constrained and covariance-matching constrained kriging,
        provide approximately unbiased prediction for nonlinear target
        values under change of support. This package
        extends the range of geostatistical tools available in R and
        provides a veritable alternative to conditional simulation for
        nonlinear spatial prediction problems with local change of
        support.
A method for modeling genetic data as a combination of discrete
    layers, within each of which relatedness may decay continuously with geographic
    distance. This package contains code for running analyses (which are implemented
    in the modeling language 'rstan') and visualizing and interpreting output. See the
    paper for more details on the model and its utility.
Process spatially- and temporally-discrete data into contact and 
   social networks, and facilitate network analysis by randomizing 
   individuals' movement paths and/or related categorical variables. To use 
   this package, users need only have a dataset containing spatial data 
   (i.e., latitude/longitude, or planar x & y coordinates), individual IDs 
   relating spatial data to specific individuals, and date/time information 
   relating spatial locations to temporal locations. The functionality of this 
   package ranges from data "cleaning" via multiple filtration functions, to 
   spatial and temporal data interpolation, and network creation and analysis. 
   Functions within this package are not limited to describing interpersonal 
   contacts. Package functions can also identify and quantify "contacts" 
   between individuals and fixed areas (e.g., home ranges, water bodies, 
   buildings, etc.). As such, this package is an incredibly useful resource 
   for facilitating epidemiological, ecological, ethological and sociological 
   research.
Common container data structures deque, set and dict (resembling
    'Python's dict type) with typical member functions to insert, delete and
    access container elements. Provides iterators and reference semantics.
Fits mixtures of multivariate contaminated normal distributions
        (with eigen-decomposed scale matrices) via the expectation conditional-
	maximization algorithm under a clustering or classification paradigm
	Methods are described in Antonio Punzo, Angelo Mazza, and Paul D McNicholas (2018) <doi:10.18637/jss.v085.i10>.
Facilitates the simulation and evaluation of context-free
    and contextual multi-Armed Bandit policies or algorithms to ease the
    implementation, evaluation, and dissemination of both existing and
    new bandit algorithms and policies.
Various utilities for evaluating continued fractions.
Bayesian analysis of complete and incomplete contingency tables.
Create contour lines for a non regular series of points, potentially from a non-regular canvas.
Provides functions for making contour plots.
  The contour plot can be created from grid data, a function,
  or a data set. If non-grid data is given, then a Gaussian
  process is fit to the data and used to create the contour plot.
One degree of freedom contrasts for lm, glm, gls, and geese objects.
Contribution table for credit assignment based on 'ggplot2'.
    This can improve the author contribution information in academic journals and personal CV.
Solves control systems problems relating to time/frequency response, LTI systems design and analysis, transfer function manipulations, and system conversion.
Nonparametric two-sample procedure for comparing survival quantiles.
Functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).
This is a pedagogical package, designed to help students understanding convergence of
             random variables. It provides a way to investigate interactively various modes of
	     convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d.
	     random variables. Visualisation of simulated sample paths is possible through interactive
	     plots. The approach is illustrated by examples and exercises through the function
	     'investigate', as described in
	     Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>.
	     The user can study his/her own sequences of random variables.
Converts graphical file formats (SVG,
    PNG, JPEG, BMP, GIF, PDF, etc) to one another. The exceptions are the
    SVG file format that can only be converted to other formats and in contrast,
    PDF format, which can only be created from others graphical formats.
    The main purpose of the package was to provide a solution for converting SVG
    file format to PNG which is often needed for exporting graphical files
    produced by R widgets.
Provides conversion functionality between a broad range of
    scientific, historical, and industrial unit types.
Quantifies and assesses the significance of convergent evolution using two different methods (and 5 different measures) as described in Stayton (2015) <DOI:  10.1111/evo.12729>.  Also displays results in a phylomorphospace framework.
Provides a simple high-level wrapper for
    'Julia' package 'Convex.jl' (see <https://github.com/JuliaOpt/Convex.jl> for
    more information),
    which makes it easy to describe and solve convex optimization problems in R.
    The problems can be dealt with include:
    linear programs,
    second-order cone programs,
    semidefinite programs,
    exponential cone programs.
Variance estimation on indicators of income concentration and
    poverty using complex sample survey designs. Wrapper around the
    survey package.
Fits convolution-based nonstationary
    Gaussian process models to point-referenced spatial data. The nonstationary
    covariance function allows the user to specify the underlying correlation
    structure and which spatial dependence parameters should be allowed to
    vary over space: the anisotropy, nugget variance, and process variance.
    The parameters are estimated via maximum likelihood, using a local
    likelihood approach. Also provided are functions to fit stationary spatial
    models for comparison, calculate the Kriging predictor and standard errors,
    and create various plots to visualize nonstationarity.
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
Read and preprocess fasta format data, and construct the co-occurrence network for downstream analyses. This R package is to construct the co-occurrence network with the algorithm developed by Du (2008) <DOI:10.1101/gr.6969007>. It could be used to transform the data with high-dimension, such as DNA or protein sequence, into co-occurrence networks. Co-occurrence network could not only capture the co-variation pattern between variables, such as the positions in DNA or protein sequences, but also reflect the relationship between samples. Although it is originally used in DNA and protein sequences, it could be also used to other kinds of data, such as RNA, SNP, etc.
Fast implementations of the co-operations: covariance,
    correlation, and cosine similarity.  The implementations are
    fast and memory-efficient and their use is resolved
    automatically based on the input data, handled by R's S3
    methods.  Full descriptions of the algorithms and benchmarks
    are available in the package vignettes.
The theory of cooperative games with transferable utility offers 
   useful insights into the way parties can share gains from cooperation and 
   secure sustainable agreements, see e.g. one of the books by Chakravarty, 
   Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, 
   ISBN:978-9027727299) for more details. A comprehensive set of tools for 
   cooperative game theory with transferable utility is provided. Users can 
   create special families of cooperative games, like e.g. bankruptcy games, 
   cost sharing games and weighted voting games. There are functions to check 
   various game properties and to compute five different set-valued solution 
   concepts for cooperative games. A large number of point-valued solution 
   concepts is available reflecting the diverse application areas of 
   cooperative game theory. Some of these point-valued solution concepts can 
   be used to analyze weighted voting games and measure the influence of 
   individual voters within a voting body. There are routines for visualizing 
   both set-valued and point-valued solutions in the case of three or four 
   players.
Computes cooperative games and allocation rules associated with linear production programming problems.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) doi:10.1111/2041-210X.13152.
Extensive functions for bivariate copula (bicopula) computations and related operations 
 for bicopula theory. The lower, upper, product, and select other bicopula are implemented along 
 with operations including the diagonal, survival copula, dual of a copula, co-copula, and
 numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical 
 derivatives and inverses of a bicopula are provided through which simulation is implemented. 
 Bicopula composition, convex combination, and products also are provided. Support 
 extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau,
 Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer-
 Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and 
 positive/negative quadrant dependency, left (right) increasing (decreasing) are available. 
 Other features include Kullback-Leibler divergence, Vuong procedure, spectral measure, and 
 Lcomoments for inference, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit.
Provides tools for fitting the copCAR (Hughes, 2015)
    <DOI:10.1080/10618600.2014.948178> regression model for discrete
	areal data. Three types of estimation are supported (continuous
	extension, composite marginal likelihood, and distributional transform),
	for three types of outcomes (Bernoulli, negative binomial, and Poisson).
Semiparametric estimation for censored time series
    with lower detection limit. The latent response is a sequence of
    stationary process with Markov property
    of order one.
    Estimation of copula parameter(COPC) and Conditional quantile estimation
    are included for five available copula functions.
    Copula selection methods based on L2 distance from empirical copula function
    are also included.
Provides functions to  compute and plot Coverage
    Probability Excursion (CoPE) sets
    for real valued functions on a 2-dimensional domain. CoPE sets are obtained
    from repeated noisy observations of the function on the entire domain.
    They are designed to bound the excursion
    set of the target function at a given level from above and below with
    a predefined probability. The target
    function can be a parameter in spatially-indexed linear regression.
    Support by NIH grant R01 CA157528 is gratefully acknowledged.
The program implements the COPPE-Cosenza Fuzzy Hierarchy Model. 
    The model was based on the evaluation of local alternatives, representing 
    regional potentialities, so as to fulfill demands of economic projects. 
    After defining demand profiles in terms of their technological coefficients, 
    the degree of importance of factors is defined so as to represent  
    the productive activity. The method can detect a surplus of supply without 
    the restriction of the distance of classical algebra, defining a hierarchy 
    of location alternatives. In COPPE-Cosenza Model, the distance between 
    factors is measured in terms of the difference between grades of memberships
    of the same factors belonging to two or more  sets under comparison. The 
    required factors are classified under the following linguistic variables: 
    Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant 
    (I). And the alternatives can assume the following linguistic variables: 
    Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and 
    Inexistent (In). The model also provides flexibility, allowing different 
    aggregation rules to be performed and defined by the Decision Maker. Such 
    feature is considered in this package, allowing the user to define other 
    aggregation matrices, since it considers the same linguistic variables 
    mentioned.
Computes the required number of patients for two time-to-event end-points as primary endpoint in phase III clinical trial.
Cluster optimized proximity scaling (COPS) refers to multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration. They achieve this by transforming proximities/distances with power functions and augment the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly for given parameters (COPS-C) for ratio, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://escholarship.org/uc/item/4ps3b5mj>), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745>, r-stress (de Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>), power-stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>) and power elastic scaling, power Sammon mapping and approximated power stress (Rusch, Mair & Hornik, 2015, <https://bach-s59.wu.ac.at/4888/>). All of these models can also solely be fit as MDS with power transformations. The package further contains a function for pattern search optimization, the "Adaptive Luus-Jakola Algorithm" (Rusch, Mair & Hornik, 2015, <https://bach-s59.wu.ac.at/4888/>).
Classes (S4) of commonly used elliptical, Archimedean,
 extreme-value and other copula families, as well as their rotations,
 mixtures and asymmetrizations. Nested Archimedean copulas, related
 tools and special functions. Methods for density, distribution, random
 number generation, bivariate dependence measures, Rosenblatt transform,
 Kendall distribution function, perspective and contour plots. Fitting of
 copula models with potentially partly fixed parameters, including
 standard errors. Serial independence tests, copula specification tests
 (independence, exchangeability, radial symmetry, extreme-value
 dependence, goodness-of-fit) and model selection based on
 cross-validation. Empirical copula, smoothed versions, and
 non-parametric estimators of the Pickands dependence function.
Estimation and statistical process control are performed under
 copula-based time-series models.
 Available are statistical methods in Long and Emura (2014 JCSA),
 Emura et al. (2017 Commun Stat-Simul) <DOI:10.1080/03610918.2015.1073303>,
 Huang and Emura (2019 Commun Stat-Simul) <DOI:10.1080/03610918.2019.1602647> and Huang, Chen and Emura (2019-, in revision).
Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. A two stage estimation method is applied for estimating model parameters. Two copula functions are used for measuring dependence. One is used for modeling serial dependence in recurrent event times. The other one is for modeling dependent censoring. The baseline hazard functions are modeled by the Weibull distributions. See Huang (2019) <https://etd.lib.nctu.edu.tw/cgi-bin/gs32/ncugsweb.cgi?o=dncucdr&s=id=%22GC106225601%22.&searchmode=basic> for detail.
Perform association analysis of bivariate survival data based on copula models.
 Two different ways to estimate the association parameter in copula models are implemented.
 A goodness-of-fit test for a given copula model is implemented.
 See Emura, Lin and Wang (2010) <doi.org/10.1016/j.csda.2010.03.013> for details.
Copula-based regression models for bivariate censored data, including 
 bivariate right-censored data and bivariate interval-censored data. Currently 
 supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For 
 marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and
 semiparametric (Cox and transformation) models. Includes methods for convenient
 prediction and plotting. Also provides a bivariate time-to-event simulation function. 
 Method details can be found in Sun et.al. (2018) < doi:10.1007/s10985-018-09459-5> 
 and Sun et.al (2019) <arXiv:1901.01918>.
Data sets used for copula modeling in addition to those in
 the package 'copula'. These include a random subsample from the US National
 Education Longitudinal Study (NELS) of 1988 and nursing home data from
 Wisconsin.
Modelling of sensitivity and specificity on their natural scale
    using copula based bivariate beta-binomial distribution to yield marginal
    mean sensitivity and specificity. The intrinsic negative correlation between
    sensitivity and specificity is modelled using a copula function. A forest plot
    can be obtained for categorical covariates or for the model with intercept only.
    Nyaga VN, Arbyn M, Aerts M (2017) <doi:10.18637/jss.v082.c01>.
Provides a platform where EDAs (estimation of
    distribution algorithms) based on copulas can be implemented and
    studied. The package offers complete implementations of various
    EDAs based on copulas and vines, a group of well-known
    optimization problems, and utility functions to study the
    performance of the algorithms. Newly developed EDAs can be easily
    integrated into the package by extending an S4 class with generic
    functions for their main components.
The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) <doi:10.1002/sim.6595>. The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) <doi:10.1177/0962280215596769> and also accounting for non-evaluable subjects in Nikoloulopoulos (2018) <arXiv:1812.03685>. The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) <doi:10.1177/0962280216682376>. The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2018) <doi:10.1177/0962280218796685>. The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2018) <arXiv:1812.05915>.
Contains several IRT and non-IRT based response similarity indices proposed in the literature for multiple-choice examinations such as the Omega index, Wollack (1997) <doi:10.1177/01466216970214002>; Generalized Binomial Test, van der Linden & Sotaridona (2006) <doi:10.3102/10769986031003283>; K index, K1 and K2 indices, Sotaridona & Meijer (2002) <doi:10.1111/j.1745-3984.2002.tb01138.x>;  and S1 and S2 indices, Sotaridona & Meijer (2003) <doi:10.1111/j.1745-3984.2003.tb01096.x>.
Calculates the co-ranking matrix to assess the
    quality of a dimensionality reduction.
Provides a bundle of basic and fundamental bioinformatics tools,
    such as network querying and alignment, subnetwork extraction and search,
    network biomarker identification.
We design algorithms with linear time complexity with respect to the dimension for three commonly studied correlation structures, including exchangeable, decaying-product and K-dependent correlation structures, and extend the algorithms to generate binary data of general non-negative correlation matrices with quadratic time complexity. Jiang, W., Song, S.,  Hou, L. and Zhao, H. "A set of efficient methods to generate high-dimensional binary data with correlation structures." Submitted.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Partition data points (variables) into communities/clusters, similar to clustering algorithms, such as k-means and hierarchical clustering.  This package implements a clustering algorithm based on a new metric CORD, defined for high dimensional parametric or semi-parametric distributions.  Read http://arxiv.org/abs/1508.01939 for more details.
We describe four different summary statistics, to ensure power and flexibility under various settings, including a new connectivity statistic that is sensitive to changes in overall covariance magnitude.
Functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of 'clusteredness' in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2017) <doi:10.1080/10618600.2017.1349664>. There is an R native version and a version that uses 'ELKI', with methods for printing, summarizing, and plotting the result. There also is an interface to the reference implementation of OPTICS in 'ELKI'.
given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.
Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.
A collection of functions and classes which serve as the foundation 
    for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This 
    package was created to abstract shared functionality from other lab package 
    releases to increase ease of maintainability and reduce code repetition in 
    current and future 'Gx' suite programs. Major features include a 'CoreSet' 
    class, from which 'RadioSet' and 'PharmaSet' are derived, along with get and 
    set methods for each respective slot. Additional functions related to 
    fitting and plotting dose response curves, quantifying statistical 
    correlation and calculating area under the curve (AUC) or survival fraction
    (SF) are included. For more details please see the included documentation, 
    as well as: 
    Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., 
      Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., 
      Lupien, M., Goldenberg, A. (2015) <doi:10.1093/bioinformatics/btv723>.
    Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M.,
      Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) <doi:10.1101/449793>.
Core Hunter is a tool to sample diverse, representative subsets from large germplasm
    collections, with minimum redundancy. Such so-called core collections have applications in plant
    breeding and genetic resource management in general. Core Hunter can construct cores based on
    genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many
    provided evaluation measures depending on the precise purpose of the core (e.g. high diversity,
    representativeness, or allelic richness). In addition, multiple measures can be simultaneously
    optimized as part of a weighted index to bring the different perspectives closer together.
    The Core Hunter library is implemented in Java 8 as an open source project (see
    <http://www.corehunter.org>).
A suite of machine learning algorithms written in C++ with the R 
 interface contains several learning techniques for classification and regression.
 Predictive models include e.g., classification and regression trees with
 optional constructive induction and models in the leaves, random forests, kNN, 
 naive Bayes, and locally weighted regression. All predictions obtained with these
 models can be explained and visualized with the 'ExplainPrediction' package.  
 This package is especially strong in feature evaluation where it contains several variants of
 Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, 
 information gain, MDL, and DKM. These methods can be used for feature selection 
 or discretization of numeric attributes.
 The OrdEval algorithm and its visualization is used for evaluation
 of data sets with ordinal features and class, enabling analysis according to the 
 Kano model of customer satisfaction. 
 Several algorithms support parallel multithreaded execution via OpenMP.  
 The top-level documentation is reachable through ?CORElearn.
Provides a minimal interface for applying
    annotators from the 'Stanford CoreNLP' java library. Methods
    are provided for tasks such as tokenisation, part of speech
    tagging, lemmatisation, named entity recognition, coreference
    detection and sentiment analysis.
Core functions for simulating quantities of interest
    from generalised linear models (GLM). This package will form the backbone of
    a series of other packages that improve the interpretation of GLM estimates.
Use to analysis case-parent trio sequencing studies. Test the compound heterozygous and recessive disease models
Fits a hidden rates model that allows different transition rate
    classes on different portions of a phylogeny by treating rate classes as hidden
    states in a Markov process and various other functions for evaluating models of
    binary character evolution.
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features 
	using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables 
	using data transformations.
A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.
We proposed a new model-based clustering method, called the clustering of 
             regression models method(CORM), which groups genes that share a similar 
             relationship to the covariate(s). This method provides a unified approach 
             for a family of clustering procedures and can be applied to data collected 
             with various experimental designs. This package includes the implementation 
             for two such clustering procedures: (1) the Clustering of Linear Models 
             (CLM) method, and (2) the Clustering of Linear Mixed Models (CLMM) method.
Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'.
    ---
    The desktop application 'Cornerstone' (<https://www.camline.com/en/products/cornerstone/cornerstone-core.html>)
    is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster.
    The engineers incorporate intensified hands-on statistics into their projects.
    They benefit from an intuitive and uniquely designed graphical Workmap concept: you design
    experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon,
    immediately, interactively, and without any programming.
    ---
    While 'Cornerstone's' interface to the statistical programming language 'R' has been available
    since version 6.0, the latest interface with 'R' is even much more efficient.
    'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the
    standard 'Cornerstone' GUI.
    Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R'
    routines, immediately and without the need to deal with programming code.
    ---
    Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel 
    (<https://www.youtube.com/watch?v=HEQHwq_laXU>) (available in German).
Implements lasso and ridge regression for dichotomised outcomes (Rauschenberger et al. 2019). Such outcomes are not naturally but artificially binary. They indicate whether an underlying measurement is greater than a threshold.
Contains an implementation of a confounding robust independent component analysis (ICA) for noisy and grouped data. The main function coroICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding based on user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/coroICA/>.
Implements a James-Stein-type shrinkage estimator for 
  the covariance matrix, with separate shrinkage for variances and correlations.  
  The details of the method are explained in Schafer and Strimmer (2005) 
  <DOI:10.2202/1544-6115.1175> and Opgen-Rhein and Strimmer (2007) 
  <DOI:10.2202/1544-6115.1252>.  The approach is both computationally as well
  as statistically very efficient, it is applicable to "small n, large p" data, 
  and always returns a positive definite and well-conditioned covariance matrix.  
  In addition to inferring the covariance matrix the package also provides 
  shrinkage estimators for partial correlations and partial variances.  
  The inverse of the covariance and correlation matrix 
  can be efficiently computed, as well as any arbitrary power of the 
  shrinkage correlation matrix.  Furthermore, functions are available for fast 
  singular value decomposition, for computing the pseudoinverse, and for 
  checking the rank and positive definiteness of a matrix.
Utility functions for the statistical analysis of corpus frequency data.
        This package is a companion to the open-source course "Statistical Inference: 
        A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL').
A set of functions used to compare co-occurrence between two corpora.
Facilitates dynamic exploration of text collections through an
    intuitive graphical user interface and the power of regular expressions.
    The package contains 1) a helper function to convert a data frame to a
    'corporaexplorerobject', 2) a 'Shiny' app for fast and flexible exploration
    of a 'corporaexplorerobject', and 3) a 'Shiny' app for simple
    retrieval/extraction of documents from a 'corporaexplorerobject' in a
    reading-friendly format.
Text corpus data analysis, with full support for international text (Unicode).  Functions for reading data from newline-delimited 'JSON' files, for normalizing and tokenizing text, for searching for term occurrences, and for computing term occurrence frequencies, including n-grams.
Provides text analysis in R, focusing on the use of a tokenized text format. In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations).
    Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences),
    similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages,
    and the possibility to reconstruct original text from tokens to facilitate interpretation.
Implementation of two-dimensional (2D) correlation analysis based
    on the Fourier-transformation approach described by Isao Noda (I. Noda
    (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot
    functions for the resulting correlation matrix: The first one creates
    colored 2D plots, while the second one generates 3D plots.
Implements non-parametric analyses for clustered
    binary and multinomial data. The elements of the cluster are assumed
    exchangeable, and identical joint distribution (also known as marginal
    compatibility, or reproducibility) is assumed for clusters of different
    sizes. A trend test based on stochastic ordering is implemented.
Using a computationally efficient method, the package can
    be used to find the corrected coverage estimate of a credible set 
    of putative causal variants from Bayesian genetic fine-mapping. 
    The package can also be used to obtain a corrected credible set
    if required; that is, the smallest set of variants required such 
    that the corrected coverage estimate of the resultant credible set is  
    within some user defined accuracy of the desired coverage.
    Maller et al. (2012) <doi:10.1038/ng.2435>,
    Wakefield (2009) <doi:10.1002/gepi.20359>,
    Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.
Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.
Correcting area under ROC (AUC) for measurement error based on probit-shift model.
There are many estimators of false discovery rate. In this package we compute the Nonlocal False
   Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery
   Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. 
   Bickel, D. R. (2016) <http://hdl.handle.net/10393/34277>.
Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.
A collection of tools for correspondence regression, i.e. the
    correspondence analysis of the crosstabulation of a categorical variable Y
    in function of another one X, where X can in turn be made up of the
    combination of various categorical variables. Consequently, correspondence
    regression can be used to analyze the effects for a polytomous or
    multinomial outcome variable.
Speeds up exploratory data analysis (EDA)
    by providing a succinct workflow and interactive visualization tools for understanding
    which features have relationships to target (response). Uses binary correlation analysis
    to determine relationship. Default correlation method is the Pearson method. 
    Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.
Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.
Calculates correlation of variables and displays the results
    graphically. Included panel functions can display points, shading, ellipses, and
    correlation values with confidence intervals. See Friendly (2002) <doi:10.1198/000313002533>.
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
A graphical display of a correlation matrix or general matrix.
    It also contains some algorithms to do matrix reordering. In addition,
    corrplot is good at details, including choosing color, text labels,
    color labels, layout, etc.
A tool for exploring correlations.
    It makes it possible to easily perform routine tasks when
    exploring correlation matrices such as ignoring the diagonal,
    focusing on the correlations of certain variables against others,
    or rearranging and visualizing the matrix in terms of the
    strength of the correlations.
Statistical summary of Structure output.
Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.
Set of methods to constrain numerical series and time series within
             arbitrary boundaries.
There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. U are proportion to different types of correlation in  6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4().  We highly recommend ST5() according to the passage New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs published in Scientific Reports.
We present 'corto' (Correlation Tool), a simple package to infer
 gene regulatory networks using DPI (Data Processing Inequality) and bootstrapping
 to recover edges. An initial step is performed to calculate all significant
 edges between a list of source nodes (centroids) and target genes.
 Then all triplets containing two centroids and one target are tested
 in a DPI step which removes edges. A bootstrapping
 process then calculates the robustness of the network, eventually re-adding
 edges previously removed by DPI. The package implements a similar pipeline as
 ARACNe-AP (Algorithm for the Reconstruction of Accurate Cellular Networks with
 Adaptive Partitioning) by Giorgi (2016) <doi:10.1093/bioinformatics/btw216>)
 with optimizations to run outside a  computing cluster (most notably
 correlation to infer feature dependencies instead of Mutual Information).
Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes.
Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with 'corx'.
Implements bound constrained optimal sample allocation (BCOSA) framework described in Bulus & Dong (2019) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Separate tools for statistical power and minimum detectable effect size computations are provided.
To identify the globally most discriminative subnetwork from gene 
             expression profiles using an optimization model and genetic algorithm
cosinor is a set of simple functions that transforms longitudinal
    data to estimate the cosinor linear model as described in Tong (1976).
    Methods are given to summarize the mean, amplitude and acrophase, to
    predict the mean annual outcome value, and to test the coefficients.
Statistical procedures for calculating population–mean cosinor, non–stationary cosinor, estimation of best–fitting period, tests of population rhythm differences and more. See Cornélissen, G. (2014). <doi:10.1186/1742-4682-11-16>.
Package encapsulates standard expressions for distances,
        times, luminosities, and other quantities useful in
        observational cosmology, including molecular line observations.
        Currently coded for a flat universe only.
A single framework, unifying, extending, and improving a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific 'parent' Gaussian process Papalexiou (2018) <doi:10.1016/j.advwatres.2018.02.013>.
COSSO is a new regularization method that automatically
        estimates and selects important function components by a
        soft-thresholding penalty in the context of smoothing spline
        ANOVA models. Implemented models include mean regression,
        quantile regression, logistic regression and the Cox regression
        models.
Parameter estimation, one-step ahead forecast and new location
    prediction methods for spatio-temporal data.
Contains functions that can determine whether a time series
	is second-order stationary or not (and hence evidence for
	locally stationarity). Given two non-stationary series (i.e.
	locally stationary series) this package can then discover
	time-varying linear combinations that are second-order stationary.
Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation.
	Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest.
	Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers
	that don't accept observation weights.
Density, distribution function, quantile function, random
    generation and survival function for the Cosine Weibull Distribution as defined
    by SOUZA, L. New Trigonometric Class of Probabilistic Distributions. 219 p.
    Thesis (Doctorate in Biometry and Applied Statistics) - Department of Statistics
    and Information, Federal Rural University of Pernambuco, Recife, Pernambuco,
    2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Count transformation models featuring 
        parameters interpretable as discrete hazard ratios, odds ratios,
        reverse-time discrete hazard ratios, or transformed expectations. 
        An appropriate data transformation for a count outcome and
        regression coefficients are simultaneously estimated by maximising
        the exact discrete log-likelihood using the computational framework
        provided in package 'mlt', technical details are given in
        Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Implements cointegration/co-trending rank selection
        algorithm in Guo and Shintani (2013) "Consistent
        co-trending rank selection when both stochastic and nonlinear
        deterministic trends are present". 
        The Econometrics Journal 16: 473-483 
        <doi:10.1111/j.1368-423X.2012.00392.x>.
        Numbered examples correspond to Feb 2011 preprint 
        <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.
Interface to the couchDB document database <http://couchdb.apache.org>.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Counts colors within color range(s) in images, and
    provides a masked version of the image with targeted pixels
    changed to a different color. Output includes the locations
    of the pixels in the images, and the proportion of the image
    within the target color range with optional background masking.
    Users can specify multiple color ranges for masking.
Implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.
A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.
Provides a function for fitting Poisson and negative binomial regression models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weiß, C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.
Flexible univariate count models based on renewal
    processes. The models may include covariates and can be specified
    with familiar formula syntax as in glm() and package 'flexsurv'.
    The methodology is described in a forthcoming paper in the Journal
    of Statistical Software <10.18637/jss.v090.i13> (included as
    vignette 'Countr_guide' in the package).
Standardize country names, convert them into one of
    eleven coding schemes, convert between coding schemes, and
    assign region descriptors.
Modeling under- and over-dispersed count data using extended Poisson process models (EPPM) as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .
Implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.
Provide data transformation functions to transform counts in RNA-seq data analysis. Please see the reference: Zhang Z, Yu D, Seo M, Hersh CP, Weiss ST, Qiu W. (2019) <doi.org/10.1038/s41598-019-41315-w>.
Quantifies United States flood impacts at the county level using
    United States Geological Service (USGS) River Discharge data for the USGS
    API. This package builds on R packages from the USGS, with the goal of
    creating county-level time series of flood status that can be more easily
    joined with county-level impact measurements, including health outcomes.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631), the Colorado Water Center,
    and the National Science Foundation, Integrative Graduate Education and
    Research Traineeship (IGERT) Grant No. DGE-0966346 "I-WATER: Integrated
    Water, Atmosphere, Ecosystems Education and Research Program" at
    Colorado State University.
Inputs date-times in Coordinated Universal Time (UTC)
    and converts to a local date-time and local date for US counties, based on
    each county's Federal Information Processing Standard (FIPS) code.
    This work was supported in part by grants from the National Institute of
    Environmental Health Sciences (R00ES022631) and the National Science
    Foundation (1331399).
Interacts with NOAA data sources (including the NCDC API at
  <http://www.ncdc.noaa.gov/cdo-web/webservices/v2> and ISD data) using
  functions from the 'rnoaa' package to obtain and compile weather time
  series for U.S. counties. This work was supported in part by grants from the
  National Institute of Environmental Health Sciences (R00ES022631) and the
  Colorado State University Water Center.
Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.
Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from 'C++' based model builder tools such as 'Rcpp'/'inline', 'TMB', or 'JAGS'.
Tests on properties of space-time covariance functions.
    Tests on symmetry, separability and for assessing 
    different forms of non-separability are available. Moreover tests on 
    some classes of covariance functions, such that the classes of 
    product-sum models, Gneiting models and integrated product models have 
    been provided.
Allows Brownian motion, fractional Brownian motion,
    and integrated Ornstein-Uhlenbeck process components to
    be added to linear and non-linear mixed effects models
    using the structures and methods of the 'nlme' package.
Produce forest plots to visualize covariate effects using either
    the command line or an interactive 'Shiny' application.
Computes p-values using the largest root test using 
    an approximation to the null distribution by Johnstone (2008) <DOI:10.1214/08-AOS605>.
Converts any word, sentence or speech into Trump's infamous
  "covfefe" format. Reference: <https://www.nytimes.com/2017/05/31/us/politics/covfefe-trump-twitter.html>.
  Inspiration thanks to: <https://codegolf.stackexchange.com/questions/123685/covfefify-a-string>.
Estimation of latent class models with covariate effects
        on underlying and measured variables. The measured variables
        are dichotomous or polytomous, all with the same number of
        categories.
Track and report code coverage for your package and (optionally)
    upload the results to a coverage service like 'Codecov' <http://codecov.io> or
    'Coveralls' <http://coveralls.io>. Code coverage is a measure of the amount of
    code being exercised by a set of tests. It is an indirect measure of test
    quality and completeness. This package is compatible with any testing
    methodology or framework and tracks coverage of both R code and compiled
    C/C++/FORTRAN code.
This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates.
The cov.nnve() function implements robust covariance estimation
        by the nearest neighbor variance estimation (NNVE) method of
        Wang and Raftery (2002) <DOI:10.1198/016214502388618780>.
Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
Model-free selection of covariates in high dimensions under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011) <DOI:10.1093/biomet/asr041> and VanderWeele and Shpitser (2011) <DOI:10.1111/j.1541-0420.2011.01619.x>. Confounder selection can be performed via either Markov/Bayesian networks, random forests or LASSO.
Functions for testing if the covariance structure of 2-dimensional data
    (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) =
    C_1 x C_2.
    A complete descriptions of the implemented tests can be found in the paper
    Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in
    nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017),
    no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.
Testing functions for Covariance Matrices. These tests include high-dimension homogeneity of covariance
  matrix testing described by Schott (2007) <doi:10.1016/j.csda.2007.03.004> and high-dimensional one-sample tests of 
  covariance matrix structure described by Fisher, et al. (2010) <doi:10.1016/j.jmva.2010.07.004>. Covariance matrix
  tests use C++ to speed performance and allow larger data sets.
Covariance is of universal prevalence across various disciplines within statistics.
    We provide a rich collection of geometric and inferential tools for convenient analysis of
    covariance structures, topics including distance measures, mean covariance estimator,
    covariance hypothesis test for one-sample and two-sample cases, and covariance estimation.
    For an introduction to covariance in multivariate statistical analysis,
    see Schervish (1987) <doi:10.1214/ss/1177013111>.
Implements a specific form of segmented linear regression
    with two independent variables. The visualization of that function looks 
    like a quarter segment of a cowbell giving the package its name. 
    The package has been specifically constructed for the case where minimum 
    and maximum value of the dependent and two independent variables 
    are known a prior, which is usually the case
    when those values are derived from Likert scales.
Provides various features that help with creating publication-quality figures
    with 'ggplot2', such as a set of themes, functions to align plots and arrange
    them into complex compound figures, and functions that make it easy to annotate
    plots and or mix plots with images. The package was originally written for
    internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package).
    It has also been used extensively in the book Fundamentals of Data
    Visualization.
Allows printing of character strings as messages/warnings/etc.
    with ASCII animals, including cats, cows, frogs, chickens, ghosts,
    and more.
This package provides routines for fitting Cox models by
        likelihood based boosting for a single endpoint or in presence
        of competing risks
Functions for generating, simulating, and visualizing expected
    durations and marginal changes in duration from the Cox proportional hazards
    model.
Fits Cox-type models based on interval-censored data from a survival
             or illness-death process.
This package fits Cox proportional hazards models containing both 
 fixed and random effects.  The random effects can have a general form, of which
 familial interactions (a "kinship" matrix) is a particular special case. 
 Note that the simplest case of a mixed effects Cox model, i.e. a single random 
 per-group intercept, is also called a "frailty" model.  The approach is based
 on Rippatti and Palgren, Biometrics 2002.
Fast algorithms for fitting a Cox mixed-effects model for e.g., genome-wide association studies. See Liang He and Alexander Kulminski (2019) <doi:10.1101/729285>.
Implements Firth's penalized maximum likelihood bias reduction method  for Cox regression
  which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function).
  The program fits profile penalized likelihood confidence intervals which were proved to outperform
  Wald confidence intervals.
Performs analysis of right-censored length-biased data using Cox model. It contains model fitting and checking, and the stationarity assumption test. The model fitting and checking methods are described in Qin and Shen (2010) <doi:10.1111/j.1541-0420.2009.01287.x> and Lee, Ning, and Shen (2018) <doi:10.1007/s10985-018-9422-y>.
Sparse estimation for Cox PH models is done via
    Minimum approximated Information Criterion (MIC) by Su, Wijayasinghe, 
    Fan, and Zhang (2016) <DOI:10.1111/biom.12484>. MIC mimics the best 
    subset selection using a penalized likelihood approach yet with no need 
    of a tuning parameter. The problem is further reformulated with a 
    re-parameterization step so that it reduces to one unconstrained non-convex
    yet smooth programming problem, which can be solved efficiently. Furthermore,
    the re-parameterization tactic yields an additional advantage in terms of
    circumventing post-selection inference.
Estimate coefficients of Cox proportional hazards model using stochastic gradient descent algorithm for batch data.
Implements weighted estimation in Cox regression as proposed by
             Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, 
             <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and 
             Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). 
             Weighted Cox regression provides unbiased average hazard ratio 
             estimates also in case of non-proportional hazards. 
             Approximated generalized concordance probability an effect size measure for clear-cut
             decisions can be obtained.
             The package provides options to estimate time-dependent effects conveniently by
             including interactions of covariates with arbitrary functions of time, with or without
             making use of the weighting option.
A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables.
A package for fitting Cox models with penalized ridge-type partial likelihood. The package includes functions for fitting simple Cox models with all covariates controlled by a ridge penalty. The weight of the penalty is optimised by using a REML type-algorithm. Models with time varying effects of the covariates can also be fitted. Some of the covariates may be allowed to be fixed and thus not controlled by the penalty. There are three different penalty functions, ridge, dynamic and weighted dynamic. Time varying effects can be fitted without the need of an expanded dataset.
Fit robustly proportional hazards regression model
Fits Cox regression based on retrospectively ascertained times-to-event. The method uses Inverse-Probability-Weighting estimating equations.
It fits a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Functions for calculating the conditional power
             for different models in survival time analysis
             within randomized clinical trials
             with two different treatments to be compared
             and survival as an endpoint.
Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.
The package includes functions to test and compare causal models.
Implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.
A Bayesian meta-analysis method for studying cross-phenotype
    genetic associations. It uses summary-level data across multiple phenotypes to
    simultaneously measure the evidence of aggregate-level pleiotropic association
    and estimate an optimal subset of traits associated with the risk locus. CPBayes
    is based on a spike and slab prior.
This package contains methods to perform Common Principal
    Component Analysis (CPCA). The stepwise method by Trendafilov is published
    in the current version. Please see Trendafilov (2010). Stepwise estimation
    of common principal components. Computational Statistics & Data Analysis,
    54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
To detect the changepoint, this package uses most recent changepoint, double cumulative sum binary segmentation, multiple changepoints in multivariate time series, analyzing each series in the panel independently, and analyzing aggregated data methods. This package is useful to simulate censored time series to detect the most recent changepoint in censored panel data as well as to assess prediction accuracy.
Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.
Probability mass function, distribution function, quantile function and random generation for the Complex Triparametric Pearson (CTP) and Complex Biparametric Pearson (CBP) distributions developed by Rodriguez-Avi et al (2003) <doi:10.1007/s00362-002-0134-7>, Rodriguez-Avi et al (2004) <doi:10.1007/BF02778271> and Olmo-Jimenez et al (2018) <doi:10.1080/00949655.2018.1482897>. The package also contains maximum-likelihood fitting functions for these models.
Functions to calculate concordance probability estimates in survival analysis.
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug's maximum concentration, (13) dlar.fn for calculating loading dose based on drug's accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
This is the R Interface to the C API of IBM ILOG CPLEX. It necessarily depends on IBM ILOG CPLEX (>= 12.1).
Likelihood-based and Bayesian methods for various compound Poisson linear models based on Zhang, Yanwei (2013) <https://link.springer.com/article/10.1007/s11222-012-9343-7>.
Provides functions to produce some circular plots for circular data, in a height- or area-proportional manner. They include barplots, smooth density plots, stacked dot plots, histograms, multi-class stacked smooth density plots, and multi-class stacked histograms. The new methodology for general area-proportional circular visualization is described in an article submitted (after revision) to Journal of Computational and Graphical Statistics.
Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points.
We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.
Calculation of distances, shortest paths and isochrones on weighted graphs using several variants of Dijkstra algorithm. 
  Proposed algorithms are unidirectional Dijkstra (Dijkstra, E. W. (1959) <doi:10.1007/BF01386390>),
  bidirectional Dijkstra (Goldberg, Andrew & Fonseca F. Werneck, Renato (2005) <https://pdfs.semanticscholar.org/0761/18dfbe1d5a220f6ac59b4de4ad07b50283ac.pdf>),
  A* search (P. E. Hart, N. J. Nilsson et B. Raphael (1968) <doi:10.1109/TSSC.1968.300136>),
  new bidirectional A* (Pijls & Post (2009) <http://repub.eur.nl/pub/16100/ei2009-10.pdf>).
Implementation of the Control Polygon Reduction and Control Net
    Reduction methods for finding parsimonious B-spline regression models.
Permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function). See <doi:10.1111/j.1467-9876.2010.00729.x>.
Calculate date of birth, age, and gender, and generate anonymous
    sequence numbers from CPR numbers.
    <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>.
Nonparametric change point estimation for survival data based on p-values of exact binomial tests.
Simulates time-to-event data
    with type I right censoring using two methods: the inverse CDF
    method and our proposed memoryless method. The latter method
    takes advantage of the memoryless property of survival and
    simulates a separate distribution between change-points. We
    include two parametric distributions: exponential and Weibull.
    Inverse CDF method draws on the work of Rainer Walke (2010), 
    <https://www.demogr.mpg.de/papers/technicalreports/tr-2010-003.pdf>.
Non-parametric test for equality of multivariate distributions.  Trains a classifier to classify (multivariate) observations as coming from one of several distributions.  If the classifier is able to classify the observations better than would be expected by chance (using permutation inference), then the null hypothesis that the distributions are equal is rejected.
Incorporates colour gradients from the 'cpt-city' web archive available at <http://soliton.vm.bytemark.co.uk/pub/cpt-city/>.
Allows to retrieve data from the
    'CPTEC/INPE' weather forecast API. 'CPTEC' stands for 'Centro de Previsão
    de Tempo e Estudos Climáticos' and 'INPE' for 'Instituto Nacional de
    Pesquisas Espaciais'. 'CPTEC' is the most advanced numerical weather and
    climate forecasting center in Latin America, with high-precision short
    and medium-term weather forecasting since the beginning of 1995. See
    <http://www.cptec.inpe.br/> for more information.
Access data from the 'Care Quality Commission', the health 
    and adult social care regulator for England. The 'Care Quality Commission' 
    operates an API 
    <https://www.cqc.org.uk/about-us/transparency/using-cqc-data#api>, with data
    available under the Open Government License. Data includes information on 
    service providers, locations such as hospitals, care homes and 
    medical clinics, and ratings and inspection reports.
Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).
Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. For large time dimensions of the panel, the computation of the proposed models involves a recursive function adapted from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)).
This package contains R-functions to perform power
        calculation in a group sequential clinical trial with censored
        survival data and possibly unequal patient allocation between
        treatment and control groups. The fuctions can also be used to
        determine the study duration in a clinical trial with censored
        survival data as the sum of the accrual duration, which
        determines the sample size in a traditional sense, and the
        follow-up duration, which more or less controls the number of
        events to be observed. This package also contains R functions
        and methods to display the computed results.
Tool for analyzing competing risks models. The main point of interest is testing differences between groups (as described in R.J Gray (1988) <doi:10.1214/aos/1176350951> and J.P. Fine, R.J Gray (1999) <doi:10.2307/2670170>) and visualizations of survival and cumulative incidence curves.
R functions for cosmological research.
  The main functions are similar to the python library, cosmolopy.
Provides R routine for the so called two-sample
        Cramer-Test.  This nonparametric two-sample-test on equality
        of the underlying distributions can be applied to 
        multivariate data as well as univariate data. It offers two 
        possibilities to approximate the critical value both of which 
        are included in this package.
Functions for completing and recalculating rankings and sorting.
A set of functions to manage 'CRAN'-like repositories
    efficiently.
'API' to the database of 'CRAN' package downloads from the
    'RStudio' 'CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>,
    see <https://github.com/r-hub/cranlogs.app> for the raw 'API'.
Core visualizations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organizing the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, producing package dependence trees, and for computing useful summaries and producing interactive visualizations from the resulting networks and summaries. The resulting networks can be coerced to 'igraph' <https://CRAN.R-project.org/package=igraph> objects for further analyses and modelling.
One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.
Various R tools for http://crantastic.org/
Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.
Fit continuous-time correlated random walk models with time indexed
    covariates to animal telemetry data. The model is fit using the Kalman-filter on
    a state space version of the continuous-time stochastic movement process.
Colored terminal output on terminals that support 'ANSI'
    color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI'
    color support is automatically detected. Colors and highlighting can
    be combined and nested. New styles can also be created easily.
    This package was inspired by the 'chalk' 'JavaScript' project.
Implements a statistical test for comparing bar plots or histograms of categorical data derived from a randomized block repeated measures layout.
Different approaches to censored or truncated regression with 
  conditional heteroscedasticity are provided. First, continuous 
  distributions can be used for the (right and/or left censored or truncated)
  response with separate linear predictors for the mean and variance. 
  Second, cumulative link models for ordinal data
  (obtained by interval-censoring continuous data) can be employed for
  heteroscedastic extended logistic regression (HXLR). In the latter type of
  models, the intercepts depend on the thresholds that define the intervals.
Provides a new method for identification of clusters of genomic
 regions within chromosomes. Primarily, it is used for calling clusters of 
 cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions
 in the tissue or cell type of interest, such as those generated from chromatin-based 
 assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements 
 within chromosomes of a given sample to identify COREs in the following steps:
 1) It identifies window size or the maximum allowed distance between the elements 
 within each CORE, 2) It identifies number of elements which should be clustered 
 as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which 
 does not pass the threshold considered in the approach.
Setup and retrieve HTTPS and SSH credentials for use with 'git' and 
    other services. For HTTPS remotes the package interfaces the 'git-credential' 
    utility which 'git' uses to store HTTP usernames and passwords. For SSH 
    remotes we provide convenient functions to find or generate appropriate SSH 
    keys. The package both helps the user to setup a local git installation, and
    also provides a back-end for git/ssh client libraries to authenticate with 
    existing user credentials.
A set of functions for computing the CreditMetrics risk model
Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization. Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster.
Evaluation of default probability of sovereign and corporate entities based on structural or intensity based models and calibration on market Credit Default Swap quotes. Damiano Brigo, Massimo Morini, Andrea Pallavicini (2013): "Counterparty Credit Risk, Collateral and Funding. With Pricing Cases for All Asset Classes".
Functions for constructing simultaneous credible bands and identifying subsets via the "credible subsets" (also called "credible subgroups") method.
It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.
Simple tidying, analysis, and visualization of conjoint (factorial) experiments, including estimation and visualization of average marginal component effects ('AMCEs') and marginal means ('MMs') for weighted and un-weighted survey data, along with useful reference category diagnostics and statistical tests. Estimation of 'AMCEs' is based upon methods described by Hainmueller, Hopkins, and Yamamoto (2014) <doi:10.1093/pan/mpt024>.
Builds a 'SQLite' database file of pre-calculated transcription 
    factor/microRNA-gene correlations (co-expression) in cancer from the 
    Cistrome Cancer Liu et al. (2011) <doi:10.1186/gb-2011-12-8-r83> and 
    'miRCancerdb' databases (in press). Provides custom classes and functions 
    to query, tidy and plot the correlation data.
Implements modeling and computational tools for conditional
    random fields (CRF) model as well as other probabilistic undirected
    graphical models of discrete data with pairwise and unary potentials.
A methodology to estimate the parameters for the cure rate frailty models with clustered current status data.
Wraps the 'CRFsuite' library <https://github.com/chokkan/crfsuite> allowing users 
    to fit a Conditional Random Field model and to apply it on existing data.
    The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models 
    for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application
    is included in the package to allow you to easily construct training data.
Tools for analyzing performances of cricketers based on stats in
    ESPN Cricinfo Statsguru. The toolset can  be used for analysis of Tests,ODIs 
    and Twenty20 matches of both batsmen and bowlers. The package can also be used to
    analyze team performances.
A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories.
Gives convenient access to publicly available police-recorded open
    crime data from large cities in the United States that are included in the
    Crime Open Database <https://osf.io/zyaqn/>.
Statistical Methods for Crime Series Linkage. This package provides 
      code for criminal case linkage, crime series identification, crime series 
      clustering, and suspect identification.
Implements convex regression with interpretable sharp partitions
    (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
Functions for phase I clinical trials using the continual reassessment method.
Text mining client for 'Crossref' (<https://crossref.org>). Includes
    functions for getting getting links to full text of articles, fetching full
    text articles from those links or Digital Object Identifiers ('DOIs'),
    and text extraction from 'PDFs'.
Implements the Cross-contribution Compensating Multiple
    standard Normalization (CCMN) method and other normalization
    algorithms.
Implements a wide range of model-based dose
    escalation designs, ranging from classical and modern continual
    reassessment methods (CRMs) based on dose-limiting toxicity endpoints to
    dual-endpoint designs taking into account a biomarker/efficacy outcome. The
    focus is on Bayesian inference, making it very easy to setup a new design
    with its own JAGS code. However, it is also possible to implement 3+3
    designs for comparison or models with non-Bayesian estimation. The whole
    package is written in a modular form in the S4 class system, making it very
    flexible for adaptation to new models, escalation or stopping rules.
The crn package provides the core functions required to
        download and format data from the Climate Reference Network.
        Both daily and hourly data are downloaded from the ftp, a
        consolidated file of all stations is created, station metadata
        is extracted. In addition functions for selecting individual
        variables and creating R friendly datasets for them is
        provided.
Functions to help implement the extraction / subsetting / indexing
    function '[' and replacement function '[<-' of custom matrix-like types
    (based on S3, S4, etc.), modeled as closely to the base matrix class as
    possible (with tests to prove it).
A dashboard supports the usage of 'cromwell'. 
 'Cromwell' is a scientific workflow engine for command line users.
 This package utilizes 'cromwell' REST APIs and provides these convenient 
 functions: timing diagrams for running workflows, 'cromwell' engine status, 
 a tabular workflow list. For more information about 'cromwell', 
 visit <http://cromwell.readthedocs.io>.
Functions to carry out the most important crystallographic calculations for crystal 
             structures made of 1d Gaussian-shaped atoms, especially useful for methods development.
             Main reference: E. Smith, G. Evans, J. Foadi (2017) <doi:10.1088/1361-6404/aa8188>.
Create, edit, and remove 'cron' jobs on your unix-alike system. The package provides a set of easy-to-use wrappers
    to 'crontab'. It also provides an RStudio add-in to easily launch and schedule your scripts.
A device closing function which is able to crop graphics (e.g.,
  PDF, PNG files) on Unix-like operating systems with the required underlying
  command-line tools installed.
Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes
             6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially,  in excel files which has been transformed
             and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell.
             The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.
A helpful tool for the identification of crop rows. Methods of this package include: Excess Green color scale <https://www.researchgate.net/publication/270613992_Color_Indices_for_Weed_Identification_Under_Various_Soil_Residue_and_Lighting_Conditions>, Otsu Thresholding <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4310076>, and Morphology <https://en.wikipedia.org/wiki/Mathematical_morphology>.
Provide the CrossClustering algorithm (Tellaroli et al. (2016)
    <doi:10.1371/journal.pone.0152333>), which is a partial clustering algorithm
    that combines the Ward's minimum variance and Complete Linkage algorithms,
    providing automatic estimation of a suitable number of clusters and
    identification of outlier elements.
Contains functions for the construction of carryover
        balanced crossover designs. In addition contains functions to
        check given designs for balance.
This package performs a test for comparing two
        multivariate distributions by using the distance between
        observations.  The input is a distance matrix and the labels of
        the two groups to be compared, the output is the number of
        cross-matches and a p-value.
This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using 
 the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
Joint distribution of number of crossings and the 
  longest run in a series of independent Bernoulli trials. The
  computations uses an iterative procedure where computations 
  are based on results from shorter series. The procedure 
  conditions on the start value and partitions by further 
  conditioning on the position of the first crossing (or none).
Cross-screening is a new method that uses both random halves of the sample to screen and test many hypotheses. It generally improves statistical power in observational studies when many hypotheses are tested simultaneously. References: 1. Qingyuan Zhao, Dylan S Small, and Paul R Rosenbaum. Cross-screening in observational studies that test many hypotheses. <arXiv:1703.02078>. 2. Qingyuan Zhao. On sensitivity value of pair-matched observational studies. <arXiv:1702.03442>.
Provides building blocks for allowing HTML widgets to communicate
    with each other, with Shiny or without (i.e. static .html files). Currently
    supports linked brushing and filtering.
Enables transformation of Verbal Autopsy data collected with the WHO 2016 questionnaire (versions 1.4.1 & 1.5.1)
  or the WHO 2014 questionnaire for automated coding of Cause of Death using the InSilicoVA (data.type = "WHO2016") and
  InterVA5 algorithms. Previous versions of this package supported user-supplied mappings (via the map_records function), but
  this functionality has been removed.  This package is made available by WHO and the Bloomberg Data for Health Initiative.
Contains generic functions for performing 
  cross validation and for computing diagnostic errors.
Defines classes and methods to cross-validate various
  binary classification algorithms used for "class prediction"
  problems.
A pair of functions for renaming and encoding data frames
	     using external crosswalk files. It is especially useful when
	     constructing master data sets from multiple smaller data
	     sets that do not name or encode variables consistently
	     across files. Based on similar commands in 'Stata'.
Generate crosswords from a list of words.
Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2018) <arXiv:1804.08715>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then the monotonicity direction classification procedure establishes it. A monotonicity test is also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.
Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
Chinese restaurant process [Pitman (1995) <doi:10.1007/BF01213386>] is used in order to compose Dirichlet process [Ferguson (1973) <doi:10.1214/aos/1176342360>]. The clustering which uses Chinese restaurant process does not need to decide the number of clusters in advance. This algorithm automatically adjusts it. And this package calculates the ambiguity of clusters as entropy [Yngvason (1999) <doi:10.1016/S0370-1573(98)00082-9>].
Cross-recurrence quantification analysis 
    of two time-series, of either categorical or
    continuous values. It provides different methods
    for profiling cross-recurrence, i.e., only looking
    at the diagonal recurrent points, as well as more
    in-depth measures of the whole cross-recurrence plot,
    e.g., recurrence rate. Please refer to by Coco and Dale (2014) 
    <doi:10.3389/fpsyg.2014.00510> for further details about the method.
Cross-recurrence quantification analysis 
    for word series, from text, known as categorical
    recurrence analysis. Uses the 'crqa' R package
    by Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510>. 
    Functions are wrappers to facilitate exploration 
    of the sequential properties of text.
In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions.
Extension of cmprsk to Stratified and Clustered data.
        Goodness of fit test for Fine-Gray model.
Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <http://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <http://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://www.sharcnet.ca>).
Based on Dutta et al. (2018) <doi:10.1016/j.jempfin.2018.02.004>, this package provides their standardized test for abnormal returns in long-horizon event studies. The methods used improve the major weaknesses of size, power, and robustness of long-run statistical tests described in Kothari/Warner (2007) <doi:10.1016/B978-0-444-53265-7.50015-9>. Abnormal returns are weighted by their statistical precision (i.e., standard deviation), resulting in abnormal standardized returns. This procedure efficiently captures the heteroskedasticity problem. Clustering techniques following Cameron et al. (2011) <10.1198/jbes.2010.07136> are adopted for computing cross-sectional correlation robust standard errors. The statistical tests in this package therefore accounts for potential biases arising from returns' cross-sectional correlation, autocorrelation, and volatility clustering without power loss.
Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model.
Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
  Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.
An algorithm for identifying candidate driver combinations in cancer. CRSO 
  is based on a theoretical model of cancer in which a cancer rule 
  is defined to be a collection of two or more events (i.e., alterations) that are minimally 
  sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively 
  are assumed to account for all of ways to cause cancer in the population. In CRSO every 
  event is designated explicitly as a passenger or driver within each patient. 
  Each event is associated with a patient-specific, event-specific passenger penalty, 
  reflecting how unlikely the event would have happened by chance, i.e., as a passenger.
  CRSO evaluates each rule set by assigning all samples to a rule in the rule set,
  or to the null rule, and then calculating the total statistical penalty from all
  unassigned event. CRSO uses a three phase procedure find the best rule set of 
  fixed size K for a range of Ks. A core rule set is then identified from among
  the best rule sets of size K as the rule set that best balances rule set size and 
  statistical penalty. 
  Users should consult the 'crso' vignette for an example walk through of a full CRSO run.
  The full description, of the CRSO algorithm is presented in: 
  Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." 
  BioRxiv 674234 [Preprint]. June 19, 2019. <doi:10.1101/674234>.
  Please cite this article if you use 'crso'.
Tidies and performs preliminary analysis of 'Coursera' research
    export data. These export data can be downloaded by anyone who has classes
    on Coursera and wants to analyze the data. Coursera is one of the leading 
    providers of MOOCs and was launched in January 2012. With over 25 million 
    learners, Coursera is the most popular provider in the world being followed 
    by EdX, the MOOC provider that was a result of a collaboration between 
    Harvard University and MIT, with over 10 million users. Coursera has over 
    150 university partners from 29 countries and offers a total of 2000+ 
    courses from computer science to philosophy. Besides, Coursera offers 180+ 
    specialization, Coursera's credential system, and four fully online Masters 
    degrees. For more information about Coursera check Coursera's
    About page on <https://blog.coursera.org/about/>.
Provides wrapper functions for running classification and
    regression tests using different machine learning techniques, such as Random
    Forests and decision trees. The package provides standardized methods for
    preparing data to suit the algorithm's needs, training a model, making
    predictions, and evaluating results. Also, some functions are provided to run
    multiple instances of a test.
Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).
Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
A simple HTTP client, with tools for making HTTP requests,
    and mocking HTTP requests. The package is built on R6, and takes
    inspiration from Ruby's 'faraday' gem (<https://rubygems.org/gems/faraday>).
    The package name is a play on curl, the widely used command line tool
    for HTTP, and this package is built on top of the R package 'curl', an
    interface to 'libcurl' (<https://curl.haxx.se/libcurl>).
The Crunch.io service <http://crunch.io/> provides a cloud-based
    data store and analytic engine, as well as an intuitive web interface.
    Using this package, analysts can interact with and manipulate Crunch
    datasets from within R. Importantly, this allows technical researchers to
    collaborate naturally with team members, managers, and clients who prefer a
    point-and-click interface.
To facilitate building custom dashboards on the Crunch data
    platform <https://crunch.io/>, the 'crunchy' package provides tools for
    working with 'shiny'. These tools include utilities to manage authentication
    and authorization automatically and custom stylesheets to help match the
    look and feel of the Crunch web application. The package also includes
    several gadgets for use in 'RStudio'.
Functions for reading in and manipulating CRU TS3.21: Climatic
    Research Unit (CRU) Time-Series (TS) Version 3.21 data.
To re-calculate the coefficients and the standard deviation when changing the reference group.
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Retrieves crypto currency current and historical information as well as information on the exchanges they are listed on. For current and historical it will retrieve the daily open, high, low and close values for all crypto currencies. This retrieves the historical market data by web scraping tables provided by 'Cryptocurrency Market Capitalizations' <https://coinmarketcap.com>.
Performs cryptographic randomness tests on a sequence of random
    integers or bits. Included tests are greatest common divisor, birthday spacings,
    book stack, adaptive chi-square, topological binary, and three random walk
    tests. Tests except greatest common divisor and birthday spacings are not
    covered by standard test suites. In addition to the chi-square goodness-of-fit
    test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are
    also generated by some of the cryptographic randomness tests.
Functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
The joint distribution of potential outcomes is not typically identified under standard identifying assumptions such as selection on observables or even when individuals are randomly assigned to being treated. This package contains methods for obtaining tight bounds on distributional treatment effect parameters when panel data is available and under a Copula Stability Assumption as in Callaway (2017) <https://ssrn.com/abstract=3028251>.
Monte Carlo conditional inference for the parameters of a
  linear nonnormal regression model.
Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers' Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.
Germline and somatic locus data which contain the total read depth and B allele 
    read depth using Bayesian model (Dirichlet Process) to cluster. Meanwhile, the cluster 
    model can deal with the SNVs mutation and the CNAs mutation.
Mines contiguous sequential patterns in text.
Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) <doi:10.1128/JCM.00696-14> that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping.
Applies factor analysis methodology to microarray data in order
    to derive connectivity scores between compounds. The package also contains an
    implementation of the connectivity score algorithm by Zhang and Gant (2008) <doi:10.1186/1471-2105-9-258>.
Package for CShapes, a GIS dataset of country boundaries (1946-today). Includes functions for data extraction and the computation of distance matrices and -lists.
Provides functions for computing the density
    and the log-likelihood function of closed-skew normal variates,
    and for generating random vectors sampled from this distribution.
    See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004).
    The closed skew normal distribution, 
    Skew-elliptical distributions and their applications: a journey beyond normality,
    Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Imports 'StatBank' data in JSON-stat format and (optionally) reshapes it into wide
	format. The Central Statistics Office (CSO) is the national statistical institute of Ireland
	and 'StatBank' is the CSOs online database of Official Statistics. This database contains current
	and historical data series compiled from CSO statistical releases and is accessed at
	<http://www.cso.ie/px/pxeirestat/statire/SelectTable/Omrade0.asp?Planguage=0>.
	A tutorial explaining and illustrating how to navigate to the 'StatBank' and how to search the
	'StatBank' by keyword or theme can be found here:
	<https://www.cso.ie/en/interactivezone/youtubevideos/statbanktutorial/>. This tutorial also
	demonstrates how a user can create, edit and download a table from the 'StatBank'. The CSO
	'StatBank' Application Programming Interface (API), which is accessed in this package, provides
	access to 'StatBank' data in JSON-stat format at <https://statbank.cso.ie/webserviceclient/>.
	This dissemination tool allows developers machine to machine access to CSO 'StatBank' data.
Provides the Correlates of State Policy data set for easy use in R.
Includes two functions to evaluate the hypothesis of complete spatial randomness (csr) in point processes.  The function 'mwin' calculates quadrat counts to estimate the intensity of a spatial point process through the moving window approach proposed by Bailey and Gatrell (1995).  Event counts are computed within a window of a set size over a fine lattice of points within the region of observation.  The function 'pielou' uses the nearest neighbor test statistic and asymptotic distribution proposed by Pielou (1959) to compare the observed point process to one generated under csr.  The value can be compared to that given by the more widely used test proposed by Clark and Evans (1954).
Cell-type specific differential expression of a microarray
        experiment of heterogeneous tissue samples, using SAM.
A collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.
Selection of the number of clusters in cluster analysis using
    stability methods.
Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014).
Exploits dynamical seasonal forecasts in order to provide
    information relevant to stakeholders at the seasonal timescale. The package
    contains process-based methods for forecast calibration, bias correction,
    statistical and stochastic downscaling, optimal forecast combination and
    multivariate verification, as well as basic and advanced tools to obtain
    tailored products. This package was developed in the context of the
    ERA4CS project MEDSCOPE and the H2020 S2S4E project.
    Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>.
    Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>.
    Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>.
    Straus et al. (2007) <doi:10.1175/JCLI4070.1>.
    Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>.
    Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>.
    D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>.
    Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>.
    Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>.
Reads and writes CSV with selected conventions.
 Uses the same generic function for reading and writing to promote consistent formats.
Functions for loading large (10M+ lines) CSV
    and other delimited files, similar to read.csv, but typically faster and
    using less memory than the standard R loader. While not entirely general,
    it covers many common use cases when the types of columns in the CSV file
    are known in advance. In addition, the package provides a class 'int64',
    which represents 64-bit integers exactly when reading from a file. The
    latter is useful when working with 64-bit integer identifiers exported from
    databases. The CSV file loader supports common column types including
    'integer', 'double', 'string', and 'int64', leaving further type
    transformations  to the user.
Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.
An interactive application for working with contingency Tables. The application has a template for solving contingency table problems like chisquare test of independence,association plot between two categorical variables. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/CategoricalDataAnalysis/>.
Estimation and inference methods for the continuous threshold expectile regression.
    It can fit the continuous threshold expectile regression and test the existence of change point,
    for the paper, "Feipeng Zhang and Qunhua Li (2016). A continuous threshold expectile regression, submitted."
Identification and network inference of genetic loci associated
  with correlation changes in quantitative traits (called correlated trait loci, CTLs).
  Arends et al. (2016) <doi:10.21105/joss.00087>.
The CTM package is designed to solve problems of text mining and is specific for Chinese document.
Functions for estimating Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal adjustment, weighted adjustment and quasi-optimization of matrix logarithm based candidate solutions, an expectation-maximization algorithm as well as a Gibbs sampler.
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework.  As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) <doi:10.1177/0962280217729845>) and the glmnet-C-TMLE algorithm (Ju et al. (2017) <arXiv:1706.10029>).
Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data.
  The package is described in Calabrese et al (2016) <doi:10.1111/2041-210X.12559>, with models and methods based on those introduced in
  Fleming & Calabrese et al (2014) <doi:10.1086/675504>,
  Fleming et al (2014) <doi:10.1111/2041-210X.12176>,
  Fleming et al (2015) <doi:10.1103/PhysRevE.91.032107>,
  Fleming et al (2015) <doi:10.1890/14-2010.1>,
  Fleming et al (2016) <doi:10.1890/15-1607>,
  Péron & Fleming et al (2016) <doi:10.1186/s40462-016-0084-7>,
  Fleming & Calabrese (2017) <doi:10.1111/2041-210X.12673>,
  Péron et al (2017) <doi:10.1002/ecm.1260>,
  Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>,
  Fleming et al (2018) <doi:10.1002/eap.1704>,
  Winner & Noonan et al (2018) <doi:10.1111/2041-210X.13027>,
  Fleming et al (2019) <doi:10.1111/2041-210X.13270>,
  and
  Noonan & Fleming et al (2019) <doi:10.1186/s40462-019-0177-1>.
Estimation of quantile regression models for survival data.
Provides functions for querying, retrieving and analysing
        protocol- and results-related information on clinical trials from
        two public registers, the European Union Clinical Trials Register
        (EUCTR, <https://www.clinicaltrialsregister.eu/>) and
        ClinicalTrials.gov (CTGOV, <https://clinicaltrials.gov/>). The
        information is transformed and then stored in a database (nodbi).
        Functions are provided for accessing and analysing the locally
        stored information on the clinical trials, as well as for
        identifying duplicate records. The package is motivated by the need
        for aggregating and trend-analysing the design, conduct and outcomes
        across clinical trials.
Models extremes of 'bursty' time series via
    Continuous Time Random Exceedances (CTRE).
    See <arXiv:1802.05218>, K. Hees, S. Nayak, P.Straka, 2018.
A simple way to assess the stability of candidate housekeeping genes is implemented in this package.
Hierarchical continuous time state space modelling, for linear
    and nonlinear systems measured by  continuous variables, with limited support for 
    binary data. The subject specific dynamic system is modelled as a stochastic 
    differential equation (SDE), measurement models are typically multivariate normal factor models. 
    Using the original ctsem formulation based on OpenMx, described in the JSS paper 
    "Continuous Time Structural Equation Modeling with R Package ctsem", with updated version
    as CRAN vignette <https://cran.r-project.org/package=ctsem/vignettes/ctsem.pdf> ,
    linear mixed effects SDE's estimated via maximum likelihood and optimization are possible.
    Using the Stan based formulation, described in <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> , 
    nonlinearity (state dependent parameters) and random effects on all parameters
    are possible, using either optimization (with optional importance sampling) or
    Stan's Hamiltonian Monte Carlo sampling. Priors may be used. For the conceptual
    overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>.
    Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> .
    Stan based functions are not available on 32 bit Windows systems at present.
An interactive document on  the topic of classification tree analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/CTShiny/>.
A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs.
A Shiny interface developed in close coordination with the CTT package, providing a GUI that guides the user through CTT analyses.
Interactive shiny application for running classical test theory (item analysis).
Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated
             packages (client-side tools), generating HTML pages and storing task view information in the repository
	     (server-side tools).
For ordinal rating data, estimate and test models within the family of
    CUB models and their extensions (where CUB stands for Combination of a 
	discrete Uniform and a shifted Binomial distributions). Simulation routines, plotting facilities 
	and fitting measures are also provided.
R wrappers around the cubature C library of Steven
    G. Johnson for adaptive multivariate integration over hypercubes
    and the Cuba C library of Thomas Hahn for deterministic and
    Monte Carlo integration. Scalar and vector interfaces for 
    cubature and Cuba routines are provided; the vector interfaces
    are highly recommended as demonstrated in the package
    vignette.
Creates a 3D data cube view of a RasterStack/Brick, typically a 
    collection/array of RasterLayers (along z-axis) with the same geographical 
    extent (x and y dimensions) and resolution, provided by package 'raster'. 
    Slices through each dimension (x/y/z), freely adjustable in location, 
    are mapped to the visible sides of the cube. The cube can be freely rotated. 
    Zooming and panning can be used to focus on different areas of the cube.
Estimating mutation and selection coefficients on synonymous
       codon bias usage based on models of ribosome overhead cost (ROC).
       Multinomial logistic regression and Markov Chain Monte Carlo are used to
       estimate and predict protein production rates with/without the presence
       of expressions and measurement errors. Work flows with examples for
       simulation, estimation and prediction processes are also provided
       with parallelization speedup. The whole framework is tested with
       yeast genome and gene expression data of Yassour, et al. (2009)
       <doi:10.1073/pnas.0812841106>.
Functions for visualizing, animating, solving and 
        analyzing the Rubik's cube. Includes data structures for
		solvable and unsolvable cubes, random moves and random 
		state scrambles and cubes, 3D displays and animations 
		using 'OpenGL', patterned cube generation, and lightweight 
		solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the
		Kociemba solver.
Regression modeling using rules with added instance-based corrections.
Utility functions that provides wrapper to descriptive base functions
  like cor, mean and table.  It makes use of the formula interface to pass
  variables to functions.  It also provides operators to concatenate (%+%), to
  repeat (%n%) and manage character vectors for nice display.
Provides tools for measuring the compositionality of signalling systems (in particular the information-theoretic measure due to Spike (2016) <http://hdl.handle.net/1842/25930> and the Mantel test for distance matrix correlation (after Dietz 1983) <doi:10.1093/sysbio/32.1.21>), functions for computing string and meaning distance matrices as well as an implementation of the Page test for monotonicity of ranks (Page 1963) <doi:10.1080/01621459.1963.10500843> with exact p-values up to k = 22.
Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.
Extends ddply to allow calculation of cumulative
        quantities.
Estimation of number and location of change points in
        mean-shift (piecewise constant) models. Particularly useful to
        model genomic sequences of continuous measurements.
Cumulative descriptive statistics for (arithmetic, geometric, harmonic) mean, median, mode, variance, skewness and kurtosis.
Access the 'Cumulocity' API and retrieve data on devices, measurements, and events. Documentation for the API can be found at <https://www.cumulocity.com/guides/reference/rest-implementation/>.
The curl() and curl_download() functions provide highly
    configurable drop-in replacements for base url() and download.file() with
    better performance, support for encryption (https, ftps), gzip compression,
    authentication, and other 'libcurl' goodies. The core of the package implements a
    framework for performing fully customized requests where data can be processed
    either in memory, on disk, or streaming via the callback or connection
    interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly
    web client see the 'httr' package which builds on this package with http
    specific tools and logic.
The currentSurvival package contains functions for the
        estimation of the current cumulative incidence (CCI) and the
        current leukaemia-free survival (CLFS). The CCI is the
        probability that a patient is alive and in any disease
        remission (e.g. complete cytogenetic remission in chronic
        myeloid leukaemia) after initiating his or her therapy (e.g.
        tyrosine kinase therapy for chronic myeloid leukaemia). The
        CLFS is the probability that a patient is alive and in any
        disease remission after achieving the first disease remission.
Partial application is the process of reducing the arity of a
    function by fixing one or more arguments, thus creating a new function
    lacking the fixed arguments. The curry package provides three different ways
    of performing partial function application by fixing arguments from either
    end of the argument list (currying and tail currying) or by fixing multiple
    named arguments (partial application). This package provides this
    functionality through the %<%, %-<%, and %><% operators which allows for
    a programming style comparable to modern functional languages. Compared
    to other implementations such a purrr::partial() the operators in curry
    composes functions with named arguments, aiding in autocomplete etc.
Computes the maximum likelihood estimator, the smoothed maximum likelihood
    estimator and pointwise bootstrap confidence intervals for the distribution 
    function under current status data. 
    Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>.
Performs multiple comparison procedures on curve observations among different treatment groups. The methods are applicable in a variety of situations (such as independent groups with equal or unequal sample sizes, or repeated measures) by using parametric bootstrap. References to these procedures can be found at Konietschke, Gel, and Brunner (2014) <doi:10.1090/conm/622/12431> and Westfall (2011) <doi:10.1080/10543406.2011.607751>.
Data recorded as paths or trajectories may be suitably described 
  by curves, which are independent of their parametrization. For the space of 
  such curves, the package provides functionalities for reading curves, sampling 
  points on curves, calculating distance between curves and for computing Tukey
  curve depth of a curve w.r.t. to a bundle of curves. For details see 
  Lafaye De Micheaux, Mozharovskyi, and Vimond (2019) <arXiv:1901.00180>.
Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.
Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, & Wagenmakers, 2009, JSS, 32:8;
        Cobb, L, 1981, Behavioral Science, 26:1, 75--78).
        Includes a cusp() function for model fitting, and several
        utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.
Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.
Customized training is a simple technique for transductive
    learning, when the test covariates are known at the time of training. The
    method identifies a subset of the training set to serve as the training set
    for each of a few identified subsets in the training set. This package
    implements customized training for the glmnet() and cv.glmnet() functions.
Create complicated drawing areas for multiple elements by combining much simpler layouts. It is an extended version of layout function from the 'graphics' package, but it also works with 'grid' graphics. It also supports arranging elements inside 'PowerPoint' slides created using the 'officer' package.
Customizable higher-order recipe step functions for 
    the 'recipes' package. These step functions take 'prep' and 'bake' helper 
    functions as inputs and create specifications of customized recipe steps 
    as output.
Provides functions for constructing and evaluating
    CUSUM charts and RA-CUSUM charts with focus on false signal probability.
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts.
Seek the significant cutoff value for a continuous variable, which will 
    be transformed into a classification, for linear regression, 
    logistic regression, logrank analysis and cox regression. First of all, 
    all combinations will be gotten by combn() function. Then n.per argument, 
    abbreviated of total number percentage, will be used to remove the combination 
    of smaller data group. In logistic, Cox regression and logrank analysis, 
    we will also use p.per argument, patient percentage, to filter the lower 
    proportion of patients in each group. Finally, p value in regression 
    results will be used to get the significant combinations and output 
    relevant parameters. In this package, there is no limit to the number of 
    cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, 
    typical Bonferroni and Duglas G (1994) <doi: 10.1093/jnci/86.11.829>, to 
    adjust the p value, Missing values will be deleted by na.omit() function 
    before analysis.
This package provides a set of tools for spatio-temporal imputation 
          in R. It includes the implementation for then CUTOFF imputation method, 
          a useful cross-validation function that can be used not only by the 
          CUOTFF method but also by some other imputation  functions to help 
          choosing an optimal value for relevant parameters, such as the number 
          of k-nearest neighbors for the KNN imputation method, or the number of
          components for the SVD imputation method. It also contains tools for 
          simulating data with missing values with respect to some specific 
          missing pattern, for example, block missing. Some useful visualisation
          functions for imputation purposes are also provided in the package.
Estimate cutpoints that optimize a specified metric in binary classification tasks
    and validate performance using bootstrapping. Some methods for more robust cutpoint
    estimation and various plotting functions are included.
Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.
This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model
Compute expected shortfall (ES) and Value at Risk (VaR) from a
    quantile function, distribution function, random number generator or
    probability density function.  ES is also known as Conditional Value at
    Risk (CVaR). Virtually any continuous distribution can be specified.
    The functions are vectorized over the arguments. The computations are
    done directly from the definitions, see e.g. Acerbi and Tasche (2002)
    <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided,
    as well.
This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
Statistical inferences for estimating the calibration equation with error-in observations
Provides some easy-to-use functions and classes to calculate 
    variability measures such as coefficient of variation with confidence 
    intervals provided with all available methods. References are 
    Panichkitkosolkul (2013) <doi:10.1155/2013/324940> ,
    Altunkaynak & Gamgam (2018) <doi:10.1080/03610918.2018.1435800> ,
    Albatineh, Kibria, Wilcox & Zogheib (2014) <doi:10.1080/02664763.2013.847405> .
Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> 
            is suitable for cluster randomized trials (CRTs) with a
            small number of clusters (e.g., 20 or fewer). The procedure of
            constrained randomization is based on the baseline values of some
            cluster-level covariates specified. The intervention effect on
            the individual outcome can then be analyzed through
            clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. 
            Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline
            values of cluster-level covariates and cluster permutation test on the individual-level outcome for cluster randomized trials.
Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.
Contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.
Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>.
Estimates a lasso penalized precision matrix via the blockwise coordinate descent (BCD). This package is a simple wrapper around the popular 'glasso' package that extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations.
              See Friedman et al (2008) <doi:10.1093/biostatistics/kxm045> for details regarding the estimation method.
Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828> (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) <doi:10.2307/3315735> and Lockhart, Spinelli and Stephens (2007) <doi:10.1002/cjs.5550350111> (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) <doi:10.2307/3316040>. Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values.
It is devoted to Cramer-von Mises goodness-of-fit tests.
    It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
Cross-validate one or multiple regression and classification models
    and get relevant evaluation metrics in a tidy format. Validate the
    best model on a test set and compare it to a baseline evaluation.
    Alternatively, evaluate predictions from an external model. Currently
    supports regression and classification (binary and multiclass).
    Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., 
    & Wambugu M. (2019, ISBN: 9781838550134).
The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient. This value can be calculated with an external data set or by cross validation.
Perform canonical variate regression (CVR) for two sets of covariates and a univariate
            response, with regularization and weight parameters tuned by cross validation.
The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
This package carries out level-dependent cross-validation
        method for the selection of thresholding value in wavelet
        shrinkage. This procedure is implemented by coupling a
        conventional cross validation with an imputation method due to
        a limitation of data length, a power of 2. It can be easily
        applied to classical leave-one-out and k-fold cross validation.
        Since the procedure is computationally fast, a level-dependent
        cross validation can be performed for wavelet shrinkage of
        various data such as a data with correlated errors.
Tools that allow developers to write functions for
        cross-validation with minimal programming effort and assist
        users with model selection.
This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.
An iterative algorithm for solving a convex
    formulation of the biclustering problem.
Alternating Minimization Algorithm (AMA) and Alternating Direction
    Method of Multipliers (ADMM) splitting methods for convex clustering.
An object-oriented modeling language for disciplined convex
    programming (DCP). It allows the user to formulate convex optimization problems
    in a natural way following mathematical convention and DCP rules. The system
    analyzes the problem, verifies its convexity, converts it into a canonical form,
    and hands it off to an appropriate solver to obtain the solution.
The 'Corpus Workbench' ('CWB', <http://cwb.sourceforge.net/>) offers a classic and mature
 approach for working with large, linguistically and structurally annotated corpora. The 'CWB'
 is memory efficient and its design makes running queries fast (Evert and Hardie 2011,
 <http://www.stefan-evert.de/PUB/EvertHardie2011.pdf>). The 'cwbtools' package offers
 pure R tools to create indexed corpus files as well as high-level wrappers for the original C
 implementation of CWB as exposed by the 'RcppCWB' package
 <https://CRAN.R-project.org/package=RcppCWB>. Additional functionality to add and
 modify annotations of corpora from within R makes working with CWB indexed corpora
 much more flexible and convenient. The 'cwbtools' package in combination with the R packages
 'RcppCWB' (<https://CRAN.R-project.org/package=RcppCWB>) and 'polmineR'
 (<https://CRAN.R-project.org/package=polmineR>) offers a lightweight infrastructure
 to support the combination of quantitative and qualitative approaches for working
 with textual data.
Miscellaneous useful or interesting functions.
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
Computes the convex hull in arbitrary dimension, based on the Qhull library (<http://www.qhull.org>). The package provides a complete description of the convex hull: edges, ridges, facets, adjacencies. Triangulation is optional.
extend cxxfunction by saving the dynamic shared objects
        for reusing across R sessions
An approach to filter out and/or identify synechococcus type cyanobacteria cells from all particles measured via flow cytometry.
    It combines known characteristics of these cyanobacteria strains alongside gating techniques developed by Mehrnoush, M. et al. (2015) <doi:10.1093/bioinformatics/btu677> 
    in the 'flowDensity' package to identify and separate these cyanobacteria cells from other cell types.
    Aside the gating techniques in the 'flowDensity' package, an EM style clustering technique
    is also developed to identify these cyanobacteria cell populations.
A suite of functions for analysing cycling data.
An interface to the cycle routing/data services provided by 'CycleStreets',
  a not-for-profit social enterprise and advocacy organisation.
  The application programming interfaces (APIs) provided by 'CycleStreets' are documented at (<https://www.cyclestreets.net/api/>).
  The focus of this package is the journey planning API, which aims to emulate the routes taken by a knowledgeable cyclist.
  An innovative feature of the routing service of its provision of fastest, quietest and balanced profiles.
  These represent routes taken to minimise time, avoid traffic and compromise between the two, respectively.
Cyclomatic complexity is a software metric (measurement),
    used to indicate the complexity of a program. It is a quantitative
    measure of the number of linearly independent paths through a program's
    source code. It was developed by Thomas J. McCabe, Sr. in 1976.
Tools for calculating coordinate representations of
    hypocycloids, epicyloids, hypotrochoids, and epitrochoids
    (altogether called 'cycloids' here) with different scaling
    and positioning options. The cycloids can be visualised with
    any appropriate graphics function in R.
This model fitting tool incorporates cyclic coordinate descent and
    majorization-minimization approaches to fit a variety of regression models
    found in large-scale observational healthcare data.  Implementations focus
    on computational optimization and fine-scale parallelization to yield
    efficient inference in massive datasets.  Please see:
    Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>.
A toolkit for querying 'Team Cymru' <http://team-cymru.org> 'IP'
    address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon
    and 'Malware' Hash Data Services.
This library contains a primary function that divides
        chewing sequences in cycles and cycles into phases.  See
        get.all.breaks for an example.
Encryption wrappers, using low-level support from
    'sodium' and 'openssl'.  'cyphr' tries to smooth over some pain
    points when using encryption within applications and data analysis
    by wrapping around differences in function names and arguments in
    different encryption providing packages.  It also provides
    high-level wrappers for input/output functions for seamlessly
    adding encryption to existing analyses.
The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.
Tools to interface with Cytobank's API via R, organized by various
    endpoints that represent various areas of Cytobank functionality. Learn more
    about Cytobank at <https://www.cytobank.org>.
Tools to process cytometry data from Cytobank into easily usable form for analysis of populations, markers, and signaling using the 'CytobankAPI' package. Learn more about Cytobank at <https://www.cytobank.org>. For more information about types of cytometry data that can be analyzed, please see: Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E. D., Krutzik, P. O., Finck, R.,... Nolan, G. P. (2011) <doi:10.1126/science.1198704> and Adan, A., Alizada, G., Kiraz, Y., Baran, Y., Nalbant, A. (2017). <doi:10.3109/07388551.2015.1128876>.
A collection of tools that leverage the CytobankAPI R package <https://cran.r-project.org/web/packages/CytobankAPI/vignettes/cytobank-quickstart.html> to complete more complex workflows, and add/extend various Cytobank features.
Calculates ecological diversity indices for a microbial
        community
An implementation of Fan plots for cytometry data in 'ggplot2'. 
    For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart 
    <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).
Given the hypothesis of a bi-modal distribution of cells for
    each marker, the algorithm constructs a binary tree, the nodes of which are
    subpopulations of cells. At each node, observed cells and markers are modeled
    by both a family of normal distributions and a family of bi-modal normal mixture
    distributions. Splitting is done according to a normalized difference of AIC
    between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, 
    Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.
Creates interactive genome browser with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Create interactive heat maps that are usable from the R console, in
    the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover
    the mouse pointer over a cell to show details, drag a rectangle to zoom, and
    click row/column labels to highlight.
This packages is intended to make it easy to create D3 JavaScript
    network, tree, dendrogram, and Sankey graphs from R using data frames.
    !!! NOTE: Active development has moved to the networkD3 package. !!!
Builds interactive 'd3.js' hierarchical visualisation easily. D3partitionR makes it easy to build and customize sunburst, circle treemap, treemap, partition chart, ...
Provides functions that offer seamless 'D3Plus' integration. The examples provided here are taken from the official 'D3Plus' website <http://d3plus.org>.
Provides a suite of functions to help ease the use of 'd3.js' in R.
              These helpers include 'htmltools::htmlDependency' functions, hierarchy
              builders, and conversion tools for 'partykit', 'igraph,' 'table',
              and 'data.frame' R objects into the 'JSON' that 'd3.js' expects.
Create and customize interactive collapsible 'D3' trees using the 'D3'
    JavaScript library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
    When in Shiny the tree layout is observed by the server and can be used as a reactive filter
    of structured data.
Various data sets used in examples and exercises in the
        book Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
        Analysis and Graphics Using R".
Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.
various data sets used in additional exercises for
        the book Maindonald, J.H. and Braun, W.J. (3rd edn 2010)
        "Data Analysis and Graphics Using R", and for a
        'Data Mining' course.  Note that a number of datasets
        that were in earlier versions of this package have been
        transferred to the DAAG package.
Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method. see Henderson, N.C. and Varadhan, R. (2018) <arXiv:1803.06673>.
Data Analysis using Bootstrap-Coupled ESTimation.
    Estimation statistics is a simple framework that avoids the pitfalls of 
    significance testing. It uses familiar statistical concepts: means, 
    mean differences, and error bars. More importantly, it focuses on the 
    effect size of one's experiment/intervention, as opposed to a false 
    dichotomy engendered by P values.
    An estimation plot has two key features:
    1. It presents all datapoints as a swarmplot, which orders each point to 
    display the underlying distribution.
    2. It presents the effect size as a bootstrap 95% confidence interval on a 
    separate but aligned axes.
    Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. 
    <doi:10.1038/s41592-019-0470-3>. 
    The free-to-view PDF is located at <https://rdcu.be/bHhJ4>.
Allows to calculate Data Agreement Criterion (DAC) scores. This can be done to determine prior-data conflict or to evaluate and compare multiple priors, which can be experts' predictions. Bousquet (2008) <doi.org/10.1080/02664760802192981>.
An implementation of data analytic methods in R for analyses for data with ceiling/floor effects. The package currently includes functions for mean/variance estimation and mean comparison tests. Implemented methods are from Aitkin (1964) <doi:10.1007/BF02289723> and Liu & Wang (in prep).
The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
The content falls into the following groupings: (i) Data, (ii)
    Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v)
    Matrix functions, (vi) Projector and canonical efficiency functions, and (vii)
    Miscellaneous functions. There is a vignette describing how to use the 
    design functions for randomizing and assessing designs available as a 
    vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of 
    information when the 'Error' function has been used in the call to 'aov'. 
    The package 'dae' can also be installed from 
  <http://chris.brien.name/rpackages/>.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Diff, patch and merge for data frames. Document changes in data
    sets and use them to apply patches. Changes to data can be made visible by using
    render_diff. The V8 package is used to wrap the 'daff.js' JavaScript library
    which is included in the package.
Quantitative comparison of microbial composition from different populations is a fundamental task in various microbiome studies. The main goal of this package is to provide a new method for two-sample testing for microbial compositional data by leveraging the phylogenetic tree information. Empirical evidence from real data sets suggests that the phylogenetic microbial composition difference between two populations is usually  sparse. Motivated by this observation, this package implements a new maximum type test, Detector of Active Flow on a Tree (DAFOT). It is shown that DAFOT is particularly powerful against sparse phylogenetic composition  difference and enjoys certain optimality.
    Chen, J., Bittinger, K., Charlson, E. S., Hoffmann, C., Lewis, J., Wu, G. D., Collman, R. G., Bushman, F. D., Li, H. (2012) <doi:10.1093/bioinformatics/bts342>.
Data and miscellanea to support the book "Introduction to
        Data analysis with R for Forensic Scientists", Curran, J.M.
        2010 CRC Press ISBN: 978-1-4200-8826-7
A port of the web-based software 'DAGitty', available at 
    <http://dagitty.net>, for analyzing structural causal models 
    (also known as directed acyclic graphs or DAGs).
    This package computes covariate adjustment sets for estimating causal
    effects, enumerates instrumental variables, derives testable
    implications (d-separation and vanishing tetrads), generates equivalent
    models, and includes a simple facility for data simulation.
Functions to draw, manipulate, evaluate directed
        acyclic graphs and simulate corresponding data.
Reverse and model the effects of changing deposition rates on geological data and rates. Based on Hohmann (2018) <doi:10.13140/RG.2.2.23372.51841> .
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. 2015. Ecology Letters 18: 844-852, <DOI:10.1111/ele.12461>.
Functions and an example dataset for the psychometric theory of
  knowledge spaces.  This package implements data analysis methods and
  procedures for simulating data and quasi orders and transforming different
  formulations in knowledge space theory.  See package?DAKS for an overview.
Machine Learning (ML) models are widely used and have various applications in classification 
  or regression. Models created with boosting, bagging, stacking or similar techniques are often
  used due to their high performance, but such black-box models usually lack of interpretability.
  DALEX package contains various explainers that help to understand the link between input variables and model output.
  The single_variable() explainer extracts conditional response of a model as a function of a single selected variable.
  It is a wrapper over packages 'pdp' (Greenwell 2017) <doi:10.32614/RJ-2017-016>, 
  'ALEPlot' (Apley 2018) <arXiv:1612.08468>  and 'factorMerger' (Sitko and Biecek 2017) <arXiv:1709.04412>.
  The single_prediction() explainer attributes parts of a model prediction to particular variables used in the model.
  It is a wrapper over 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>.
  The variable_dropout() explainer calculates variable importance scores based on variable shuffling 
  (Fisher at al. 2018) <arXiv:1801.01489>.
  All these explainers can be plotted with generic plot() function and compared across different models.
  'DALEX' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Machine Learning models are widely used and have various applications in classification or regression tasks. 
    Due to increasing computational power, availability of new data sources and new methods, ML models are more and more complex. 
    Models created with techniques like boosting, bagging of neural networks are true black boxes. 
    It is hard to trace the link between input variables and model outcomes. 
    They are used because of high performance, but lack of interpretability is one of their weakest sides.
    In many applications we need to know, understand or prove how input variables are used in the model and what impact do they have on final model prediction. 
    DALEX2 is a collection of tools that help to understand how complex predictive models are working.
    DALEX2 is a part of DrWhy universe for tools for Explanation, Exploration and Visualisation for Predictive Models.
Provides wrapper of various machine learning models. 
  In applied machine learning, there 
  is a strong belief that we need to strike a balance 
  between interpretability and accuracy. 
  However, in field of the interpretable machine learning, 
  there are more and more new ideas for explaining black-box models, 
  that are implemented in 'R'. 
  'DALEXtra' creates 'DALEX' Biecek (2018) <arXiv:1806.08915> explainer for many type of models
  including those created using 'python' 'scikit-learn' and 'keras' libraries, 'java' 'h2o' library and
  'mljar' API. Important part of the package is Champion-Challenger analysis and innovative approach
  to model performance across subsets of test data presented in Funnel Plot. 
  Third branch of 'DALEXtra' package is aspect importance analysis
  that provides instance-level explanations for the groups of explanatory variables.
Automates fitting of double GLM in 'JAGS'. Includes automatic
    generation of 'JAGS' scripts, running 'JAGS' via 'rjags', and summarizing the
    resulting output.
The DALY Calculator is a free, open-source Graphical User
             Interface (GUI) for stochastic disability-adjusted life year
             (DALY) calculation.
A collection of functions which aim to assist common computational workflow for analysis of matabolomic data..
Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.
Miscellaneous set of functions I use in my teaching either at the University of Western Ontario or the Inter-university Consortium for Political and Social Research (ICPSR) Summer Program in Quantitative Methods.  Broadly, the functions help with presentation and interpretation of LMs and GLMs, but also implement some new tools like Alternating Least Squares Optimal Scaling for dependent variables, a Bayesian analog to the ALSOS algorithm.  There are also tools to help understand interactions in both LMs and binary GLMs.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
Loads behavioural data from the widely used Drosophila Activity Monitor System (DAMS, TriKinetics <https://trikinetics.com/>) into the rethomics framework.
The single largest source of dams in the United States is the
    National Inventory of Dams (NID) <http://nid.usace.army.mil> from the US
    Army Corps of Engineers. Entire data from the NID cannot be obtained all at
    once and NID's website limits extraction of more than a couple of thousand
    records at a time. Moreover, selected data from the NID's user interface
    cannot not be saved to a file. In order to make the analysis of this data
    easier, all the data from NID was extracted manually. Subsequently, the raw
    data was checked for potential errors and cleaned. This package provides
    sample cleaned data from the NID and provides functionality to access the
    entire cleaned NID data.
Contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.
Provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size 
 calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to 
 design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.
Discriminant Adaptive Nearest Neighbor Classification is a 
    variation of k nearest neighbors where the neighborhood is elongated
    along class boundaries. This package implements dann and sub_dann from
    Hastie (1995) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
An implementation of Discriminant Analysis via Projections (DAP) method for high-dimensional binary classification in the case of unequal covariance matrices. See Irina Gaynanova and Tianying Wang (2018) <arXiv:1711.04817v2>.
An easy-to-use, dependency-free set of functions for iterating over
    elements of various input objects. Functions are wrappers around base
    apply()/lapply()/vapply() functions but designed to have similar
    functionality to the mapping functions in the 'purrr' package
    <https://purrr.tidyverse.org/>. Specifically, function names more explicitly
    communicate the expected class of the output and functions also allow for
    the convenient shortcut of '~ .x' instead of the more verbose
    'function(.x) .x'.
The recovery of visual sensitivity in a dark environment is known
    as dark adaptation. In a clinical or research setting the recovery is typically
    measured after a dazzling flash of light and can be described by the Mahroo,
    Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take
    dark adaptation data and use nonlinear regression to find the parameters of the
    model that 'best' describe the data. They do this by firstly, generating rapid
    initial objective estimates of data adaptation parameters, then a multi-start
    algorithm is used to reduce the possibility of a local minimum. There is also a
    bootstrap method to calculate parameter confidence intervals. The functions rely
    upon a 'dark' list or object. This object is created as the first step in the
    workflow and parts of the object are updated as it is processed.
Estimation of dark diversity using species co-occurrences. 
    It includes implementations of probabilistic dark diversity based on the 
    Hypergeometric distribution, as well as estimations based on the Beals index,
    which can be transformed to binary predictions using different thresholds, 
    or transformed into a favorability index. All methods include the possibility of
    using a calibration dataset that is used to estimate the indication matrix 
    between pairs of species, or to estimate dark diversity directly on a single 
    dataset. See De Caceres and Legendre (2008) <doi:10.1007/s00442-008-1017-y>, 
    Lewis et al. (2016) <doi:10.1111/2041-210X.12443>, 
    Partel et al. (2011) <doi:10.1016/j.tree.2010.12.004>, Real et al. (2017) <doi:10.1093/sysbio/syw072>
    for further information.
Provides programmatic access to the 'Dark Sky' 'API' 
    <https://darksky.net/dev/docs>, which provides current or historical global 
    weather conditions.
Functions are provided that facilitate the import and analysis of
    SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. The main focus is on data generated
    by DarT (Diversity Arrays Technology). However, once SNP or related fragment
    presence/absence data from any source is imported into a genlight object many
    of the functions can be used. Functions are available for input and output of
    SNP and silicodart data, for reporting on and filtering on various criteria
    (e.g. CallRate, Heterozygosity, Reproducibility, maximum allele frequency).
    Advanced filtering is based on Linkage Disequilibrium and HWE (Hardy-Weinberg equilibrium). Other functions
    are available for visualization after PCoA (Principle Coordinate Analysis), or to facilitate transfer of data
    between genlight/genind objects and newhybrids, related, phylip, structure, faststructure packages.
Are you aiming at the right spot in darts? Maybe not! Use
        this package to find your optimal aiming location. For a better
        explanation, go to
        http://www-stat.stanford.edu/~ryantibs/darts/ or see the paper
        "A Statistician Plays Darts".
The dashboard package allows users to create web pages which display 
	interactive data visualizations working in a standard modern browser. It displays them locally 
	using the Rook server. Nor knowledge about web technologies nor Internet connection are 
	required. D3.js is a JavaScript library for manipulating documents based on data. 
	D3 helps the dashboard package bring data to life using HTML, SVG and CSS.
Provides methods for reading, displaying,
    processing and writing files originally arranged for
    the 'DSSAT-CSM' fixed width format. The 'DSSAT-CSM' 
    cropping system model is described at J.W. Jones,
    G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor,
    L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, 
    J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.
An implementation of common higher order functions with syntactic
    sugar for anonymous function. Provides also a link to 'dplyr' for common
    transformations on data frames to work around non standard evaluation by
    default.
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and traverse the
    tree in various orders. Aggregate, cumulate, print, plot, convert to and from
    data.frame and more. Useful for decision trees, machine learning, finance,
    conversion from and to JSON, and many other applications.
High-level tools for working with 'data.world' data sets. 'data.world' is a platform 
    where you can find interesting data, store and showcase your own data and data projects, 
    and find and collaborate with other members. In addition to exploring, querying and 
    charting data on the data.world site, you can access data via 'API' endpoints and 
    integrations. Use this package to access, query and explore data sets, and to 
    publish your insights. Visit <https://data.world>, for additional information.
Provides methods for using differential equations as modelling  
 objects as described in J. Ramsay G. and Hooker (2017,ISBN 978-1-4939-7188-6) 
 Dynamic Data Analysis, New York: Springer. Data sets and script files for 
 analyzing many of the examples in this book are included. This version 
 corrects bugs and adds a new demo, CruiseControl. 'Matlab' versions of the 
 code and sample analyses are available by ftp from 
 <http://www.psych.mcgill.ca/misc/fda/downloads/Data2LD/>.  There you find 
 a set of .zip files containing the functions and sample analyses, as well 
 as two .txt files giving instructions for installation and some additional 
 information.
An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server', 
    'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', and 'SQLite'). Also includes support for
    fetching data as 'ffdf' objects. Uses 'Java Database Connectivity' ('JDBC') to connect to databases (except SQLite).
Provides external JAR dependencies for the 'DatabaseConnector' package.
Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called databionic swarm (DBS) is introduced. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The package is based on the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>. A comparison to 26 common clustering algorithms on 15 datasets is presented on the website.
Functions to check variables against a
    set of data quality rules. A rule file can be accompanied by look-up tables. In
    addition, there are some convenience functions that may
    serve as an example for defining clearer 'data rules'. An
    HTML based user interface facilitates initial exploration of the
    functionality.
Includes functions that researchers or practitioners may use to clean
    raw data, transferring html, xlsx, txt data file into other formats. And it
    also can be used to manipulate text variables, extract numeric variables from
    text variables and other variable cleaning processes. It is originated from a
    author's project which focuses on creative performance in online education
    environment. The resulting paper of that study will be published soon.
Tools for combining and cleaning data sets, particularly
    with grouped and time series data.
Easy comparison of two tabular data
    objects in R. Specifically designed to show differences between two sets of
    data in a useful way that should make it easier to understand the differences,
    and if necessary, help you work out how to remedy them. Aims
    to offer a more useful output than all.equal() when your two data sets do not
    match, but isn't intended to replace all.equal() as a way to test for equality.
An R interface for Rho's 'web-codebook' 'JavaScript' charting library, which provides a simple interactive framework for exploring data. 'datadigest' includes two key functions (codebook() and explorer()) that deliver a concise summary of every variable in a data frame, along with interactive features such as real-time filters, grouping, and highlighting. Each function has an associated 'RStudio' addin/'Shiny' app for quick and convenient exploration of one or more data frames.
Query for metrics from 'Datadog' (<https://www.datadoghq.com/>) via its API.
Methods for dividing data into subsets, applying analytical
    methods to the subsets, and recombining the results.  Comes with a generic
    MapReduce interface as well.  Works with key-value pairs stored in memory,
    on local disk, or on HDFS, in the latter case using the R and Hadoop
    Integrated Programming Environment (RHIPE).
This is a GUI application for defining
  attributes and setting valid values of variables, and then,
  entering questionnaire data in a data.frame.
Functions intended to work with the API of the Spain Government <https://datos.gob.es/en/apidata> that allows you to download, analyze and generate information through datasets obtained from the API. The API containing information about multiples topics from the municipalities and organizations of Spain, it is possible to find URIs corresponding to the primary sector taxonomy and the identification of geographical coverage defined in Annexes IV and V <https://www.boe.es/diario_boe/txt.php?id=BOE-A-2013-2380> (NTI).
Automated data exploration process for analytic tasks and predictive modeling, so
    that users could focus on understanding data and extracting insights. The package scans and
    analyzes each variable, and visualizes them with typical graphical techniques. Common
    data processing methods are also available to treat and format data.
Writes data frames to xls files. It supports 
        multiple sheets and basic formatting.
Automatic generation of finite state machine models of dynamic 
    decision-making that both have strong predictive power and are 
    interpretable in human terms. We use an efficient model representation and 
    a genetic algorithm-based estimation process to generate simple 
    deterministic approximations that explain most of the structure of complex 
    stochastic processes. We have applied the software to empirical data, and 
    demonstrated it's ability to recover known data-generating processes by 
    simulating data with agent-based models and correctly deriving the 
    underlying decision models for multiple agent models and degrees of
    stochasticity.
Functions to save either '.dtable' or '.dtbin' files that can be read by DataGraph, a graphing and analysis application for macOS. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://www.visualdatatools.com/DataGraph/Help/DataFiles/R/index.html>.
Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.
Data screening is an important first step of any statistical
    analysis. dataMaid auto generates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset.
datamap utilizes variable bindings and objects of class
        "UserDefinedDatabase" to provide a simple mapping system to
        foreign objects. Maps can be used as environments or attached
        to the search path, and changes to either are persistent.
        Mapped foreign objects are fetched in real-time and are never
        cached by the mapping system.
Easily create interactive choropleth maps then add bubbles and arcs by coordinates or region name. 
    These maps can be used directly from the console, from 'RStudio', in 'Shiny' apps and 'R Markdown' documents. 'Shiny' 
    proxies allow to interactively add arcs and bubbles, change choropleth values, or change labels.
Provides an S4 infrastructure for unified handling
    of internal datasets and web based data sources. The package is
    currently in beta; things may break, change or go away without
    warning.
Designed to create a basic data dictionary and append to the original dataset's attributes list. The package makes use of a tidy dataset and creates a data frame that will serve as a linker that will aid in building the dictionary. The dictionary is then appended to the list of the original dataset's attributes. The user will have the option of entering variable and item descriptions by writing code or use alternate functions that will prompt the user to add these.
Post Global Financial Crisis derivatives reforms have lifted the 
  veil off over-the-counter (OTC) derivative markets. Swap Execution Facilities
  (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are 
  traded on or reported to those facilities (respectively). This package provides
  you the ability to get this data from supported sources.
Provides read and write access to data and metadata from
    the DataONE network <https://www.dataone.org> of data repositories.  
    Each DataONE repository implements a consistent repository application 
    programming interface. Users call methods in R to access these remote 
    repository functions, such as methods to query the metadata catalog, get 
    access to metadata for particular data packages, and read the data objects 
    from the data repository. Users can also insert and update data objects on 
    repositories that support these methods.
Provides a flexible container to transport and manipulate complex
    sets of data. These data may consist of multiple data files and associated
    meta data and ancillary files. Individual data objects have associated system
    level meta data, and data files are linked together using the OAI-ORE standard
    resource map which describes the relationships between the files. The OAI-
    ORE standard is described at <https://www.openarchives.org/ore>. Data packages
    can be serialized and transported as structured files that have been created
    following the BagIt specification. The BagIt specification is described at
    <https://tools.ietf.org/html/draft-kunze-bagit-08>.
Work with 'Frictionless Data Packages' (<https://frictionlessdata.io/specs/data-package/>). Allows to load and validate any descriptor for a data package profile, create and modify descriptors and provides expose methods for reading and streaming data in the package. When a descriptor is a 'Tabular Data Package', it uses the 'Table Schema' package (<https://CRAN.R-project.org/package=tableschema.r>) and exposes its functionality, for each resource object in the resources field.
A framework to help construct R data packages in a 
  reproducible manner. Potentially time consuming processing of 
  raw data sets into analysis ready data sets is done in a reproducible 
  manner and decoupled from the usual R CMD build process so that
  data sets can be processed into R objects in the data package and 
  the data package can then be shared, built, and installed by others
  without the need to repeat computationally costly data processing.
  The package maintains data provenance by turning the data processing
  scripts into package vignettes, as well as enforcing documentation 
  and version checking of included data objects. Data packages can be
  version controlled in github, and used to share data for manuscripts,
  collaboration and general reproducibility.
RStudio addins and R functions that make copy-pasting vectors and tables to text painless.
Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of data.table efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.
The package performs variable level data quality checks including
    missing values, unique values, frequency tables, and generates summary
    statistics
Collection of functions to help retrieve U.S. Geological Survey
    (USGS) and U.S. Environmental Protection Agency (EPA) water quality and
    hydrology data from web services. USGS web services are discovered from 
    National Water Information System (NWIS) <https://waterservices.usgs.gov/> and <https://waterdata.usgs.gov/nwis>. 
    Both EPA and USGS water quality data are obtained from the Water Quality Portal <https://www.waterqualitydata.us/>.
Contains data organized by topics: categorical data, regression model, 
            means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.
For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.
The Datasaurus Dozen is a set of datasets with the same summary statistics. They 
             retain the same summary statistics despite having radically different distributions.
             The datasets represent a larger and quirkier object lesson that is typically taught
             via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet
             contains four very different distributions with the same summary statistics and as 
             such highlights the value of visualisation in understanding data, over and above
             summary statistics. As well as being an engaging variant on the Quartet, the data
             is generated in a novel way. The simulated annealing process used to derive datasets 
             from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating 
             Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" 
             <doi:10.1145/3025453.3025912>.
Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.
Visual interface for loading datasets in RStudio from all installed (unloaded) packages, also includes command line interfaces.
Provides a convenient API interface to access immunological data
    within 'the CAVD DataSpace'(<https://dataspace.cavd.org>), a data sharing 
    and discovery tool that facilitates exploration of HIV immunological data 
    from pre-clinical and clinical HIV vaccine studies.
For estimation of a variable of interest using Kalman filter by incorporating results from previous assessments, i.e. through development weighted estimates where weights are assigned inversely proportional to the variance of existing and new estimates. For reference see Ehlers et al. (2017) <doi:10.20944/preprints201710.0098.v1>.
Based on a SAS data step. This allows for row-wise dynamic building
    of data, iteratively importing slices of existing dataframes, conducting
    analyses, and exporting to a results frame. This is particularly useful for
    differential or time-series analyses, which are often not well suited to vector-
    based operations.
Implementation of advanced data structures such as hashmaps,
    heaps, or queues. Advanced data structures are essential
    in many computer science and statistics problems, for example graph
    algorithms or string analysis. The package uses 'Boost' and 'STL' data
    types and extends these to R with 'Rcpp' modules.
It allows the user to retrieve the data from the systems of 
    DATASUS (SUS IT department related to the Brazilian Ministry of Health, 
    see <http://datasus.saude.gov.br/informacoes-de-saude/tabnet/> for more
    information) much in the same way that is done in the online portal.
    For now the package allows access to the SINASC and SIM's (ICD-10) 
    systems, that is, the 'Estatísticas Vitais'.
Contains facilities such as getting the current timestamp in decimal seconds, computing interval w.r.t. a reference timestamp, and custom plotting with error bars.
Provides access to Dataverse version 4 APIs <https://dataverse.org/>, 
    enabling data search, retrieval, and deposit. For Dataverse versions <= 4.0, 
    use the deprecated 'dvn' package <https://cran.r-project.org/package=dvn>.
Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of 'DataVisualizations' is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data presented in Thrun et al. (2019) <arXiv:1908.06081>. The MD-plot outperforms the box-and-whisker diagram (box plot) and bean plot. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, 'DataVisualizations' makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <doi:10.1007/978-3-658-20540-9>.
Gives access to data visualisation methods that are relevant from the statistician's point of view. Using 'D3''s existing data visualisation tools to empower R language and environment. The throw chart method is a line chart used to illustrate paired data sets (such as before-after, male-female).
Provides methods for working with nominal dates, times, and 
 durations. Base R has sophisticated facilities for handling time, but these 
 can give unexpected results if, for example, timezone is not handled properly. 
 This package provides a more casual approach to support cases which 
 do not require rigorous treatment.  It systematically deconstructs the 
 concepts origin and timezone, and de-emphasizes the display of seconds. It 
 also converts among nominal durations such as seconds, hours, days, and weeks.
 See '?datetime' and '?duration' for examples. Adapted from 'metrumrg' 
 <http://r-forge.r-project.org/R/?group_id=1215>.
Utilities for handling dates and times, such
   as selecting particular days of the week or month,
   formatting timestamps as required by RSS feeds, or
   converting timestamp representations of other software
   (such as 'MATLAB' and 'Excel') to R. The package is
   lightweight (no dependencies, pure R implementations) and
   relies only on R's standard classes to represent dates
   and times ('Date' and 'POSIXt'); it aims to provide
   efficient implementations, through vectorisation and the
   use of R's native numeric representations of timestamps
   where possible.
This package performs voxel-wise deconvolution analysis of
        DCE-MRI contrast agent concentration versus time data and
        generates the Impulse Response Function, which can be used to
        approximate commonly utilized kinetic parameters such as Ktrans
        and ve.  An interactive advanced voxel diagnosis tool (AVDT) is
        also provided to facilitate easy navigation of voxel-wise data.
A thin wrapper around the 'Datorama' API.
    Ideal for analyzing marketing data from <https://datorama.com>.
Provee una versión traducida de los siguientes conjuntos de datos: 
  'airlines', 'airports', 'AwardsManagers', 'babynames', 'Batting', 'diamonds', 'faithful', 'fueleconomy', 
  'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers', 'mpg', 'mtcars', 'atmos', 'People, 
  'Pitching', 'planes', 'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b', 'table5', 
  'vehicles', 'weather', 'who'. 
  English: It provides a Spanish translated version of the datasets listed above.
Interface with the 'Dat' p2p network protocol <https://datproject.org>. Clone archives from the network, share your own files, and install packages from the network.
Profiles datasets (collecting statistics and informative summaries 
    about that data) on data frames and 'ODBC' tables: maximum, minimum, mean, standard deviation, nulls,
    distinct values, data patterns, data/format frequencies.
A collection of functions accompanying the book "Data Analysis in Vegetation Ecology". 3rd ed. CABI, Oxfordshire, Boston.
Various utilities for the Davies distribution.
In applications it is usual that some additional information is available. This package dawai (an acronym for Discriminant Analysis With Additional Information) performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means. It also computes several estimations of the true error rate.
Programmatic interface to the 'Daymet' web services
    (<http://daymet.ornl.gov>). Allows for easy downloads of
    'Daymet' climate data directly to your R workspace or your computer.
    Routines for both single pixel data downloads and
    gridded (netCDF) data are provided.
Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/MH sampler. Also serves as a drop-in replacement for package 'BayesTree'.
Using widgets provided by gWidgets this creates a nice
        usable interface to help facilitate the learning of SQL.  It
        also just gives a nice interface for more advanced users.  Not
        all features are provided in the GUI but it is possible for
        more advanced users to achieve these goals inside the GUI.
Goodness-of-fit and two sample comparison tests using sample entropy
A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.
A tool to ensure the validity of database writes.
  It provides a set of utilities to analyze and type check the properties
  of data frames that are to be written to databases with SQL support.
Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) <doi:10.1037/1082-989X.5.1.87>. 
              The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small.
Reads and writes files to SQLite databases <https://www.sqlite.org/index.html> as flobs 
    (a flob is a blob that preserves the file extension).
This package provides methods and examples to support a
        method of Gene Set Analysis (GSA).  DBGSA is a novel
        distance-based gene set enrichment analysis method. We consider
        that, the distance between 2 groups with different phenotype by
        focusing on the gene expression should be larger, if a certain
        gene functional set was significantly associated with a
        particular phenotype.
Provides an implementation of a mixture of hidden Markov models 
    (HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering 
    (DBHC) algorithm. The DBHC algorithm is an HMM Clustering 
    algorithm that finds a mixture of discrete-output HMMs while using 
    heuristics based on Bayesian Information Criterion (BIC) to search for the 
    optimal number of HMM states and the optimal number of clusters.
A database interface definition for communication
    between R and relational database management systems.  All classes in
    this package are virtual and need to be extended by the various R/DBMS
    implementations.
A helper that tests 'DBI' back ends for conformity
    to the interface.
Allows for nonparametric graduation of mortality rates using fixed or adaptive discrete beta kernel estimator.
Use EM algorithm to compute the NPMLE of CDF and also the
        two censoring distributions. For doubly censored data (as
        described in Chang and Yang (1987) Ann. Stat. 1536-47). You can
        also specify a constraint, it will return the constrained NPMLE
        and the -2 log empirical likelihood ratio. This can be used to
        test the hypothesis about the constraint and find confidence
        intervals for probability or quantile via empirical likelihood
        ratio theorem. Influence function of hat F may also be
        calculated (but may be slow).
It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links) and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).
Trains logistic regression model by discretizing continuous variables via gradient boosting approach. The proposed method tries to achieve a tradeoff between interpretation and prediction accuracy for logistic regression by discretizing the continuous variables. The variable binning is accomplished in a supervised fashion. The model trained by this package is still a single 
  logistic regression model, but not a sequence of logistic regression models. The fitted model
  object returned from the model training consists of two tables. One table is used to give the
  boundaries of bins for each continuous variable as well as the corresponding coefficients,
  and the other one is used for discrete variables. This package can also be used for binning
  continuous variables for other statistical analysis.
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>.
             Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). 
             Relies on 'spatstat' for some core calculation.
This tool is for parsing the 'DrugBank' XML database <http://drugbank.ca/>. The parsed 
    data are then returned in a proper 'R' dataframe with the ability to save 
    them in a given database.
Leverages 'dplyr' to process the calculations of a plot inside a database. 
    This package provides helper functions that abstract the work at three levels:
    outputs a 'ggplot', outputs the calculations, outputs the formula
    needed to calculate bins.
A 'dplyr' back end for databases that allows you to
    work with remote database tables as if they are in-memory data frames.
    Basic features works with any database that has a 'DBI' back end; more
    advanced features require 'SQL' translation to be provided by the
    package author.
A fast reimplementation of several density-based algorithms of
    the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial
    clustering of applications with noise) and OPTICS (ordering points to identify
    the clustering structure) clustering algorithms HDBSCAN (hierarchical DBSCAN) and the LOF (local outlier
    factor) algorithm. The implementations use the kd-tree data structure (from
    library ANN) for faster k-nearest neighbor search. An R interface to fast kNN
    and fixed-radius NN search is also provided. 
    See Hahsler M, Piekenbrock M and Doran D (2019) <doi:10.18637/jss.v091.i01>.
Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.
Provides select, insert, update, upsert, and delete database operations. Supports 'PostgreSQL', 'MySQL', 'SQLite', and more, and plays nicely with the 'DBI' package.
Performs differential network analysis to infer disease specific gene networks.
Finding dominant latent signals that regulate dynamic correlation between many pairs of variables.
Functions for analyzing dichotomous choice contingent valuation (CV) data. It provides 
   functions for estimating parametric and nonparametric models for single-, one-and-one-half-, 
   and double-bounded CV data.
A differential community detection (DCD) based approach to effectively locate differential sub-networks in paired scale-free biological networks, e.g. case vs control - Raghvendra Mall et al (2017) <doi:10.1145/3107411.3107418>.
A collection of routines and documentation that allows one to
  perform voxel-wise quantitative analysis of dynamic contrast-enhanced MRI 
  (DEC-MRI) and diffusion-weighted imaging (DWI) data, with emphasis on 
  oncology applications.
Data cloud geometry (DCG) applies random walks in finding community structures for social networks. 
    Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).
Functions for 1) gene filtration; 2) link filtration; 3) differential co-expression analysis: DCG (Differential Coexpressed
        Gene) identification and DCL (Differentially Coexpressed Link)
        identification; and 4) differential regulation analysis: DRG (Differential Regulated
        Gene) identification, DRL (Differential Regulated Link)
        identification, DRL visualization and regulator ranking.
There lacks a package for analysing domain-centric ontologies and annotations, particularly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a comprehensive domain-centric database for annotating protein domains using a panel of ontologies including Gene Ontology. With the package, users are expected to analyse and visualise domain-centric ontologies and annotations. Supported analyses include but are not limited to: easy access to a wide range of ontologies and their domain-centric annotations; able to build customised ontologies and annotations; domain-based enrichment analysis and visualisation; construction of a domain (semantic similarity) network according to ontology annotations; significance analysis for estimating a contact (statistical significance) network via Random Walk with Restart; and high-performance parallel computing. The new functionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input protein sequences (precisely domain content in the form of architectures) plus to assess the predictions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony.
Provides several algorithms for the purpose of detecting chaotic signals inside univariate time series. We focus on methods derived from chaos theory which estimate the complexity of a dataset through exploring the structure of the attractor. We have taken into account the Lyapunov exponents as an ergodic measure. We have implemented the Jacobian method by a fit through neural networks in order to estimate both the largest and the spectrum of Lyapunov exponents. We have considered the full sample and three different methods of subsampling by blocks (non-overlapping, equally spaced and bootstrap) to estimate them. In addition, it is possible to make inference about them and know if the estimated Lyapunov exponents values are or not statistically significant. This library can be used with time series whose time-lapse is fixed or variable. That is, it considers time series whose observations are sampled at fixed or variable time intervals. For a review see David Ruelle and Floris Takens (1971) <doi:10.1007/BF01646553>, Ramazan Gencay and W. Davis Dechert (1992) <doi:10.1016/0167-2789(92)90210-E>, Jean-Pierre Eckmann and David Ruelle (1995) <doi:10.1103/RevModPhys.57.617>, Mototsugu Shintani and Oliver Linton (2004) <doi:10.1016/S0304-4076(03)00205-7>, Jeremy P. Huke and David S. Broomhead (2007) <doi:10.1088/0951-7715/20/9/011>.
Functions for reading DCP and CDF.bin files generated by the dChip software.
Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
Low level functions for implementing
    maximum likelihood estimating procedures for
    complex models using data cloning and Bayesian
    Markov chain Monte Carlo methods
    as described in Solymos 2010 (R Journal 2(2):29--37).
    Sequential and parallel MCMC support
    for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.
Contains a single function dclust() for divisive hierarchical clustering based on 
    recursive k-means partitioning (k = 2). Useful for clustering large datasets
    where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records).
    For further information see Steinbach, Karypis and Kumar (2000) <http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf>.
A set of functions for the detection of spatial clusters
        of disease using count data. Bootstrap is used to estimate
        sampling distributions of statistics.
Model-based methods for the detection of disease clusters
  using GLMs, GLMMs and zero-inflated models. These methods are described
  in 'V. Gómez-Rubio et al.' (2019) <doi:10.18637/jss.v090.i14> and
  'V. Gómez-Rubio et al.' (2018) <doi:10.1007/978-3-030-01584-8_1>.
Data Converter Module (DCM) converts the dataset format from split into stack and to the reverse.
A set of asymptotic methods that can be used to directly estimate the expected (Fisher) information matrix by Liu, Tian, and Xin (2016) <doi:10.3102/1076998615621293> in diagnostic classification models or cognitive diagnostic models are provided when marginal maximum likelihood estimation is used. For these methods, both the item and structural model parameters are considered simultaneously. Specifically, the observed information matrix, the empirical cross-product information matrix and the sandwich-type co-variance matrix that can be used to estimate the asymptotic co-variance matrix (or the model parameter standard errors) within the context of diagnostic classification models are provided.
S4 classes around infrastructure provided by the
    'coda' and 'dclone' packages to make package development easy as a breeze
    with data cloning for hierarchical models.
Data cleaning scripts typically contain a lot of 'if this change that'
    type of statements. Such statements are typically condensed expert knowledge.
    With this package, such 'data modifying rules' are taken out of the code and
    become in stead parameters to the work flow. This allows one to maintain, document,
    and reason about data modification rules as separate entities.
Traversal graph algorithm for listing linear n-peptide constraints for overlapping protein regions. (Lebre and Gascuel, The combinatorics of overlapping genes, freely available from arXiv at : http://arxiv.org/abs/1602.04971).
Computing and plotting the distance covariance and correlation
             function of a univariate or a multivariate time series. Both versions
             of biased and unbiased estimators of distance covariance and correlation are provided.
             Test statistics for testing pairwise independence are also implemented. 
             Some data sets are also included.
A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by
  easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density,
  Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)
  <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of
  the loglikelihood and a random generator for Davidian curves.
This package performs several conventional
        Cross-validation statistical methods for climate-growth model
        in the climate reconstruction from tree rings, including Sign
        Test statistic, Reduction of Error statistic, Product Mean
        Test, Durbin-Watson statistic etc. This package is at its
        primary stage, the functions have not been tested exhaustively
        and more functions would be added in the comming days.
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 <doi:10.18637/jss.v091.i05>).
Implements maximum likelihood and bootstrap methods based on
 the diversity-dependent birth-death process to test whether
 speciation or extinction are diversity-dependent, under various
 models including various types of key innovations.
 See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309,
 <DOI:10.1098/rspb.2011.1439>,
 Etienne & Haegeman 2012, Am. Nat. 180: E75-E89,
 <DOI:10.1086/667574> and
 Etienne et al. 2016. Meth. Ecol. Evol. 7: 1092-1099,
 <DOI:10.1111/2041-210X.12565>.
 Also contains functions to simulate the diversity-dependent
 process.
Solves ordinary and delay differential equations, where
    the objective function is written in either R or C.  Suitable only
    for non-stiff equations, the solver uses a 'Dormand-Prince' method
    that allows interpolation of the solution at any point.  This
    approach is as described by Hairer, Norsett and Wanner (1993)
    <ISBN:3540604529>.  Support is also included for iterating
    difference equations.
Contains the normalizing and variance stabilizing
	Data-Driven Haar-Fisz algorithm. Also contains related algorithms
	for simulating from certain microarray gene intensity models and
	evaluation of certain transformations. Contains cDNA and shipping
	credit flow data.
The Data Driven I-V Feature Extraction is used to extract 
    Current-Voltage (I-V) features from I-V curves. I-V curves indicate the 
    relationship between current and voltage for a solar cell or Photovoltaic (PV) 
    modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), 
    series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), 
    fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) 
    contain important information of the performance for PV modules. The traditional 
    method uses the single diode model to model I-V curves and extract I-V features. 
    This package does not use the diode model, but uses data-driven a method which 
    select different linear parts of the I-V curves to extract I-V features. 
    This method also uses a sampling method to calculate uncertainties when extracting 
    I-V features. Also, because of the partially shaded array, "steps" occurs in 
    I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves.
    This material is based upon work supported by the U.S. Department of Energy’s 
    Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy 
    Technologies Office (SETO) Agreement Number DE-EE0007140.
Useful functions for various DDI (Data Documentation Initiative) related inputs and outputs.
A set of three two-census methods to the estimate the degree of death registration coverage for a population. Implemented methods include the Generalized Growth Balance method (GGB), the Synthetic Extinct Generation method (SEG), and a hybrid of the two, GGB-SEG. Each method offers automatic estimation, but users may also specify exact parameters or use a graphical interface to guess parameters in the traditional way if desired.
Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) <doi:10.1145/782010.782021>, Angiulli, F., & Pizzuti, C. (2002) <doi:10.1007/3-540-45681-3_2>, Hautamaki, V., & Ismo, K. (2004) <doi:10.1109/ICPR.2004.1334558> and Zhang, K., Hutter, M. & Jin, H. (2009) <doi:10.1007/978-3-642-01307-2_84>. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) <doi:10.1007/3-540-47887-6_53>, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) <doi:10.1007/11731139_68>, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) <doi:10.1137/1.9781611973440.63>, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) <doi:10.1007/978-3-540-73499-4_6>, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) <doi:10.1109/ICDE.2003.1260802>, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) <doi:10.1145/342009.335388>, Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) <doi:10.1145/1645953.1646195>, Zhu, Q., Feng, Ji. & Huang, J. (2016) <doi:10.1016/j.patrec.2016.05.007>, Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) <doi:10.1016/j.knosys.2015.10.014>, Tang, B. & Haibo, He. (2017) <doi:10.1016/j.neucom.2017.02.039> and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) <doi:10.1007/978-3-642-20847-8_23>.
Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>)  by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.
An interface to explore, analyze, and visualize droplet digital PCR
    (ddPCR) data in R. This is the first non-proprietary software for analyzing
    two-channel ddPCR data. An interactive tool was also created and is available
    online to facilitate this analysis for anyone who is not comfortable with
    using R.
A nonparametric Bayesian approach to survival analysis. The functions perform inference via MCMC simulations from the posterior distributions for a Dependent Dirichlet Process-Gaussian Process prior. To maximize computational efficiency, some of the computations are performed in 'Rcpp'.
Functions designed to connect disease-related differential proteins and 
  co-expression network. It provides the basic statics analysis included t test, ANOVA analysis. 
  The network construction is not offered by the package, you can used 'WGCNA' package which you 
  can learn in Peter et al. (2008) <doi:10.1186/1471-2105-9-559>. It also provides module analysis 
  included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and 
  hub analysis.
Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.
Allows to build Multi-Data-Driven Sparse Partial Least Squares models. Multi-blocks with 
    high-dimensional settings are particularly sensible to this. It comes with visualization
    functions and uses 'Rcpp' functions for fast computations and 'doParallel' to parallelize cross-validation.
    This is based on H Lorenzo, J Saracco, R Thiebaut (2019) <arXiv:1901.04380>.
    Many applications have been successfully realized.
    See <https://hadrienlorenzo.netlify.com/> for more information, documentation and examples.
Smooth testing of goodness of fit. These tests are data
    driven (alternative hypothesis is dynamically selected based on data). In this
    package you will find various tests for exponent, Gaussian, Gumbel and uniform
    distribution.
Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.
Bayesian networks with continuous and/or discrete
        variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
Dealing GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) RAW.tar file using the robust multi-array average expression measure and returning expression profile.
deamer provides deconvolution algorithms for the
        non-parametric estimation of the density f of an error-prone
        variable x with additive noise e. The model is y = x + e where
        the noisy variable y is observed, while x is unobserved.
        Estimation may be performed for i) a known density of the error
        ii) with an auxiliary sample of pure noise and iii) with an
        auxiliary sample of replicate (repeated) measurements.
        Estimation is performed using adaptive model selection and
        penalized contrasts.
Set of functions for Data Envelopment Analysis. It runs both classic and fuzzy DEA models.See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). <doi:10.1287/mnsc.30.9.1078>, Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). <doi:10.1016/0377-2217(78)90138-8> and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). <doi:10.1287/mnsc.27.6.668>.
The 'debar' sequence processing pipeline is designed for denoising high throughput 
    sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package 
    is designed to detect and correct insertion and deletion errors within sequencer outputs. 
    This is accomplished through comparison of input sequences against a profile hidden Markov 
    model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, 
    ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted 
    for through the introduction of a placeholder character. Since the PHMM is a probabilistic 
    representation of the COI barcode, corrections are not always perfect. For this reason 
    'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder 
    characters (default is 7 base pairs in either direction, this feature can be disabled).
    Testing has shown that this censorship results in the correct sequence length being restored, 
    and erroneous base pairs being masked the vast majority of the time (>95%).
A Bayesian framework for parameter inference in differential equations.
    This approach offers a rigorous methodology for parameter inference as well as
    modeling the link between unobservable model states and parameters, and
    observable quantities. Provides templates for the DE model, the
    observation model and data likelihood, and the model parameters and their prior
    distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs
    to estimate the posterior distributions of the parameters and any derived
    quantities, including the model trajectories. Further functionality is provided
    to facilitate MCMC diagnostics and the visualisation of the posterior distributions
    of model parameters and trajectories.
Specify debug messages as special string constants, and
    control debugging of packages via environment variables.
Tool to print out the value of R objects/expressions while running
  an R script. Outputs can be made dependent on user-defined conditions/criteria. 
  Debug messages only appear when a global option for debugging is set. 
  This way, 'debugr' code can even remain in the debugged code for later use 
  without any negative effects during normal runtime.
Calculates various estimates for measures of educational
        differentials, the relative importance of primary and secondary
        effects in the creation of such differentials and compares the
        estimates obtained from two datasets.
Provides constrained triangulation of polygons. Ear cutting (or 
 ear clipping) applies constrained triangulation by successively 'cutting'
 triangles from a polygon defined by path/s. Holes are supported by introducing
 a bridge segment between polygon paths. This package wraps the 'header-only' 
 library 'earcut.hpp' <https://github.com/mapbox/earcut.hpp.git> which includes
 a reference to the method used by Held, M. (2001) <doi:10.1007/s00453-001-0028-4>.
Contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.
Aides in the multi objective decision analysis process by simplifying 
  the creation of value hierarchy tree plots, calculating and plotting single and 
  multi attribute value function scores, and conducting sensitivity analysis. Linear, 
  exponential, and categorical single attribute value functions are supported. For 
  details see Parnell (2013, ISBN:978-1-118-17313-8) Kirkwood (1997, ISBN:0-534-51692-0).
Supporting the quantitative analysis of binary welfare based
    decision making processes using Monte Carlo simulations. Decision support
    is given on two levels: (i) The actual decision level is to choose between
    two alternatives under probabilistic uncertainty. This package calculates
    the optimal decision based on maximizing expected welfare. (ii) The meta
    decision level is to allocate resources to reduce the uncertainty in the
    underlying decision problem, i.e to increase the current information to
    improve the actual decision making process. This problem is dealt with
    using the Value of Information Analysis. The Expected Value of
    Information for arbitrary prospective estimates can be calculated as
    well as Individual Expected Value of Perfect Information.
    The probabilistic calculations are done via Monte Carlo
    simulations. This Monte Carlo functionality can be used on its own.
Makes 'deck.gl' <https://deck.gl/>, a WebGL-powered open-source JavaScript framework
  for visual exploratory data analysis of large datasets, available within R via the 'htmlwidgets' package.
  Furthermore, it supports basemaps from 'mapbox' <https://www.mapbox.com/> via
  'mapbox-gl-js' <https://github.com/mapbox/mapbox-gl-js>.
Researchers can characterize and learn about the properties of
    research designs before implementation using `DeclareDesign`. Ex ante
    declaration and diagnosis of designs can help researchers clarify the 
    strengths and limitations of their designs and to improve their 
    properties, and can help readers evaluate a research strategy prior
    to implementation and without access to results. It can also make it
    easier for designs to be shared, replicated, and critiqued.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Main function "decode" is used to decode coded key values to plain
    text. Function "code" can be used to code plain text to code if there is a
    1:1 relation between the two. The concept relies on 'keyvalue' objects used
    for translation. There are several 'keyvalue' objects included in the areas
    of geographical regional codes, administrative health care unit codes,
    diagnosis codes and more. It is also easy to extend the use by arbitrary 
    code sets.
Predict future values with hybrid combinations of Pattern Sequence based
        Forecasting (PSF), Autoregressive Integrated Moving Average (ARIMA), Empirical Mode
        Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) methods based
        hybrid methods.
Two global-value-chain decompositions are implemented. Firstly, the
    Wang-Wei-Zhu (Wang, Wei, and Zhu, 2013) algorithm splits bilateral gross exports
    into 16 value-added components. Secondly, the Leontief decomposition (default)
    derives the value added origin of exports by country and industry, which is also
    based on Wang, Wei, and Zhu (Wang, Z., S.-J. Wei, and K. Zhu. 2013. "Quantifying
    International Production Sharing at the Bilateral and Sector Levels.").
This package contains a collection of functions to deal
        with nonparametric measurement error problems using
        deconvolution kernel methods. We focus two measurement error
        models in the package: (1) an additive measurement error model,
        where the goal is to estimate the density or distribution
        function from contaminated data; (2) nonparametric regression
        model with errors-in-variables. The R functions allow the
        measurement errors to be either homoscedastic or
        heteroscedastic. To make the deconvolution estimators
        computationally more efficient in R, we adapt the "Fast Fourier
        Transform" (FFT) algorithm for density estimation with
        error-free data to the deconvolution kernel estimation. Several
        methods for the selection of the data-driven smoothing
        parameter are also provided in the package. See details in:
        Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
        measurement error models: The R package decon. Journal of
        Statistical Software, 39(10), 1-24.
Takes sample information in the form of the fraction of mutations
    in each of 96 trinucleotide contexts and identifies the weighted combination
    of published signatures that, when summed, most closely reconstructs the
    mutational profile.
Empirical Bayes methods for learning prior distributions from data.
    An unknown prior distribution (g) has yielded (unobservable) parameters, each of
    which produces a data point from a parametric exponential family (f). The goal
    is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
    Bayes methods.
DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
    of Artificial Training Examples) builds an ensemble of J48 trees by recursively
    adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6(1), 99-111. <doi:10.1016/j.inffus.2004.04.001>").
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
A example GUI plug-in package to serve as a template.
A Deducer plug-in for factor analysis, reliability
        analysis and discriminant analysis, using psych, GPArotation
        and mvnormtest packages.
A Deducer plug-in for spatial data analysis. Includes The
        ability to plot and explore open street map and Bing satellite
        images.
Adds Kaplan-Meier Survival Analysis to the Deducer GUI
A collection of methods for automated data cleaning where all actions are logged.
Attempt to repair inconsistencies and missing values in data 
    records by using information from valid values and validation rules 
    restricting the data.
Explore neural networks in a layer oriented way, the framework
    is intended to give the user total control of the
    internals of a net without much effort. Use classes like PerceptronLayer
    to create a layer of Percetron neurons, and specify how many you want. The
    package does all the tricky stuff internally leaving you focused in what
    you want. I wrote this package during a neural networks course to help me
    with the problem set.
Provides deep boosting models training, evaluation, predicting and
 hyper parameter optimising using grid search and cross validation.
 Based on Google's Deep Boosting algorithm, and Google's C++ implementation.
 Cortes, C., Mohri, M., & Syed, U. (2014) <http://machinelearning.wustl.edu/mlpapers/papers/icml2014c2_cortesb14>.
Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) 
    <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures 
    to multiple layers. Each layer contains a set of latent variables that follow 
    a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension 
    reduction is applied at each layer by way of factor models.
A wrapper for the 'DeepL' API, a web service for translating texts between different languages. 
    Access to the official API (see <https://www.deepl.com/translator>) is subject to a monthly fee.
    No authentication key is required for the undocumented DeepL JSON-RPC API. The package provides functions
    for both types of API calls.
Implement some deep learning architectures and neural network
    algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
Provides mean square error (MSE) and plot the kernel densities related to extreme value distributions.
    By using Gumbel and Weibull Kernel. See Salha et al. (2014) <doi:10.4236/ojs.2014.48061> and Khan and Akbar (2019).
A simple syntax to change the default values for function
  arguments, whether they are in packages or defined locally.
Creates a directory of archived files with a descriptive 'PDF'
 document at the root level (i.e. 'define.pdf') containing tables
 of definitions of data items and relative-path hyperlinks to 
 the documented files. Converts file extensions to 'txt' per FDA
 expectations and converts 'CSV' files to 'SAS' Transport format.
 Relies on data item descriptors stored as per R package 'spec'.
 See 'package?define'. See also '?define'. 
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
Simple functions to deflate nominal Brazilian Reais using several popular price indexes downloaded from the Brazilian Institute for Applied Economic Research.
Numerical quadrature of functions of one variable over a finite or infinite interval
	with double exponential formulas.
Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.
Provides a low-level interface for a deisotoper container 
  implemented in the 'Java' programming language and means of S3 helper 
  functions for plotting and debugging isotopes of mass spectrometric data. 
  The deisotoper algorithm detects and aggregates peaks which belong to the 
  same isotopic cluster of a given mass spectrum.
Provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution. The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.
Provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is homogeneous across the individual subject. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.
Calculates the Delaunay triangulation and the Dirichlet
	or Voronoi tessellation (with respect to the entire plane) of
	a planar point set. Plots triangulations and tessellations in
	various ways.  Clips tessellations to sub-windows. Calculates
	perimeters of tessellations.  Summarises information about the
	tiles of the tessellation.
Implements the DeLorean model (Reid & Wernisch (2016)
    <doi:10.1093/bioinformatics/btw372>) to estimate pseudotimes for
    single cell expression data. The DeLorean model uses a Gaussian process
    latent variable model to model uncertainty in the capture time of
    cross-sectional data.
We implement methods for estimating multivariate densities.
  We include a discretized kernel estimator,
  an adaptive histogram (a greedy histogram and a CART-histogram), 
  stagewise minimization, and bootstrap aggregation.
Measure of agreement delta was originally by Martín & Femia (2004) <DOI:10.1348/000711004849268>. 
    Since then has been considered as agreement measure for different 
    fields, since their behavior is usually better than the usual kappa index
    by Cohen (1960) <DOI:10.1177/001316446002000104>. The main issue with delta 
    is that can not be computed by hand contrary to kappa. The current algorithm
    is based on the Version 5 of the delta windows program that can be found on
    <https://www.ugr.es/~bioest/software/delta/cmd.php?seccion=downloads>.
The deltaPlotR package implements Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported (Magis and Facon (2014) <doi:10.18637/jss.v059.c01>).
Computes the regional correction factor of 14C age offset in marine-derived samples (Delta R). This is possible with the recently established "marine13" calibration curve and the 'BchronCalibrate' function from the 'Bchron' package. The algorithm of Delta R computation includes four steps: measure radiocarbon age of a marine sample; identify its true age; compute its modeled radiocarbon age corresponding to the true age; and finally calculate the difference between its measured and modeled radiocarbon ages. This package has functions that compute Delta R with three methods: by using marine samples with known collection dates (before 1950s), usually molluscan shells from museum collections, (function dr_shell()); by measuring other radioactive isotopes, mainly uranium-thorium (230Th/234U), ratio (dr_pair() function and the "pair" method in the dr_df() function); and finally by using a pair of coeval samples, one being marine and the other terrestrial (dr_pair() function and the "pair" method for the dr_df() function as well). Usually such samples originate from archaeological sites where the context, in which paired samples were found, is taken as a guarantee of their synchronicity.  
 References: 
 Oeschger H, Siegenthaler U, Schotterer U, Gugelmann A (1975) <doi:10.3402/tellusa.v27i2.9900>
 Reimer PJ, Bard E, Bayliss A, Beck JW, Blackwell PG, Bronk Ramsey C, Buck CE, Cheng H, Edwards RL, Friedrich M, Grootes PM, Guilderson TP, Haflidason H, Hajdas I, Hatté C, Heaton TJ, Hoffmann DL, Hogg AG, Hughen KA, Kaiser KF, Kromer B, Manning SW, Niu M, Reimer RW, Richards DA, Scott EM, Southon JR, Staff RA, Turney CSM, van der Plicht J (2013) <doi:10.2458/azu_js_rc.55.16947>
 Soulet G (2015) <doi:10.1016/j.quageo.2015.05.023>
 Stuiver M, Braziunas TF (1993) <doi:10.1017/S0033822200013874> .
A collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. 
    Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull.
    For details see Chen (2000), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.
Functions to calculate pairwise relatedness on diploid genetic datasets. Different estimators for relatedness can be combined with information on geographical distances. Information on heterozygosity, allele- and genotype diversity as well as genetic F-statistics are provided for each population.
This package allows to calculate the fixation index Gst
  (Nei, 1973) and the differentiation index D (Jost, 2008) pairwise
  between or averaged over several populations.  P-values, stating the
  significance of differentiation, and 95 percent confidence intervals
  can be estimated using bootstrap resamplings. In the case that more
  than two populations are compared pairwise, the p-values are
  adjusted by bonferroni correction and in several other ways due to
  the multiple comparison from one data set.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Two general demographic decomposition methods are offered: Pseudo-continuous decomposition proposed by Horiuchi, Wilmoth, and Pletcher (2008) <doi:10.1353/dem.0.0033> and stepwise replacement decomposition proposed by Andreev, Shkolnikov and Begun (2002) <doi:10.4054/DemRes.2002.7.14>.
Construction and analysis of matrix population models in R.
The Demographic Table in R combines contingency table for categorical variables, mean and standard deviation for continuous variables. t-test, chi-square test and Fisher's exact test calculated the p-value of two groups. The standardized mean difference were performed with 95 % confident interval, and writing table into document file.
Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.
Demonstration code showing how (univariate) kernel
	     density estimates are computed, at least conceptually,
	     and allowing users to experiment with different kernels,
	     should they so wish.  NOTE: the density function in the
	     stats package should be used for computational efficiency.
Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.
Implements the Determinantal point process (DPP) based optimal design
    emulator described in Pratola, Lin and Craigmile (2018) <arXiv:1804.02089> for
    Gaussian process regression models.  See <http://www.matthewpratola.com/software>
    for more information and examples.
Offers a set of functions for extending
    'dendrogram' objects in R, letting you visualize and compare trees of
    'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters
    - the color, size, type, etc of its branches, nodes and labels. (2)
    Visually and statistically compare different 'dendrograms' to one another.
Provides extra functions to manipulate dendrograms
    that build on the base functions provided by the 'stats' package. The main
    functionality it is designed to add is the ability to colour all the edges
    in an object of class 'dendrogram' according to cluster membership i.e. each
    subtree is coloured, not just the terminal leaves. In addition it provides
    some utility functions to cut 'dendrogram' and 'hclust' objects and to 
    set/get labels.
Various functions to import, verify, process and plot high-resolution dendrometer using daily and stem-cycle approaches.
Provides functions for the calculation and plotting of synchrony in 
      tree growth from tree-ring width chronologies (TRW index). It combines
      variance-covariance (VCOV) mixed modelling with functions that quantify 
      the degree to which the TRW chronologies contain a common temporal 
      signal. It also implements temporal trends in spatial synchrony using a 
      moving window. These methods can also be used with other kind of ecological
      variables that have temporal autocorrelation corrected.
Provides novel dendroclimatological methods, primarily used by the
    Tree-ring research community. There are four core functions. The first one is 
    daily_response(), which finds the optimal sequence of days that are related 
    to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), 
    which implements partial correlations in the analysis of daily response functions.
    For the enthusiast of monthly data, there is monthly_response() function.
    The last core function is compare_methods(), which effectively compares several 
    linear and nonlinear regression algorithms on the task of climate reconstruction.
Re-arranges a dendrogram to optimize visualisation-based cost functions
An implementation of functions to optimize ordering of nodes in
    a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can
    be sorted based on the average distance of subtrees, or based on the smallest
    distance value. These sorting methods improve readability and interpretability
    of tree structure, especially for tasks such as comparison of different distance
    measures or linkage types and identification of tight clusters and outliers. As
    a result, it also introduces more meaningful reordering for a coupled heatmap
    visualization.
Provides functions for parameter estimation and real-time predictions  of a single dengue 
 outbreak taking into account model uncertainty using  model averaging.
Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of  a bottom-up model for PCR amplification. This model is  developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
Estimate a low rank matrix from noisy data using singular values
    thresholding and shrinking functions. Impute missing values with matrix completion.
An integrated toolset for the analysis of de novo (sporadic)
    genetic sequence variants. denovolyzeR implements a mutational model that
    estimates the probability of a de novo genetic variant arising in each human
    gene, from which one can infer the expected number of de novo variants in a
    given population size. Observed variant frequencies can then be compared against
    expectation in a Poisson framework. denovolyzeR provides a suite of functions
    to implement these analyses for the interpretation of de novo variation in human
    disease.
We provide tools to 
  (1) visualize multivariate density functions and density estimates 
      with level set trees,
  (2) visualize level sets with shape trees,
  (3) visualize multivariate data with tail trees, 
  (4) visualize scales of multivariate density estimates with 
      mode graphs and branching maps, and
  (5) visualize anisotropic spread with 2D volume functions and
      2D probability content functions.
  Level set trees visualize mode structure,
  shape trees visualize shapes of level sets of unimodal densities,
  and tail trees visualize connected data sets.
  The kernel estimator is implemented
  but the package may  also be applied for visualizing other density estimates.
Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.
Implementation in the density framework of the non-combinatorial algorithm and its greedy version, introduced by Magalhães and Rozenholc (2014), for T-estimation Hold-Out proposed in Birgé (2006, Section 9). The package provide an implementation which uses several families of estimators (regular and irregular histograms, kernel estimators) which may be used alone or combined. As a complement, provides also a comparison with other Held-Out derived from least-squares and maximum-likelihood. This package implements also the T-estimation Hold-Out derived from the test introduced in Baraud (2011).
An improved implementation (based on k-nearest neighbors) of the 
    density peak clustering algorithm, originally described by Alex Rodriguez 
    and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets
    (> 100, 000 samples) very efficiently. It was initially implemented by 
    Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by 
    Xiaojie Qiu to handle large datasets with kNNs.
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.
Density ratio estimation.
    The estimated density ratio function can be used in many applications such as
    anomaly detection, change-point detection, covariate shift adaptation.
    The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>),
    RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>),
    and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds"
    in <doi:10.1098/rsif.2019.0234>.  Using the functions in the package,
    that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data
    using other functions in this package (as in the manuscript) or from some other source.
    The package provides a vignette which walks through the analysis in the publication, as well as a function
    to generate a project skeleton for such an analysis.
Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
Differential Evolution (DE) stochastic algorithms for global
  optimization of problems with and without constraints.
  The aim is to curate a collection of its state-of-the-art variants that
  (1) do not sacrifice simplicity of design,
  (2) are essentially tuning-free, and
  (3) can be efficiently implemented directly in the R language.
  Currently, it only provides an implementation of the 'jDE' algorithm by
  Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.
Estimation and testing methods for dependently truncated data.
 Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>,
 and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
 Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>.
 A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>.
 Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
Plots dependency logos from a set of input sequences.
Traditional phasing programs are limited to diploid organisms.
 Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo
 (MCMC) approaches, and builds a generic framework that allows haplotype searches
 in a multiple infection setting. This package is primarily developed as part of
 the Pf3k project, which is a global collaboration using the latest
 sequencing technologies to provide a high-resolution view of natural variation
 in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from
 patient blood sample, which often contains more than one parasite strain, with
 unknown proportions. This package is used for deconvoluting mixed haplotypes,
 and reporting the mixture proportions from each sample.
Fits (multigroup) mixtures of latent or hidden Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge.
Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).
Tools for depth functions methodology applied 
        to multivariate analysis. Besides allowing calculation 
        of depth values and depth-based location estimators, the package
        includes functions or drawing contour plots and perspective plots
        of depth functions. Euclidian and spherical depths are supported.
Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
Data depth concept offers a variety of powerful and user friendly
    tools for robust exploration and inference for multivariate data. The offered
    techniques may be successfully used in cases of lack of our knowledge on
    parametric models generating data due to their nature. The
    package consist of among others implementations of several data depth techniques
    involving multivariate quantile-quantile plots, multivariate scatter estimators,
    multivariate Wilcoxon tests and robust regressions.
depthTools is a package that implements different
        statistical tools for the description and analysis of gene
        expression data based on the concept of data depth, namely, the
        scale curves for visualizing the dispersion of one or various
        groups of samples (e.g. types of tumors), a rank test to decide
        whether two groups of samples come from a single distribution
        and two methods of supervised classification techniques, the DS
        and TAD methods. All these techniques are based on the Modified
        Band Depth, which is a recent notion of depth with a low
        computational cost, what renders it very appropriate for high
        dimensional data such as gene expression data.
Queues, stacks, and 'deques' are list-like, abstract data types. 
    These are meant to be very cheap to "grow", or insert new objects into.
    A typical use case involves storing data in a list in a streaming fashion,
    when you do not necessarily know how may elements need to be stored.
    Unlike R's lists, the new data structures provided here are not
    necessarily stored contiguously, making insertions and deletions at the
    front/end of the structure much faster.  The underlying implementation
    is new and uses a head/tail doubly linked list; thus, we do not rely on R's
    environments or hashing.  To avoid unnecessary data copying, most operations
    on these data structures are performed via side-effects.
Modeling the zero coupon yield curve using the dynamic De Rezende and
             Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable
             or fixed decaying parameters. For explanatory purposes, the package also
             includes various short datasets of interest rates for the BRICS countries.
R-based solution for symbolic differentiation. It admits
    user-defined function as well as function substitution
    in arguments of functions to be differentiated. Some symbolic
    simplification is part of the work.
A set of pricing and expository functions that should
    be useful in teaching a course on financial derivatives.
Discrete event simulation (DES) involves modeling of systems
   having discrete, i.e. abrupt, state changes. For instance, when a job
   arrives to a queue, the queue length abruptly increases by 1.  This
   package is an R implementation of the event-oriented approach to DES;
   see the tutorial in Matloff (2008) 
   <http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf>.
Tools to read, write, create, and manipulate DESCRIPTION files.
    It is intended for packages that create or manipulate other packages.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
Weighted frequency and contingency tables of categorical
       variables and of the comparison of the mean value of a numerical
       variable by the levels of a factor, and methods to produce xtable
       objects of the tables and to plot them. There are also functions to
       facilitate the character encoding conversion of objects, to quickly
       convert fixed width files into csv ones, and to export a data.frame to
       a text file with the necessary R and SPSS codes to reread the data.
Helper functions for descriptive tasks such as making print-friendly bivariate 
  tables, sample size flow counts, and visualizing sample distributions. Also 
  contains 'R' approximations of some common 'SAS' and 'Stata' functions such 
  as 'PROC MEANS' from 'SAS' and 'ladder', 'gladder', and 'pwcorr' from 'Stata'.
Produce publication quality graphics from output of 'GGobi'
    describe display plugin.
Allows users to quickly and easily describe data using
    common descriptive statistics.
Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface.
Generate descriptive statistics such as measures of location,
    dispersion, frequency tables, cross tables, group summaries and multiple
    one/two way tables.
It generates summary statistics on the input dataset using different descriptive univariate 
    statistical measures on entire data or at a group level. Though there are other packages which does 
    similar job but each of these are deficient in one form or other, in the measures generated, in
    treating numeric, character and date variables alike, no functionality to view these measures on a
    group level or the way the output is represented. Given the foremost role of the descriptive statistics
    in any of the exploratory data analysis or solution development, there is a need for a more constructive, 
    structured and refined version over these packages. This is the idea behind the package and it brings 
    together all the required descriptive measures to give an initial understanding of the data quality, 
    distribution in a faster,easier and elaborative way.The function brings an additional capability to be 
    able to generate these statistical measures on the entire dataset or at a group level. It calculates measures 
    of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, 
    standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on 
    the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures 
    are generated based on the data types as required by them,rather than applying numerical measures on character and 
    data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful 
    when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a 
    summary report for the data.
Easily create descriptive and comparative tables.
    It makes use and integrates directly with the tidyverse family of packages, and pipes.
    Tables are produced as data frames/lists of data frames for easy manipulation after creation,
    and ready to be saved as csv, or piped to DT::datatable() or pander::pander() to integrate into reports.
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
'RStudio' as of recently offers the option to define addins and assign shortcuts to them. This package contains addins for a few most frequently used functions in a data scientist's (at least mine) daily work (like str(), example(), plot(), head(), view(), Desc()). Most of these functions will use the current selection in the editor window and send the specific command to the console while instantly executing it. Assigning shortcuts to these addins will save you quite a few keystrokes.
Deseasonalize daily or monthly time series.
The package provides R scripts for designing genetical
        genomics experiments.
Use simulated annealing to find optimal designs for Poisson regression models with blocks.
A simple interface to build designs using the package 'DeclareDesign'. 
    In one line of code, users can specify the parameters of individual 
    designs and diagnose their properties. The designers can also be used 
    to compare performance of a given design across a range of combinations 
    of parameters, such as effect size, sample size, and assignment probabilities.
Includes functions for the construction of matched samples that are balanced and representative by design.  Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs.  Also, they can be used for the design of randomized experiments, for example, for matching before randomization.  By default, 'designmatch' uses the 'GLPK' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface.  For their installation, please follow the instructions at <http://user.gurobi.com/download/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>.  We have also included directions in the gurobi_installation file in the inst folder.
Generate balanced factorial designs with crossed and nested random and fixed effects <https://github.com/mmrabe/designr>.
Functions for (1) ranking, selecting, and prioritising  genes,
    proteins, and metabolites from high dimensional biology experiments, (2)
    multivariate hit calling in high content screens, and (3) combining data
    from diverse sources.
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
Harrington and Derringer-Suich type desirability functions
This package implements a statistical data mining method to
    compare whole genome gene expression profiles, with respect to the presence
    of a recurrent genetic disturbance event, to identify the affected target
    genes.
Functions that solve initial value problems of a system
        of first-order ordinary differential equations ('ODE'), of
        partial differential equations ('PDE'), of differential
        algebraic equations ('DAE'), and of delay differential
        equations.  The functions provide an interface to the FORTRAN
        functions 'lsoda', 'lsodar', 'lsode', 'lsodes' of the 'ODEPACK'
        collection, to the FORTRAN functions 'dvode', 'zvode' and 'daspk'
        and a C-implementation of solvers of the 'Runge-Kutta' family with
        fixed or variable time steps.  The package contains routines
        designed for solving 'ODEs' resulting from 1-D, 2-D and 3-D
        partial differential equations ('PDE') that have been converted
        to 'ODEs' by numerical differencing.
A function for plotting maps of agricultural field experiments that
    are laid out in grids.
Builds both ROC (Receiver Operating Characteristic) and DET (Detection Error Tradeoff) curves from a set of predictors, which are the
    results of a binary classification system. The curves give a general vision of the performance of the classifier,
    and are useful for comparing performance of different systems.
Create a details HTML tag around R objects to place
    in a Markdown, 'Rmarkdown' and 'roxygen2' documentation.
Models for analyzing site occupancy and count data models
  with detection error, including single-visit based models,
  conditional distance sampling and time-removal models.
  Package development was supported by the
  Alberta Biodiversity Monitoring Institute (www.abmi.ca)
  and the Boreal Avian Modelling Project (borealbirds.ca).
Allows users to quickly and easily detect data containing
    Personally Identifiable Information (PII) through convenience functions.
Detection of runs of homozygosity and of heterozygosity
    in diploid genomes using two methods: sliding windows (Purcell et al (2007)
    <doi:10.1086/519795>) and consecutive runs (Marras et al (2015)
    <doi:10.1111/age.12259>).
Solvers and test set for stiff and non-stiff differential equations, and 
  differential algebraic equations. 
  'Mazzia, F., Cash, J.R. and K. Soetaert, 2012. DOI: 10.1016/j.cam.2012.03.014'.
Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.
Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.
DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust
    Linear Regression.
Simple GUI to perform some standard tree-ring analyses.
Detrend fluorescence microscopy image series for fluorescence 
    fluctuation and correlation spectroscopy ('FCS' and 'FFS') analysis. This 
    package contains functionality published in a 2016 paper 
    <doi:10.1093/bioinformatics/btx434> but it has been extended since then with
    the Robin Hood algorithm and thus contains unpublished work.
Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
In the new era of population genomics, surveys of genetic polymorphism ("genome scans") offer the opportunity to distinguish locus-specific from genome wide effects at many loci. Identifying presumably neutral regions of the genome that are assumed to be influenced by genome-wide effects only, and excluding presumably selected regions, is therefore critical to infer population demography and phylogenetic history reliably. Conversely, detecting locus-specific effects may help identify those genes that have been, or still are, targeted by natural selection. The software package DetSel has been developed to identify markers that show deviation from neutral expectation in pairwise comparisons of diverging populations.
Compare detrital zircon suites by uploading univariate,
      U-Pb age, or bivariate, U-Pb age and Lu-Hf data, in a 'shiny'-based
      user-interface. Outputs publication quality figures using 'ggplot2', and
      tables of statistics currently in use in the detrital zircon geochronology
      community.
A concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code.
Differential expression analysis tools for data aggregation, visualization, exploratory analysis, and project organization.
Provides a non-drawing graphic device for benchmarking purpose.
    In order to properly benchmark graphic drawing code it is necessary
    to factor out the device implementation itself so that results are not 
    related to the specific graphics device used during benchmarking. The 
    'devoid' package implements a graphic device that accepts all the required
    calls from R's graphic engine but performs no action. Apart from 
    benchmarking it is unlikely that this device has any practical use.
Data sets and sample analyses from Jay L. Devore (2008),
        "Probability and Statistics for Engineering and the Sciences
        (7th ed)", Thomson.
A set of functions to quantify the relationship between development
    rate and temperature and to build phenological models. The package comprises 
    a set of models and estimated parameters borrowed from a literature review 
    in ectotherms. The methods and literature review are described in Rebaudo 
    et al. (2018) <doi:10.1111/2041-210X.12935> and Rebaudo and Rabhi (2018) 
    <doi:10.1111/eea.12693>. An example can be found in Rebaudo et al. (2017) 
    <doi:10.1007/s13355-017-0480-5>.
Collection of package development tools.
A system for the management, assessment, and psychometric analysis of data from educational and psychological tests.
Classical Test and Item analysis, 
  Item Response analysis and data management for educational and psychological tests.
Conditional Maximum Likelihood Calibration and data management of multistage tests. 
  Functions for calibration of the Extended Nominal Response and the Interaction models, DIF and profile analysis.
  See Robert J. Zwitser and Gunter Maris (2015)<doi:10.1007/s11336-013-9369-6>.
It handles numerics, characters, factors, and logicals.
Computes small-sample degrees of freedom adjustment for
    heteroskedasticity robust standard errors, and for clustered standard errors
    in linear regression. See Imbens and Kolesár (2016)
    <doi:10.1162/REST_a_00552> for a discussion of these adjustments.
Phase I/II adaptive dose-finding design for combination
   studies where toxicity rates are supposed to increase with both agents.
Compares two dataframes with a common key
            and returns the delta records. The package will return
            three dataframes that contain the added, changed,
            and deleted records.
An implementation of multivariate linear process bootstrap (MLPB) method and sliding window technique to assess the dynamic functional connectivity (dFC) estimate by providing its confidence bands, based on Maria Kudela (2017) <doi: 10.1016/j.neuroimage.2017.01.056>.
  It also integrates features to visualize non-zero coverage for selected a-priori regions of interest estimated by the dynamic functional connectivity model (dFCM) and dynamic functional connectivity (dFC) curves for reward-related a-priori regions of interest where the activation-based analysis reported.
Provides functions to run the CRM and
        TITE-CRM in phase I trials and calibration tools for trial
        planning purposes.
Quickly and graphically show missing values and classes of each variable and each observation of a data.frame. The aim is to show pattern of missing values and if there is wrong class attribution.
A set of functions to perform Raju, van der Linden and Fleer's
    (1995, doi:10.1177/014662169501900405) Differential Functioning of Items
    and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item
    Parameter Replication approach (Oshima, Raju, & Nanda, 2006, doi:10.1111/j.
    1745-3984.2006.00001.x) for obtaining the associated statistical significance
    tests cut-off points. They may also be used for a priori and post-hoc power
    calculations (Cervantes, 2017, doi:10.18637/jss.v076.i05).
Meta-analysis approaches for Phase I dose finding early phases clinical trials in order to better suit requirements in terms of maximum tolerated dose (MTD) and maximal dose regimen (MDR). This package has currently three different approaches: (a) an approach proposed by Zohar et al, 2011, <doi:10.1002/sim.4121> (denoted as ZKO), (b) the Variance Weighted pooling analysis (called VarWT) and (c) the Random Effects Model Based (REMB) algorithm, where user can input his/her own model based approach or use the existing random effect logistic regression model (named as glimem) through the 'dfmeta' package.
Phase I/II adaptive dose-finding design for single-agent
   Molecularly Targeted Agent (MTA), according to the paper "Phase
   I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
   Determination using Adaptive Randomization", Riviere Marie-Karelle et
   al. (2016) <doi:10.1177/0962280216631763>.
Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.
Provides R-implementation of Decision forest algorithm, which combines the predictions of
    multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel 
    pattern-recognition method which can be used to analyze: (1) DNA microarray data; 
    (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry  (SELDI-TOF-MS) data; and 
    (3) Structure-Activity Relation (SAR) data.
    In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV.  
    run Dforest() to see more instructions.
    Weida Tong (2003) <doi:10.1021/ci020058s>.
A unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults, is proposed in the 'dfped' package. The dose range can be calculated under three extrapolation methods: linear, allometry and maturation adjustment, using pharmacokinetic (PK) data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose-toxicity and dose-efficacy relationships can be obtained using early phase adult toxicity and efficacy data at several dose levels through 'dfped' package. Priors are used into the dose finding process through a Bayesian model selection or adaptive priors, to facilitate adjusting the amount of prior information to differences between adults and children. This calibrates the model to adjust for misspecification if the adult and paediatric data are very different. User can use his/her own Bayesian model written in Stan code through the 'dfped' package. A template of this model is proposed in the examples of the corresponding R functions in the package. Finally, in this package you can find a simulation function for one trial or for more than one trial. These methods are proposed by Petit et al, (2016) <doi:10.1177/0962280216671348>.
Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed  without making any parametric assumptions on the stable (in-control) distribution.
Statistical methods involving PK measures are provided, in the dose allocation process during a Phase I clinical trials. These methods, proposed by Ursino et al, (2017) <doi:10.1002/bimj.201600084>, enter pharmacokinetics (PK) in the dose finding designs in different ways, including covariates models, dependent variable or hierarchical models. This package provides functions to generate data from several scenarios and functions to run simulations which their objective is to determine the maximum tolerated dose (MTD).
Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
Feature selection from high dimensional data using a diploid
    genetic algorithm with Incomplete Dominance for genotype to phenotype mapping
    and Random Assortment of chromosomes approach to recombination.
Performs differential correlation analysis on input
    matrices, with multiple conditions specified by a design matrix. Contains
    functions to filter, process, save, visualize, and interpret differential
    correlations of identifier-pairs across the entire identifier space, or with
    respect to a particular set of identifiers (e.g., one). Also contains several
    functions to perform differential correlation analysis on clusters (i.e., modules)
    or genes. Finally, it contains functions to generate empirical p-values for the
    hypothesis tests and adjust them for multiple comparisons. Although the package
    was built with gene expression data in mind, it is applicable to other types of
    genomics data as well, in addition to being potentially applicable to data from
    other fields entirely. It is described more fully in the manuscript
    introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.
Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.
Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.
Model fitting and evaluation tools for double generalized linear
    models (DGLMs). This class of models uses one generalized linear model (GLM)
    to fit the specified response and a second GLM to fit the deviance of the first
    model.
Model estimation, dispersion testing and diagnosis of hyper-Poisson
    Saez-Castillo, A.J. and Conde-Sanchez, A. (2013) 
    <doi:10.1016/j.csda.2012.12.009> and Conway-Maxwell-Poisson Huang, A. (2017)
    <doi:10.1177/1471082X17697749> regression models.
Dynamic graphical models for multivariate time series data to estimate directed
    dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et al. (2017) <doi:10.1101/198887>.
A set of functions have been implemented to generate random data to perform Monte Carlo simulations on structural models with formative constructs and interaction and nonlinear effects (Two-Step PLS Mode B structural models). The setup of the true model considers a simple structure with three formative exogenous constructs related to one formative endogenous construct. The routines take into account the interaction and nonlinear effects of the exogenous constructs on the endogenous construct.
Fit dynamic group-level item response theory (IRT) and multilevel
    regression and poststratification (MRP) models from item response data. dgo
    models latent traits at the level of demographic and geographic groups,
    rather than individuals, in a Bayesian group-level IRT approach developed by
    Caughey and Warshaw (2015) <doi:10.1093/pan/mpu021>. The package also
    estimates subpopulations' average responses to single survey items with a
    dynamic MRP model proposed by Park, Gelman, and Bafumi (2004)
    <doi:10.11126/stanford/9780804753005.003.0011>.
Provides data used by package 'dgo' in examples and vignettes.
This package contains a proposed revision to the
        stats::ks.test() function and the associated ks.test.Rd help
        page.  With one minor exception, it does not change the
        existing behavior of ks.test(), and it adds features necessary
        for doing one-sample tests with hypothesized discrete
        distributions.  The package also contains cvm.test(), for doing
        one-sample Cramer-von Mises goodness-of-fit tests.
This is a visualization tool for vegetation structure/succession
    in space and/or time mainly for forest gap models. However, it could also be used to
    visualize observed forest stands. If used for models, they should contain either
    individual trees or cohorts (e.g. LPJ-GUESS by Smith et al. (2014) <doi:10.5194/bg-11-2027-2014>).
    For a list of required and additional data fields see the vignette.
The 'DHARMa' package uses a simulation-based approach to create
    readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
    models. Currently supported are linear and generalized linear (mixed) models from 'lme4' 
    (classes 'lmerMod', 'glmerMod'), 'glmmTMB' and 'spaMM', generalized additive models ('gam' from 
    'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
    classes. Moreover, externally created simulations, e.g. posterior predictive simulations 
    from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. 
    The resulting residuals are standardized to values between 0 and 1 and can be interpreted 
    as intuitively as residuals from a linear regression. The package also provides a number of 
    plot and test functions for typical model misspecification problems, such as 
    over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.
Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.
Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.
Implements double hierarchical generalized linear models in which the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
The density, cumulative distribution, quantiles, 
    and i.i.d random variables of a heavy-headed distribution.
    For more information, please see the vignette.
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regression.
Calculates key indicators such as fertility rates (Total Fertility Rate (TFR), General Fertility Rate (GFR), 
  and Age Specific Fertility Rate (ASFR)) using Demographic and Health Survey (DHS) women/individual data, 
  and childhood mortality probabilities and rates such as Neonatal Mortality Rate (NNMR), Post-neonatal Mortality Rate (PNNMR), 
  Infant Mortality Rate (IMR), Child Mortality Rate (CMR), and Under-five Mortality Rate (U5MR). 
  In addition to the indicators, the 'DHS.rates' package estimates sampling errors indicators such as Standard Error (SE), 
  Design Effect (DEFT), Relative Standard Error (RSE) and Confidence Interval (CI). 
  The package is developed according to the DHS methodology of calculating the fertility indicators and 
  the childhood mortality rates outlined in the 
  "Guide to DHS Statistics" (Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. 2018, <https://dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm>) 
  and the DHS methodology of estimating the sampling errors indicators outlined in 
  the "DHS Sampling and Household Listing Manual" (ICF International 2012, <https://dhsprogram.com/pubs/pdf/DHSM4/DHS6_Sampling_Manual_Sept2012_DHSM4.pdf>).
Contains an implementation of the
	     d-variable Hilbert Schmidt independence criterion
	     and several hypothesis tests based on it, as described
	     in Pfister et al. (2017) <doi:10.1111/rssb.12235>.
A set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. 
             The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood 
             and represents a proxy measure of aging and also can be served as
             a sensitive predictor of survival. For more information, see 
             (i)"Accumulation of Deficits as a Proxy Measure of Aging" 
             by Arnold B. Mitnitski et al. (2001), 
             The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>;
             (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" 
             by Arnold B Mitnitski et al. (2001), 
             BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.
Fast functions for effective sample size, weighted multivariate mean and variance computation, 
    and weight diagnostic plot for generic importance sampling type results.
Provides methods by Steinhauser et al. (2016) <DOI:10.1186/s12874-016-0196-1> for meta-analysis of diagnostic accuracy studies with several cutpoints.
Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the 'decompr' and 'gvc' packages have each country-industry combination occur along both edges of the matrix.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, 
     visualising webs, electrical networks, etc.
     Support for the book "A practical guide to ecological modelling -
     using R as a simulation platform"
     by Karline Soetaert and Peter M.J. Herman (2009), Springer.
     and the book "Solving Differential Equations in R"
     by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012), Springer.
     Includes demo(flowchart), demo(plotmat), demo(plotweb).
Build graph/network structures using functions for stepwise addition and
    deletion of nodes and edges. Work with data available in tables for bulk
    addition of nodes, edges, and associated metadata. Use graph selections
    and traversals to apply changes to specific nodes or edges. A wide
    selection of graph algorithms allow for the analysis of graphs. Visualize
    the graphs and take advantage of any aesthetic properties assigned to
    nodes and edges.
Allows for export of DiagrammeR Graphviz objects to SVG.
Performs Diallel Analysis with R using Griffing's and Hayman's approaches. Four different methods (1: Method-I (Parents + F1's + reciprocals); 2: Method-II (Parents and one set of F1's); 3: Method-III (One set of F1's and reciprocals); 4: Method-IV (One set of F1's only)) and two methods (1: Fixed Effects Model; 2: Random Effects Model) can be applied using Griffing's approach.
Parse, format, and validate international phone
    numbers using Google's 'libphonenumber' java library,
    <https://github.com/googlei18n/libphonenumber>.
Collects 'libphonenumber' jars required for the
    'dialr' package.
Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.
Visualize one-factor data frame. 
  Beads plot consists of diamonds of each factor of each data series. 
  A diamond indicates average and range. 
  Look over a data frame with many numeric columns and a factor column.
This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.
Space-Filling Designs and Uniformity Criteria.
Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
Efficient Global Optimization (EGO) algorithm and adaptations for
  parallel infill (multipoint EI), problems with noise, and problems with
  constraints.
Performs cluster analysis using an ensemble
    clustering framework, Chiu & Talhouk (2018)
    <doi:10.1186/s12859-017-1996-y>.  Results from a diverse set of
    algorithms are pooled together using methods such as majority voting,
    K-Modes, LinkCluE, and CSPA. There are options to compare cluster
    assignments across algorithms using internal and external indices,
    visualizations such as heatmaps, and significance testing for the
    existence of clusters.
View 2D/3D sections or contours of computer experiments designs, surrogates or test functions.
Collapse red-green or green-blue distinctions to simulate
        the effects of different types of color-blindness.
This package contains the dictionaries for reading microdata
        from IBGE (Brazilian Institute of Geography and Statistics)
        surveys PNAD, PME and POF.
Given  an initial set of points, this package minimizes the number of elements to discard from this set such that there exists at least one monotonic and convex mapping within pre-specified upper and lower bounds.
The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group.  Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time.  This package contains tools for computing average treatment effect parameters in Difference in Differences models with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2018) <https://ssrn.com/abstract=3148250>.  The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time.  These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these.  There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.
A basic, clear implementation of tree-based gradient boosting
    designed to illustrate the core operation of boosting models. Tuning
    parameters (such as stochastic subsampling, modified learning rate, or
    regularization) are not implemented. The only adjustable parameter is the
    number of training rounds. If you are looking for a high performance boosting
    implementation with tuning parameters, consider the 'xgboost' package.
Enables the user to build a citation network/graph from bibliographic data and, based on modularity and heterocitation metrics, assess the degree of awareness/cross-fertilization between two corpora/communities. This toolset is optimized for Scopus data.
This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
             customer transaction data. The function consumes a data frame with Transaction Number,
             Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
             The function returns a data frame with RFM data for the sales information.
An implementation of the metrics dief@t and dief@k to measure the diefficiency (or continuous efficiency) of incremental approaches, see Acosta, M., Vidal, M. E., & Sure-Vetter, Y. (2017) <doi:10.1007/978-3-319-68204-4_1>. The metrics dief@t and dief@k allow for measuring the diefficiency during an elapsed time period t or while k answers are produced, respectively. dief@t and dief@k rely on the computation of the area under the curve of answer traces, and thus capturing the answer rate concentration over a time interval.
Physical constants. Gold, silver and glass permittivities,
    together with spline interpolation functions.
Identification of debris-containing droplets 
        from a droplet-based single cell/nucleus RNA-seq. 'diem' uses 
        the expression profile of background RNA droplets, followed 
        by expectation maximization (EM), to assign candidates as 
        debris or nuclei. See Alvarez M, Rahmani E, et al (2019) 
        <doi:10.1101/786285> for details.
Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Froese & Pauly (2000, ISBN: 9718709991).
A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.
Performs detection of Differential Item Functioning using the method DIFboost as proposed in Schauberger and Tutz (2015): Detection of Differential item functioning in Rasch models by boosting techniques, British Journal of Mathematical and Statistical Psychology.
Estimation of DIFferential COexpressed NETworks using diverse and user metrics.
			 This package is basically used for three functions related to the estimation
			 of differential coexpression. 
			 First, to estimate differential coexpression where
			 the coexpression is estimated, by default, by Spearman correlation. For this,
			 a metric to compare two correlation distributions is needed. The package includes
			 6 metrics. Some of them needs a threshold. A new metric can also be specified as
			 a user function with specific parameters (see difconet.run). The significance is
			 be estimated by permutations.
			 Second, to generate datasets with controlled differential correlation data. This 
			 is done by either adding noise, or adding specific correlation structure.
			 Third, to show the results of differential correlation analyses. Please see
			 <http://bioinformatica.mty.itesm.mx/difconet> for further information.
A method for identifying pattern changes between 2 experimental
         conditions in correlation networks (e.g., gene co-expression networks),
         which builds on a commonly used association measure, such as Pearson's
         correlation coefficient. This package includes functions to calculate
         correlation matrices for high-dimensional dataset and to test
         differential correlation, which means the changes in the correlation
         relationship among variables (e.g., genes and metabolites) between 2
         experimental conditions.
The package includes functions to calculate confidence
        intervals for the difference of dependent proportions. There
        are two functions implemented to edit the data (dichotomising
        with the help of cutpoints, counting accordance and discordance
        of two tests or situations). For the calculation of the
        confidence intervals entries of the fourfold table are needed.
Functions for comparing two data.frames against 
    each other. The core functionality is to provide a detailed breakdown of any differences 
    between two data.frames as well as providing utility functions to help narrow down the 
    source of problems and differences.
This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.
Compare functional enrichment between two experimentally-derived groups of genes or proteins (Peterson, DR., et al.(2018)) <doi: 10.1371/journal.pone.0198139>. Given a list of gene symbols, 'diffEnrich'  will
  perform differential enrichment analysis using the Kyoto Encyclopedia of Genes
  and Genomes (KEGG) REST API. This package provides a number of functions that are 
  intended to be used in a pipeline. Briefly, the user provides a KEGG formatted species id for either human, mouse or rat, and the package will
  download and clean species specific ENTREZ gene IDs and map them to their respective
  KEGG pathways by accessing KEGG's REST API. KEGG's API is used to guarantee the most up-to-date pathway data from KEGG. Next, the user will identify significantly
  enriched pathways from two gene sets, and finally, the user will identify 
  pathways that are differentially enriched between the two gene sets. In addition to 
  the analysis pipeline, this package also provides a plotting function.
An interface to 'DifferentialEquations.jl' <http://docs.juliadiffeq.org/latest/> from the R programming language.
  It has unique high performance methods for solving ordinary differential equations (ODE), stochastic differential equations (SDE),
  delay differential equations (DDE), differential-algebraic equations (DAE), and more. Much of the functionality,
  including features like adaptive time stepping in SDEs, are unique and allow for multiple orders of magnitude speedup over more common methods.
  'diffeqr' attaches an R interface onto the package, allowing seamless use of this tooling by R users.
Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.
Package to fit diffusion-based IRT models to response and 
	response time data. Models are fit using marginal maximum 
	likelihood. Parameter restrictions (fixed value and equality 
	constraints) are possible. In addition, factor scores (person drift 
	rate and person boundary separation) can be estimated. Model fit 
	assessment tools are also available. The traditional diffusion model 
	can be estimated as well.
An algorithm based on graph theory tools to detect differentiation
    problems. A differentiation problem occurs when aggregated 
    data are disseminated according to two different nomenclatures. By making the
    difference for an additive variable X between an aggregate composed of categories
    of the first nomenclature and an other aggregate, included in that first 
    aggregate, composed of categories of the second nomenclature, 
    it is sometimes possible to derive X on a small aggregate of records 
    which could then lead to a break of confidentiality.
    The purpose of this package is to detect the set of aggregates composed of 
    categories of the first nomenclature which lead to a differentiation problem, 
    when given a confidentiality threshold.
This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution (i.e., the conceptually highest fidelity).
Provides an implementation of statistically significant 
    differential sub-network analysis for paired biological networks.
Generate a colorized diff of two R objects for an intuitive
    visualization of their differences.
An implementation of major general-purpose mechanisms for privatizing
    statistics, models, and machine learners, within the framework of differential
    privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms
    include the Laplace mechanism for releasing numeric aggregates, and the 
    exponential mechanism for releasing set elements. A sensitivity sampler 
    (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target 
    non-private function sensitivity; combined with the generic mechanisms, it 
    permits turn-key privatization of arbitrary programs.
An R interface to the 'codediff' JavaScript library (a copy of which is included in the package,
  see <https://github.com/danvk/codediff.js> for information).
  Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.
Provides data structures and methods for measuring, coding, 
    and analysing text within text corpora. The package allows for manual as 
    well computer aided coding on character, token and text pair level.
Interactive visualizations and tabulations for diffrprojects. 
    All presentations are based on the htmlwidgets framework allowing for 
    interactivity via HTML and Javascript, Rstudio viewer integration, 
    RMarkdown integration, as well as Shiny compatibility.
Various diffusion models to forecast new product growth. Currently
    the package contains Bass, Gompertz and Gamma/Shifted Gompertz curves. See
    Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.
Implements diffusion map method of data
    parametrization, including creation and visualization of
    diffusion map, clustering with diffusion K-means and
	  regression using adaptive regression model.
	  Richards (2009) <doi:10.1088/0004-637X/691/1/32>.
Tools for performing inference and analysis on a class of quadratic diffusion processes for both scalar and bivariate diffusion systems. For scalar diffusions, a module is provided for solving first passage time problems for both time-homogeneous and time-inhomogeneous GQDs.
Tools for performing inference and analysis using a data-imputation scheme and the method of lines.
Tools for performing inference and analysis on a class of quadratic jump diffusion processes.
Implementation of network diffusion algorithms such as
  heat diffusion or Markov random walks. Network diffusion algorithms generally
  spread information in the form of node weights along the edges of a graph to other nodes.
  These weights can for example be interpreted as temperature, an initial amount
  of water, the activation of neurons in the brain, or the location of a random
  surfer in the internet. The information (node weights) is iteratively propagated
  to other nodes until a equilibrium state or stop criterion occurs.
Statistical hypothesis testing of pattern heterogeneity via
    differences in underlying distributions across two or more contingency
    tables. Three tests are included: the comparative chi-squared test
    (Song et al, 2014) <doi:10.1093/nar/gku086> (Zhang et al, 2015)
    <doi:10.1093/nar/gkv358>, the Sharma-Song test, and the heterogeneity test.
    Under the null hypothesis that row and column variables are statistically
    independent and joint distributions are equal, their test statistics all
    follow an asymptotically chi-squared distribution. These options test for
    heterogeneous patterns that differ in either the first order (marginal) or
    the second order (joint distribution deviation from product of marginals).
    Second-order differences may reveal more fundamental changes than
    first-order differences across heterogeneous patterns.
Performs DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.
Detection of differential item functioning (DIF) among dichotomously scored items and differential distractor functioning (DDF) among unscored items with non-linear regression procedures based on generalized logistic regression models (Drabinova and Martinkova, 2017, doi:10.1111/jedm.12158).
Provides a collection of standard methods to detect differential item functioning among dichotomously scored items. Methods for uniform and non-uniform DIF, based on test-score or IRT methods, for comparing two or more than two groups of respondents, are available (Magis, Beland, Tuerlinckx and De Boeck,A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning, Behavior Research Methods, 42, 2010, 847-862 <doi:10.3758/BRM.42.3.847>).
Item focussed recursive partitioning for simultaneous selection of items and variables that induce Differential Item Functioning (DIF) in dichotomous or polytomous items.
Implementation of a function 'digest()' for the creation 
 of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 
 'crc32', 'xxhash', 'murmurhash' and 'spookyhash' algorithms) permitting easy
 comparison of R language objects, as well as functions such as'hmac()' to
 create hash-based message authentication code. Please note that this package
 is not meant to be deployed for cryptographic purposes for which more
 comprehensive (and widely tested) libraries such as 'OpenSSL' should be
 used.
Digiroo2 is an R package developed by researchers at the
        University of Queensland to investigate association patterns
        and social structure in wild animal populations. Proximity
        between individuals is generally considered to be an
        appropriate proxy for associations and pairwise association
        indices are the most widely used technique for analysing animal
        social structure. However, little attention is given to
        identifying how patterns of spatial overlap affect these
        association patterns. For example, do individuals associate
        randomly with others with whom they share home ranges, or do
        some individuals go out of their way to associate with or avoid
        particular individuals? This program builds a null model of
        random associations based on an individual's space use
        determined using home range methodologies. Random points may be
        generated within a specified home range contour or according to
        the Utilization Distribution (UD). Expected associations of
        individuals are extracted based on probability of occurrence
        and the proximity between home range weighted random points.
        Association matrices can be generated from multiple
        permutations for analysis using SOCPROG 2.4 (Whitehead 2009) to
        create 'expected' pairwise half-weight association indices
        (HWIs). These may be compared with the 'observed' HWIs from
        field observations to reveal whether pairs of animals associate
        more (= attraction) or less (= avoidance) than expected by
        chance.
The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
Import data from a digital image; it requires user input for
    calibration and to locate the data points. The end result is similar to
    'DataThief' and other other programs that 'digitize' published plots or
    graphs.
Combine multiple-relationship networks into a single weighted
    network.  The approach is similar to factor analysis in the that
    contribution from each constituent network varies so as to maximize the
    information gleaned from the multiple-relationship networks.
    This implementation uses Principal Component Analysis calculated using
    'prcomp' with bootstrap subsampling.  Missing links are imputed using
    the method of Chen et al.
    (2012).
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
The implemented methods are: Bass Standard model, Bass Generalized model (with rectangular shock, exponential shock, mixed shock and armonic shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function.
A collection of dimensionality reduction
    techniques from R packages and a common
    interface for calling the methods.
Estimate the Deterministic Input, Noisy "And" Gate (DINA)
    cognitive diagnostic model parameters using the Gibbs sampler described
    by Culpepper (2015) <doi:10.3102/1076998615595403>.
This function implements the DiNAMIC procedure for
        assessing the statistical significance of recurrent DNA copy
        number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
Decomposition of (income) inequality by population sub groups. 
    For a decomposition on a single variable the mean log deviation can be used
      (see Mookherjee Shorrocks (1982) <DOI:10.2307/2232673>).
    For a decomposition on multiple variables a regression based technique can be 
      used (see Fields (2003) <DOI:10.1016/s0147-9121(03)22001-x>).
    Recentered influence function regression for marginal effects of the (income 
      or wealth) distribution  (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>).
    Some extensions to inequality functions to handle weights and/or missings.
S3 classes and methods to create and work
    with year-quarter, year-month and year-isoweek vectors. Basic
    arithmetic operations (such as adding and subtracting) are supported,
    as well as formatting and converting to and from standard R date
    types.
Implements the algorithm described in Jun Li and Alicia T. Lamere, 
    "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes"  (Unpublished). 
    Detects not only genes that show different 
    average expressions ("differential expression", DE), but also genes that show different 
    diversities of expressions in different groups ("differentially dispersed", DD). DD genes 
    can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the 
    quasi-likelihood function, and thus has superior robustness against outliers and other noise.
    Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi.
    (2) Added a function for estimating the proportion of outliers in the data.
    (3) Modified the input parameter names for clarity, and modified the output format for the main function.
Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization.
    Yu, R., and Rosenbaum, P. R. (2019). <doi:10.1111/biom.13098>.
Works as "add-ons" to packages like 'shiny', 'future', as well as 
    'rlang', and provides utility functions. Just like dipping sauce adding 
    flavors to potato chips or pita bread, 'dipsaus' for data analysis and 
    visualizations adds handy functions and enhancements to popular packages. 
    The goal is to provide simple solutions that are frequently asked for 
    online, such as how to synchronize 'shiny' inputs without freezing the app,
    or how to get memory size on 'Linux' or 'MacOS' system. The enhancements 
    roughly fall into these four categories: 1. 'shiny' input widgets; 2. 
    high-performance computing using 'RcppParallel' and 'future' package; 3. 
    modify R calls and convert among numbers, strings, and other objects. 4. 
    utility functions to get system information such like CPU chipset, memory 
    limit, etc.
Compute Hartigan's dip test statistic for unimodality /
 multimodality and provide a test with simulation based p-values,  where
 the original public code has been corrected.
A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data.  It estimates the number of clusters directly from the data using a Dirichlet-process prior.  See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.
Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. 
    It allows to compute clustering coefficients that are not present in 'igraph' package. 
    A description of clustering coefficients can be found in "Directed clustering in weighted networks: a new perspective", Clemente, G.P., Grassi, R. (2017),  	
    <doi:10.1016/j.chaos.2017.12.007>.
A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216>.
A collection of functions for directional data (including massive, with millions of observations, data) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the "Directional Statistics" by Mardia, K. V. and Jupp, P. E. (2000). Other references include  Phillip J. Paine, Simon P. Preston Michail Tsagris and Andrew T. A. Wood (2018). An elliptically symmetric angular Gaussian distribution. Statistics and Computing 28(3): 689-697. <doi:10.1007/s11222-017-9756-4>, P. J. Paine, S. P. Preston, M. Tsagris and Andrew T. A. Wood (2019). Spherical regression models with general covariates and anisotropic errors. Statistics and Computing (to appear). <doi:10.1007/s11222-019-09872-2>.
An extensible framework
 for automatically placing direct labels onto multicolor 'lattice' or
 'ggplot2' plots.
 Label positions are described using Positioning Methods
 which can be re-used across several different plots.
 There are heuristics for examining "trellis" and "ggplot" objects
 and inferring an appropriate Positioning Method.
Represents the content of a directory as an interactive collapsible tree. Offers
    the possibility to assign a text (e.g., a 'Readme.txt') to each folder
    (represented as a clickable node), so that when the user hovers the pointer
    over a node, the corresponding text is displayed as a tooltip.
Direction analysis is a set of tools designed to identify
    combinatorial effects of multiple treatments/conditions on pathways
    and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics
    data. See Yang P et al (2014) <doi:10.1093/bioinformatics/btt616>; and Yang P et al. (2016) <doi:10.1002/pmic.201600068>.
Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.
Perform nonparametric Bayesian analysis using Dirichlet 
    processes without the need to program the inference algorithms. 
    Utilise included pre-built models or specify custom 
    models and allow the 'dirichletprocess' package to handle the 
    Markov chain Monte Carlo sampling. 
    Our Dirichlet process objects can act as building blocks for a variety 
    of statistical models including and not limited to: density estimation, 
    clustering and prior distributions in hierarchical models.
    See Teh, Y. W. (2011) 
    <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, 
    among many other sources.
Implements Dirichlet regression models in R.
Implementation of Directional Metropolis Hastings Algorithm for
    MCMC.
Estimate parameters in Dirichlet-Multinomial and compute
        profile log-likelihoods.
Fits disaggregation regression models using 'TMB' ('Template Model
    Builder'). When the response data are aggregated to polygon level but
    the predictor variables are at a higher resolution, these models can be
    useful. Regression models with spatial random fields. A useful reference for disaggregation modelling is 
    Lucas et al. (2019) <doi:10.1101/548719>.
Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) <doi:10.1093/biostatistics/kxy071>. There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function.
Client for the Discourse API. Discourse is a open source
    discussion forum platform (<https://www.discourse.org/>). It comes with 'RESTful'
    API access to an installation. This client requires that you are authorized
    to access a Discourse installation, either yours or another.
Computes discrete fast Fourier transform of river discharge data and the derived metrics.
  The methods are described in J. L. Sabo, D. M. Post (2008) <doi:10.1890/06-1340.1> and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) <doi:10.1126/science.aao1053>.
Discrete Laplace exponential family for models such as a generalized linear model
Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
Concordance and discordance of homologous gene regulation allows comparing
  reaction to stimuli in different organisms, 
  for example human patients and animal models of a disease. The package
  contains functions to calculate discordance and concordance score
  for homologous gene pairs, identify concordantly or
  discordantly regulated transcriptional modules and visualize the results.
  It is intended for analysis of transcriptional data.
Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Currently, the package contains data restructuring functions; functions for generating genetically- and environmentally-informed data for kin pairs.
Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Structure learning of Bayesian network using coordinate-descent
    algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
    and we use a multi-logit model to do the regression.
    The algorithm is described in Gu, Fu and Zhou (2016) <arXiv:1403.2310>.
Multiple testing procedures described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input a set of observed p-values and their discrete support under the null. A function to compute such p-values and supports for Fisher's exact tests is also provided, along with a wrapper allowing to apply discrete procedures directly from contingency tables.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.
Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.
Multiple testing procedures for discrete test statistics,
        that use the known discrete null distribution of the p-values
        for simultaneous inference.
We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003) <DOI:10.1073/pnas.1530509100>. The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, pi_0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi_0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi_0 estimators which take into account the discrete distribution of the P-values.
Create, manipulate, transform, and simulate from discrete random
    variables. The syntax is modeled after that which is used in mathematical
    statistics and probability courses, but with powerful support for more
    advanced probability calculations. This includes the creation of joint
    random variables, and the derivation and manipulation of their conditional
    and marginal distributions.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.
This package is a collection of supervised discretization
        algorithms. It can also be grouped in terms of top-down or
        bottom-up, implementing the discretization algorithms.
Bindings for additional classification models for use with the 
    'parsnip' package. Models include flavors of discriminant analysis, such as 
    linear (Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized 
    (Friedman (1989) <doi:10.1080/01621459.1989.10478752>), and flexible 
    (Hastie, Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), 
    as well as naive Bayes classifiers (Hand and Yu (2007) 
    <doi:10.1111/j.1751-5823.2001.tb00465.x>).
'discrimARTs' discriminates with explicit confidence the alternative reproductive tactics (ARTs) in dimorphic systems with bimodal traits by computing the maximum likelihood estimate of a mixture of distributions of a measured ARTs trait. Supported distributions include a mixture of 2 normal distributions and a mixture of 2 facing gamma distributions.
Functions for Discriminant Analysis and Classification purposes
    covering various methods such as descriptive, geometric, linear, quadratic,
    PLS, as well as qualitative discriminant analyses
Provides data transformations, estimation utilities,
    predictive evaluation measures and simulation functions for discrete time
    survival analysis.
Formatting of population and case data, calculation of Standardized
    Incidence Ratios, and fitting the BYM model using INLA.
Model cell type heterogeneity of bulk renal cell carcinoma. The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions.
Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.
Implements methods for calculating disproportionate impact: the percentage point gap, proportionality index, and the 80% index.
 California Community Colleges Chancellor's Office (2017).  Percentage Point Gap Method. <https://www.cccco.edu/-/media/CCCCO-Website/About-Us/Divisions/Digital-Innovation-and-Infrastructure/Research/Files/PercentagePointGapMethod2017.ashx>.
 California Community Colleges Chancellor's Office (2014).  Guidelines for Measuring Disproportionate Impact in Equity Plans. <https://www.cccco.edu/-/media/CCCCO-Website/About-Us/Divisions/Digital-Innovation-and-Infrastructure/Network-Operations/Accountability/Files/GUIDELINES-FOR-MEASURING-DISPROPORTIONATE-IMPACT-IN-EQUITY-PLANS.ashx>.
A disk-based data manipulation tool for working with 
  large-than-RAM datasets. Aims to lower the barrier-to-entry for 
  manipulating large datasets by adhering closely to popular and 
  familiar data manipulation paradigms like dplyr verbs and 
  data.table syntax.
A pipeline to analyze photographs of disk diffusion plates. This removes the need to analyze the plates themselves, and thus analysis can be done separate from the assay. Furthermore, diskImageR removes potential researcher bias, by quantitative assessment of drug resistance as the zone diameter at multiple cutoff values of growth inhibition. This method also extends the disk diffusion assay by measuring drug tolerance (in addition to drug resistance) as the fraction of the subpopulation that is able to grow above the resistance point ("FoG"), and drug sensitivity as the rate of change from no growth to full growth ("slope").
Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites and the environment at these sites.
The disparity filter algorithm is a network reduction technique to
    identify the 'backbone' structure of a weighted network without destroying
    its multi-scale nature. The algorithm is documented in M. Angeles Serrano,
    Marian Boguna and Alessandro Vespignani in "Extracting the multiscale
    backbone of complex weighted networks", Proceedings of the National Academy
    of Sciences 106 (16), 2009. This implementation of the algorithm supports
    both directed and undirected networks.
A package containing R functions for displaying data and
        results from high-throughput screening experiments.
Functions for estimating Gaussian dispersion regression models (Aitkin, 1987 <doi:10.2307/2347792>), overdispersed binomial logit models (Williams, 1987 <doi:10.2307/2347977>), and overdispersed Poisson log-linear models (Breslow, 1984 <doi:10.2307/2347661>), using a quasi-likelihood approach.
Create disposable R packages for testing.
    You can create, install and load multiple R packages with a single
    function call, and then unload, uninstall and destroy them with another
    function call. This is handy when testing how some R code or an R package
    behaves with respect to other packages.
A modular package for measuring disparity from multidimensional matrices. Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics (Guillerme (2018) <doi:10.1111/2041-210X.13022>). The package also provides functions for looking at disparity in a serial way (e.g. disparity through time - Guillerme and Cooper (2018) <doi:10.1111/pala.12364>) or per groups as well as visualising the results. Finally, this package provides several basic statistical tests for disparity analysis.
Spatial downscaling of coarse grid mapping to fine grid
    mapping using predictive covariates and a model fitted using the 'caret'
    package. The original dissever algorithm was published by Malone et al. 
    (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al.
    (2017) <doi:10.1016/j.compag.2017.08.021>.
Tools to fit sample selection models in case of discrete response 
  variables, through a parametric formulation which represents a natural 
  extension of the well-known Heckman selection model are provided in the 
  package. The response variable can be of Bernoulli, Poisson or Negative 
  Binomial type. The sample selection mechanism allows to choose among a 
  Normal, Logistic or Gumbel distribution.
This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.
A simple way of fitting detection functions to distance sampling
    data for both line and point transects. Adjustment term selection, left and
    right truncation as well as monotonicity constraints and binning are
    supported. Abundance and density estimates can also be calculated (via a
    Horvitz-Thompson-like estimator) if survey area information is provided.
Calculates the study size (either number of detections, or
    proportion of region that should be covered) to achieve a target precision for
    the estimated abundance. The calculation allows for the penalty due to unknown
    detection function, and for overdispersion. The user must specify a guess at the
    true detection function.
Provides tools for constructing, manipulating and using distance metrics.
Provides tools to assess the dissimilarity between multivariate time-series. It is based on the psi measure described  by Birks and Gordon (1985 <doi:10.1002/jqs.3390020110>), which computes dissimilarity between irregular time-series constrained by sample order. However, in this package the original idea has been extended to work with any kind of multivariate time-series, no matter whether they are regular, irregular, aligned or unaligned. Furthermore, the package allows to assess the significance of dissimilarity values by applying a restricted permutation test, allows to measure the contribution of individual variables to dissimilarity, and offers tools to transfer attributes (generally time or age, but other are possible) between sequences based on the similarity of their samples.
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations.
Implementing algorithms and fitting models when sites (possibly remote) share
  computation summaries rather than actual data over HTTP with a master R process (using
  'opencpu', for example). A stratified Cox model and a singular value decomposition are
  provided. The former makes direct use of code from the R 'survival' package. (That is,
  the underlying Cox model code is derived from that in the R 'survival' package.)
  Sites may provide data via several means: CSV files, Redcap API, etc. An extensible
  design allows for new methods to be added in the future and includes facilities
  for local prototyping and testing. Web applications are provided (via 'shiny') for
  the implemented methods to help in designing and deploying the computations.
Creates discretised versions of continuous 
      distribution functions by mapping continuous values 
      to an underlying discrete grid, based on a (uniform) 
      frequency of discretisation, a valid discretisation 
      point, and an integration range. For a review of 
      discretisation methods, see 
      Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>.
Contains a range of functions covering the present
  development of the distributional method for the dichotomisation of continuous outcomes. 
  The method provides estimates with standard error of a comparison of proportions 
  (difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means. 
  See the URL below or <arXiv:1809.03279> for more information.
Download data from the FlorKart database of the floristic field mapping in Germany in a convenient way. The database incorporates distribution data for plants in Germany on the basis of quadrants on a topographical map with a resolution of 1 : 25000 (TK 25). The data is owned and provided by the German Federal Agency for Nature Conservation (BfN) and the Network Phytodiversity in Germany (NetPhyD). For further information please visit <http://www.floraweb.de/pflanzenarten/hintergrundtexte_florkart_organisation.html>. The author of this package is in no way associated with the BfN or NetPhyD.
Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.
Scientific and technical article format for the web. 'Distill' articles 
    feature attractive, reader-friendly typography, flexible layout options
    for visualizations, and full support for footnotes and citations.
Some very simple method functions for confidence interval calculation, bootstrap resampling, and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.
Provides a high level API to interface over sources storing distance, dissimilarity, similarity matrices with matrix style extraction, replacement and other utilities. Currently, in-memory dist object backend is supported.
Geodesic distance between phylogenetic trees and
        associated functions.
S4-classes and methods for distributions.
An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) <doi:10.1007/978-0-387-98144-4> for sampling the Multivariate Normal distribution and Michael et al. (1976) <doi:10.2307/2683801> for sampling the Wald distribution.
Provides documentation in form of a common vignette to packages 'distr',
        'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.
Functions for visualizing distributional regression models fitted using the 'gamlss', 'bamlss' or 'betareg' R package. The core of the package consists of a 'shiny' application, where the model results can be interactively explored and visualized.
Distribution (S4-)classes for elliptically contoured distributions (based on
            package 'distr').
Extends package 'distr' by functionals, distances, and conditional distributions.
A different way for calculating pdf/pmf, cdf, quantile and random data such that the user is able to consider the name of related distribution as an argument and so easily can changed by a changing argument by user. It must be mentioned that the core and computation base of package 'DISTRIB' is package 'stats'. Although similar functions are introduced previously in package 'stats', but the package 'DISTRIB' has some special applications in some special computational programs.
Fits Gaussian Mixtures by applying evolution. As fitness function a mixture of the chi square test for distributions and a novel measure for approximating the common area under curves between multiple Gaussians is used. The package presents an alternative to the commonly used Likelihood Maximization as is used in Expectation Maximization. The evolution is based on the 'GA' package: Luca Scrucca (2013) <doi:10.18637/jss.v053.i04> while the Gaussian Mixture Logic stems from 'AdaptGauss': Alfred Ultsch, Michael Thrun, Onno Hansen-Goos, Jorn Lotsch (2015) <doi:10.3390/ijms161025897>.
Tools to create and manipulate probability
    distributions using S3.  Generics random(), pdf(), cdf() and
    quantile() provide replacements for base R's r/d/p/q style functions.
    Functions and arguments have been named carefully to minimize
    confusion for students in intro stats courses. The documentation for
    each distribution contains detailed mathematical notes.
Provides new types of omnibus tests which are generally much more powerful than traditional tests (including the Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling tests),see Zhang (2002) <doi:10.1111/1467-9868.00337>.
Utilities are provided which are of use in the
    packages I have developed for dealing with
    distributions. Currently these packages are GeneralizedHyperbolic,
    VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these
    packages requires DistributionUtils. Functionality includes sample
    skewness and kurtosis, log-histogram, tail plots, moments by
    integration, changing the point about which a moment is
    calculated, functions for testing distributions using inversion
    tests and the Massart inequality. Also includes an implementation
    of the incomplete Bessel K function.
Implements S4 classes for probability models based on packages 'distr' and
            'distrEx'.
Fast distributed/parallel estimation for multinomial logistic regression via Poisson factorization and the 'gamlr' package.  For details see: Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>.
Tools to estimate and manage empirical distributions,
    which should work with survey data. One of the main features is the 
    possibility to create data cubes of estimated statistics, that include
    all the combinations of the variables of interest (see for example functions
    dcc5() and dcc6()).
S4-distribution classes based on package distr for distributions from packages
        'fBasics' and 'fGarch'.
S4-classes for setting up a coherent framework for simulation within the distr
        family of packages.
Provides flexible examples of LLN and CLT for teaching purposes in secondary
        school.
Evaluation (S4-)classes based on package distr for evaluating procedures
        (estimators/tests) at data/simulation in a unified way.
A full definition for Weibull tails and Full-Tails Gamma and tools for fitting these distributions to empirical tails. This package build upon the paper by del Castillo, Joan & Daoudi, Jalila & Serra, Isabel. (2012) <doi:10.1017/asb.2017.9>.
Provides convenient methods for accessing the data in 'dist' objects with minimal memory and computational overhead. 'disttools' can be used to extract the distance between any pair or combination of points encoded by a 'dist' object using only the indices of those points. This is an improvement over existing functionality, which requires either coercing a 'dist' object into a matrix or calculating the one dimensional index corresponding to a pair of observations. Coercion to a matrix is undesirable because doing so doubles the amount of memory required for storage. In contrast, there is no inherent downside to the latter solution. However, in part due to several edge cases, correctly and efficiently implementing such a solution can be challenging. 'disttools' abstracts away these challenges and provides a simple interface to access the data in a 'dist' object using the latter approach.
Provides tools for quality checks of georeferenced plant
        species accessions.
Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.
Contains functions for the 'DivE' estimator <doi:10.1371/journal.pcbi.1003646>. The 'DivE' estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.
Utilities to represent, visualize, filter, analyse, and summarize
	     time-depth recorder (TDR) data.  Miscellaneous functions for
	     handling location data are also provided.
Computes the most common diversity measures used in social and other sciences, and includes new measures from interdisciplinary research.
Contains a number of comparative 'phylogenetic' methods,
  mostly focusing on analysing diversification and character
  evolution.  Contains implementations of 'BiSSE' (Binary State
  'Speciation' and Extinction) and its unresolved tree extensions,
  'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE',
  'GeoSSE', and 'BiSSE-ness' Other included methods include Markov
  models of discrete and continuous trait evolution and constant rate
  'speciation' and extinction.
Allows the calculation of both genetic diversity partition 
  statistics, genetic differentiation statistics, and locus informativeness 
  for ancestry assignment. 
  It also provides users with various option to calculate 
  bootstrapped 95\% confidence intervals both across loci, 
  for pairwise population comparisons, and to plot these results interactively. 
  Parallel computing capabilities and pairwise results without 
  bootstrapping are provided. 
  Also calculates F-statistics from Weir and Cockerham (1984). 
  Various plotting features are provided, as well as Chi-square tests of 
  genetic heterogeneity. 
  Functionality for the calculation of various diversity parameters is 
  possible for RAD-seq derived SNP data sets containing thousands of marker loci.
  A shiny application for the development of microsatellite multiplexes is 
  also available.
Predictions of alpha diversity are fitted from presence data, first abundance is modeled from occupancy models and then, several diversity indices are calculated and finally GLM models are used to predict diversity in different environments and select priority areas.
Provides tools to sort DICOM-format medical image files, and
    convert them to NIfTI-1 format.
This package has tools for analyzing DNA melting data to
        generate HRM scores, the DNA diversity measure output of the
        HRM Diversity Assay.  For additional documentation visit
        http://code.google.com/p/divmelt/.
A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.
Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some 'Fortran' code has been included to the original dixon2002() function of the 'ecespa' package to improve speed.
For outlier detection in small and normally distributed 
    samples the ratio test of Dixon (Q-test) can be used. Density,
    distribution function, quantile function and random generation 
    for Dixon's ratio statistics are provided as wrapper functions.
    The core applies McBane's Fortran functions <doi:10.18637/jss.v016.i03> 
    that use Gaussian quadrature for a numerical solution.
Perform multistage deterministic linkages, apply case definitions to datasets, and deduplicate records.
    Records (rows) from datasets are linked by different matching criteria and sub-criteria (columns) in a specified order of certainty. 
    The linkage process handles missing data and conflicting matches based on this same order of certainty.
    For episode grouping, rows of dated events (e.g. sample collection) or interval of events (e.g. hospital admission) are 
    grouped into chronological episodes beginning with a "Case". The process permits several options such as 
    episode lengths and recurrence periods which are used to build custom preferences for case assignment (definition).
    The record linkage and episode grouping processes assign unique group IDs to matching records or those grouped into episodes. 
    This then allows for record deduplication or sub-analysis within these groups.
Implements various decision support tools related to the Econometrics & Technometrics.
             Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, etc.
Provides functions to facilitate access to the 'DKAN' API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the 'DKAN' REST API (metadata), and the 'DKAN' datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the 'DKAN' datastore.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Baltagi (2011) <doi:10.1007/978-3-642-20059-5> for more information.
An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.
The Dirichlet Laplace shrinkage prior in Bayesian linear regression and variable selection, featuring:
    utility functions in implementing Dirichlet-Laplace priors such as visualization; 
    scalability in Bayesian linear regression; 
    penalized credible regions for variable selection.
Interface for 'Rcpp' users to 'dlib' <http://dlib.net> which is a
    'C++' toolkit containing machine learning algorithms and computer vision tools.
    It is used in a wide range of domains including robotics, embedded devices,
    mobile phones, and large high performance computing environments. This package
    allows R users to use 'dlib' through 'Rcpp'.
Provides routines for Maximum likelihood,
    Kalman filtering and smoothing, and Bayesian
    analysis of Normal linear State Space models, also known as 
    Dynamic Linear Models.
QTL mapping in a mixed model framework with separate
        detection and localization stages. The first stage detects the
        number of QTL on each chromosome based on the genetic variation
        due to grouped markers on the chromosome; the second stage uses
        this information to determine the most likely QTL positions.
        The mixed model can accommodate general fixed and random
        effects, including spatial effects in field trials and pedigree
        effects. Applicable to backcrosses, doubled haploids,
        recombinant inbred lines, F2 intercrosses, and association
        mapping populations.
Development, simulation testing, and implementation of management
    procedures for data-limited fisheries 
    (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).
Collection of functions for distributed lag linear and non-linear models.
A collection of tools that support data diagnosis, exploration, and transformation. 
    Data diagnostics provides information and visualization of missing values and outliers and 
    unique and negative values to help you understand the distribution and quality of your data. 
    Data exploration provides information and visualization of the descriptive statistics of 
    univariate variables, normality tests and outliers, correlation of two variables, and 
    relationship between target variable and predictor. Data transformation supports binning 
    for categorizing continuous variables, imputates missing values and outliers, resolving skewness. 
    And it creates automated reports that support these three tasks.
Inference functionalities for distributed-lag linear structural equation models (DLSEMs). DLSEMs are Markovian structural causal models where each factor of the joint probability distribution is a distributed-lag linear regression (Magrini, 2018 <doi:10.2478/bile-2018-0012>; Magrini et al., 2019 <doi:10.1007/s11135-019-00855-z>). DLSEMs account for temporal delays in the dependence relationships among the variables and allow to perform dynamic causal inference by assessing causal effects at different time lags. Endpoint-constrained quadratic, quadratic decreasing and gamma lag shapes are available.
Monthly download stats of 'CRAN' and 'Bioconductor' packages.
	     Download stats of 'CRAN' packages is from the 'RStudio' 'CRAN mirror', see <http://cranlogs.r-pkg.org>.
	     'Bioconductor' package download stats is at <https://bioconductor.org/packages/stats/>.
Dynamic model averaging for binary and continuous
        outcomes.
Computes measurement nonequivalence effect size indices described in Nye and Drasgow (2011) <doi:10.1037/a0022955>.
Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (<DOI:10.1016/0304-4076(80)90070-6>).
The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Dyadic mixed model analysis with multi-trait responses and
  pedigree-based partitioning of individual variation into a range of
  environmental and genetic variance components for individual and 
  maternal effects.
Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.
The framework provides functions to generate ODEs of reaction
    networks, parameter transformations, observation functions, residual functions,
    etc. The framework follows the paradigm that derivative information should be
    used for optimization whenever possible. Therefore, all major functions produce
    and can handle expressions for symbolic derivatives.
Perform differential analysis for
        methylation array data. Detect differentially
        methylated regions (DMRs) from array M-values. 
        The core is a Non-homogeneous Hidden Markov Model
        for estimating spatial correlation and a novel Constrained 
        Gaussian Mixture Model for modeling the M-value pairs of each individual locus.
Model selection algorithms for regression and classification, where the predictors can be numerical and categorical and the number of regressors exceeds the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka (2015) <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk (2015) <http://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>.
Work within the 'dplyr' workflow to add random variates to your data frame. 
  Variates can be added at any level of an existing column.  Also, bounds can be specified 
  for simulated variates.
This package includes functions and data accompanying the book 
	     "Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Functions and data accompanying the second edition of the book  "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.
Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and  often impose further restrictions on the parameters.
Computationally efficient tools for comparing all pairs of profiles
    in a DNA database. The expectation and covariance of the summary statistic
    is implemented for fast computing. Routines for estimating proportions of
    close related individuals are available. The use of wildcards (also called F-
    designation) is implemented. Dedicated functions ease plotting the results.
The DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as "is.xxdistribution" are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt().
The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.
Provides a modified hierarchical test (Liu (2017) <doi:10.1214/17-AOS1539>) for detecting the structural difference between two Semiparametric Gaussian graphical models. The multiple testing procedure asymptotically controls the false discovery rate (FDR) at a user-specified level. To construct the test statistic, a truncated estimator is used to approximate the transformation functions and two R functions including lassoGGM() and lassoNPN() are provided to compute the lasso estimates of the regression coefficients.
Provides density, distribution function, quantile function and random
             generation for the split normal and split-t distributions, and computes their
             mean, variance, skewness and kurtosis for the two distributions (Li, F,
             Villani, M. and Kohn, R. (2010) <doi:10.1016/j.jspi.2010.04.031>).
Using Local Moran's I for detection of differential network local consistency.
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Functions are provided to fit temporal lag models to dynamic
    networks. The models are build on top of exponential random graph models (ERGM) framework. There are
    functions for simulating or forecasting networks for future time points.
    Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models. Mallik, Almquist (2017, under review).
Flexibly convert data between long and wide format using just two
    functions: reshape_toLong() and reshape_toWide().
Provides Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains.
A dimension reduction technique for outlier detection. DOBIN: a Distance 
    based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier 
    detection. This is not an outlier detection method; rather it is a pre-processing 
    method for outlier detection. It brings outliers to the fore-front using fewer basis 
    vectors (Kandanaarachchi, Hyndman 2019) <doi:10.13140/RG.2.2.15437.18403>.
Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.
Contains:
    1) Facilities for working with grouped data: 'do' something to data
    stratified 'by' some variables.
    2) LSmeans (least-squares means), general linear contrasts. 
    3) Miscellaneous other utilities.
Build a Dockerfile straight from your R session. 'dockerfiler' allows 
    you to create step by step a Dockerfile, and provide convenient tools to wrap 
    R code inside this Dockerfile.
Gives you the ability to use arbitrary Docker images
    (including custom ones) to process Rmarkdown code chunks.
Build a lightweight Docker r-base container image for your R 
    package, in which the package itself is available. The Docker image seeks 
    to mirror your R session as close as possible with respect to R specific 
    dependencies. Both dependencies on CRAN R packages as well as local non-CRAN 
    R packages will be included in the Docker container image.
Define a command-line interface by just giving it
    a description in the specific format.
A direct approach to optimal designs for copula models based on
    the Fisher information. Provides flexible functions for building joint PDFs,
    evaluating the Fisher information and finding optimal designs. It includes an
    extensible solution to summation and integration called 'nint', functions for
    transforming, plotting and comparing designs, as well as a set of tools for
    common low-level tasks.
Provides the ability to display something analogous to
    Python's docstrings within R.  By allowing the user to document
    their functions as comments at the beginning of their function
    without requiring putting the function into a package we allow
    more users to easily provide documentation for their functions.
    The documentation can be viewed just like any other help files
    for functions provided by packages as well.
Production of R packages from tagged comments introduced within the code 
             and a minimum of additional documentation files.
Have you ever been tempted to create 'roxygen2'-style documentation
    comments for one of your functions that was not part of one of your
    packages (yet)?
    This is exactly what this package is about: running 'roxygen2' on
    (chunks of) a single code file.
It is sometimes necessary to create documentation for all files in a directory. Doing so by hand can be very tedious. This task is made fast and reproducible using the functionality of 'documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion.
Connect to the 'DocuSign' Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, 
  which supports embedded signing, and sending of documents.
A set of helper functions for using R Markdown to create documents
    in docx format, especially documents for use in a classroom or workshop
    setting.
'Microsoft Word' 'docx' files provide an 'XML' structure that is fairly
    straightforward to navigate, especially when it applies to 'Word' tables and
    comments. Tools are provided to determine table count/structure, comment count
    and also to extract/clean tables and comments from 'Microsoft Word' 'docx' documents.
    There is also nascent support for '.doc' files.
A variety of sampling plans are able to be compared using evaluations of their operating characteristics (OC), average outgoing quality (OQ), average total inspection (ATI) etc.
Distances on dual-weighted directed graphs using priority-queue
    shortest paths (Padgham (2019) <doi:10.32866/6945>). Weighted directed
    graphs have weights from A to B which may differ from those from B to A.
    Dual-weighted directed graphs have two sets of such weights. A canonical
    example is a street network to be used for routing in which routes are
    calculated by weighting distances according to the type of way and mode of
    transport, yet lengths of routes must be calculated from direct distances.
Detect Differences in rhythmic time series. Using linear
    least squares and the robust semi-parametric rfit() method. Differences in
    harmonic fitting could be detected as well as differences in scale of the
    noise distribution.
Creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.
'CRAN' packages 'DoE.base' and 'Rmosek' and non-'CRAN' package 'gurobi' are enhanced with functionality for the creation of optimized arrays for experimentation, where optimization is in terms of generalized minimum aberration. It is also possible to optimally extend existing arrays to larger run size. Optimization requires the availability of at least one of the commercial products 'Gurobi' or 'Mosek' (free academic licenses available for both). For installing 'Gurobi' and its R package 'gurobi', follow instructions at <http://www.gurobi.com/downloads/gurobi-optimizer> and <http://www.gurobi.com/documentation/7.5/refman/r_api_overview.html> (or higher version). For installing 'Mosek' and its R package 'Rmosek', follow instructions at <https://www.mosek.com/downloads/> and <http://docs.mosek.com/8.1/rmosek/install-interface.html>, or use the functionality in the stump CRAN R package 'Rmosek'.
Construct multi-response experimental designs, such as a Unique Factor 
    Central Composite Design (UF-CCD), given information (from screening or expert knowledge) about 
    which factors are related to each response variable (Wilmina M. Marget & Max D. Morris, 2019
    <doi:10.1080/00401706.2018.1549102>).
Various kinds of designs for
 (industrial) experiments can be created. The package uses, and sometimes enhances,
        design generation routines from other packages. 
        So far, response surface designs from package rsm, latin hypercube
        samples from packages lhs and DiceDesign, and 
        D-optimal designs from package AlgDesign have been implemented.
Rare variant association test integrating variant position information. It aims to identify the presence of clusters of disease-risk variants in specific gene regions. For more details, please read the publication from Persyn et al. (2017)  <doi:10.1371/journal.pone.0179364>.
Provides a '%dopar%' adapter such that any type of futures can
    be used as backends for the 'foreach' framework.
Provides a parallel backend for the %dopar% function using
        the multicore functionality of the parallel package.
Functions to calculate ADI (Average Dominance Index) and FDI (Frequency-Based Dominance Index). Functions to visualize the Data with Social Network Graphs with Dual
    Directions and Music Notation Graph.
Dominance analysis is a method that allows to compare the
             relative importance of predictors in multiple regression models:
             ordinary least squares, generalized linear models, 
             hierarchical linear models and dynamic linear models. The main principles and methods of 
             dominance analysis are described in
             Budescu, D. V. (1993) <doi:10.1037/0033-2909.114.3.542> and  
             Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>
             for ordinary least squares regression. Subsequently, the extensions 
             for multivariate regression, logistic regression and
             hierarchical linear models were described in 
             Azen, R., & Budescu, D. V. (2006) <doi:10.3102/10769986031002157>,
             Azen, R., & Traxel, N. (2009) <doi:10.3102/1076998609332754> and
             Luo, W., & Azen, R. (2013) <doi:10.3102/1076998612458319>,
             respectively.
A wrapper on top of the 'Domino Command-Line Client'. It lets you
    run 'Domino' commands (e.g., "run", "upload", "download") directly from your
    R environment. Under the hood, it uses R's system function to run the 'Domino'
    executable, which must be installed as a prerequisite. 'Domino' is a service
    that makes it easy to run your code on scalable hardware, with integrated
    version control and collaboration features designed for analytical workflows
    (see <http://www.dominodatalab.com> for more information).
Provides a parallel backend for the %dopar% function using
        the Rmpi package.
Finds the k nearest neighbours in a dataset of specified points, 
  adding the option to wrap certain variables on a torus.  The user chooses
  the algorithm to use to find the nearest neighbours. Three such algorithms,
  provided by the packages 'RANN' <https://cran.r-project.org/package=RANN>, 
  'RANN.L1' <https://cran.r-project.org/package=RANN.L1>, and 'nabor' 
  <https://cran.r-project.org/package=nabor>, are suggested.
Provides a parallel backend for the %dopar% function using
        the parallel package.
A Redis parallel backend for the %dopar% function
Provides models to fit the dynamics of a regulated
    system experiencing exogenous inputs. The underlying models use differential
    equations and linear mixed-effects regressions to estimate the characteristic
    parameters of the equation (the coefficients) and an estimated signal. The
    package also provides print, summary, plot and predict functions, specific for
    the models outputs.
Provides functions to perform
    reproducible parallel foreach loops, using independent
    random streams as generated by L'Ecuyer's combined
    multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>].
    It enables to easily convert standard %dopar% loops into
    fully reproducible loops, independently of the number
    of workers, the task scheduling strategy, or the chosen
    parallel environment and associated foreach backend.
Contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.
Contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010)  <doi:10.1007/978-1-4419-1213-8>.
Identification of causal effects from arbitrary observational and experimental probability 
    distributions via do-calculus and standard probability manipulations using a search-based algorithm. 
    Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>), missing data (Mohan, K. and Pearl, J. and Tian., J. 
    (2013) <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations of these. Also supports
    identification in the presence of context-specific independence (CSI) relations through labeled directed
    acyclic graphs (LDAG). For details on CSIs see Corander et al. (2019) <doi:10.1016/j.apal.2019.04.004>.
    For further information on the search-based approach see Tikka et al. (2019) <arXiv:1902.01073>.
The DoseFinding package provides functions for the design and analysis
	     of dose-finding experiments (with focus on pharmaceutical Phase
	     II clinical trials). It provides functions for: multiple contrast
	     tests, fitting non-linear dose-response models (using Bayesian and
	     non-Bayesian estimation), calculating optimal designs and an
	     implementation of the MCPMod methodology (Pinheiro et al. (2014)
	     <doi:10.1002/sim.6052>).
Provides a parallel backend for the %dopar% function using
        the snow package of Tierney, Rossini, Li, and Sevcikova.
Estimates dose-response relations from summarized dose-response
    data and to combines them according to principles of (multivariate)
    random-effects models.
A small package containing helper utilities for creating function
    for computing statistics.
Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.
Calculation of cluster typicality coefficients as being generated by fuzzy k-means clustering.
Provides .C64(), which is an enhanced version of .C()
    and .Fortran() from the foreign function interface. .C64() supports long
    vectors, arguments of type 64-bit integer, and provides a mechanism to
    avoid unnecessary copies of read-only and write-only arguments. This
    makes it a convenient and fast interface to C/C++ and Fortran code.
Use '..' on the right hand side of the ':=' operator as a shorthand for the 
    left hand side, so that 'var := f(..) + ..' is equivalent to
    'var <- f(var) + var'. This permits the user to be explicit about growing an
    object or overwriting it using its previous value, avoids repeating a 
    variable name, and saves keystrokes, time, visual space and cognitive load.
Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.
Quick and easy dot-and-whisker plots of regression results.
Contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola  & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].
Likelihood-based inference methods with doubly-truncated data are developed under various models.
 Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and
 Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>.
 Parametric models from the special exponential family (SEF) are based on
 Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and
 Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>.
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.
Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.
Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.
'Kevin Dowd's' book Measuring Market Risk is a widely read book 
          in the area of risk measurement by students and 
          practitioners alike. As he claims, 'MATLAB' indeed might have been the most 
          suitable language when he originally wrote the functions, but,
          with growing popularity of R it is not entirely 
	  valid. As 'Dowd's' code was not intended to be error free and were mainly 
	  for reference, some functions in this package have inherited those 
	  errors. An attempt will be made in future releases to identify and correct 
	  them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. 
          It should be noted that 'Dowd' offers both
          'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more 
          recent version of 'MMR1' toolbox and they both have mostly similar 
          function. The toolbox mainly contains different parametric and non 
	  parametric methods for measurement of market risk as well as 
	  backtesting risk measurement methods.
Provides a wrapper for the download.file function,
    making it possible to download files over HTTPS on Windows, Mac OS X, and
    other Unix-like platforms. The 'RCurl' package provides this functionality
    (and much more) but can be difficult to install because it must be compiled
    with external dependencies. This package has no external dependencies, so
    it is much easier to install.
Downscales species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) doi:10.18637/jss.v086.c03.
Downscaling the coarse-resolution remote sensing 
       images into fined-resolution images is important for 
       preprocessing. This package provides the functions for this
       with high accuracy. Currently, the resautonet is used 
       as the deep learning algorithm for downscaling. 
       This work was done based on this paper, 
       Lianfa Li (2019) <doi:10.3390/rs11111378>.
Toggles the test and production versions of a large workflow.
A GUI or command-line operated data analysis tool, for
        analyzing time-dependent simulation data in which multiple
        instantaneous or time-lagged relations are assumed. This
        package uses Structural Equation Modeling (the sem package). It
        is aimed to deal with time-dependent data and estimate whether
        a causal diagram fits data from an (agent-based) simulation
        model.
A Scannerless GLR parser/parser generator.  Note that GLR standing for "generalized LR", where L stands for "left-to-right" and
   R stands for "rightmost (derivation)".  For more information see <https://en.wikipedia.org/wiki/GLR_parser>. This parser is based on the Tomita
   (1987) algorithm. (Paper can be found at <http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/J/J87/J87-1004.pdf>).
   The original 'dparser' package documentation can be found at <http://dparser.sourceforge.net/>.  This allows you to add mini-languages to R (like
   RxODE's ODE mini-language Wang, Hallow, and James 2015 <DOI:10.1002/psp4.12052>) or to parse other languages like 'NONMEM' to automatically translate
   them to R code.   To use this in your code, add a LinkingTo dparser in your DESCRIPTION file and instead of using #include <dparse.h> use
   #include <dparser.h>.  This also provides a R-based port of the make_dparser <http://dparser.sourceforge.net/d/make_dparser.cat> command called
   mkdparser().  Additionally you can parse an arbitrary grammar within R using the dparse() function, which works on most OSes and is mainly for grammar
   testing.  The fastest parsing, of course, occurs at the C level, and is suggested.
Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.
Differential partial correlation identification with the ridge and the fusion penalties.
Analysis, visualisation and simulation of digital polymerase chain
    reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). 
    Supports data formats of commercial systems (Bio-Rad QX100 and
    QX200; Fluidigm BioMark) and other systems.
fits the primal graphical lasso, via one-at-a-time
        block-coordinate descent.
Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.
Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.
The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in  Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
A fast, consistent tool for working with data frame like objects,
    both in memory and out of memory.
A 'Teradata' backend for 'dplyr'. It makes it possible to operate
  'Teradata' database <https://www.teradata.com/products-and-services/teradata-database/>
  in the same way as manipulating data frames with 'dplyr'.
An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
    You can learn each steps of data manipulation by clicking your mouse without coding.
    You can get resultant data (as a 'tibble') and the code for data manipulation.
Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.
This MCMC method takes a data numeric vector (Y) and assigns the elements of Y
  to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred.
  Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
Computations for approximations and alternatives for the 'DPQ'
  (Density (pdf), Probability (cdf) and Quantile) functions for probability
  distributions in R.
  Primary focus is on (central and non-central) beta, gamma and related
  distributions such as the chi-squared, F, and t.
  --
  This is for the use of researchers in these numerical approximation
  implementations, notably for my own use in order to improve R`s own
   pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.
  -- We plan to complement with 'DPQmpfr' to be suggested later.
Provides a generalized method for printing tabular data within 
	the R environment in order to make the process of presenting high quality 
	tabular output seamless for the user.  Output is directed to the R graphics 
	device so that tables can be exported to any file format supported by the 
	graphics device. Utilizes a formula interface to specify the contents of 
	tables often found in manuscripts or business reports.  In addition, formula 
	interface provides inline formatting of the numeric cells of a table and 
	renaming column labels.
Contains functions to perform copula estimation 
	by the non-parametric Bayesian method, 
	Dirichlet-based Polya Tree. See Ning (2018) <doi:10.1080/00949655.2017.1421194>.
Use Dirichlet process Weibull mixture model and dependent Dirichlet process Weibull mixture model for survival data with and without competing risks. Dirichlet process Weibull mixture model is used for data without covariates and dependent Dirichlet process model is used for regression data. The package is designed to handle exact/right-censored/ interval-censored observations without competing risks and exact/right-censored observations for data with competing risks. Inside each cluster of Dirichlet process, we assume a multiplicative effect of covariates as in Cox model and Fine and Gray model.
Several fast random number generators are provided as C++
  header only libraries: The PCG family by O'Neill (2014
  <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>) as well as
  Xoroshiro128+ and Xoshiro256+ by Blackman and Vigna (2018
  <arXiv:1805.01407>). In addition fast functions for generating random
  numbers according to a uniform, normal and exponential distribution
  are included. The latter two use the Ziggurat algorithm originally
  proposed by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>).
  These functions are exported to R and as a C++ interface and are
  enabled for use with the default 64 bit generator from the PCG family,
  Xoroshiro128+ and Xoshiro256+ as well as the 64 bit version of the 20 rounds
  Threefry engine (Salmon et al., 2011 <doi:10.1145/2063384.2063405>) as
  provided by the package 'sitmo'.
Provides highly customizable modules to enhance your shiny apps.
    Includes layout independent collapsible boxes and value boxes, a very fast
    autocomplete input, rhandsontable extensions for filtering and paging and
    much more.
Functions, methods, and datasets for fitting dimension
 reduction regression, using slicing (methods SAVE and SIR), Principal
 Hessian Directions (phd, using residuals and the response), and an
 iterative IRE.  Partial methods, that condition on categorical
 predictors are also available.  A variety of tests, and stepwise
 deletion of predictors, is also included.  Also included is
 code for computing permutation tests of dimension.  Adding additional
 methods of estimating dimension is straightforward.
 For documentation, see the vignette in the package.   With version 3.0.4,
 the arguments for dr.step have been modified.
Models the relationship between dose levels and responses in a pharmacological experiment using the 4 Parameter Logistic model. Traditional packages on dose-response modelling such as 'drc' and 'nplr' often draw errors due to convergence failure especially when data have outliers or non-logistic shapes. This package provides robust estimation methods that are less affected by outliers and other initialization methods that work well for data lacking logistic shapes. We provide the bounds on the parameters of the 4PL model that prevent parameter estimates from diverging or converging to zero and base their justification in a statistical principle. These methods are used as remedies to convergence failure problems.  Gadagkar, S. R. and Call, G. B. (2015) <doi:10.1016/j.vascn.2014.08.006> Ritz, C. and Baty, F. and Streibig, J. C. and Gerhard, D. (2015) <doi:10.1371/journal.pone.0146021>.
Fits epidemic data to and generates stochastic profiles of a model with constant or time-dependent behavior modification parameters. Two parameters, p and q, describe the effect of reduced contact rate of susceptible and infectious populations, respectively as described by Brauer (2011, ISSN:1471-2458). In the absence of behavior modification, p=q=1, we recover the familiar compartmental Susceptible-Infectious-Recovered (SIR) equations. 'DRAFT' supports both constant values for p and q and a time-dependent form which smoothly changes p and q from their initial, pre-epidemic value of 1.0 to the user chosen values that are between 0 and 1. The start and transient time of behavior change are set by the user. 'DRAFT' can be used to compare forecasts of epidemic incidence with and without behavior modification.  Additional parameters and data fitting methods are explained in Ben-Nun et al (2019) <doi:10.1371/journal.pcbi.1007013>.
Visualization and manipulation of the mineral-chemistry network across deep time on earth.
Statistical tests and test statistics to identify events in a
    dataset that are dragon kings (DKs). The statistical methods in this
    package were reviewed in Wheatley & Sornette (2015) <doi:10.2139/ssrn.2645709>.
Move elements between containers in 'Shiny' without explicitly using 'JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables.
A general-purpose computational engine for data
  analysis, drake rebuilds intermediate data objects when their
  dependencies change, and it skips work when the results are already up
  to date.  Not every execution starts from scratch, there is native
  support for parallel and distributed computing, and completed projects
  have tangible evidence that they are reproducible.  Extensive
  documentation, from beginner-friendly tutorials to practical examples
  and more, is available at the reference website
  <https://docs.ropensci.org/drake/> and the online manual
  <https://books.ropensci.org/drake/>.
Analysis of preprocessed dramatic texts, with respect to literary research. 
  The package provides functions to analyse and visualise information about characters,
  stage directions, the dramatic structure and the text itself.
  The dramatic texts are expected to be in CSV format, which can be installed from within 
  the package, sample texts are provided. The package and the reasoning behind it are described in 
  Reiter et al. (2017) <doi:10.18420/in2017_119>.
Creation and use of R Repositories via helper functions 
 to insert packages into a repository, and to add repository information 
 to the current R session. Two primary types of repositories are support:
 gh-pages at GitHub, as well as local repositories on either the same machine
 or a local network. Drat is a recursive acronym: Drat R Archive Template.
A set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings.
  Compatible with and based on the R 'grid' package.
We present DRaWR, a network-based method for ranking genes or
    properties related to a given gene set. Such related genes or properties are
    identified from among the nodes of a large, heterogeneous network of biological
    information. Our method involves a random walk with restarts, performed on
    an initial network with multiple node and edge types, preserving more of the
    original, specific property information than current methods that operate
    on homogeneous networks. In this first stage of our algorithm, we find the
    properties that are the most relevant to the given gene set and extract a
    subnetwork of the original network, comprising only the relevant properties. We
    then rerank genes by their similarity to the given gene set, based on a second
    random walk with restarts, performed on the above subnetwork.
We offer an implementation of the series representation put 
  forth in "A series representation for multidimensional 
  Rayleigh distributions" by Wiegand and Nadarajah <DOI: 
  10.1002/dac.3510>. Furthermore we have implemented an 
  integration approach proposed by Beaulieu et al. for 3 and 
  4-dimensional Rayleigh densities (Beaulieu, Zhang,  "New 
  simplest exact forms for the 3D and 4D multivariate Rayleigh 
  PDFs with applications to antenna array geometrics", <DOI: 
  10.1109/TCOMM.2017.2709307>).
Feed longitudinal data into a Bayesian Latent Factor Model to obtain a low-rank representation. Parameters are estimated using a Hamiltonian Monte Carlo algorithm with STAN. See G. Weinrott, B. Fontez, N. Hilgert and S. Holmes, "Bayesian Latent Factor Model for Functional Data Analysis", Actes des JdS 2016.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
A penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.
A somewhat outdated package of basic and easy-to-use functions for
  fitting dose-response curves to continuous dose-response data, calculating some
  toxicological parameters and plotting the results. Please consider using
  the more powerful and actively developed 'drc' package.  Functions that are
  fitted are the cumulative density function of the log-normal distribution
  ('probit' fit), of the logistic distribution ('logit' fit), of the Weibull
  distribution ('weibull' fit) and a linear-logistic model ('linlogit' fit),
  derived from the latter, which is used to describe data showing stimulation at
  low doses (hormesis). In addition, functions checking, plotting and retrieving
  dose-response data retrieved from a database accessed via 'odbc' are included.
  As an alternative to the original fitting methods, the algorithms from the 'drc'
  package can be used.
Fit restricted mean models for the conditional association
  between an exposure and an outcome, given covariates. Three methods
  are implemented: O-estimation, where a nuisance model for the
  association between the covariates and the outcome is used;
  E-estimation where a nuisance model for the association
  between the covariates and the exposure is used, and doubly robust (DR)
  estimation where both nuisance models are used. In DR-estimation,
  the estimates will be consistent when at least one of the nuisance
  models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) <doi:10.1515/em-2014-0021>.
Performs the identification of differential risk hotspots (Briz-Redon et al. 2019) <doi:10.1016/j.aap.2019.105278> along a linear network. Given a marked point pattern lying on the linear network, the method implemented uses a network-constrained version of kernel density estimation (McSwiggan et al. 2017) <doi:10.1111/sjos.12255> to approximate the probability of occurrence across space for the type of event specified by the user through the marks of the pattern (Kelsall and Diggle 1995) <doi:10.2307/3318678>. The goal is to detect microzones of the linear network where the type of event indicated by the user is overrepresented.
Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors' MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>.
Concept drift refers to the change in the data distribution or 
  in the relationships between variables over time.
  'drifter' calculates distances between variable distributions or 
  variable relations and identifies both types of drift. 
  Key functions are: 
  calculate_covariate_drift() checks distance between corresponding variables in two datasets,
  calculate_residuals_drift() checks distance between residual distributions for two models,
  calculate_model_drift() checks distance between partial dependency profiles for two models,
  check_drift() executes all checks against drift.
  'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
A tidy implementation of equations that correct for instrumental drift in 
    continuous water quality monitoring data.  There are many sources of water quality data 
    including private (ex: YSI instruments) and open source (ex: USGS and NDBC), each of 
    which are susceptible to errors/inaccuracies due to drift. This package allows the 
    user to correct their data using one or two standard reference values in a uniform, 
    reproducible way. The equations implemented are from Hasenmueller (2011)
    <doi:10.7936/K7N014KS>.
Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.
R codes for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events ('PCAreduce', 'SC3', 'PCA', 't-SNE', 'Monocle', 'TSCAN', etc). 'DrImpute' can improve the performance of such software by imputing dropout events.
This is a connectivity mapping-based drug repurposing tool that identifies drugs that can potentially reverse query disease phenotype or have similar functions with query drugs.
This is a collection of functions for discontinuous regression
	     analysis and image processing (DRIP). A recent addition is the
	     blind image deblurring via jump-preserving extrapolation.
Contains quality control routines for multiplex immunoassay data, 
    including several approaches for: treating the background noise of the assay,  
    fitting the dose-response curves and estimating the limits of quantification.
Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations, at least 5, and the more the better) rather than a great number of replicates (no need of three replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts or already pretreated metabolomic data in log scale. For further details see Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.
Drop out analysis for psychologists in a R based web application.
    Shiny is used to visualize and analyze drop outs tailored to the methods of
    online survey methodology. Concept and app presented at the SCIP Conference
    in Long Beach, California.
Generates simulated data representing the LOX drop testing
    process (also known as impact testing). A simulated process allows for
    accelerated study of test behavior. Functions are provided to simulate
    trials, test series, and groups of test series. Functions for creating plots
    specific to this process are also included. Test attributes and criteria can
    be set arbitrarily. This work is not endorsed by or affiliated with NASA.
    See "ASTM G86-17, Standard Test Method for Determining Ignition Sensitivity
    of Materials to Mechanical Impact in Ambient Liquid Oxygen and Pressurized
    Liquid and Gaseous Oxygen Environments" <doi:10.1520/G0086-17>.
An Implementation of Dimensionality Reduction
    via Regression using Kernel Ridge Regression.
Provides tools for assessing the shape of a dose-response
    curve by testing linearity and non-linearity at user-defined cut-offs. It
    also provides two methods of estimating a threshold dose, or the dose at
    which the dose-response function transitions to significantly increasing:
    bi-linear (based on pkg 'segmented') and smoothed with splines (based on
    pkg 'mgcv').
Targeted minimum loss-based estimators of counterfactual means and
    causal effects that are doubly-robust with respect both to consistency and
    asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
    van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
An implementation of a Machine Learning Framework for prediction of new drugs Side Effects.
    Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores.
    Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm.
    This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways.
    Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).
Perform assessment of synergy/antagonism for drug combinations based
    on the Loewe additivity model, following Harbron's approach (Statistics in
    Medicine, 2010, <doi:10.1002/sim.3916>). The package allows flexible modeling
    of the drug interaction index and supports "2-stage" estimation in addition 
    to "1-stage" estimation, including bootstrap-based confidence intervals.
    The method requires data on the monotherapy responses and the package 
    accommodates both checkerboard and ray designs. Functions are available for 
    graphical exploration of model goodness-of-fit and diagnostics, as well as 
    for synergy/antagonism assessment in 2D and 3D.
Includes various functions for playing drum sounds. beat() plays a drum sound
  from one of the six included drum kits. tempo() sets spacing between calls to beat()
  in bpm. Together the two functions can be used to create many different drum patterns.
Performs various analyzes of descriptive statistics, including correlations, graphics and tables.
All data sets required for the examples and exercises in the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2019), freely available at <https://bookdown.org/hneth/ds4psy/>. The book and course introduce principles and methods of data science to students of psychology and other biological or social sciences. The 'ds4psy' package primarily provides datasets, but also functions for graphics and text-manipulation that are used in the book and its exercises.
Seasonal- and calendar adjustment of time series with daily frequency using the DSA approach developed by Ollech, Daniel (2018): Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.
A collection of 'shiny' apps that allow for the simulation and
    exploration of various infectious disease transmission dynamics scenarios.
    The purpose of the package is to help individuals learn 
    about infectious disease epidemiology from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on
    what to do with the models.
A collection of 'shiny' apps that allow for the simulation and
    exploration of various within-host immune response scenarios.
    The purpose of the package is to help individuals learn 
    about within-host infection and immune response modeling from a dynamical systems perspective.
    All apps include explanations of the underlying models and instructions on
    what to do with the models. 
    The development of this package was partially supported by NIH grant U19AI117891.
Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure.  The normalizing constant of the target distribution needs not to be known.
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.
Tools for multivariate, linear, time-invariant,
	time series models. This includes ARMA and state-space representations,
	and methods for converting between them. It also includes simulation
	methods and several estimation functions. The package has functions 
	for looking at model roots, stability, and forecasts at different 
	horizons. The ARMA model representation is general, so that VAR, VARX, 
	ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
	filter and smoother estimates can be obtained from the state space
	model, and state-space model reduction techniques are implemented. 
	An introduction and User's Guide is available in a vignette.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
Density surface modelling of line transect data. A Generalized
    Additive Model-based approach is used to calculate spatially-explicit estimates
    of animal abundance from distance sampling (also presence/absence and strip
    transect) data. Several utility functions are provided for model checking,
    plotting and variance estimation.
An expressive language to facilitate simulation and visualization
    of two-dimensional dynamical systems. The basic elements of the language are
    a model wrapping around a function(x,y) which outputs a list(x
    = xprime, y = yprime), and a range. The language supports three
    types of visual objects: visualizations, features, and backgrounds. Visualizations, including dots and arrows,
    depict the behavior of the dynamical system over the entire range.
    Features display
    user-defined curves and points, and their images under the system.
    Backgrounds define and color regions of interest, such as basins of attraction.
    The language
    can also approximate attractors and their basins through simulation.
Fits inhomogeneous Poisson process spatial models
    to line transect sampling data and provides estimate of
    abundance within a region.
A set of functions to compute and compare directly standardized rates, rate differences and ratios. A variety of user defined options for analysis (e.g confidence intervals) and formatting are included.
Perform a test of a simple null hypothesis about a 
    directly standardized rate and obtain the matching confidence 
    interval using a choice of methods.
Creates survey designs for distance sampling surveys. These
    designs can be assessed for various effort and coverage statistics.
    Once the user is satisfied with the design characteristics they can 
    generate a set of transects to use in their distance sampling survey.
    Many of the designs implemented in this R package were first made 
    available in our 'Distance' for Windows software and are detailed in 
    Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, 
    ISBN-13: 978-0199225873). Find out more about estimating animal/plant 
    abundance with distance sampling at <http://distancesampling.org/>.
Performs distance sampling simulations.It repeatedly generates
    instances of a user defined population within a given survey region, generates
    realisations of a survey design (currently these must be generated using
    Distance software in advance <http://distancesampling.org/>) and simulates
    the detection process. The data are then analysed so that the results can
    be compared for accuracy and precision across all replications. This will
    allow users to select survey designs which will give them the best accuracy
    and precision given their expectations about population distribution. Any
    uncertainty in population distribution or population parameters can be
    included by running the different survey designs for a number of different
    population descriptions. An example simulation can be found in the help file for
    make.simulation.
Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments.  A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>).  There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.
Building discrete-time survival trees and bagged trees based on
    the functionalities of the rpart package. Splitting criterion maximizes the
    likelihood of a covariate-free logistic discrete time hazard model.
Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. This package was designed and implemented for identifying disease similarities. It contains five functions which are 'DCEA', 'DCpathway', 'DS', 'comDCGL' and 'comDCGLplot'. In 'DCEA' function, differentially co-expressed genes and differentially co-expressed links are extracted from disease vs. health samples. Then 'DCpathway' function assigns differential co-expression values of pathways to be the average differential co-expression value of their component genes. Then 'DS' employs partial correlation coefficient of pathways as the disease similarity for each disease pairs. And 'DS' contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, 'comDCGL' and 'comDCGLplot' sort out shared differentially co-expressed genes and differentially co-expressed links with regulation information and visualize them.
Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny). The
    'DataTables' library has been included in this R package. The package name
    'DT' is an abbreviation of 'DataTables'.
Towards automation of descriptive frequencies and statistics tables.
Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.
DTAT is a methodologic framework allowing dose individualization to be
             conceived as a continuous learning process that begins in early-phase
             clinical trials and continues throughout drug development, on into
             clinical practice. This package includes code that researchers may use
             to reproduce or extend key results of the DTAT research programme, plus
             tools for trialists to design and simulate a '3+3/PC' dose-finding study.
             Please see Norris (2017) <doi:10.12688/f1000research.10624.3> and
             Norris (2017) <doi:10.1101/240846>.
To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method.
    The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.
This package contains functions to compare the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
Provides fast methods to work with Merton's distance to default 
  model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. 
  The methods includes simulation and estimation of the parameters.
This package implements different algorithms for analyzing
        randomly truncated data, one-sided and two-sided (i.e. doubly)
        truncated data. Two real data sets are included.
Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.
Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-Álvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
             modality, that measures diffusion of water in tissues like the human
             brain. The package contains R-functions to process diffusion-weighted
             data. The functionality includes diffusion tensor imaging (DTI),
             diffusion kurtosis imaging (DKI), modeling for high angular resolution
             diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
             tensor mixture models, several methods for structural adaptive
             smoothing including POAS and msPOAS, and a streamline fiber tracking
             for tensor and tensor mixture models.
             The package provides functionality to manipulate and visualize results
             in 2D and 3D.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Dunnett modified Tukey-Kramer test (a.k.a. the
        T3 Procedure), create two vectors: one for your observations
        and one for the factor level of each observation. The function,
        gl.unequal, provides a means to more conveniently produce a
        factor vector with unequal sample sizes. Next, use the DTK.test
        function to conduct the test and save the output as an object
        to input into the DTK.plot function, which produces a
        confidence interval plot for each of the pairwise comparisons.
        Lastly, the function TK.test conducts the original Tukey-Kramer
        test.
A series of functions which aid in both simulating and
        determining the properties of finite, discrete-time, discrete
        state markov chains.  Two functions (DTMC, MultDTMC) produce n
        iterations of a Markov Chain(s) based on transition
        probabilities and an initial distribution.  The function FPTime
        determines the first passage time into each state.  The
        function statdistr determines the stationary distribution of a
        Markov Chain.
Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.
Provides the dose transition pathways (DTP) to project in advance
    the doses recommended by a model-based design for subsequent patients (stay,
    escalate, deescalate or stop early) using all the accumulated toxicity
    information; See Yap et al (2017) <doi: 10.1158/1078-0432.CCR-17-0582>. DTP
    can be used as a design and an operational tool and can be displayed as a
    table or flow diagram. The 'dtpcrm' package also provides the modified
    continual reassessment method (CRM) and time-to-event CRM (TITE-CRM) with
    added practical considerations to allow stopping early when there is
    sufficient evidence that the lowest dose is too toxic and/or there is a
    sufficient number of patients dosed at the maximum tolerated dose.
Provides a data.table backend for 'dplyr'. The goal of 'dtplyr' 
    is to allow you to write 'dplyr' code that is automatically translated to 
    the equivalent, but usually much faster, data.table code.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
Combines various decision tree algorithms, plus both
             linear regression and ensemble methods into one package.
             Allows for the use of both continuous and categorical outcomes.
             An optional feature is to quantify the (in)stability to the
             decision tree methods, indicating when results can be trusted
             and when ensemble methods may be preferential.
We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.
Dynamic treatment regime estimation and inference via G-estimation,
    dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference
    via bootstrap and (for G-estimation) recursive sandwich estimation. Estimation
    and inference for survival outcomes via Dynamic Weighted Survival Modeling
    (DWSurv).
Basic time series functionalities such as listing of missing
    values, application of arbitrary aggregation as well as rolling window
    functions and automatic detection of periodicity. As it is mainly based on
    'data.table', it is fast and - in combination with the 'R6' package - offers
    reference semantics. In addition to its native R6 interface, it provides an
    S3 interface inclusive an S3 wrapper method generator for those who prefer
    the latter.
This package provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).
Manipulates date (Date), datetime (POSIXct) and time (hms) vectors.
    Date/times are considered discrete and are floored whenever encountered.
    Times are wrapped and time zones are maintained unless explicitly altered 
    by the user.
A comprehensive implementation of dynamic time warping (DTW) algorithms in R.  DTW computes the optimal (least cumulative distance) alignment between points of two time series.  Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.  Provides cumulative distances, alignments, specialized plot styles, etc.
Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).
Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions. Implementations of
    DTW barycenter averaging, a distance based on global alignment kernels,
    and the soft-DTW distance and centroid routines are also provided. 
    All included distance functions have custom loops optimized for the 
    calculation of cross-distance matrices, including parallelization support.
    Several cluster validity indices are included.
Provides an implementation of the Time-Weighted Dynamic Time
    Warping (TWDTW) method for land cover mapping using satellite image time series.
    TWDTW is based on the Dynamic Time Warping technique and has achieved high
    accuracy for land cover classification using satellite data. The method is
    based on comparing unclassified satellite image time series with a set of known
    temporal patterns (e.g. phenological cycles associated with the vegetation).
    Using 'dtwSat' the user can build temporal patterns for land cover types, apply
    the TWDTW analysis for satellite datasets, visualize the results of the time
    series analysis, produce land cover maps, create temporal plots for land cover
    change, and compute accuracy assessment metrics.
Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) <DOI:10.1109/MLSP.2017.8168165>: a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided.
Automatic differentiation is achieved by using dual numbers without
  providing hand-coded gradient functions. The output value of a mathematical 
  function is returned with the values of its exact first derivative 
  (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind
  (2018) <http://jmlr.org/papers/volume18/17-468/17-468.pdf>.
Create shareable data sets from raw data files that
	     contain protected elements. Relying on master crosswalk
	     files that list restricted variables, package functions
	     warn users about possible violations of data usage
	     agreement and prevent writing protected elements.
Provides an operator for assigning nested components of a list to
  names via a concise pattern matching syntax. This is especially convenient for
  assigning individual names to the multiple values that a function may return
  in the form of a list, and for extracting deeply nested list components.
Programmatic access to the DuckDuckGo Instant Answer API <https://api.duckduckgo.com/api>.
Expands factors, characters and other eligible classes
        into dummy/indicator variables.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.
For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Identifies code blocks that have a high level of similarity
  within a set of R files.
Estimation of piecewise constant mixed proportional hazard competing risk model with NPMLE.
 The model is described in S. Gaure et al. (2007) <doi:10.1016/j.jeconom.2007.01.015>,
 J. Heckman and B. Singer (1984) <doi:10.2307/1911491>, and
 B.G. Lindsay (1983) <doi:10.1214/aos/1176346059>.
Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.
Contains functions that do something convenient (e.g. create BMI categories), functions for calculating moving-window statistics efficiently, and functions for generating various figures (e.g. histograms with fitted probability mass/density function).
A set of wrapper functions for 'data.world' REST API endpoints <https://apidocs.data.world>.
Solving large scale distance weighted discrimination.
  The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <arXiv:1604.05473> for more details.
The 'DWD' provides gridded radar data for Germany in binary format. 
  'dwdradar' reads these files and enables a fast conversion into numerical format.
Infers networks with hubs using degree weighted Lasso method.
This linear model solution is useful when both predictor and response have associated uncertainty. The doubly weights linear model solution is invariant on which quantity is used as predictor or response. Based on the results by Reed(1989) <doi:10.1119/1.15963> and Ripley & Thompson(1987) <doi:10.1039/AN9871200377>.
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>) as described in Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>.
Finds regular and chaotic intervals in the data using 
    the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) 
	<DOI:10.1137/080718851>.
An R interface to the 'dygraphs' JavaScript charting library
    (a copy of which is included in the package). Provides rich facilities
    for charting time-series data in R, including highly configurable
    series- and axis-display and interactive features like zoom/pan and
    series/point highlighting.
Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.
Add a "Did You Mean" feature to the R interactive. With this
    package, error messages for misspelled input of variable names or package names
    suggest what you really want to do in addition to notification of the mistake.
Time series regression.  The dyn class interfaces ts,
        irts(), zoo() and zooreg() time series classes to lm(), glm(),
        loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(),
        quantreg::rq(), randomForest::randomForest() and other regression
        functions allowing those functions to be used with time series
        including specifications that may contain lags, diffs and
        missing values.
While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).
The package is aimed at dynamically visualizing probability
    distributions and their moments and all the commonly used distributions are
    included.
Emulating and solving inverse problems for dynamic computer experiments.
	     It contains two major functionalities: (1) localized GP model for large-scale
	     dynamic computer experiments using the algorithm proposed by
	     Zhang et al. (2018) <arXiv:1611.09488>; (2) solving inverse problems
	     in dynamic computer experiments. The current version only supports 64-bit
	     version of R.
Interactive graphical tool for manipulating graphs
Contains functions that lets you fit dynamic hazard models using 
  state space models. The first implemented model is described in Fahrmeir 
  (1992) <doi:10.1080/01621459.1992.10475232> and Fahrmeir (1994) 
  <doi:10.1093/biomet/81.2.317>. Extensions hereof are available where the  
  Extended Kalman filter is replaced by an unscented Kalman filter and other 
  options including particle filters. The implemented particle filters support
  more general state space models.
Contains methods for detection of clusters in hierarchical clustering dendrograms.
An implementation of the method proposed in Lund and Hansen (2018) for fitting 3-dimensional dynamical array models.  The implementation is based on the glamlasso package, see Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>, for efficient design matrix free lasso regularized estimation in a generalized linear array model. The implementation uses a block relaxation scheme to fit each individual component in the model using functions from the glamlasso package.
Provides functions for inferring longitudinal dominance hierarchies, which describe dominance relationships and their dynamics in a single latent hierarchy over time. Strauss & Holekamp (in press).
A native R implementation and enhancement of the Dynamic TOPMODEL
    semi-distributed hydrological model. Includes some preprocessing, utility and
    routines for displaying outputs.
Inference by sequential Monte Carlo for 
  dynamic tree regression and classification models
  with hooks provided for sequential design and optimization, 
  fully online learning with drift, variable selection, and 
  sensitivity analysis of inputs.  Illustrative 
  examples from the original dynamic trees paper are facilitated
  by demos in the package; see demo(package="dynaTree").
A GUI to solve dynamic biplots and classical biplot. Try matrices
    of 2-way and 3-way. The GUI can be run in multiple languages.
DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
While there are many well-established measures for identifying critical fluctuations and phase transitions, these measures only work with many points of measurement and thus are unreliable when studying short and coarse-grained time series. This package provides a measure for complexity in a time series that does not rely on long time series (Kaiser (2017), <doi:10.17605/OSF.IO/GWTKX>).
Computes dynamical correlation estimates and percentile
        bootstrap confidence intervals for pairs of longitudinal
        responses, including consideration of lags and derivatives.
Provides a common interface for applying dimensionality reduction methods,
  such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, 
  Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), 
  and Uniform Manifold Approximation and Projection ('UMAP'). 
  Has built-in support for sparse matrices.
An anonymization algorithm to resist neighbor label attack in a dynamic network.
Fits semiparametric dynamic frailty models according to the methodology of Putter and van Houwelingen (2015) <doi:10.1093/biostatistics/kxv002>. Intermediate models, where the frailty is piecewise constant on prespecified intervals, are also supported. The frailty process is taken to have a specific auto-correlation structure, and the supported distributions include gamma, inverse Gaussian, power variance family (PVF) and positive stable.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, mgcv::gam and gam::gam model objects.
Implements a method based on the fast Fourier transform to estimate delays of expression initiation between trajectories to integrate and
    analyse time course omics data.
Computes the first stage GMM estimate of a dynamic linear model with p lags of the dependent variables.
Provides tools for describing parameters of algorithms in an abstract way.
  Description can include an id, a description, a domain (range or list of values),
  and a default value. 'dynparam' can also convert parameter sets to a 'ParamHelpers' format, 
  in order to be able to use 'dynparam' in conjunction with 'mlrMBO'.
The dynpred package contains functions for dynamic prediction in survival analysis.
A domain-specific language for specifying translating recursions
    into dynamic-programming algorithms. See 
    <https://en.wikipedia.org/wiki/Dynamic_programming> for a description
    of dynamic programming.
Intensive longitudinal data have become increasingly prevalent in
    various scientific disciplines. Many such data sets are noisy, multivariate,
    and multi-subject in nature. The change functions may also be continuous, or
    continuous but interspersed with periods of discontinuities (i.e., showing
    regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package
    that implements a set of computationally efficient algorithms for handling a
    broad class of linear and nonlinear discrete- and continuous-time models with
    regime-switching properties under the constraint of linear Gaussian measurement
    functions. The discrete-time models can generally take on the form of a state-
    space or difference equation model. The continuous-time models are generally
    expressed as a set of ordinary or stochastic differential equations. All
    estimation and computations are performed in C, but users are provided with the
    option to specify the model of interest via a set of simple and easy-to-learn
    model specification functions in R. Model fitting can be performed using single-
    subject time series data or multiple-subject longitudinal data.
Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.
Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in  Matias and Miele (2016) <doi:10.1111/rssb.12200>.
Dynamic simulations and graphical depictions of autoregressive
    relationships.
Time-varying coefficient models for interval censored and
    right censored survival data including
    1) Bayesian Cox model with time-independent, time-varying or
    dynamic coefficients for right censored and interval censored data studied by
    Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and
    Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>,
    2) Spline based time-varying coefficient Cox model for right censored data
    proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and
    3) Transformation model with time-varying coefficients for right censored data
    using estimating equations proposed by
    Peng and Huang (2007) <doi:10.1093/biomet/asm058>.
Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators.
Provides common functionality for the 'dynverse' packages. 
  'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods.
  For more information, check out <https://dynverse.org>.
Provides functionality to infer trajectories from single-cell data,
  represent them into a common format, and adapt them. Other biological information
  can also be added, such as cellular grouping, RNA velocity and annotation.
  Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.
Functions for dyadic binary/dichotomous sequence analyses are implemented in this contribution.
             The focus is on estimating actor-partner-interaction models using various approaches, for  
             instances the approach of Bakeman & Gottman's (1997) <DOI:10.1017/cbo9780511527685>,
             generalized multi-level models, and basic Markov models. Moreover, coefficients of one model 
             can be translated into those of the other models. Finally, simulation-based power analyses are
             provided.
MCMC method to estimate and predict skewed spatial processes. A real data set is included. Reference: Wang, Yang and Majumdar (2018) <doi.org/10.1007/s00180-017-0741-3>.
Functions for latent class analysis, short time Fourier
	     transform, fuzzy clustering, support vector machines,
	     shortest path computation, bagged clustering, naive Bayes
	     classifier, ...
Process and manage the data from the Empatica E4. All functions operate on the EDA data stream, but other streams will be added soon. The Empatica E4 is a wearable physiological monitor made by Empatica (Empatica is not associated with any of this code). You can find more information about the E4 at Empatica's website <https://www.empatica.com/research/e4/>.
Plots of the empirical attainment function for two objectives.
An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and  outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This  opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.
Estimator augmentation methods for statistical inference on high-dimensional data, 
    as described in Zhou, Q. (2014) <arXiv:1401.4425v2>
    and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>.
    It provides several simulation-based inference methods: (a) Gaussian and 
    wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
    lasso and their de-biased estimators, (b) importance sampler for approximating
    p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with 
    applications in post-selection inference.
A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.
Computes the most important properties of four 'Bayesian' early gating 
             designs (two single arm and two randomized controlled designs), such 
             as minimum required number of successes in the experimental group to 
             make a GO decision, operating characteristics and average operating 
             characteristics with respect to the sample size. 
             These might aid in deciding what design to use for the early phase trial.
Implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) <doi:10.1093/aje/kwt133>.
The Early-Warning-Signals Toolbox provides methods for estimating
    statistical changes in timeseries that can be used for identifying nearby
    critical transitions. Based on Dakos et al (2012) Methods for Detecting
    Early Warnings of Critical Transitions in Time Series Illustrated Using
    Simulated Ecological Data. PLoS ONE 7(7):e41010
Build regression models using the techniques in Friedman's
    papers "Fast MARS" and "Multivariate Adaptive Regression
    Splines" <doi:10.1214/aos/1176347963>.
    (The term "MARS" is trademarked and thus not used in
    the name of the package.)
This is a port of 'Fortran ETERNA 3.4' 
             <http://igets.u-strasbg.fr/soft_and_tool.php> by H.G. Wenzel
             for calculating synthetic Earth tides using the 
             Hartmann and Wenzel (1994) <doi:10.1029/95GL03324> or 
             Kudryavtsev (2004) <doi:10.1007/s00190-003-0361-2> tidal catalogs.
Downloads a satellite image via Google Maps/Earth (these are
    originally from a variety of aerial photography sources), 
    translates the image into a perceptually uniform color space,
    runs one of a few different clustering algorithms on the colors in the image 
    searching for a user-supplied number of colors,
    and returns the resulting color palette.
Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Given the scores from decision makers, the analytic hierarchy process can be conducted easily.
Alluvial plots are similar to sankey diagrams and visualise categorical data 
    over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in 
    Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694> 
    Their graphical grammar however is a bit more complex then that of a regular x/y 
    plots. The 'ggalluvial' package made a great job of translating that grammar into 
    'ggplot2' syntax and gives you many options to tweak the appearance of an alluvial 
    plot, however there still remains a multi-layered complexity that makes it difficult
    to use 'ggalluvial' for explorative data analysis. 'easyalluvial' provides a simple 
    interface to this package that allows you to produce a decent alluvial plot from any 
    dataframe in either long or wide format from a single line of code while also handling 
    continuous data. It is meant to allow a quick visualisation of entire dataframes 
    with a focus on different colouring options that can make alluvial plots a great 
    tool for data exploration.
Perform analysis of variance and other important complementary
  analyses.  The functions are easy to use.  Performs analysis in various
  designs, with balanced and unbalanced data.
Univariate and multivariate methods for compositional data 
    analysis, based on logratios. The package implements the approach in the
    book Compositional Data Analysis in Practice by Michael Greenacre (2018),
    where accent is given to simple pairwise logratios. Selection can be made
    of logratios that account for a maximum percentage of logratio variance.
    Various multivariate analyses of logratios are included in the package.
Allows users to easily read multiple comma separated tables and create a data frame under the same name.
    Is able to read multiple comma separated tables from a local directory, a zip file or a zip file on a remote directory.
Descriptive analysis is essential for publishing medical articles.
  This package provides an easy way to conduct the descriptive analysis.
  1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test.
  2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, 'Tukey' for the numeric variables and 'FDR' for the factor variables.
  3. T test, ANOVA or Fisher test can be forced to apply.
  4. Mean and standard deviation can be forced to display.
It is a package that can be used to send HTML reports easily.
Contains a few utility functions for working with capture-history data, a function for simulating capture-history data, and a function to fit this data, using a Gibbs sampler.
Utilities for building certain kinds of common matrices and models in 
    the extended structural equation modeling package, 'OpenMx'.
Set of wrappers for the 'ncdf4' package to simplify and extend its reading/writing capabilities into/from multidimensional R arrays.
Easily load and install multiple packages from different sources, 
    including CRAN and GitHub. The libraries function allows you to load or attach 
    multiple packages in the same function call. The packages function will load one 
    or more packages, and install any packages that are not installed on your system 
    (after prompting you). Also included is a from_import function that allows you 
    to import specific functions from a package into the global environment.
Power analysis is used in the estimation of sample sizes for
    experimental designs. Most programs and R packages will only output the highest
    recommended sample size to the user. Often the user input can be complicated
    and computing multiple power analyses for different treatment comparisons can
    be time consuming. This package simplifies the user input and allows the user
    to view all of the sample size recommendations or just the ones they want to see.
    The calculations used to calculate the recommended sample sizes are from the
    'pwr' package.
Provides various functions for reading and preparing the Panel Study of Income Dynamics (PSID) for longitudinal analysis, including functions that read the PSID's fixed width format files directly into R, rename all of the PSID's longitudinal variables so that recurring variables have consistent names across years, simplify assembling longitudinal datasets from cross sections of the PSID Family Files, and export the resulting PSID files into file formats common among other statistical programming languages ('SAS', 'STATA', and 'SPSS').
Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Makes difficult operations easy. Includes these types of functions: 
    shorthand, type conversion, data wrangling, and workflow. 
    Also includes some helpful data objects: NA strings, U.S. state list, color blind charting colors. 
    Built and shared by Oliver Wyman Actuarial Consulting. Accepting proposed contributions through GitHub.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models.
The main function, ProtectTable(), performs table suppression according to a 
 frequency rule with a data set as the only required input. Within this function, 
 protectTable(), protectLinkedTables() or runArgusBatchFile() in package 'sdcTable' is called. 
 Lists of level-hierarchy (parameter 'dimList') and other required input to these functions 
 are created automatically. 
 The function, PTgui(), starts a graphical user interface based on the shiny package.
This SVG elements generator can easily generate 
    SVG elements such as rect, line, circle, ellipse, polygon, 
    polyline, text and group. Also, it can combine and 
    output SVG elements into a SVG file.
Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.
Fitting and testing multi-attribute probabilistic choice
  models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
  Terry, 1952 <doi:10.1093/biomet/39.3-4.324>; Luce, 1959),
  elimination-by-aspects (EBA) models (Tversky, 1972 <doi:10.1037/h0032955>),
  and preference tree (Pretree) models (Tversky & Sattath, 1979
  <doi:10.1037/0033-295X.86.6.542>).
Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
We propose a new sample size calculation method for trial-based
    cost-effectiveness analyses. Our strategy is based on the value of perfect
    information that would remain after the completion of the study.
Empirical Bayes thresholding using the methods developed
    by I. M. Johnstone and B. W. Silverman. The basic problem is to
    estimate a mean vector given a vector of observations of the mean
    vector plus white noise, taking advantage of possible sparsity in
    the mean vector. Within a Bayesian formulation, the elements of
    the mean vector are modelled as having, independently, a
    distribution that is a mixture of an atom of probability at zero
    and a suitable heavy-tailed distribution. The mixing parameter can
    be estimated by a marginal maximum likelihood approach. This leads
    to an adaptive thresholding approach on the original data.
    Extensions of the basic method, in particular to wavelet
    thresholding, are also implemented within the package.
Infer the adjacency matrix of a
	network from time course data using an empirical Bayes
	estimation procedure based on Dynamic Bayesian Networks.
Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
  package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.
Functions for querying the EBI Metagenomics Portal <https://www.ebi.ac.uk/metagenomics/>. The current main focus is on taxa abundance data, but the intention is that this package should evolve into a general purpose package for working with EBI Metagenomics data using R.
Tools to download, map, plot and analyze eBird Status
    and Trends data (<https://ebird.org/science/status-and-trends>). eBird 
    (<https://ebird.org>) is a global database of bird observations collected by 
    citizen scientists. eBird Status and Trends uses these data to analyse 
    continental bird abundances, range boundaries, habitats, and trends.
Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.
EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy.  No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for  each individual in testing data, and evaluate the PRS according to AUC and predictive r2.
Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors.  A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.
Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.
Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
Provides an interface to the European Central Bank's Statistical
    Data Warehouse API, allowing for programmatic retrieval of a vast quantity
    of statistical data.
Draw sample paths for endpoint-conditioned continuous time Markov chains via modified rejection sampling or uniformization.
Elliptic lambda distribution and lambda option pricing model
    have been evolved into a framework of
    stable-law inspired distributions,
    such as the extended stable lambda distribution for asset return,
    stable count distribution for volatility,
    and Lihn-Laplace process as a leptokurtic extension of Wiener process.
    This package contains functions for the computation of
    density, probability, quantile, random variable, fitting procedures,
    option prices, volatility smile. It also comes with sample financial data,
    and plotting routines.
Data sets for econometrics, including political science.
Computes and plots a transformed empirical CDF (ecdf) as a
    diagnostic for heavy tailed data, specifically data with power law decay on the
    tails.  Routines for annotating the plot, comparing data to a model, fitting a
    nonparametric model, and some multivariate extensions are given.
Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on 'spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.
Testing the equality of several covariance functions of functional data. Four different methods are implemented: L2-norm with W-S naive, L2-norm with W-S bias-reduced, L2-norm (Zhang 2013) <ISBN:9781439862735>, and sup-norm with resampling (Guo et al. 2017) <arXiv:1609.04232>.
Functions to update data sets in Ecdat and to create,
          manipulate, plot and analyze those and similar data sets.
A goodness-of-fit test for elliptical distributions with diagnostic
  capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2019) <arXiv:1902.03622>.
Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
Easily create interactive charts by leveraging the 'Echarts Javascript' library which includes
    34 chart types, themes, 'Shiny' proxies and animations.
Simulates cyclic voltammetry, linear-sweep voltammetry 
    (both with and without stirring of the solution), and single-pulse 
    and double-pulse chronoamperometry and chronocoulometry 
    experiments using the implicit finite difference method outlined in 
    Gosser (1993, ISBN: 9781560810261) and in Brown (2015) 
    <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide 
    ways to display and to examine the results of these simulations.
    The primary purpose of this package is to provide tools for
    use in courses in analytical chemistry.
Provides a function (echo_find()) designed to find rhythms 
    from data using extended harmonic oscillators. For more information,
    see H. De los Santos et al. (2017) <doi:10.1145/3107411.3107420> .
Easily import multi-frequency acoustic data stored in 'HAC' files (see <http://biblio.uqar.ca/archives/30005500.pdf> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.
An R interface to United States Environmental 
    Protection Agency (EPA) Environmental Compliance 
    History Online ('ECHO') Application Program Interface
    (API). 'ECHO' provides information about EPA permitted 
    facilities, discharges, and other reporting info 
    associated with permitted entities. Data are obtained 
    from <https://echo.epa.gov/>.
Provides a function that quickly computes the fine structure
    isotope patterns of a set of chemical formulas to a given degree of
    accuracy (up to the limit set by errors in floating point arithmetic). A
    data-set comprising the masses and isotopic abundances of individual
    elements is also provided.
Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.
Companion package to the paper: An analytic approach for 
    interpretable predictive models in high dimensional data, in the presence of 
    interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. 
    This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.
Functions for easy building of error correction models (ECM) for time series regression.
Programmatic interface to the European Centre for Medium-Range
    Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>)
    and Copernicus's Climate Data Store (CDS; 
    <https://cds.climate.copernicus.eu>). Allows for easy downloads of weather 
    forecasts and climate reanalysis data in R.
Implements the Bayesian and likelihood methods proposed 
  in Imai, Lu, and Strauss (2008 <DOI: 10.1093/pan/mpm017>) and 
  (2011 <DOI:10.18637/jss.v042.i05>) for ecological inference in 2 
  by 2 tables as well as the method of bounds introduced by Duncan and 
  Davis (1953).  The package fits both parametric and nonparametric 
  models using either the Expectation-Maximization algorithms (for 
  likelihood models) or the Markov chain Monte Carlo algorithms (for 
  Bayesian models).  For all models, the individual-level data can be 
  directly incorporated into the estimation whenever such data are available.
  Along with in-sample and out-of-sample predictions, the package also
  provides a functionality which allows one to quantify the effect of data
  aggregation on parameter estimation and hypothesis testing under the
  parametric likelihood models.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.
The ecoengine ('ecoengine';
    <https://ecoengine.berkeley.edu/>). provides access to more than 5 million georeferenced
    specimen records from the University of California, Berkeley's Natural History
    Museums.
Management and exploratory analysis of spatial data in landscape genetics. Easy integration of information from multiple sources with "ecogen" objects.
Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.
Provides a flexible foundation for scientists, 
  engineers, and policy makers to base teaching exercises as well as for 
  more applied use to model complex eco-hydrological interactions.
Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.
Provides measures to characterize the complexity of classification 
    and regression problems based on aspects that quantify the linearity of the 
    data, the presence of informative feature, the sparsity and dimensionality 
    of the datasets. This package provides bug fixes, generalizations and 
    implementations of many state of the art measures. The measures are 
    described in the papers: Lorena et al. (2019) <doi:10.1145/3347711> and 
    Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
  All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11. 
  The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
  Solutions to model projects are in the same subdirectories.
Coalescent-Based Simulation of Ecological Communities as proposed
    by Munoz et al. (2017) <doi:10.13140/RG.2.2.31737.26728>. The package includes
    a tool for estimating parameters of community assembly by using Approximate 
    Bayesian Computation.
Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018), which examine the determinants of US campaign contributions when legislators care about the behavior of other legislators to whom they are socially connected. For additional details, see the vignette.
Randomly generate a wide range of interaction networks with
             specified size, average degree, modularity, and topological
             structure. Sample nodes and links from within simulated networks
             randomly, by degree, by module, or by abundance. Simulations
             and sampling routines are implemented in 'FORTRAN', providing
             efficient generation times even for large networks. Basic
             visualization methods also included. Algorithms implemented
             here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.
A collection of advanced tools, methods and models specifically  designed  for  analyzing different  types  of  ecological  networks - especially  antagonistic  (food  webs,  host-parasite),  mutualistic (plant-pollinator, plant-fungus, etc)  and competitive networks, as well as their variability in time and space. Statistical models are developed to describe and understand the mechanisms that determine species interactions, and to decipher the organization of these (multi-layer) ecological networks.
A wrapper of different computation methods to create bipartite 
  networks such as countries and their exported products, and different kind of
  relations such as countries and their spoken languages. This package 
  implements the equations described in 
  Mariani, et al. (2015) <doi:10.1140/epjb/e2015-60298-7> and 
  Hausmann, et al. (2014) <doi:10.7551/mitpress/9647.001.0001>.
Tools for using null models to analyse ecological
    networks (e.g. food webs, flower-visitation networks, seed-dispersal
    networks) and detect resource preferences or non-random interactions among
    network nodes. Tools are provided to run null models, test for and plot
    preferences, plot and analyse bipartite networks, and export null model
    results in a form compatible with other network analysis packages. The 
    underlying null model was developed by Agusti et al. (2003) 
    <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to 
    ecological networks by Vaughan et al. (2017) econullnetr: an R package 
    using null models to analyse the structure of ecological networks and 
    identify resource selection. Methods in Ecology & Evolution, in press.
Estimating individual-level covariate-outcome associations 
 using aggregate data ("ecological inference") or a combination of 
 aggregate and individual-level data ("hierarchical related regression").
Creates an R interface to the Bacen <http://api.bcb.gov.br/> and Sidra <http://api.sidra.ibge.gov.br> APIs and IPEA data <http://www.ipeadata.gov.br/Default.aspx>.
Classes and methods for implementing aquatic ecosystem models,
  for running these models, and for visualizing their results.
Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org.
R interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.
Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness.
    Functions are written so they will work in a parallel-computing environment.
Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.
A simple approach to using a probit or logit analysis to calculate
        lethal concentration (LC) or time (LT) and the appropriate fiducial 
        confidence limits desired for selected LC or LT for
        ecotoxicology studies (Finney 1971; Wheeler et al. 2006; 
        Robertson et al. 2007). The simplicity of 'ecotox' comes from the 
        syntax it implies within its functions which are similar to functions 
        like glm() and lm(). In addition to the simplicity of the syntax, 
        a comprehensive data frame is produced which gives the user a 
        predicted LC or LT value for the desired level and a suite of important 
        parameters such as fiducial confidence limits and slope.   
        Finney, D.J. (1971, ISBN: 052108041X);
        Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>;
        Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K.
        (2007, ISBN: 0849323312).
Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis.
    Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book.
    Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included.
    Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard.
    Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
Functions for evaluating and visualizing
  ecological assessment procedures for surface waters
  containing physical, chemical and biological assessments
  in the form of value functions.
Computer simulations of classical ecological models as a
    learning resource.
Implements various procedures for finding 
	     multiple change-points. Two methods make use of dynamic 
	     programming and pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Easy and rapid quantitative estimation of small terrestrial ectotherm temperature regulation effectiveness in R. ectotemp is built on classical formulas that evaluate temperature regulation by means of various indices, inaugurated by Hertz et al. (1993) <doi: 10.1086/285573>. Options for bootstrapping and permutation testing are included to test hypotheses about divergence between organisms, species or populations.
Functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively
    Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by
    us, as well as logistic regression and isotonic regression. Besides, the package also supports
    comparison between two estimation results.
Energy decomposition analysis for measuring contributions of components
             and factors on energy or carbon emission changes, as described in 
             B.W. Ang (2005) <doi:10.1016/j.enpol.2003.10.010>. Includes Log Mean 
             Devisia Index method and multi-year energy decomposition analysis using 
             five methods. Please refer P. Wu (2019) <doi:10.1016/j.jclepro.2019.02.200> 
             to use the package.
Provides data sets and R Codes for Williams, E.R., Matheson, A.C. and Harwood, C.E. (2002). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.
Functions useful for exploratory data analysis
    using random forests which can be used to compute multivariate partial
    dependence, observation, class, and variable-wise marginal and joint permutation
    importance as well as observation-specific measures of distance 
    (supervised or unsupervised). All of the aforementioned functions are
    accompanied by 'ggplot2' plotting functions.
This package provides a unified approach for Economic
        Design of Control Charts. The main purpose of this package is
        to find out the optimal parameters to minimize the ECH
        (Expected Cost per Hour) of the process.
Detection of edge points in images based on the difference
        of two asymmetric M-kernel estimators. Linear and circular
        regression clustering based on redescending M-estimators.
        Detection of linear edges in images.
Finds and downloads raw Evaporative Demand Drought
    Index (EDDI) data, then reads the data into 'R' using the 'raster'
    package. The EDDI product detects drought at multiple time scales,
    from weekly "flash droughts" to long-term droughts. More information
    about the EDDI data product can be found at
    <https://www.esrl.noaa.gov/psd/eddi/>.
Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package 'bupaR'.
This package will allow to connect with email server through Internet Message Access Protocol (IMAP) and extract header information e.g. from, to, cc, subject, date and time. User will supply their email address and password along with other options. Initially this package is developed only for Gmail. To run the functions from this package user have to have IMAP enabled Gmail account.
Import physiologic data stored in
    the European Data Format (EDF and EDF+) into R.
    Both EDF and EDF+ files are supported. Discontinuous
    EDF+ files are not yet supported.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>,
    BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>,
    and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>.
    The files are read in two steps: first the header is read
    and then the signals (using the header object as a parameter).
Easily creating empirical distribution functions from data: 'dfun', 'pfun',
    'qfun' and 'rfun'.
In the USA, companies file different forms with the U.S. 
  Securities and Exchange Commission (SEC) through EDGAR (Electronic 
  Data Gathering, Analysis, and Retrieval system). The EDGAR 
  database automated system collects all the different necessary 
  filings and makes it publicly available. Investors, regulators, 
  and researchers often require these forms for various purposes. 
  This package helps in bulk data gathering and textual analysis of 
  EDGAR filings. It downloads filings from SEC server in bulk with 
  a single query. Additionally, it provides various useful functions:
  extracts 8-K triggering events, extract "Business (Item 1)" and 
  "Management's Discussion and Analysis(Item 7)" sections of annual 
  statements, search filings for desired words, provides sentiment 
  measures, parse filing header information, and provides HTML 
  view of SEC filings.
A set of methods to access and parse live filing information from the
    U.S. Securities and Exchange Commission (SEC - <https://sec.gov>) including
    company and fund filings along with all associated metadata.
Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.
Facilitates basic spatial edge correction to point pattern data.
Extends edgeR functionality by improving on exactTest using an unconditional exact test of negative binomial means.
Package EDISON (Estimation of Directed Interactions from
    Sequences Of Non-homogeneous gene expression) runs an MCMC
    simulation to reconstruct networks from time series data, using
    a non-homogeneous, time-varying dynamic Bayesian network.
    Networks segments and changepoints are inferred concurrently,
    and information sharing priors provide a reduction of the
    inference uncertainty.
An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame'
    without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. 
    These modules are intended for reuse across applications.
An integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach as described in Kim et al. (2015) <doi:10.1080/01621459.2015.1040881>. In this approach, the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
Facilitates reading and manipulating (multivariate) data restrictions
    (edit rules) on numerical and categorical data. Rules can be defined with common R syntax
    and parsed to an internal (matrix-like format). Rules can be manipulated with
    variable elimination and value substitution methods, allowing for feasibility checks
    and more. Data can be tested against the rules and erroneous fields can be found based
    on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with 
    using the 'igraph' package.
Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
Implementations of (1) mutual dependence measures and mutual independence tests in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532>;
    (2) independent component analysis methods based on mutual dependence measures in 
    Jin, Z., and Matteson, D. S. (2017) <arXiv:1709.02532> 
    and Pfister, N., et al. (2018) <doi:10.1111/rssb.12235>;
    (3) conditional mean dependence measures and conditional mean independence tests in 
    Shao, X., and Zhang, J. (2014) <doi:10.1080/01621459.2014.887012>,
    Park, T., et al. (2015) <doi:10.1214/15-EJS1047>,
    and Lee, C. E., and Shao, X. (2017) <doi:10.1080/01621459.2016.1240083>.
A non-parametric framework based on  estimation statistics principle. Its main purpose is to  infer orders of empirical distributions from different categories based on a probability of finding a value in one distribution that is greater than an expectation of another distribution. Given a set of ordered-pair of real-category values the framework is capable of 1) inferring orders of  domination  of  categories  and  representing  orders  in  the form of a graph; 2) estimating  magnitude  of  difference  between  a  pair  of categories in forms of mean-difference confidence intervals; and 3) visualizing  domination  orders  and  magnitudes  of  difference of categories. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2019) <arXiv:1911.06723>.
The library contains R-functions to estimate the effective
        dimension reduction space in 'multi-index' regression models.
Reduction methods through slice inverse regression approaches. It mainly designed for illustrating the articles "A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches" (Liquet, B., Saracco, J. (2012) <doi:10.1007/s00180-011-0241-9>) and "Comparison of sliced inverse regression approaches for underdetermined cases”.
Provides convenience functions and pre-programmed Stan models
    related to item response theory. Its purpose is to make fitting
    common item response theory models using Stan easy.
Read in and analysis functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: OECD <http://www.oecd.org/>, including PISA, TALIS, PIAAC, and IEA <http://www.iea.nl/>, including TIMSS, TIMSS Advanced, PIRLS, ICCS, ICILS, and CivEd.
Easily compute education inequality measures and the distribution 
  of educational attainments for any group of countries, using the data set 
  developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>. 
  The package offers the possibility to compute not only the Gini index, but 
  also generalized entropy measures for different values of the sensitivity 
  parameter. In particular, the package includes functions to compute the 
  mean log deviation, which is more sensitive to the bottom part of the 
  distribution; the Theil’s entropy measure, equally sensitive to all parts 
  of the distribution; and finally, the GE measure when the sensitivity 
  parameter is set equal to 2, which gives more weight to differences in 
  higher education. The decomposition of these measures in the components 
  between-country and within-country inequality is also provided. Two 
  graphical tools are also provided, to analyse the evolution of the
  distribution of educational attainments: The cumulative distribution 
  function and the Lorenz curve.
Data from the seven Australian Federal Elections (House of
    Representatives) between 2001 and 2019, and from the four Australian
    Censuses over the same period. Includes tools for visualizing and
    analysing the data, as well as imputing Census data for years in
    which a Census does not occur. This package incorporates
    data that is copyright Commonwealth of Australia (Australian
    Electoral Commission and Australian Bureau of Statistics) 2019.
Provides tools for analysing education trials. Making different
    methods accessible in a single place is essential for sensitivity analysis
    of education trials, particularly the implication of the different methods in
    analysing simple randomised trials, cluster randomised trials and multisite
    trials.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Compute the extended empirical log likelihood ratio (Tsao & Wu, 2014) for the mean and parameters defined by estimating equations.
Read raw EEM data and prepares them for further analysis.
Provides various tools for preprocessing Emission-Excitation-Matrix (EEM) for Parallel Factor Analysis (PARAFAC). Different
  methods are also provided to calculate common metrics such as humification index and fluorescence index.
Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, and identify cut points. Also includes three example data 
    sets of administrative education records for learning how to process records 
    with errors.
Provides functions to create simulated time series of environmental
    exposures (e.g., temperature, air pollution) and health outcomes for use in
    power analysis and simulation studies in environmental epidemiology. This
    package also provides functions to evaluate the results of simulation studies
    based on these simulated time series. This work was supported by a grant
    from the National Institute of Environmental Health Sciences (R00ES022631) and
    a fellowship from the Colorado State University Programs for Research and
    Scholarly Excellence.
Performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 
    <doi:10.1037/a0023353>) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 
    2011 <doi:10.1080/00273171.2011.564527>) for assessing the dimensionality
    of a set of variables using minimum rank factor analysis (see ten Berge & 
    Kiers, 1991 <doi:10.1007/BF02294464> for more information). The package 
    also includes the option to compute minimum rank factor analysis by 
    itself, as well as the greater lower bound calculation.
A number of utility function for exploratory factor analysis
    are included in this package. In particular, it computes standard errors for
    parameter estimates and factor correlations under a variety of conditions.
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
    in an image. The image is first transformed into the wavelet domain in
    order to decorrelate any noise components, following which the coefficients
    at each resolution are standardised. Statistical tests (in a multiple
    hypothesis testing setting) are then carried out to find the anomalies. The
    power of EFDR exceeds that of standard FDR, which would carry out tests on
    every wavelet coefficient: EFDR choose which wavelets to test based on a
    criterion described in Shen et al. (2002). The package also provides
    elementary tools to interpolate spatially irregular data onto a grid of the
    required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
    'Nonparametric hypothesis testing for a spatial signal.' Journal of the
    American Statistical Association 97.460 (2002): 1122-1140.
Variable selection and Bayesian effect fusion for categorical predictors in linear and 
    logistic regression models. Effect fusion aims at the question which categories have a similar 
	effect on the response and therefore can be fused to obtain a sparser representation of the model. 
    Effect fusion and variable selection can be obtained either with a prior that has an interpretation 
	as spike and slab prior on the level effect differences or with a sparse finite mixture prior on 
	the level effects. The regression coefficients are estimated with a flat uninformative prior after 
	model selection or by taking model averages. Posterior inference is accomplished by an MCMC 
	sampling scheme which makes use of a data augmentation strategy (Polson, Scott & Windle 
	(2013)) based on latent Polya-Gamma random variables in the case of logistic regression. 
	The code for data augmentation is taken from Polson et al. (2013), who own the copyright.
Use structural equation modeling to estimate average and
    conditional effects of a treatment variable on an outcome variable, taking into
    account multiple continuous and categorical covariates.
Predicts cytoplasmic effector proteins using genomic data by searching for motifs of interest using regular expression searches and hidden Markov models (HMM) based in Haas et al. (2009) <doi:10.1038/nature08358>.
Graphical and tabular effect displays, e.g., of interactions, for 
  various statistical models with linear predictors.
Provide utilities to work with indices of effect size and standardized parameters for a wide variety of models (see support list of insight; Lüdecke, Waggoner & Makowski (2019) <doi:10.21105/joss.01412>), allowing computation and conversion of indices such as Cohen's d, r, odds, etc.
Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) <doi:10.1016/j.beth.2010.08.006> was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.
Functions to test for changes of a response to a stimulus grouping relative 
  to a background or baseline response.
Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.
Provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package.
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
An implementation for using efficient initials to compute the
    maximal eigenpair in R. It provides three algorithms to find the efficient
    initials under two cases: the tridiagonal matrix case and the general matrix
    case. Besides, it also provides two algorithms for the next to the maximal eigenpair under
    these two cases.
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.
A collection of functions to compute the standardized 
  effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). 
  The computation algorithms have been optimized to allow efficient computation even 
  with very large data sets.
The European Eddy Fluxes Database Cluster distributes fluxes of different Green House Gases measured mainly using the eddy covariance technique acquired in sites involved in EU projects but also single sites in Europe, Africa and others continents that decided to share their measurements in the database <http://gaia.agraria.unitus.it>. The package provides two functions to load and row-wise bind CSV files distributed by the database. Currently only L2, L3, and L4 (L=Level), half-hourly and daily (aggregation) files are supported.
Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().
The binary file format 'netCDF' is developed primarily for climate, ocean and
    meteorological data, and 'efts' is a package to read and write Ensemble Forecast Time 
    Series data in 'netCDF'. 'netCDF' has traditionally been used to store time slices 
    of gridded data, rather than complete time series of point data. 
    'efts' facilitates data handling stored in 'netCDF' files that follow a convention
    devised in the domain of ensemble hydrologic forecasting, but possibly applicable 
    in other domains. 'efts' uses reference class objects to 
    provide a high level interface to read and write such data, wrapping 
    lower level operations performed using 'ncdf4'.
Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids
    in both mg/mL and mmol/L units.
An implementation of the Exploratory Graph Analysis (EGA) framework for dimensionality assessment. EGA is part of a new area called network psychometrics that focuses on the estimation of undirected network models in psychological datasets. EGA estimates the number of dimensions or factors using graphical lasso or Triangulated Maximally Filtered Graph (TMFG) and a weighted network community analysis. A bootstrap method for verifying the stability of the estimation is also available. The fit of the structure suggested by EGA can be verified using confirmatory factor analysis and a direct way to convert the EGA structure to a confirmatory factor model is also implemented. Documentation and examples are available. Golino, H. F., & Epskamp, S. (2017) <doi:10.1371/journal.pone.0174035>. Golino, H. F., & Demetriou, A. (2017) <doi:10.1016/j.intell.2017.02.007> Golino, H., Shi, D., Garrido, L. E., Christensen, A. P., Nieto, M. D., Sadana, R., & Thiyagarajan, J. A. (2018) <doi:10.31234/osf.io/gzcre>. Christensen, A. P. & Golino, H.F. (2019)  <doi:10.31234/osf.io/9deay>.
An easy-to-use implementation of the Engle-Granger
  two-step procedure for identifying pairs of cointegrated series.  It is geared towards 
  the analysis of pairs of securities.  Summary and plot functions are provided, 
  and the package is able to fetch closing prices of securities from Yahoo.
  A variety of unit root tests are supported, and an improved unit root test is included.
Miscellaneous functions to help customise 'ggplot2' objects. High-level functions are provided to post-process 'ggplot2' layouts and allow alignment between plot panels, as well as setting panel sizes to fixed values. Other functions include a custom 'geom', and helper functions to enforce symmetric scales or add tags to facetted plots.
An implementation of Bayesian hierarchical models
    for faecal egg count data to assess anthelmintic
    efficacy. Bayesian inference is done via MCMC sampling using 'Stan' <https://mc-stan.org/>.
Tools for importing, analyzing and visualizing ego-centered
    network data. Supports several data formats, including the export formats of
    'EgoNet', 'EgoWeb 2.0' and 'openeddi'. An interactive (shiny) app for the
    intuitive visualization of ego-centered networks is provided. Also included
    are procedures for creating and visualizing Clustered Graphs 
    (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).
Estimation of ego-temporal exponential random graph models with two-stage estimation including initialization through k-means clustering on temporal exponential random graph model parameters and EM as per Campbell (2018) <doi:10.7910/DVN/TWHEZ9>.
Statistics and graphics for streamflow history,
    water quality trends, and the statistical modeling algorithm: Weighted
    Regressions on Time, Discharge, and Season (WRTDS). The modeling
    method is introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>,
    and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>.
Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis. The WRTDS modeling
    method was initially introduced and discussed in Hirsch et al. (2010) <doi:10.1111/j.1752-1688.2010.00482.x>,
    and expanded in Hirsch and De Cicco (2015) <doi:10.3133/tm4A10>. The 
    paper describing the uncertainty and confidence interval calculations 
    is Hirsch et al. (2015) <doi:10.1016/j.envsoft.2015.07.017>.
Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of fat storage in the adipose tissue by dysregulation of genes expressed in adipose tissue. We present a method eGST (eQTL-based genetic subtyper) that integrates tissue-specific eQTLs with GWAS data for a complex trait to probabilistically assign a tissue of interest to the phenotype of each individual in the study. eGST estimates the posterior probability that an individual's phenotype can be assigned to a tissue based on individual-level genotype data of tissue-specific eQTLs and marginal phenotype data in a genome-wide association study (GWAS) cohort. Under a Bayesian framework of mixture model, eGST employs a maximum a posteriori (MAP) expectation-maximization (EM) algorithm to estimate the tissue-specific posterior probability across individuals. Methodology is available from: A Majumdar, C Giambartolomei, N Cai, MK Freund, T Haldar, T Schwarz, J Flint, B Pasaniuc (2019) <doi:10.1101/674226>.
Sampling of risk sets in Cox regression, selections in 
        the Lexis diagram, bootstrapping. Parametric proportional 
        hazards fitting with left truncation and right censoring for 
        common families of distributions, piecewise constant hazards, 
        and discrete models. Parametric accelerated failure time models 
        for left truncated and right censored data.
Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), and corrected Akaike's information criterion (CAIC).
By overloading the R help() function, this package allows users to use "docstring" style comments within their own defined functions.
Process and analyze Electronic Health Record (EHR) data. Frequency and 
    contingency tables for many binary outcomes and a binary exposure variable can be 
    generated more efficiently. Phenome Wide Association Study (PheWAS) performed 
    using EHR data can be analyzed using three commonly used statistical analysis 
    methods: Firth's penalized-likelihood logistic regression; logistic regression 
    with likelihood ratio test; conventional logistic regression with Wald test.
The 'EHRtemporalVariability' package contains functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds uncovering the patterns of the data latent temporal variability. Dataset shifts can be explored and identified through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional 'EHRtemporalVariability' Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding.
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.
Provides API access to data from the 'US Energy Information Administration' ('EIA') <https://www.eia.gov/>. 
    Use of the API requires a free API key obtainable at <https://www.eia.gov/opendata/register.php>.
    The package includes functions for searching 'EIA' data categories and importing time series and geoset time series datasets. 
    Datasets returned by these functions are provided in a tidy format or alternatively in more raw form. 
    It also offers helper functions for working with 'EIA' date strings and time formats and for inspecting different summaries of series metadata.
    The package also provides control over API key storage and caching of API request results. 
    This package has been peer-reviewed by rOpenSci (v. 0.3.4).
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API <https://www.eia.gov/opendata/>.
Compares estimates from three ecological inference routines, based on King (1997) <ISBN: 0691012407>, <http://gking.harvard.edu/eicamera/kinroot.html>; King et. al. (2004) <ISBN: 0521542804>, <http://gking.harvard.edu/files/abs/ecinf04-abs.shtml>.
Solves the ``inverse eigenvalue problem'' which is to
        generate a real-valued matrix that has the specified real
        eigenvalue spectrum.  It can generate infinitely many dense
        matrices, symmetric or asymmetric, with the given set of
        eigenvalues.  Algorithm can also generate stochastic and doubly
        stochastic matrices.
Estimation of the parameters in a model for
        symmetric relational data (e.g., the above-diagonal part of a
        square matrix), using a model-based eigenvalue decomposition
        and regression. Missing data is accommodated, and a posterior
        mean for missing data is calculated under the assumption that
        the data are missing at random. The marginal distribution of
        the relational data can be arbitrary, and is fit with an
        ordered probit specification. See Hoff (2007) <arXiv:0711.1146> 
        for details on the model.
Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
Implementation of Efficient Inference of Local Ancestry 
	     using fused quantile regression and k-means classifier
Provides methods for analyzing R by C ecological contingency
        tables using the extreme case analysis, ecological regression,
        and Multinomial-Dirichlet ecological inference models.  Also
        provides tools for manipulating higher-dimension data objects.
Estimate district-level bounds for 2x2 ecological inference based on the approach 
    described in the forthcoming article Jiang et al. (2019), "Ecological Regression with Partial
    Identification", Political Analysis. Interval data regression is used to bound the 
    nonidentified regression parameter in a linear contextual effects model, from which 
    district-level bounds are derived. The approach here can be useful as a baseline of comparison
    for future work on ecological inference.
Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.
This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression.  It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.
This package allows to use the hybrid Multinomial-Dirichlet-Model
    of Ecological Inference for estimating inner Cells of RxC-Tables. This was
    already implemented in the eiPack-package. eiwild-package now has the
    possibility to use individual level data to support the aggregate level
    data and using different Hyperpriori-Distributions.
Structure mining from 'XGBoost' and 'LightGBM' models.
    Key functionalities of this package cover: visualisation of tree-based ensembles models,
    identification of interactions, measuring of variable importance,
    measuring of interaction importance, explanation of single prediction 
    with break down plots (based on 'xgboostExplainer' and 'breakDown' packages). 
    To download the 'LightGBM' use the following link: <https://github.com/Microsoft/LightGBM>.
    'EIX' is a part of the 'DrWhy.AI' universe.
Emissions are the mass of pollutants released into the atmosphere. Air quality models need emissions data, with spatial and temporal distribution, to represent air pollutant concentrations. This package, eixport, creates inputs for the air quality models 'WRF-Chem' Grell et al (2005) <doi:10.1016/j.atmosenv.2005.04.027>, 'BRAMS-SPM' Freitas et al (2005) <doi:10.1016/j.atmosenv.2005.07.017> and 'RLINE' Snyder et al (2013) <doi:10.1016/j.atmosenv.2013.05.074>. See the eixport website (<https://atmoschem.github.io/eixport/>) for more information, documentations and examples. More details in Ibarra-Espinosa et al (2018) <doi.org/10.21105/joss.00607>.
Functions for estimating catalytic constant and Michaelis-Menten constant
            for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs 
            sampler based on the Bayesian framework. Additionally, a function to create 
            plot to identify the goodness-of-fit is included.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Functions for computing critical values and implementing the one-sided/two-sided EL tests.
Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.
Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' API
    (<https://www.elastic.co/products/elasticsearch>), including functions for
    setting connection details to 'Elasticsearch' instances, loading bulk data,
    searching for documents with both 'HTTP' query variables and 'JSON' based body
    requests. In addition, 'elastic' provides functions for interacting with API's
    for 'indices', documents, nodes, clusters, an interface to the cat API, and
    more.
Description: Uses k-fold cross-validation and elastic-net regularization to estimate the
              Ising model on binary data. Produces 3D plots of the cost function as a function
              of the tuning parameter in addition to the optimal network structure.
Provides functions for fitting the entire
        solution path of the Elastic-Net and also provides functions
        for estimating sparse Principal Components. The Lasso solution
        paths can be computed by the same function. First version:
        2005-10.
A lightweight R interface to 'Elasticsearch' - a NoSQL search-engine and 
    column store database (see <https://www.elastic.co/products/elasticsearch> for more 
    information). This package implements a simple Domain-Specific Language (DSL) for indexing, 
    deleting, querying, sorting and aggregating data using 'Elasticsearch'.
This is a bizarre collection of functions written to do
        various sorts of statistical election audits.  There are also
        functions to generate simulated voting data, and simulated
        "truth" so as to do simulations to check characteristics of
        these methods.
An extension of the elec package intended for use on
        election audits using stratified random samples.  Includes
        functions to obtain conservative and exact p-values, and
        functions that give sample sizes that may make election audits
        more efficient.
Digital simulation of electrochemical processes.
             Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.
             Bibliography regarding this methods can be found in the following texts.
             Dieter Britz, Jorg Strutwolf (2016) <ISBN:978-3-319-30292-8>.
             Allen J. Bard, Larry R. Faulkner (2000) <ISBN:978-0-471-04372-0>.
Functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the 'msm' package using age as the time-scale.
This includes a dataset on the outcomes of the USA presidential elections since 1920, and various predictors, as used in <http://vanderwalresearch.com/blog/15-elections>.
Offers a set of functions to easily download and clean 
    Brazilian electoral data from the Superior Electoral Court website. 
    Among others, the package retrieves data on local and
    federal elections for all positions (city councilor, mayor, state deputy,
    federal deputy, governor, and president) aggregated by
    state, city, and electoral zones.
Provides all electivity algorithms (including Vanderploeg and Scavia 
    electivity) that were examined in Lechowicz (1982) <doi:10.1007/BF00349007>, 
    plus the example data that were provided for moth resource utilisation.
Highest averages & largest remainders allocating seats methods and
    several party system scores.
    Implemented highest averages allocating seats methods are D'Hondt, Webster,
    Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague,
    equal proportions and Adams.
    Implemented largest remainders allocating seats methods are Hare, Droop,
    Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders.
    The main advantage of this package is that ties are always reported
    and not incorrectly allocated.
    Party system scores provided are competitiveness, concentration,
    effective number of parties, party nationalization score,
    party system nationalization score and volatility.
    References.
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>.
    Norris (2004, ISBN:0-521-82977-1).
    Consejo Nacional Electoral del Ecuador (2014)<http://cne.gob.ec/documents/Estadisticas/Atlas/ATLAS/CAPITULO%206%20web.pdf>.
    Laakso & Taagepera (1979) <http://journals.sagepub.com/doi/pdf/10.1177/001041407901200101>.
    Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>.
    Pedersen (1979) <http://janda.org/c24/Readings/Pedersen/Pedersen.htm>.
Aims to facilitate the reduction of elemental microchemistry data from solid-phase LAICPMS analysis (laser ablation inductive coupled plasma mass spectrometry). The 'elementR' package provides a reactive and user friendly interface (based on a 'shiny' application) and a set of 'R6' classes for conducting all steps needed for an optimal data reduction while leaving maximum control for user.
Useful when reading the book above mentioned, in the
        documentation referred to as `the book'.
Several web services are available that provide access to elevation
             data. This package provides access to several of those services and 
             returns elevation data either as a SpatialPointsDataFrame from 
             point elevation services or as a raster object from raster 
             elevation services.  Currently, the package supports access to the
             Amazon Web Services Terrain Tiles <https://aws.amazon.com/public-datasets/terrain/> 
             and the USGS Elevation Point Query Service <http://ned.usgs.gov/epqs/>.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
Density, distribution function, quantile function
    and random generation for the Kumaraswamy Complementary Weibull
    Geometric (Kw-CWG) lifetime probability distribution proposed
    in Afify, A.Z. et al (2017) <doi:10.1214/16-BJPS322>.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
To run data analysis for enzyme-link immunosorbent assays (ELISAs). 
	Either the five- or four-parameter logistic model will be fitted for data of single ELISA. 
	Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. 
	Feng (2018) <doi:10.1101/483800>.
Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name "elitism" is an acronym of "e"quipment for "l"ogarithmic and l"i"near "ti"me "s"tepwise "m"ultiple hypothesis testing. Version 1.0.0 was released on June 26, 2019. See Gou, J., and Zhang, F. (2020). Quick multiple test procedures and p-value adjustments. Technical report.
Correlation chart of two set (x and y) of data.  
  Using Quantiles.  Visualize the effect of factor.
Contains various routines for drawing
    ellipses and ellipse-like confidence regions, implementing the plots
    described in Murdoch and Chow (1996), A graphical display of large
    correlation matrices, The American Statistician 50, 178-180. There are
    also routines implementing the profile plots described in Bates and
    Watts (1988), Nonlinear Regression Analysis and its Applications.
The ellipsis is a powerful tool for extending functions. Unfortunately 
    this power comes at a cost: misspelled arguments will be silently ignored. 
    The ellipsis package provides a collection of functions to catch problems
    and alert the user.
A suite of elliptic and related functions including Weierstrass and
 Jacobi forms.  Also includes various tools for manipulating and
 visualizing complex functions.
Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
An implementation of the algorithm described in "Efficient Large-
    Scale Internet Media Selection Optimization for Online Display Advertising"
    by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below
    for journal text/citation and <http://www-bcf.usc.edu/~gareth/research/ELMSO.pdf>
    for a full-text version of the paper). The algorithm here is designed to 
    allocate budget across a set of online advertising opportunities using a 
    coordinate-descent approach, but it can be used in any resource-allocation 
    problem with a matrix of visitation (in the case of the paper, website page-
    views) and channels (in the paper, websites). The package contains allocation
    functions both in the presence of bidding, when allocation is dependent on 
    channel-specific cost curves, and when advertising costs are fixed at each channel.
A flexible framework for calculating Elo ratings and resulting
    rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
    etc.). This implementation is capable of evaluating a variety of matchups,
    Elo rating updates, and win probabilities, all based on the basic Elo
    rating system. It also includes methods to benchmark performance,
    including logistic regression and Markov chain models.
Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.
Provides an implementation of the maximum likelihood methods for deriving
    Elo scores as published in Foerster, Franz et al. (2016) <DOI:10.1038/srep35404>.
Provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries' I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.
Flexible implementation of Elston-Stewart algorithm
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models. 
             Empirical likelihood tests within a Cox model, for parameters defined via 
			 both baseline hazard function and regression parameters.
The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) <doi:10.1016/j.fss.2011.05.022>.
We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.
Do data management functions common in real-time monitoring (also called: ecological momentary assessment, experience sampling, micro-longitudinal) data, including creating power curves for multilevel data, centering on participant means and merging event-level data into momentary data sets where you need the events to correspond to the nearest data point in the momentary data. This is VERY early release software, and more features will be added over time.
Implementation of Expectation Maximization (EM) regression of general linear models.
    The package currently supports Poisson and Logistic regression with variable weights, with underlying theory included in the vignettes. 
    New users are recommended to look at the em.glm() and small.em() functions - the outputs of which are supported by AIC(), BIC(),  and logLik() calls.
    Several plot functions have been included for useful diagnostics and model exploration.  
    Methods are based on the theory of Dempster et al (1977, ISBN:00359246), and follow the methods of Hastie et al. (2009) <doi:10.1007/978-0-387-21606-5_7>  and A. 
    Zeileis et al (2017) <doi:10.18637/jss.v027.i08>.
Analysis and visualization of an ensemble of boolean models for 
  biomarker discovery in cancer cell networks. The package allows to easily 
  import the data results of a software pipeline that predicts synergistic drug 
  combinations in cancer cell lines, developed by the DrugLogics research group 
  in NTNU. It has generic functions that can be used to split a boolean model 
  dataset to model groups with regards to the models predictive performance (number of true 
  positive predictions or Matthews correlation coefficient score) or synergy prediction based on a given set 
  of observed synergies and find the average activity difference per network 
  node between all group pairs. Thus, given user-specific thresholds,
  important nodes (biomarkers) can be accessed in the sense that they make the 
  models predict specific synergies (synergy biomarkers) or have better 
  performance in general (performance biomarkers). Lastly, if the 
  boolean models have a specific equation form and differ only in their link operator, 
  link operator biomarkers can also be assessed.
Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").
Predictors can be converted to one or more numeric representations using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737>. All encoding methods are supervised.
Provides a smooth mapping of multidimensional points into
    low-dimensional space defined by a self-organizing map. Designed to work
    with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019)
    <doi:10.1101/496869>.
Tools to analyze the embryo growth and the sexualisation thermal reaction norms.
random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement
Computes and visualizes empirical joint distribution of multivariate data with optimized algorithms and multi-thread computation. There is a faster algorithm using dynamic programming to compute the whole empirical joint distribution of a bivariate data. There are optimized algorithms for computing empirical joint CDF function values for other multivariate data. Visualization is focused on bivariate data. Levelplots and wireframes are included.
EM algorithms and several efficient
        initialization methods for model-based clustering of finite
        mixture Gaussian distribution with unstructured dispersion
        in both of unsupervised and semi-supervised learning.
For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral
        analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical 
        Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Functions that support estimating, assessing and mapping regional
    disaggregated indicators. So far, estimation methods comprise direct estimation
    and the model-based approach Empirical Best Prediction (see "Small area
    estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), 
    as well as their precision estimates. The assessment of the used model
    is supported by a summary and diagnostic plots. For a suitable presentation of
    estimates, map plots can be easily created. Furthermore, results can easily be
    exported to excel. For a detailed description of the package and the methods used
    see "The {R} Package {emdi} for Estimating and Mapping Regionally Disaggregated Indicators"
    by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>.
Package providing calculation of Earth Mover's Distance
        (EMD).
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
Simulate and fitting exponential multivariate Hawkes model. 
    This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.1093/biomet/58.1.83>, with an exponential kernel and fits the parameters from the data. 
    Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. 
    The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with 'maxLik' package.
Various Expectation-Maximization (EM) algorithms are implemented for item response theory
				(IRT) models. The current implementation includes IRT models for binary and ordinal
				responses, along with dynamic and hierarchical IRT models with binary responses. The
				latter two models are derived and implemented using variational EM.  Subsequent edits
				also include variational network and text scaling models.
Creates emissions for use in air quality models. Vehicular emissions 
  are estimated by a top-down approach, total emissions are calculated using the 
  statistical description of the fleet of vehicles, the emission is spatially 
  distributed according to satellite images or openstreetmap data 
  <https://www.openstreetmap.org> and then distributed temporarily 
  (Vara-Vela et al., 2016, <doi:10.5194/acp-16-777-2016>).
Provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The 'EmiStatR' package implements modular R methods. This enables 
    to add new functionalities through the R framework.
Work with Ecological Metadata Language ('EML') files. 
    'EML' is a widely used metadata standard in the ecological and
    environmental sciences, described in Jones et al. (2006),
    <doi:10.1146/annurev.ecolsys.37.091305.110031>.
This is a utility for transforming Ecological Metadata Language
        ('EML') files into 'JSON-LD' and back into 'EML.'  Doing so creates a
        list-based representation of 'EML' in R, so that 'EML' data can easily
        be manipulated using standard 'R' tools. This makes this package an
        effective backend for other 'R'-based tools  working with 'EML.' By
        abstracting away the complexity of 'XML' Schema, developers can
        build around native 'R' list objects and not have to worry about satisfying
        many of the additional constraints of set by the schema (such as element
        ordering, which is handled automatically). Additionally, the 'JSON-LD' 
        representation enables the use of developer-friendly 'JSON' parsing and
        serialization that may facilitate the use of 'EML' in contexts outside of 'R,'
        as well as the informatics-friendly serializations such as 'RDF' and
        'SPARQL' queries.
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.
The evolutionary model-based multiresponse approach (EMMA)
        is a novel methodology to process optimisation and product
        improvement. The approach is suitable to contexts in which the
        experimental cost and/or time limit the number of implementable
        trials.
End-member modelling analysis of grain-size data is an approach 
    to unmix a data set's underlying distributions and their contribution to 
    the data set. EMMAgeo provides deterministic and robust protocols for 
    that purpose.
This package includes functions to read and write to an
        EMME/2 databank
Obtain estimated marginal means (EMMs) for many linear, generalized 
  linear, and mixed models. Compute contrasts or linear functions of EMMs,
  trends, and comparisons of slopes. Plots and other displays.
  Least-squares means are discussed, and the term "estimated marginal means"
  is suggested, in Searle, Speed, and Milliken (1980) Population marginal means 
  in the linear model: An alternative to least squares means, The American 
  Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Functions to fit finite mixture of multivariate canonical fundamental skew t (FM-CFUST) distributions, random sample generation, 2D and 3D contour plots. See Lee and McLachlan (2017) <doi:10.18637/jss.v083.i03> for more information and examples.
Provides unsupervised selection and clustering of microarray data
    using mixture models. Following the methods described in McLachlan, Bean and
    Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected
    based one the likelihood ratio statistic for the test of one versus two
    components when fitting mixtures of t-distributions to the expression data
    for each gene. The dimensionality of this gene subset is further reduced through
    the use of mixtures of factor analyzers, allowing the tissue samples to be
    clustered by fitting mixtures of normal distributions.
We provide functions to fit finite mixtures of multivariate normal or t-distributions to 
    data with various factor analytic structures adopted for the covariance/scale matrices. The 
    factor analytic structures available include mixtures of factor analyzers and mixtures of common 
    factor analyzers. The latter approach is so termed because the matrix of factor loadings is
    common to components before the component-specific rotation of the component factors to 
    make them white noise. Note that the component-factor loadings are not common after 
    this rotation. Maximum likelihood estimators of model parameters are obtained via the 
    Expectation-Maximization algorithm. See descriptions of the algorithms used in
    McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8>
    McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> 
    McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> 
    McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> 
    Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> 
    Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> 
    McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.
EM algorithm for Fitting Mixture of Multivariate Skew Normal and Skew t Distributions. An implementation of the algorithm described in Wang, Ng, and McLachlan (2009) <doi:10.1109/DICTA.2009.88>.
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
        of evolutionary multiobjective optimization algorithms.
An implementation of using emoji and fontawesome for using in both
    base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.
Evaluates the empirical characteristic function of univariate and multivariate samples.
    This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level.
Routines for performing empirical calibration of observational
  study estimates. By using a set of negative control hypotheses we can
  estimate the empirical null distribution of a particular observational
  study setup. This empirical null distribution can be used to compute a
  calibrated p-value, which reflects the probability of observing an
  estimated effect size when the null hypothesis is true taking both random
  and systematic error into account. A similar approach can be used to
  calibrate confidence intervals, using both negative and positive controls.
Auxiliary functions for the DESeq2 package to simulate read counts according to the null hypothesis (i.e., with empirical sample size factors, per-gene total counts and dispersions, but without effects of predictor variables) and to compute the empirical false discovery rate.
Empirical likelihood ratio tests for means/quantiles/hazards
 	from possibly censored and/or truncated data. Now does regression too.
	This version contains some C code.
Calculates the p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with censored data.
Collection of functions for data analysis and
    editing of clinical and epidemiological data.
    Most of them are related to benchmark with prediction models.
Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
Fits a Bayesian shrinkage regression model that can incorporate structural information. 
             Changgee Chang, Suprateek Kundu, Qi Long (2018) <doi:10.1111/biom.12882>.
It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.
Computes Euclidean Minimum Spanning Trees (EMST) using the fast 
    Dual-Tree Boruvka algorithm (March, Ram, Gray, 2010, 
    <doi:10.1145/1835804.1835882>) implemented in 'mlpack' - the C++ Machine 
    Learning library (Curtin et al., 2013). 'emstreeR' heavily relies on 
    'RcppMLPACK' and 'Rcpp', working as a wrapper to the C++ fast EMST 
    algorithm. Thus, R users do not have to deal with the R-'Rcpp'-C++ 
    integration. The package also provides functions and an S3 method for 
    readily plotting Minimum Spanning Trees (MST) using either 'base' R, 
    'scatterplot3d' or 'ggplot2' style.
The package provides functions to carry out a
        Goodness-of-fit test for discrete multivariate data. It is
        tested if a given observation is likely to have occurred under
        the assumption of an ab-initio model. A p-value can be
        calculated using different distance measures between observed
        and expected frequencies. A Monte Carlo method is provided to
        make the package capable of solving high-dimensional problems.
Allows one to estimate the output of a computer program,
 as a function of the input parameters, without actually running it.
 The computer program is assumed to be a Gaussian process, whose
 parameters are estimated using Bayesian techniques that give a PDF of
 expected program output.  This PDF is conditional on a training set
 of runs, each consisting of a point in parameter space and the model
 output at that point.  The emphasis is on complex codes that take
 weeks or months to run, and that have a large number of undetermined
 input parameters; many climate prediction models fall into this
 class.  The emulator essentially determines Bayesian posterior
 estimates of the PDF of the output of a model, conditioned on results
 from previous runs and a user-specified prior linear model.  A vignette is
 provided and the help pages include examples.
Provides the next iteration of the EMU Speech 
    Database Management System (EMU-SDMS) with database management, data 
    extraction, data preparation and data visualization facilities.
An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization paths for linear regression. Rockova and George (2014) <doi:10.1080/01621459.2013.869223>.
Provides algorithms for the analysis of ecological networks.
Implements an S3 class for storing 'UTF-8' strings, based on regular character vectors.
    Also contains routines to portably read and write 'UTF-8' encoded text files,
    to convert all strings in an object to 'UTF-8',
    and to create character vectors with various encodings.
We describe fifteen different splice site sequence encoding schemes that have been used in earlier studies for mapping of splice site sequences into numeric feature vectors. These encoding schemes will also be helpful for transforming other nucleotide sequences into numeric forms, provided they are of equal length. These encoding schemes will help the computational biologist working in the field of classification (binary or multiclass) or prediction involving nucleic acid sequences of equal length.
Interconverts between ordered lists and compact string notation.  
 Useful for capturing code lists, and pair-wise codes and decodes, for text storage.
 Analogous to factor levels and labels. Generics encode() and decode()
 perform interconversion, while codes() and decodes() extract components of an encoding.
 The function encoded() checks whether something is interpretable as an encoding.
 If a vector has an encoded 'guide' attribute, as_factor() uses it to coerce to factor.
It is important to ensure that sensitive data is protected. 
    This straightforward package is aimed at the end-user. 
    Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns.
    A public key can be shared to allow others to encrypt data to be sent to you. 
    This is particularly aimed a healthcare settings so patient data can be pseudonymised.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
endogMNP is an R package that fits a Bayesian multinomial
        probit model with endogenous selection, which is sometimes
        called an endogenous switching model.  This can be used to
        model discrete choice data when respondents select themselves
        into one of several groups.  This package is based on the MNP
        package by Kosuke Imai and David A. van Dyk.  This package
        modifies their code.
Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) <DOI:10.1093/pan/mpr031> to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy 
             distance, testing for multivariate normality, distance components (disco) for non-parametric 
             analysis of structured data, and other energy statistics/methods are implemented.
Provides a function for distribution free control chart based on the change point model, for multivariate statistical process control. The main constituent of the chart is the energy test that focuses on the discrepancy between empirical characteristic functions of two random vectors. This new control chart highlights in three aspects. Firstly, it is distribution free, requiring no knowledge of the random processes. Secondly, this control chart can monitor mean and variance simultaneously. Thirdly it is devised for multivariate time series which is more practical in real data application. Fourthly, it is designed for online detection (Phase II), which is central for real time surveillance of stream data. For more information please refer to O. Okhrin and Y.F. Xu (2017) <https://github.com/YafeiXu/working_paper/raw/master/CPM102.pdf>.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <DOI:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.
Provides a quantitative toolkit to explore elastic net families and to uncover correlates contributing to prediction under a cross-validation framework. Fits linear, binomial (logistic), multinomial and Cox regression models. Candia J and Tsang JS, BMC Bioinformatics (2019) 20:189 <doi:10.1186/s12859-019-2778-5>.
Allow numbers to be presented in an English language
        version, one, two, three, ...  Ordinals are also available,
        first, second, third, ...
Datasets from Nelson, Coffin and Copeland "Introductory
        Statistics for Engineering Experimentation" (Elsevier, 2003)
        with sample code.
Analyzing English & European soccer results data from 1871-2016.
A set of tools for the analysis of ecological niche of species and calculation of habitat suitability maps.
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006) or maxnet (Phillips et al. 2017).
The ENmisc package contains utility function for different
        purposes: mtapply and mlapply (multivariate version of tapply
        and lapply), wtd.boxplot (a boxplot with weights), and a visual
        interface to restructuring mosaic plots.
An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.
Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See (<http://amp.pharm.mssm.edu/Enrichr/>) for further details.
These programs are used for calculating enrichment
        factors, drawing enrichment curves to evaluate virtual
        screening approaches.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm', 'lm', 'glm' and 'betareg'. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the 'enriched_glm' function that has the same interface as 'glm' but results in objects of class 'enriched_glm'. In addition to the usual components in a `glm` object, 'enriched_glm' objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.
An implementation of the clustering methods of categorical data
    discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical 
    data via ensembling dissimilarity matrices.  Preprint <arXiv:1506.07930>.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
        from ensemble forecasts and weather observations.
Extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Ensemble Model Output Statistics to create probabilistic
        forecasts from ensemble forecasts and weather observations.
Extends the base classes and methods of 'EnsembleBase' package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in 'EnsembleBase' package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in 'EnsembleBase' package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.
Functions to use ensembles of several machine learning models
    specified in caret package.
Elastic net regression models are controlled by two parameters,
  lambda, a measure of shrinkage, and alpha, a metric defining the model's
  location on the spectrum between ridge and lasso regression.  
  glmnet provides tools for selecting lambda via cross
  validation but no automated methods for selection of alpha.  Elastic Net
  SearcheR automates the simultaneous selection of both lambda and alpha.
  Developed, in part, with support by NICHD R03 HD094912.
Add simple runtime contracts to R values. These ensure that values
    fulfil certain conditions and will raise appropriate errors if they do not.
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>.
             'entropart' provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
             Estimation-bias corrections are available.
This package implements various estimators of entropy, such
  as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood 
  and the Millow-Madow estimator, various Bayesian estimators, and the 
  Chao-Shen estimator.  It also offers an R interface to the NSB estimator.
  Furthermore, it provides functions for estimating Kullback-Leibler divergence,
  chi-squared, mutual information, and chi-squared statistic of independence.
  In addition there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Rows of two matrices are compared for Shannon entropy,
     coefficient of variation, and expression. P-values can be requested for all metrics.
Tools for Markov Chain Monte Carlo (MCMC) simulation and performance analysis. Simulate MCMC algorithms including adaptive MCMC, evaluate their convergence rate, and compare candidate MCMC algorithms for a same target density, based on entropy and Kullback-Leibler divergence criteria. MCMC algorithms can be simulated using provided functions, or imported from external codes. This package is based upon work starting with Chauveau, D. and Vandekerkhove, P. (2013) <doi:10.1051/ps/2012004> and next articles.
Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991) <https://www.jstor.org/stable/2937655>.
Small toolbox for data analyses in environmental chemistry and
    ecotoxicology. Provides, e.g., calibration() to calculate calibration curves
    and corresponding limits of detection (LODs) and quantification (LOQs)
    according to German DIN 32645:2008-11. texture() makes it easy to estimate
    soil particle size distributions from hydrometer measurements (ASTM
    D422-63(2007)e2).
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
Prints out information about the R working environment
    (system, R version,loaded and attached packages and versions) from a single
    function "env_doc()".  Optionally adds information on git repository,
    tags, commits and remotes (if available).
A collection of functions for microbial ecology and other
   applications of genomics and metagenomics. Companion package for the
   Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016
   <DOI:10.7287/peerj.preprints.1900v1>).
Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.
Fast and very memory-efficient calculation of isotope patterns,
    subsequent convolution to theoretical envelopes (profiles) plus valley
    detection and centroidization or intensoid calculation. Batch processing,
    resolution interpolation, wrapper, adduct calculations and molecular
    formula parsing. 
	Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) 
	<doi:10.1021/acs.analchem.5b00941>.
Sequential partitioning, clustering and peak detection of
    centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
    data plot.
Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.
Methods to perform a Probabilistic Environmental Risk assessment from exposure to toxic substances - i.e. USEPA (1997) <https://www.epa.gov/risk/guiding-principles-monte-carlo-analysis> -.
Functions and datasets to support the book by Nhu D Le and James V Zidek, Springer (2006).
Envelope methodology and aster modeling are combined to provide users with precise estimation of expected Darwinian fitness.
Set of functions to keep track of user-defined environment names
    (which cannot be retrieved with the built-in function environmentName()).
    The package also provides functionality to search for objects in environments,
    deal with function calling chains, and retrieve an object's
    memory address.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).
Graphical and statistical analyses of environmental data, with 
  focus on analyzing chemical concentrations and physical parameters, usually in 
  the context of mandated environmental monitoring.  Major environmental 
  statistical methods found in the literature and regulatory guidance documents, 
  with extensive help that explains what these methods do, how to use them, 
  and where to find them in the literature.  Numerous built-in data sets from 
  regulatory guidance documents and environmental statistics literature.  Includes 
  scripts reproducing analyses presented in the book "EnvStats:  An R Package for 
  Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, 
  <http://www.springer.com/book/9781461484554>).
Provides wrap functions to export and import graphics and
             data frames in R to 'microsoft' office. And This package also 
             provide write out figures with lots of different formats.
             Since people may work on the platform without GUI support, the package 
             also provide function to easily write out figures
             to lots of different type of formats. Now this package provide function 
             to extract colors from all types of figures and pdf files.
This data management package provides some helper
    classes for publicly available data sources (HMD, DESTATIS) in
    Demography. Similar to ideas developed in the Bioconductor
    project <https://bioconductor.org> we strive to encapsulate data in easy
    to use S4 objects.
    If original data is provided in a text file, the resulting S4
    object contains all information from that text file. But the
    information is somehow structured (header, footer, etc).
    Further the classes provide methods to make a subset for selected
    calendar years or selected regions. The resulting subset
    objects still contain the original header and footer information.
A collection of nice plotting functions directly from a
        data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Enables simulation of water piping networks using 'EPANET'.
    The package provides functions from the 'EPANET' programmer's toolkit as R
    functions so that basic or customized simulations can be carried out from R.
    The package uses 'EPANET' version 2.1 from Open Water Analytics
    <https://github.com/OpenWaterAnalytics/EPANET/releases/tag/v2.1>.
Reads water network simulation data in 'Epanet' text-based
    '.inp' and '.rpt' formats into R. Also reads results from 'Epanet-msx'.
    Provides basic summary information and plots.  The README file has a 
    quick introduction. See <http://www2.epa.gov/water-research/epanet> 
    for more information on the Epanet software for modeling
    hydraulic and water quality behavior of water piping systems.
The top-performing ensemble-based Penalized Cox Regression (ePCR) framework developed during the DREAM 9.5 mCRPC Prostate Cancer Challenge <https://www.synapse.org/ProstateCancerChallenge> presented in Guinney J, Wang T, Laajala TD, et al. (2017) <doi:10.1016/S1470-2045(16)30560-5> is provided here-in, together with the corresponding follow-up work. While initially aimed at modeling the most advanced stage of prostate cancer, metastatic Castration-Resistant Prostate Cancer (mCRPC), the modeling framework has subsequently been extended to cover also the non-metastatic form of advanced prostate cancer (CRPC). Readily fitted ensemble-based model S4-objects are provided, and a simulated example dataset based on a real-life cohort is provided from the Turku University Hospital, to illustrate the use of the package. Functionality of the ePCR methodology relies on constructing ensembles of strata in patient cohorts and averaging over them, with each ensemble member consisting of a highly optimized penalized/regularized Cox regression model. Various cross-validation and other modeling schema are provided for constructing novel model objects.
Tools to download and manipulate the Permanent Household Survey from Argentina
    (EPH is the Spanish acronym for Permanent Household Survey).
    e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets,
    calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology.
    organize_panels() is used to concatenate observations from different periods, and organize_labels()
    adds the official labels to the data. The implemented methods are based on INDEC (2016) <http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf>.
    As this package works with the argentinian Permanent Household Survey and its main audience is from this country,
    the documentation was written in Spanish.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data, in
  particular representation, manipulation and simulation of multistate 
  data - the Lexis suite of functions, which includes interfaces to
  'mstate', 'etm' and 'cmprsk' packages. 
  Also contains functions for Age-Period-Cohort and Lee-Carter
  modeling and a function for interval censored data and some useful
  functions for tabulation and plotting, as well as a number of
  epidemiological data sets.
Contains elementary tools for analysis of
        common epidemiological problems, ranging from sample size
        estimation, through 2x2 contingency table analysis and basic
        measures of agreement (kappa, sensitivity/specificity).
        Appropriate print and summary statements are also written to
        facilitate interpretation wherever possible.  Source code is 	commented throughout to facilitate modification.  The target audience includes advanced undergraduate and graduate students
        in epidemiology or biostatistics courses, and clinical researchers.
A collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.
Routines for epidemiological contact tracing
    and visualisation of network of contacts.
Creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.
The Economic Policy Institute (<http://www.epi.org/>) provides
    researchers, media, and the public with easily accessible, up-to-date, and
    comprehensive historical data on the American labor force. It is compiled
    from Economic Policy Institute analysis of government data sources. Use
    it to research wages, inequality, and other economic indicators over time
    and among demographic groups. Data is usually updated monthly.
Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<http://medipe.psu.ac.th/epicalc>'.
Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.
Tools to quantify transmissibility throughout
    an epidemic from the analysis of time series of incidence as described in
    Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) 
    <doi:10.1093/aje/kwh255>.
Provides functions and classes designed to handle and visualise
    epidemiological flows between locations. Also contains a statistical method
    for predicting disease spread from flow data initially described in
    Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>.
    This package is part of the RECON (<http://www.repidemicsconsortium.org/>)
    toolkit for outbreak analysis.
Functions to perform robust epistasis detection in genome-wide
  association studies, as described in Slim et al. (2018) <doi:10.1101/442749>.
  The implemented methods identify pairwise interactions between a particular
  target variant and the rest of the genotype, using a propensity score
  approach. The propensity score models the linkage disequilibrium between the
  target and the rest of the genotype. All methods are penalized regression
  approaches, which differently incorporate the propensity score to only
  recover the synergistic effects between the target and the genotype.
Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).
Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks.
Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.
Tools for simulating mathematical models of infectious disease dynamics. 
    Epidemic model classes include deterministic compartmental models, stochastic 
    individual-contact models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs) 
    from the Statnet suite of software packages in R. Standard templates for epidemic 
    modeling include SI, SIR, and SIS disease types. EpiModel features 
    an API for extending these templates to address novel scientific research aims.
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
A toolbox to make it easy to analyze plant disease epidemics. It
    provides a common framework for plant disease intensity data recorded over
    time and/or space. Implemented statistical methods are currently mainly
    focused on spatial pattern analysis (e.g., aggregation indices, Taylor and
    binary power laws, distribution fitting, SADIE and mapcomp methods). See
    Madden LV, Hughes G, van den Bosch F (2007, ISBN: 978-089054-354-2) for
    further information on these methods. Several data sets that were mainly
    published in plant disease epidemiology literature are also included in this
    package.
Tools for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
Drafting an epidemiological report in 'Microsoft Word' format for a given disease,
  similar to the Annual Epidemiological Reports published by the European Centre 
  for Disease Prevention and Control. Through standalone functions, it is specifically 
  designed to generate each disease specific output presented in these reports and includes:
  - Table with the distribution of cases by Member State over the last five years;
  - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Trend plot with the trend and number of cases at the European Union / European Economic Area level, 
  by month, over the past five years;
  - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level.
  Two types of datasets can be used:
  - The default dataset of salmonella 2012-2016 data;
  - Any dataset specified as described in the vignette.
Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). <doi:10.1038/ejhg.2010.196>. 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011).  <doi:10.1093/bioinformatics/btr218>.)
Basic sensitivity analysis of the observed relative risks adjusting
    for unmeasured confounding and misclassification of the
    exposure/outcome, or both. It follows the bias analysis methods and
    examples from the book by Lash T.L, Fox M.P, and Fink A.K.
    "Applying Quantitative Bias Analysis to Epidemiologic Data",
    ('Springer', 2009).
A collection of R functions supporting the text book
  Modern Epidemiology, Second Edition, by Kenneth J.Rothman and Sander Greenland.
  ISBN 13: 978-0781755641  See <http://www.krothman.org/> for more information.
Exploring time series for signal detection. It is specifically designed
    to detect possible outbreaks using infectious disease surveillance data
    at the European Union / European Economic Area or country level.
    Automatic detection tools used are presented in the paper
    "Monitoring count time series in R: aberration detection in public health surveillance",
    by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>.
    The package includes:
    - Signal Detection tool, an interactive 'shiny' application
      in which the user can import external data and perform basic signal detection analyses;
    - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs.
      This report can also be stratified by population characteristics (see 'Population' variable).
    This project was funded by the European Centre for Disease Prevention and Control.
Produce one-dimensional density estimates using 
 exponential epi-splines. The user may incorporate soft information, by 
 imposing constraints that (i) require unimodality; (ii) require that the 
 density be monotone non-increase or non-decreasing; (iii) put upper bounds
 on first or second moments; (iv) bound the density's values at mesh points;
 (v) require that the estimate be continuous or continuously differentiable;
 and more.
An efficient multi-core package to reconstruct an underlying network of
             genomic signatures of high-dimensional epistatic selection from 
             partially observed genotype data. The phenotype that we consider is viability. 
			 The network captures the conditional dependent short- and long-range linkage 
			 disequilibrium structure of genomes and thus reveals aberrant marker-marker 
			 associations that are due to epistatic selection. We target on high-dimensional
			 genotype data where number of variables (markers) is larger than number of 
			 sample sizes (p >> n). The computations is memory-optimized using the sparse 
			 matrix output.
Provides set of functions aimed at epidemiologists. 
 They include commands for measures of association and impact for case control studies and cohort studies. 
 They may be particularly useful for outbreak investigations and include univariate and stratified analyses.
 The functions for cohort studies include the CS(), CSTable() and CSInter() commands. 
 The functions for case control studies include the CC(), CCTable() and CCInter() commands.
Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.
Builds contingency tables that cross-tabulate multiple 
    categorical variables and also calculates various summary measures.
    Export to a variety of formats is supported, including: 'HTML', 
    'LaTeX', and 'Excel'.
Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
A collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymise data, and fitting discretised Gamma distributions.
Aim: Adjust the graphical parameters to create nicer
    longitudinal series plots. The default set of graphical parameters is very
    general, and can be improved upon when we are interested in plotting data
    points observed over time. Functions facilitate plotting those kind of
    series, univariate plots, bivariate plots (with vertical axis on both left
    and right hand sides), multivariate plots and plots which allow to examine
    whether a new observation is 'unusual' via construction and visualization
    of prediction intervals around it.
A rich toolkit of using the whole building simulation program
    'EnergyPlus'(<https://energyplus.net>), which enables programmatic
    navigation, modification of 'EnergyPlus' models and makes it less painful to
    do parametric simulations and analysis.
Evaluate a function over a data frame of expressions.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
      AY + U + R = 0
  and EPoC G where
      Y = GU + E,
  the matrices R and E are so far treated as noise. For
  details see the manual page of 'lassoshooting' and the
  article Rebecka Jörnsten, Tobias Abenius, Teresia Kling, 
  Linnéa Schmidt, Erik Johansson, Torbjörn E M Nordling, 
  Bodil Nordlander, Chris Sander, Peter Gennemark, 
  Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander
  (2011) <doi:10.1038/msb.2011.17>.
Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the corpus of LIVIVO. The ranked result lists of neurological drug terms co-occurring with named entities from the epilepsy ontologies EpSO, ESSO, and EPILONT are aggregated in order to generate two different results: an overview table of drugs that are relevant to epilepsy created with the method createNeuroTable, and a plot of tanimoto similarity coefficients between the aggregated list of drug terms against the list of drug terms from each of the ontologies created with the method createTanimotoBaseline(). The alignment of the Top-K Ranked Lists is conducted using the R-package TopKLists <https://cran.r-project.org/package=TopKLists>. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558> and Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>.
Performs analysis of polynomial regression in simple designs with quantitative treatments.
Implements the conditional estimation procedure of
  Lee, Sun, Sun and Taylor (2016) <doi:10.1214/15-AOS1371>.
  This procedure allows hypothesis testing on the mean of
  a normal random vector subject to linear constraints.
For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Donghoh Kim, Guebin Choi and Hee-Seok Oh (2019) <arXiv:1904.03643>.
Provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    The 'epubr' package provides functions supporting the reading and parsing of internal e-book content from EPUB files. 
    E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. 
    E-book formatting is not completely standardized across all literature. 
    It can be challenging to curate parsed e-book content across an arbitrary collection of e-books 
    perfectly and in completely general form, to yield a singular, consistently formatted output. 
    Many EPUB files do not even contain all the same pieces of information in their respective metadata. 
    EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. 
    However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. 
    There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with 'epubr'. 
    Text is read 'as is' for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. 
    Substantive changes are expected to be performed subsequently by the user as part of their text analysis. 
    Additional text cleaning can be performed at the user's discretion, such as with functions from packages like 'tm' or 'qdap'.
Import data from 'Epidata' XML files '.epx' and convert it to R data structures.
EQ-5D is a popular health related quality of life instrument used 
    in the clinical and economic evaluation of health care. Developed by the 
    EuroQol group <https://www.euroqol.org>, the instrument consists of two 
    components: health state description and evaluation. For the description 
    component a subject self-rates their health in terms of five dimensions; 
    mobility, self-care, usual activities, pain/discomfort, and 
    anxiety/depression using either a three-level (EQ-5D-3L,
    <https://www.euroqol.org/eq-5d-instruments/eq-5d-3l-about>) or a five-level
    (EQ-5D-5L, <https://www.euroqol.org/eq-5d-instruments/eq-5d-5l-about>) 
    scale. Frequently the scores on these five dimensions are converted to a 
    single utility index using country specific value sets, which can be used
    in the clinical and economic evaluation of health care as well as in 
    population health surveys. The eq5d package provides methods to calculate 
    index scores from a subject's dimension scores. 25 TTO and 11 VAS EQ-5D-3L value 
    sets including those for countries in Szende et al (2007) 
    <doi:10.1007/1-4020-5511-0> and Szende et al (2014) 
    <doi:10.1007/978-94-007-7596-1>, 18 EQ-5D-5L EQ-VT value sets from the EuroQol
    website, and the EQ-5D-5L crosswalk value sets developed by van Hout et al. (2012) 
    <doi:10.1016/j.jval.2012.02.008> are included. Additionally, a shiny web tool is 
    included to enable the calculation, visualisation and automated statistical 
    analysis of EQ-5D index values via a web browser using EQ-5D dimension scores 
    stored in CSV or Excel files.
Computation of the EQL for a given family of variance
        functions, Saddlepoint-approximations and related auxiliary
        functions (e.g. Hermite polynomials).
If you have a mathematical formula and the need to have that
    formula in the form of scalable vector graphics (SVG), you'll be
    delighted by what 'eqn2svg' will let you do. The incoming LaTeX math formula
    will be nicely converted to SVG tags. And you can use that code wherever
    SVGs are accepted.
Transitioning from EQS to R for structural equation modeling (SEM)
    is made easier with a set of functions to convert .out files into R code.
    The EQS output can be converted into lavaan syntax and run in the R
    environment.  Other functions parse descriptive statistics and the covariance matrix
    from an EQS .out file.  A heat map plot of a covariance matrix is also
    included.
Analysis of experimental crosses to identify genes (called
        quantitative trait loci, QTLs) contributing to variation in
        quantitative traits.
Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.
The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.
Functions for examining measurement invariance via equivalence testing along with adjusted RMSEA(root mean square error of approximation; Steiger & Lind, 1980) cutoff values. In particular, a projection-based method is implemented to test the equality of latent factor means across groups without assuming the equality of intercepts.
Contains methods for observed-score linking
  and equating under the single-group, equivalent-groups,
  and nonequivalent-groups with anchor test(s) designs.
  Equating types include identity, mean, linear, general
  linear, equipercentile, circle-arc, and composites of
  these. Equating methods include synthetic, nominal
  weights, Tucker, Levine observed score, Levine true
  score, Braun/Holland, frequency estimation, and chained
  equating. Plotting and summary methods, and methods for
  multivariate presmoothing and bootstrap error estimation
  are also provided.
Computation of direct, chain and average (bisector) equating coefficients with 
  standard errors using Item Response Theory (IRT) methods for dichotomous items 
  (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, 
  Battauz (2015) <doi:10.18637/jss.v068.i07>). 
  Test scoring can be performed by true score equating and observed score equating methods. 
  DIF detection can be performed using a Wald-type test  
  (Battauz (2018) <doi:10.1007/s10260-018-00442-w>).
Equating of multiple forms using Item Response Theory (IRT) methods (Battauz M. (2017) <doi:10.1007/s11336-016-9517-x> and Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>).
Provides statistical tests and graphics for assessing tests
        of equivalence.  Such tests have similarity as the alternative
	hypothesis instead of the null.  Sample data sets are included.
Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results.
Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph
             ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition''
             (Chapman&Hall/CRC).
Implementation of uniformly most powerful invariant equivalence
    tests for one- and two-sample problems (paired and unpaired) as described 
    in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives 
    (non-inferiority and non-superiority tests) are supported. Basically a variant 
    of a t-test with (relaxed) null and alternative hypotheses exchanged.
Provides an equivalent measure of partial correlation coefficients for high-dimensional Gaussian Graphical Models to learn and visualize the underlying relationships between variables from single or multiple datasets. You can refer to Liang, F., Song, Q. and Qiu, P. (2015) <doi:10.1080/01621459.2015.1012391> for more detail. Based on this method, the package also provides the method for constructing networks for Next Generation Sequencing Data, jointly estimating multiple Gaussian Graphical Models, constructing single graphical model for heterogeneous dataset, inferring graphical models from high-dimensional missing data and estimating moral graph for Bayesian network.
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) <doi:10.1021/acs.analchem.6b02927>.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together
    with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks.
A template package to demonstrate the use of user-specified statistics for use in 'ergm' models as part of the Statnet suite of packages.
Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM).
    Includes functions to conduct mediation and moderation analyses and to diagnose
    multicollinearity.
    URL: <http://github.com/sduxbury/ergMargins>.
    BugReports: <http://github.com/sduxbury/ergMargins/issues>.
    Duxbury, Scott W (2019) <doi:10.31235/osf.io/9bs4u>.
    Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>.
    Mize, Trenton D. (2019) <doi:10.15195/v6.a4>.
    Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>.
    Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>.
Data for use with the Sage Introduction to Exponential
        Random Graph Modeling text by Jenine K. Harris.  Network data
        set consists of 1283 local health departments and the
        communication links among them along with several attributes.
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).
A set of user-friendly functions to aid in organizing, plotting
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn
    method to explore ERP data. Should be useful to those without a background
    in computer programming, and to those who are new to ERPs (or new to the
    more advanced ERP software available). Emphasis has been placed on highly
    automated processes using functions with as few arguments as possible.
    Expects processed (cleaned) data.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Messages should provide users with readable information 
    about R objects without flooding their console. 
    'cc()' concatenates vector and data frame values 
    into a grammatically correct string using commas, an ellipsis and conjunction. 
    'cn()' allows the user to define a string which varies based on a count.
    'co()' combines the two to produce a customizable object aware string.
    The package further facilitates this process by providing five 'sprintf'-like 
    types such as '%n' for the length of an object and '%o' for its name as
    well as wrappers for pasting objects and issuing errors, warnings and messages.
Builds and analyzes error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.
Provides a function to convert existing R functions into "errorized" versions 
    with added logging and handling functionality when encountering errors or warnings. 
    The errorize function accepts an existing R function as its first argument and 
    returns a R function with the exact same arguments and functionality. However, 
    if an error or warning occurs when running that "errorized" R function, it will save a 
    .Rds file to the current working directory with the relevant objects and information 
    required to immediately recreate the error.
Errors in data can be located and removed using validation rules from package 'validate'.
Support for measurement errors in R vectors, matrices and arrays:
    automatic uncertainty propagation and reporting.
    Documentation about 'errors' is provided in the paper by Ucar, Pebesma &
    Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a
    vignette; see 'citation("errors")' for details.
Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.
Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories.
    Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>.
    Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.
Implementation of the Edge Selection Algorithm
Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges' d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality).
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).
It allows to construct two types of indicators used in the study of
    Electoral Systems and Party Systems starting from electoral results data.
    The Effective Number of Parties (Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>)
    and Electoral Volatility in its three versions (Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>,
    Powell and Tucker (2014) <doi:10.1017/S0007123412000531> and Torcal and Lago (2015, ISBN:9788415260356)).
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>)
    in R. Based on the input, the effect size can be returned as standardized mean 
    difference, Cohen's f, Hedges' g, Pearson's r or Fisher's
    transformation z, odds ratio or log odds, or eta squared effect size.
Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.
A tool which allows users to create and evaluate ensembles 
    of species distribution model (SDM) predictions. 
    Functionality is offered through R functions or a GUI (R Shiny app). 
    This tool can assist users in identifying spatial uncertainties and 
    making informed conservation and management decisions. The package is 
    further described in Woodman et al (2019) <doi:10.1111/2041-210X.13283>.
The package can identify the dysregulated canonical pathways by investigating the changes of biological relationships of pathways in the context of gene expression data. (1) The ESEA package constructs a background set of edges by extracting pathway structure (e.g. interaction, regulation, modification, and binding etc.) from the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther) and the edge sets of pathways for each of the above databases. (2) The ESEA package can can quantify the change of correlation between genes for each edge based on gene expression data with cases and controls. (3) The ESEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an edge enrichment score (EES), which reflects the degree to which a given pathway is associated the specific phenotype. (4) The ESEA package can provide the visualization of the results.
Environmental seismology is a scientific field that studies the  
    seismic signals, emitted by Earth surface processes. This package 
    provides all relevant functions to read/write seismic data files, prepare, 
    analyse and visualise seismic data, and generate reports of the processing 
    history.
The package presents a "Scenarios" class containing
        general parameters, risk parameters and projection results.
        Risk parameters are gathered together into a ParamsScenarios
        sub-object. The general process for using this package is to
        set all needed parameters in a Scenarios object, use the
        customPathsGeneration method to proceed to the projection, then
        use xxx_PriceDistribution() methods to get asset prices.
Toolkit for Monte Carlo simulations of financial assets and
    interest rates models, involved in an Economic Scenario Generator (ESG).
    The underlying simulation loops have been implemented in C++.
Computes shrinkage estimators for regression problems. Selects
    penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution.
Functions for classification and group membership probability estimation are given. 
             The issue of non-informative features in the data is addressed by utilizing the ensemble method. 
             A few optimal models are selected in the ensemble from an initially large set of base k-nearest neighbours (KNN) models, generated on subset of features from the training data.
             A two stage assessment  is applied in selection of optimal models for the ensemble in the training function. 
             The prediction functions for classification and class membership probability estimation returns class outcomes and class membership probability estimates for the test data. 
             The package includes measure of classification error and brier score, for classification and probability estimation tasks respectively.
Misc functions programmed by Eduard Szöcs. 
    Provides read_regnie() to read gridded precipitation data from German Weather 
    Service  (DWD, see <http://www.dwd.de/> for more information).
Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.
A 'shiny' gadget to create 'ggplot2' charts interactively with drag-and-drop to map your variables.
    You can quickly visualize your data accordingly to their type, export to 'PNG' or 'PowerPoint',
    and retrieve the code to reproduce the chart.
Simultaneous modeling of the quantile and the expected shortfall of a response variable given 
    a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.
Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.
Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
Estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
An implementation of sequential testing that uses evidence ratios
    computed from the weights of a set of models. These weights correspond either
    to Akaike weights computed from the Akaike Information Criterion (AIC) or the
    Bayesian Information Criterion (BIC) and following Burnham & Anderson
    (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA
    weights computed from the WAIC or the LOO-IC of models fitted
    with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
Provides tools for determining estimability of linear functions 
  of regression coefficients, and 'epredict' methods that handle 
  non-estimable cases correctly. Estimability theory is discussed in
  many linear-models textbooks including Chapter 3 of Monahan, JF (2008), 
  "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4).
Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / eBIC) or crossvalidation.
A routine for parameter estimation for any probability density or
    mass function implemented in R via maximum likelihood (ML) given a data set. This
    routine is a wrapper function specifically developed for ML estimation. There are
    included optimization procedures such as 'nlminb' and 'optim' from base package,
    and 'DEoptim' Mullen (2011) <doi: 10.18637/jss.v040.i06>. Standard errors are
    estimated with 'numDeriv' Gilbert (2011) <http://CRAN.R-project.org/package=numDeriv>
    or the option 'Hessian = TRUE' of 'optim' function.
Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.
Implements the methods of McGrath et al. (2019) <arXiv:1903.10498> 
    for estimating the sample mean and standard deviation from commonly reported
    quantiles in meta-analysis. These methods can be applied to studies that 
    report the sample median, sample size, and one or both of (i) the sample 
    minimum and maximum values and (ii) the first and third quartiles.
Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, <doi:10.1214/17-AOAS1026> for more details.
This package is intended to speedup the process of
        creating model-comparing tables common in Macroeconomics. The
        function collection stores the estimates of several models and
        formats it to a table of the form estimate starred and std.err.
        below. The default output is LaTeX but output to CSV for later
        editing in a spreadsheet tool is possible as well. It works for
        linear models (lm) and panel models from the "plm"-package
        (plm). Two further implemented functions "descsto" and
        "desctab" enable you to export descriptive statistics of
        data-frames and single variables to LaTeX and CSV.
Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. 
    The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. 
    The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>.
    The second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>.
	And the third by Wooldridge (2009) <doi.org/10.1016/j.econlet.2009.04.026>.
This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
An implementation of a most commonly used event study methodology,
    including both parametric and nonparametric tests. It contains variety
    aspects of the rate of return estimation (the core calculation is done in
    C++), as well as three classical for event study market models: mean
    adjusted returns, market adjusted returns and single-index market models.
    There are 6 parametric and 6 nonparametric tests provided, which examine
    cross-sectional daily abnormal return (see the documentation of the
    functions for more information). Parametric tests include tests proposed by 
    Brown and Warner (1980) <DOI:10.1016/0304-405X(80)90002-1>, Brown and Warner
    (1985) <DOI:10.1016/0304-405X(85)90042-X>, Boehmer et al. (1991)
    <DOI:10.1016/0304-405X(91)90032-F>, Patell (1976) <DOI:10.2307/2490543>, and
    Lamb (1995) <DOI:10.2307/253695>. Nonparametric tests covered in estudy2 are
    tests described in Corrado and Zivney (1992) <DOI:10.2307/2331331>,
    McConnell and Muscarella (1985) <DOI:10.1016/0304-405X(85)90006-6>,
    Boehmer et al. (1991) <DOI:10.1016/0304-405X(91)90032-F>, Cowan (1992)
    <DOI:10.1007/BF00939016>, Corrado (1989) <DOI:10.1016/0304-405X(89)90064-0>,
    Campbell and Wasley (1993) <DOI:10.1016/0304-405X(93)90025-7>, Savickas (2003)
    <DOI:10.1111/1475-6803.00052>, Kolari and Pynnonen (2010)
    <DOI:10.1093/rfs/hhq072>. Furthermore, tests for the cumulative
    abnormal returns proposed by Brown and Warner (1985)
    <DOI:10.1016/0304-405X(85)90042-X> and Lamb (1995) <DOI:10.2307/253695>
    are included.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
Fits the space-time Epidemic Type Aftershock Sequence
    ('ETAS') model to earthquake catalogs using a stochastic 'declustering' 
    approach. The 'ETAS' model is a 'spatio-temporal' marked point process
    model and a special case of the 'Hawkes' process. The package is based 
    on a Fortran program by 'Jiancang Zhuang'
    (available at <http://bemlar.ism.ac.jp/zhuang/software.html>),
    which is modified and translated into C++ and C such that it 
    can be called from R. Parallel computing with 'OpenMP' is possible 
    on supported platforms.
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood. The two estimation steps are alternated until convergence is obtained. For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present, together with some diagnostic tools. Some descriptive functions for earthquakes catalogs are  present; also plot, print, summary, profile methods are defined for main output (objects of class 'etasclass').
The package allows selecting those treatments of a one-way layout
 being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
 (TOST) and related simultaneous confidence intervals are given for both
 differences or ratios of means of normally distributed data. For the case of
 equal variances and balanced sample sizes for the treatment groups, the
 single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
 non-normal data, the Wilcoxon test is applied.
Interacts with the open-source, public 'Ethereum' <https://www.ethereum.org/> blockchain.
    It provides a distributed computing platform via smart contracts. This package provides
    functions which interrogate blocks and transactions in the 'Ethereum' blockchain.
Calculate common quantitative ethnobotany indices to assess the cultural significance of plant species based on informant consensus. The package closely follows the paper by Tardio and Pardo-de-Santayana (2008). Tardio, J., and M. Pardo-de-Santayana, 2008. Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain) 1. Economic Botany, 62(1), 24-39. <doi:10.1007/s12231-007-9004-5>.
Reliable and rapid ethnicity annotation from whole exome sequencing data.
A predictable and pipeable framework for performing ETL 
    (extract-transform-load) operations on publicly-accessible medium-sized data 
    set. This package sets up the method structure and implements generic 
    functions. Packages that depend on this package download specific data sets 
    from the Internet, clean them up, and import them into a local or remote 
    relational database management system.
Provides functions to facilitate the use of the 'ff' package
    in interaction with big data in 'SQL' databases (e.g. in 'Oracle', 'MySQL',
    'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf' objects
    using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility functions to
    do fast left outer join merging based on 'match', factorisation of data and a
    basic function for re-coding vectors.
The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 <doi:10.18637/jss.v038.i04>). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 <doi:10.1007/s00180-010-0200-x>).
Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
The remit of the European Clinical Trials Data Base (EudraCT <https://eudract.ema.europa.eu/> ) is to provide open access to summaries of all registered clinical trial results; thus aiming to prevent non-reporting of negative results and provide open-access to results to inform future research. The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by EudraCT and formats them into the precise requirements to directly upload an XML file into the web portal, with no further data entry by hand.
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
Generate area-proportional Euler diagrams
    using numerical optimization. An Euler diagram is a generalization of a Venn
    diagram, relaxing the criterion that all interactions need to be
    represented. Diagrams may be fit with ellipses and circles via
    a wide range of inputs and can be visualized in numerous ways.
An R Client for the Europe PubMed Central RESTful Web Service
    (see <https://europepmc.org/RestfulWebService> for more information). It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
This dataset contains population estimates of all European cities 
    with at least 10,000 inhabitants during the period 1500-1800. These data are
    adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).
Bayesian sample size calculation software and examples for EuroSARC
    clinical trials which utilise Bayesian methodology. These trials rely on
    binomial based endpoints so the majority of programs found here relate to this
    sort of endpoint. Developed as part of the EuroSARC FP7 grant.
Tools to download data from the Eurostat database
    <http://ec.europa.eu/eurostat> together with search and
    manipulation utilities.
Implementation of trawl processes and an extension of such processes into a univariate latent model for extreme values.
    Inference, simulation and initialization tools are available. See Noven et al. (2018) <DOI:10.21314/JEM.2018.179> which can be found on arXiv (<arXiv:1511.08190>) .
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
Provides functions for evaluating (time series) model
	estimation methods. These facilitate Monte Carlo experiments of repeated
	simulations and estimations. Also provides methods for
	looking at the distribution of the results from these experiments,
	including model roots (which are an equivalence class invariant).
Parsing and evaluation tools that make it easy to recreate the
    command line behaviour of R.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
An open source risk analysis toolkit based on the OpenFAIR ontology 
  <https://www2.opengroup.org/ogsys/catalog/C13K> and risk assessment standard 
  <https://www2.opengroup.org/ogsys/catalog/C13G>. Empowers an organization to 
  perform a quantifiable, repeatable, and data-driven risk review.
Conducts sensitivity analyses for unmeasured confounding for either an observational study or a meta-analysis of observational studies. For a single observational study, the package reports E-values, defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. You can use one of the evalues.XX() functions to compute E-values for the relevant outcome types. Outcome types include risk ratios, odds ratio with common or rare outcomes, hazard ratios with common or rare outcomes, and standardized differences in outcomes. Optionally, you can use the bias_plot() function to plot the bias factor as a function of two sensitivity parameters. (See VanderWeele & Ding, 2017 [<http://annals.org/aim/article/2643434>] for details.) For a meta-analysis, use the function confounded_meta to compute point estimates and inference for: (1) the proportion of studies with true causal effect sizes more extreme than a specified threshold of scientific importance; and (2) the minimum bias factor and confounding strength required to reduce to less than a specified threshold the proportion of studies with true effect sizes of scientifically significant size. The functions sens_plot() and sens_table() create plots and tables for visualizing these meta-analysis metrics across a range of bias values. (See Mathur & VanderWeele, 2019 [<https://amstat.tandfonline.com/doi/full/10.1080/01621459.2018.1529598#.XKIJtOtKjdc>] for details.) Most of the analyses available in this package can also be conducted using web-based graphical interfaces (for a single observational study: [<https://evalue.hmdc.harvard.edu>]; for a meta-analysis: [<https://mmathur.shinyapps.io/meta_gui_2/>]).
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Different evidential distance-based classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: the
    evidential K-nearest neighbor rule and the evidential neural network.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the objects.
    The algorithms are: Evidential c-Means (ECM), Relational Evidential c-Means (RECM),
    Constrained Evidential c-Means (CECM), EVCLUS and EK-NNclus.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.
Provides functions for the bayesian analysis of extreme
        value models, using MCMC methods.
Functions for setting up and analyzing event history data.
Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages 'edeaR', 'processmapR', etc.
Detect / classify events in time-series data. Combines multiple well-known R packages like 'forecast' to deliver an easily configurable tool for event detection.
Functions for analysis of rate changes in sequential events.
Stores, manipulates, aggregates and otherwise messes with event
    data from KEDS/TABARI or any other extraction tool with similar output
Implements event extraction and early classification of events in data streams in R. 
    It has the functionality to generate 2-dimensional data streams with events belonging to 
    2 classes. These events can be extracted and features computed. The event features extracted
    from incomplete-events can be classified using a partial-observations-classifier 
    (Kandanaarachchi et al. 2018) <doi:10.13140/RG.2.2.10051.25129>.
Perform Event Studies from through our <http://EventStudyTools.com> Application Programming Interface, parse the results, visualize it, and / or use the results in further analysis.
Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package:
        Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) <doi:10.1038/ejhg.2010.47>.
        Strug, L. J., & Hodge, S. E. (2006) <doi:10.1159/000094709>.
        Royall, R. (1997) <ISBN:0-412-04411-0>.
Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach.  The functions in the package also make extensive use of graphical displays for data exploration and model comparison.
Integrated Sensitivity Analysis of Evidence Factors in Observational Studies.
Functions for extreme value theory, which may be 
  divided into the following groups; exploratory data analysis, 
  block maxima, peaks over thresholds (univariate and bivariate), 
  point processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
    model diagnostics for univariate stationary extreme value mixture models
    are provided. Kernel density estimation including various boundary
    corrected kernel density estimation methods and a wide choice of kernels,
    with cross-validation likelihood based bandwidth estimator.
    Reasonable consistency with the base functions in the 'evd' package is
    provided, so that users can safely interchange most code.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
Provides functions for covariance matrix comparisons, estimation
    of repeatabilities in measurements and matrices, and general evolutionary
    quantitative genetics tools.
A comprehensive set of tools to illustrate the
    core concepts of evolutionary game theory, such as
    evolutionary stability or various evolutionary dynamics, 
    for teaching and academic research.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible 
      package providing optimization driven parameter estimation methods using metaheuristics  and 
      evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization 
      for continuous domains, Tabu Search, Evolutionary Strategies, ...)  which could be more efficient and require, 
      in some cases, fewer model evaluations than alternatives relying on experimental design.  Currently there 
      are built in support for models developed with 'Repast Simphony'  Agent-Based framework (<https://repast.github.io/>)  
      and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP) 
	     and peaks over random threshold (PORT) Hill methodologies. 
             Besides, also computes moment, generalised Hill and mixed moment estimates for EVI. 
             Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.
Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.
Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.
An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds as well as performs simulations to obtain operating characteristics.
Implements escalation with overdose control (EWOC) trial designs using two drug combinations described by Tighiouart, Li and Rogatko (2017) <doi:10.1002/sim.6961>. It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics.
Performs unconditional exact tests and power calculations for 2x2 contingency tables.
Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or  exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case. Gives boundary-optimized rejection region test (Gabriel, et al, 2018, <DOI:10.1002/sim.7579>), an unconditional exact test for the situation where the controls are all expected to fail.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
        difference between two independent or dependent proportions.
Provides a replacement for the 'extract' function from the 'raster' package
    that is suitable for extracting raster values using 'sf' polygons.
Monte Carlo and MCMC goodness of fit tests for log-linear
        models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
Computes exact conditional p-values and quantiles using an
 implementation of the Shift-Algorithm by Streitberg & Roehmel.
Take the examples written in your documentation of
    functions and use them to create shells (skeletons which must be
    manually completed by the user) of test files to be tested with the
    'testthat' package. Sort of like python 'doctests' for R.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single-choice and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include standalone
	files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOLAT,
	ARSnova, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format
	(NOPS) is provided that can be printed, scanned, and automatically evaluated.
Tools for constructing confidence regions for exceedance regions
    and contour lines.
Computes confidence intervals for the exceedance probability of normally distributed estimators. Currently only supports general linear models. Please see Segal (2019) <arXiv:1803.03356> for more information.
Allows access to data in running instance of Microsoft Excel
    (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
    'xl[a1] = current.graphics()'. Additionally there are function for reading/writing 
    'Excel' files - 'xl.read.file'/'xl.save.file'. They are not fast but able to read/write 
    '*.xlsb'-files and password-protected files. There is an Excel workbook with 
    examples of calling R from Excel in the 'doc' folder. It tries to keep things as
    simple as possible - there are no needs in any additional
    installations besides R, only 'VBA' code in the Excel workbook.
    Microsoft Excel is required for this package.
An R interface to 'jExcel' library to create web-based interactive tables and spreadsheets compatible with 'Excel' or any other spreadsheet software.
This is an R interface to the
    python package 'excerpts' (<https://pypi.python.org/pypi/excerpts>).
Implementation of a function which calculates the empirical excess mass 
	for given \eqn{\lambda} and given maximal number of modes (excessm()). Offering 
	powerful plot features to visualize empirical excess mass (exmplot()). This 
	includes the possibility of drawing several plots (with different maximal 
	number of modes / cut off values) in a single graph.
Interactive tools to explore topographic-like data
    sets.  Such data sets take the form of a matrix in which the rows and
    columns provide location/frequency information, and the matrix elements
    contain altitude/response information.  Such data is found in cartography,
    2D spectroscopy and chemometrics.  The functions in this package create
    interactive web pages showing the contoured data, possibly with
    slices from the original matrix parallel to each dimension. The interactive
    behavior is created using the 'D3.js' 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.
Performs frequentist inference for the extremal index of a 
    stationary time series.  Two types of methodology are used.  One type is
    based on a model that relates the distribution of block maxima to the 
    marginal distribution of series and leads to the semiparametric maxima 
    estimators described in Northrop (2015) <doi:10.1007/s10687-015-0221-5> and 
    Berghaus and Bucher (2018) <doi:10.1214/17-AOS1621>.  Sliding block maxima
    are used to increase precision of estimation. The other type of methodology
    uses a model for the distribution of threshold inter-exceedance times
    (Ferro and Segers (2003) <doi:10.1111/1467-9868.00401>). Two 
    versions of this type of approach are provided, following Suveges (2007) 
    <doi:10.1007/s10687-007-0034-2> and Suveges and Davison (2010)  
    <doi:10.1214/09-AOAS292>.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Reads, writes, and edits EXIF and other file metadata
    using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>,
    returning read results as a data frame. ExifTool supports many
    different metadata formats including EXIF, GPS, IPTC, XMP, JFIF,
    GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, as
    well as the maker notes of many digital cameras by Canon, Casio,
    DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf,
    Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson,
    Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung,
    Sanyo, Sigma/Foveon and Sony.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.
Provides a shiny-based front end (the 'ExPanD' app) and
    a set of functions for exploratory panel data analysis. Run as a web-based app, 
    'ExPanD' enables users to assess the robustness of empirical evidence without 
    providing them access to the underlying data. You can also use the functions 
    of the package to support your exploratory data analysis workflow. Refer to 
    the vignettes of the package for more information on how to use 'ExPanD' 
    and/or the functions of this package.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
Expanding Ploidy and Allele Frequency on Nested Subpopulations (expands) characterizes coexisting subpopulations in a single tumor sample using copy number and allele frequencies derived from exome- or whole genome sequencing input data (<http://www.ncbi.nlm.nih.gov/pubmed/24177718>). The model detects coexisting genotypes by leveraging run-specific tradeoffs between depth of coverage and breadth of coverage. This package predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation, tumor purity and phylogeny. The main function runExPANdS() provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of subpopulation predictions increases with the number of mutations provided. It is recommended that at least 200 mutations are used as input to obtain stable results. Updates in version 2.1 include: (i) new parameter ploidy in runExPANdS.R allows specification of non-diploid background ploidies (e.g. for near-triploid cell lines); (ii) parallel computing option is available. Further documentation and FAQ available at <http://dna-discovery.stanford.edu/software/expands>.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD)(qualitative treatments); residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.
Pacote destinado a analise de delineamentos experimentais simples (DIC, DBC e DQL), experimentos em esquema de fatorial duplo (em DIC e DBC), experimentos em esquema de parcelas subdivididas no tempo (em DIC e DBC), experimentos em esquema de fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em esquema de fatorial triplo (em DIC e DBC) e experimentos em esquema de fatorial triplo com um tratamento adicional (em DIC e DBC); realizando a analise de variancia e comparacao de medias pelo ajuste de modelos de regressao ate o terceiro grau (tratamentos quantitativos) ou por testes de comparacao multipla: teste de Tukey, teste de Student-Newman-Keuls (SNK), teste de Scott-Knott, teste de Duncan, teste t (LSD), teste t de Bonferroni (LSD protegido) e teste Bootstrap (tratamentos qualitativos); analise de residuos (Ferreira, Cavalcanti e Nogueira (2014) <doi:10.4236/am.2014.519280>).
Provides various statistical methods for
  designing and analyzing randomized experiments. One functionality
  of the package is the implementation of randomized-block and
  matched-pair designs based on possibly multivariate pre-treatment
  covariates. The package also provides the tools to analyze various
  randomized experiments including cluster randomized experiments,
  two-stage randomized experiments, randomized experiments with 
  noncompliance, and randomized experiments with missing data.
Expert opinion (or judgment) is a body of techniques to
  estimate the distribution of a random variable when data is scarce
  or unavailable. Opinions on the quantiles of the distribution are
  sought from experts in the field and aggregated into a final
  estimate. The package supports aggregation by means of the Cooke,
  Mendel-Sheridan and predefined weights models.
The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.
Generates explanations for classification and regression models and visualizes them.
 Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
 are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
 in the model. The IME method is slower as it samples from all feature subsets.
 For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, 
 and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
 All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided 
 and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Interactive data exploration with one line of code or use an easy to remember set of tidy functions for exploratory data analysis. Introduces three main verbs. explore() to graphically explore a variable or table, describe() to describe a variable or table and report() to create an automated report.
Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.
Computation of the matrix exponential, logarithm, sqrt,
  and related quantities.
Easily export 'R' graphs and statistical output to 'Microsoft
    Office' / 'LibreOffice', 'Latex' and 'HTML' Documents, using sensible defaults
    that result in publication-quality output with simple, straightforward commands.
    Output to 'Microsoft Office' is in editable 'DrawingML' vector format for
    graphs, and can use corporate template documents for styling. This enables
    the production of standardized reports and also allows for manual tidy-up
    of the layout of 'R' graphs in 'Powerpoint' before final publication. Export
    of graphs is flexible, and functions enable the currently showing R graph
    or the currently showing 'R' stats object to be exported, but also allow the
    graphical or tabular output to be passed as objects. The package relies on package
    'officer' for export to 'Office' documents,and output files are also fully compatible
    with 'LibreOffice'. Base 'R', 'ggplot2' and 'lattice' plots are supported, as
    well as a wide variety of 'R' stats objects, via wrappers to xtable(), broom::tidy() 
    and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as
    well as matrices and data frames and many more...
Estimate individual and average joint effects of chemical mixtures, dose-response relationships, and potential interactions in environmental epidemiology.
    The visualization of interactions and the plotting of all the objects. For more information please, check next work: Youssef Oulhote, Marie-Abele Bind, Brent Coull, Chirag, Patel, Philippe Grandjean (2017) <https://www.biorxiv.org/content/early/2017/06/30/147413.article-info>.
A variety of descriptive multivariate analyses with the singular value decomposition,
    such as principal components analysis, correspondence analysis, and multidimensional scaling.
    See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Tools and data to accompany Schlicht, L., Valcu, M., & Kempenaers, B. (2015) <doi:10.1111/1365-2656.12293>. Spatial patterns of extra-pair paternity: beyond paternity gains and losses. Journal of Animal Ecology, 84(2), 518-531.
A set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. It has been written to accompany the forthcoming paper 'Computing expectations and marginal likelihoods for permutations'. Publication details will be updated as soon as they are finalized.
Allows to calculate the probabilities  of occurrences of an event in a 
 great number of repetitions of Bernoulli experiment, through the application of  
 the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. 
 Gives the possibility to show the results graphically and analytically, and to compare
 the results obtained by the application of the above  theorems with those calculated by
 the direct application of the Binomial formula. Is basically useful for educational
 purposes.
The aim of this package is to provide practitioners of statistics in
  geology, hydrology, etc. a tool to derive prior distributions for Bayesian 
  inference. Prior distributions summarize knowledge from studies at similar sites. 
  The main features of the package are to (i) generate prior distributions based 
  on external data only; (ii) to account for possible autocorrelation
  in the data, and (iii) to account for available soft data, say, in 
  the form of expert information on bounds and moments.
Given a continuous-time dynamic network, this package allows one to fit a stochastic blockmodel where nodes belonging to the same group create interactions and non-interactions of similar lengths. This package implements the methodology described by R. Rastelli and M. Fop (2019) <arXiv:1901.09828>.
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Package computes and displays tables with support for 'SPSS'-style 
        labels, multiple and nested banners, weights, multiple-response variables 
        and significance testing. There are facilities for nice output of tables 
        in 'knitr', 'Shiny', '*.xlsx' files, R and 'Jupyter' notebooks. Methods 
        for labelled variables add value labels support to base R functions and to 
        some functions from other packages. Additionally, the package brings 
        popular data transformation functions from 'SPSS' Statistics and 'Excel': 
        'RECODE', 'COUNT', 'COMPUTE', 'DO IF', 'COUNTIF', 'VLOOKUP' and etc. 
        These functions are very useful for data processing in marketing research 
        surveys. Package intended to help people to move data 
        processing from 'Excel' and 'SPSS' to R.
Creation of an exposure table with rows for policy-intervals from a table
    with a unique policy number key and beginning and ending dates for each policy. 
    Methods for assigning supplemental data containing dates and policy numbers
    to the corresponding interval from the created exposures table.
Tests for the composite hypothesis of exponentiality
Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
Estimation for high conditional quantiles based on quantile regression.
The package provides tools for botanists, plant taxonomists,
    curators of plant genebanks and perhaps other biological collections.
If 'dplyr' is a grammar for data manipulation, 'extdplyr' is like
    a short paragraph written in 'dplyr'. 'extdplyr' extends 'dplyr' and
    'tidyr' verbs to some common "routines" that manipulate data sets. It uses
    the same interface and preserves all the features from 'dplyr', has good 
    performance, and supports various data sources.
If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.
This is a package containing the function extfunnel()
        which produces a funnel plot including additional augmentations
        such as statistical significance contours and heterogeneity
        contours.
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.
For multiple full/partial ranking lists, R package 'ExtMallows' can (1) detect whether the input ranking lists are over-correlated, and (2) use the Mallows model or extended Mallows model to integrate the ranking lists, and (3) use hierarchical extended Mallows model for rank integration if there are groups of over-correlated ranking lists.
This package tests for differences in minor allele
        frequency between groups and is based on an extra-binomial
        variation model for pooled sequencing data.
By using a multispectral image and ESRI shapefile (Point/Polygon), a data table will be generated for classification or regression. The data table will be contained by band wise raster values and class ids.
Density, distribution function, quantile function
    and random generation for a number of univariate
    and multivariate distributions. This package implements the
    following distributions: Bernoulli, beta-binomial, beta-negative
    binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
    bivariate normal, bivariate Poisson, categorical, Dirichlet,
    Dirichlet-multinomial, discrete gamma, discrete Laplace,
    discrete normal, discrete uniform, discrete Weibull, Frechet,
    gamma-Poisson, generalized extreme value, Gompertz,
    generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t,
    Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy,
    Laplace, location-scale t, logarithmic, Lomax, multivariate
    hypergeometric, multinomial, negative hypergeometric, 
    non-standard beta, normal mixture, Poisson mixture, Pareto,
    power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam,
    slash, triangular, truncated binomial, truncated normal,
    truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
    zero-inflated negative binomial, zero-inflated Poisson.
Tools to using fonts other than the standard PostScript fonts.
    This package makes it easy to use system TrueType fonts and with PDF or
    PostScript output files, and with bitmap output files in Windows. extrafont
    can also be used with fonts packaged specifically to be used with, such as
    the fontcm package, which has Computer Modern PostScript fonts with math
    symbols. See https://github.com/wch/extrafont for instructions and
    examples.
Package for holding the database for the extrafont package
Speed up common tasks, particularly logical or
  relational comparisons and routine follow up tasks such as finding the
  indices and subsetting. Inspired by mathematics, where something like:
  3 < x < 6 is a standard, elegant and clear way to assert that
  x is both greater than 3 and less than 6
  (see for example <https://en.wikipedia.org/wiki/Relational_operator>),
  a chaining operator is implemented. The chaining operator, %c%,
  allows multiple relational operations to be used in quotes on the right
  hand side for the same object, on the left hand side.
  The %e% operator allows something like set-builder notation 
  (see for example <https://en.wikipedia.org/wiki/Set-builder_notation>) 
  to be used on the right hand side.
  All operators have built in prefixes defined for all, subset, and which
  to reduce the amount of code needed for common tasks, such as return those
  values that are true.
Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method.
A set of procedures for modelling parametrically and non-parametrically the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. Adapts the methodologies derived in Beranger et al. (2019) <arxiv:1904.08251>, Beranger et al. (2017) <doi:10.1111/sjos.12240>, Beranger and Padoan (2015) <arxiv:1508.05561>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Marcon et al. (2016) <doi:10.1214/16-EJS1162>. It also refers to the works of Bortot (2010) <https://pdfs.semanticscholar.org/b0dc/1cb608d35bf515c76e39aacc14b4de82e281.pdf>, Padoan (2011) <doi:10.1016/j.jmva.2011.01.014>, Cooley et al. (2010) <doi:10.1016/j.jmva.2010.04.007>, Husler and Reiss (1989) <doi:10.1016/0167-7152(89)90106-5>, Engelke et al. (2015) <doi:10.1111/rssb.12074>, Coles and Tawn (1991) <doi:10.1111/j.2517-6161.1991.tb01830.x>, Nikoloulopoulos et al. (2011) <doi:10.1007/s10687-008-0072-4>, Opitz (2013) <doi:10.1016/j.jmva.2013.08.008>, Tawn (1990) <doi:10.2307/2336802>, Azzalini (1985) <https://www.jstor.org/stable/pdf/4615982.pdf>, Azzalini and Capitanio (2014) <doi:10.1017/CBO9781139248891>, Azzalini (2003) <doi:10.1111/1467-9469.00322>, Azzalini and Capitanio (1999) <doi:10.1111/1467-9868.00194>, Azzalini and Dalla Valle (1996) <doi:10.1093/biomet/83.4.715>, Einmahl et al. (2013) <doi:10.1007/s10687-012-0156-z>, Naveau et al (2009) <doi:10.1093/biomet/asp001> and Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Extreme value theory, nonparametric kernel estimation, tail
    conditional probabilities, extreme conditional quantile, adaptive estimation,
    quantile regression, survival probabilities.
An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.
Functions for performing extreme value analysis.
Code to fit, plot and compare several (extreme value)
    distribution functions. Can also compute (truncated) distribution quantile estimates and
    draw a plot with return periods on a linear scale.
Detect outliers in one-dimensional data.
Conducts inference in statistical models for extreme values (de Carvalho et al (2012), <doi:10.1080/03610926.2012.709905>; de Carvalho and Davison (2014), <10.1080/01621459.2013.872651>; Einmahl et al (2016), <doi:10.1111/rssb.12099>).
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
It implements the subjectively censored Weibull MLE and censored Weibull mixture methods for the lower quantile estimation. Quantile estimates from these two methods are robust to model misspecification in the lower tail. It also includes functions to evaluation the standard error of the resulting quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for the Type-I or Type-II right censored data.
Testing for and dating periods of explosive
    dynamics (exuberance) in time series using the univariate and panel
    recursive unit root tests proposed by Phillips et al. (2015)
    <doi:10.1111/iere.12132> and Pavlidis et al. (2016)
    <doi:10.1007/s11146-015-9531-2>.  The recursive least-squares
    algorithm utilizes the matrix inversion lemma to avoid matrix
    inversion which results in significant speed improvements. Simulation
    of a variety of periodically-collapsing bubble processes.
Imports plain-text ASC data files from EyeLink eye trackers 
    into (relatively) tidy data frames for analysis and visualization.
Misc function for working with eyetracking data
A set of tools that address tasks along the pipeline from raw
    data to analysis and visualization for eye-tracking data. Offers several
    popular types of analyses, including linear and growth curve time analyses,
    onset-contingent reaction time analyses, as well as several non-parametric
    bootstrapping approaches.
A set of functions for organising and analysing datasets from
    experiments run using 'Eyelink' eye-trackers. Organising functions help
    to clean and prepare eye-tracking datasets for analysis, and mark up
    key events such as display changes and responses made by participants.
    Analysing functions help to create means for a wide range of standard
    measures (such as 'mean fixation durations'), which can then be fed into
    the appropriate statistical analyses and graphing packages as necessary.
Facilitates easy analysis of factorial experiments, including
    purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss
    designs, and mixed within-and-between-Ss designs. The functions in this package
    aim to provide simple, intuitive and consistent specification of data analysis
    and visualization. Visualization functions also include design visualization for
    pre-analysis data auditing, and correlation matrix visualization. Finally, this
    package includes functions for non-parametric analysis, including permutation
    tests and bootstrap resampling. The bootstrap function obtains predictions
    either by cell means or by more advanced/powerful mixed effects models, yielding
    predictions and confidence intervals that may be easily visualized at any level
    of the experiment's design.
A tool to operate a batch of univariate or multivariate 
    Cox models and return tidy result.
Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.
Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.
This package implements a simplified version of least
        squares, and logistic regression for efficiently selecting the
        significant non-additive interactions between two variables.
An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.
Analysis of enzyme activities on fluorogenic and chromogenic substrates using Michaelis-Menten kinetics. It outputs plots and relevant statistics regarding standard curves and saturation curves. It also enables the user to customize plots to their particular datasets. 
    This package is based on concepts described in: German et al. (2011) <doi:10.1016/j.soilbio.2011.03.017>, Sinsabaugh et al. (2014) <doi:10.1007/s10533-014-0030-y>, and Steen and Arnosti (2011) <doi:10.1016/j.marchem.2010.10.006>.
Choosing any rectangular data file using interactive GUI dialog box, and seamlessly manipulating tidy data between an 'Excel' window and R session.
Wrapper for the 'ggplot2' package that creates a variety of common
  charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce
  typing.
ezsim provides a handy way to run simulation and examine its result
Contains two functions that are intended to make
    tuning supervised learning methods easy. The eztune function uses a
    genetic algorithm or Hooke-Jeeves optimizer to find the best 
    set of tuning parameters. The user can choose the optimizer, the 
    learning method, and if optimization will be based on accuracy 
    obtained through validation error, cross validation, or resubstitution. 
    The function eztune.cv will compute a cross validated error rate. The purpose 
    of eztune_cv is to provide a cross validated accuracy or MSE when 
    resubstitution or validation data are used for optimization because 
    error measures from both approaches can be misleading.
Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <https://arxiv.org/abs/1612.08287>.
Provides a collection of commonly used univariate and multivariate
    time series forecasting models including automatically selected exponential 
    smoothing (ETS) and autoregressive integrated moving average (ARIMA) models.
    These models work within the 'fable' framework provided by the 'fabletools'
    package, which provides the tools to evaluate, visualise, and combine models 
    in a workflow consistent with the tidyverse.
Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <https://www.jstor.org/stable/25703571>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).
Helps you imagine your data before you collect it. Hierarchical data structures
   and correlated data can be easily simulated, either from random number generators or
   by resampling from existing data sources. This package is faster with 'data.table' and
   'mvnfast' installed.
We implement the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing <doi: 10.1007/s11222-017-9744-8>.
Provides an interface to the Facebook API and builds collections of elements that reflects the graph architecture of Facebook.
  See <https://developers.facebook.com/docs/graph-api> for more information.
Provides an interface to the 'Kairos' Face Recognition API <https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.
A tool for simulating a variety of spatial individual-based models of
    plant-plant interactions. User-created models can include any number of species, each of
    which can be structured in any number of life-stages, where each life-stage has specific
    death, growth and reproduction rates, as well as specific interaction radius, dispersal
    radius, and interaction effects over each other species/life-stage. Life stages were modeled
    so as to be a stochastic, individual-based version of differential Matrix Population
    Models (Caswell 2001, ISBN:0-87893-096-5). Interactions can be positive
    (facilitation) or negative (competition) and can affect death rates, growth rates or
    reproduction rates. Interactions from multiple numbers are additive, so as to best
    approximate classic population dynamics models such as the logistic model and
    Lotka-Volterra model (Britton 2004, ISBN:9781852335366). All models work in continuous time, implemented as an optimized version
    of the Gillespie algorithm (Gillespie 1976 <doi:10.1016/0021-9991(76)90041-3>) for independent exponential times, and continuous space.
This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.
The package estimates Factor Mixture Analysis via the EM
        algorithm
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.
Some functions of 'ade4' and 'stats' are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. 
  First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. See, for example: 
      Lebart, L. and Piron, M. and Morineau, A.  (2006).
      Statistique Exploratoire Multidimensionnelle, Dunod, Paris.
  In order to permit to have different weights of the elements to be clustered, the function 'kmeansW', programmed in C++, is included. It is a modification of 'kmeans'.   
  Some graphical functions include the option: 'gg=FALSE'.  When 'gg=TRUE', they  use the 'ggplot2' and 'ggrepel' packages to avoid  the super-position of the labels.
Provides some easy-to-use functions to extract and visualize the
    output of multivariate data analyses, including 'PCA' (Principal Component
    Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence
    Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple
    Factor Analysis) functions from different R packages. It contains also functions
    for simplifying some clustering analysis steps and provides 'ggplot2' - based
    elegant data visualization.
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).
Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.
Identifies change-points in the common and the idiosyncratic components via factor modelling.
Provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2019+), and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.
Used for the design and analysis of a 2x2 factorial trial for
    a time-to-event endpoint.  Performs power calculations and significance
    testing.  Important reference papers include
    Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609>
    Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507>
    Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2019)
    <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al%20Factorial.pdf>.
The Merging Path Plot is a methodology for adaptive fusing of k-groups 
    with likelihood-based model selection. This package contains tools for 
    exploration and visualization of k-group dissimilarities. 
    Comparison of k-groups is one of the most important issues
    in exploratory analyses and it has zillions of applications. 
    The traditional approach is to use pairwise post hoc tests
    in order to verify which groups differ significantly. However, 
    this approach fails with a large number of groups in both interpretation 
    and visualization layer.
    The Merging Path Plot solves this problem by using an easy-to-understand 
    description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic (Sitko, Biecek 2017) <arXiv:1709.04412>.
    'factorMerger' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
    Work on this package was financially supported by the 'NCN Opus grant 2016/21/B/ST6/02176'.
Methods to calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models.
Package to fit Bayesian quantile regression models that
        assume a factor structure for at least part of the design
        matrix.
It identifies the factors significantly related to species richness, and their relative contribution, using multiple regressions and support vector machine models. It uses an output file of 'ModestR' (<http://www.ipez.es/ModestR>) with data of richness of the species and environmental variables in a cell size defined by the user. The residuals of the support vector machine model are shown on a map. Negative residuals may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors.
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian estimation of latent factor stochastic volatility models with interweaving <doi:10.1080/10618600.2017.1322091>. Sparsity can be achieved through the usage of Normal-Gamma priors on the factor loading matrix <doi:10.1016/j.jeconom.2018.11.007>.
Function factories are functions that make functions. They can be 
    confusing to construct. Straightforward techniques can produce functions 
    that are fragile or hard to understand. While more robust techniques exist 
    to construct function factories, those techniques can be confusing. This 
    package is designed to make it easier to construct function factories.
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
Per the Factual.com website, "Factual is a platform where
        anyone can share and mash open, living data on any subject."
        The data is in the form of tables and is accessible via REST
        API.  The factualR package is a thin wrapper around the
        Factual.com API, to make it even easier for people working with
        R to explore Factual.com data sets.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a  
   factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
    (see Friedman et al. (2010)), sparse linear discriminant analysis (see
    Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
    analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Probability distributions that are sometimes useful in hydrology.
Data and functions for the book "Multivariate Statistical 
              Modelling Based on Generalized Linear Models", first edition, by 
              Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.
More comfortable interface to work with R data or source files
    in a key-value fashion.
An implementation of the fair data adaptation with quantile preservation described in
    Plecko & Meinshausen (2019) <arXiv:1911.06685>. 
    The adaptation procedure uses the specified causal graph to pre-process the 
    given training and testing data in such a way to remove the bias caused by 
    the protected attribute. The procedure uses tree ensembles for quantile 
    regression.
Offers various metrics of algorithmic fairness. Fairness in machine learning is an emerging 
  topic with the overarching aim to critically assess algorithms (predictive and classification models) whether
  their results reinforce existing social biases. While unfair algorithms can propagate such biases and offer
  prediction or classification results with a disparate impact on various sensitive subgroups of populations (defined
  by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities), fair algorithms possess
  the underlying foundation that these groups should be treated similarly / should have similar outcomes. The fairness
  R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population
  subgroups. These methods are described by Calders and Verwer (2010) <doi:10.1007/s10618-010-0190-x>, Chouldechova 
  (2017) <doi:10.1089/big.2016.0047>, Feldman et al. (2015) <doi:10.1145/2783258.2783311> , Friedler et al. 
  (2018) <doi:10.1145/3287560.3287589> and Zafar et al. (2017) <doi:10.1145/3038912.3052660>. The package also 
  offers convenient visualizations to help understand fairness metrics.
It is used for systematic analysis of decisions based on attributes and its levels.
Use R as a minimal build system. This might come in handy
        if you are developing R packages and can not use a proper build
        system. Stay away if you can (use a proper build system).
Generates fake data from a dataset of different variable types.
    The package contains the functions simulate_dataset and simulate_dataset_ts 
    to simulate time-independent and time-dependent data. It randomly samples 
    character and factor variables from contingency tables and  numeric and 
    ordered factors from a multivariate normal distribution. It currently supports the 
    simulation of stationary and zero-inflated count time series.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data.  Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.  The implemented method is based on the paper: Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (under review). Allele-specific copy number estimation by whole Exome sequencing.
Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.
Functionality provided for conditional simulation, likelihoods and plotting of pedigrees, mostly as a wrapper for 'paramlink'. Users typically start by exporting from the Windows version of 'Familias'.
Simulates age-at-onset traits associated with a segregating major gene in family data 
     obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment 
     correction is utilized to estimate age-dependent penetrance functions either parametrically from 
     the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm 
     can infer missing genotypes and carrier probabilities estimated from family's genotype and
     phenotype information or from a fitted model. Plot functions include pedigrees of simulated 
     families and predicted penetrance curves based on specified parameter values.
An interface to the core Familias functions (www.familias.name), 
 which are programmed in C++.
Fits penalized linear and logistic regression models with pairwise interaction terms.
Estimate parameters of univariate probability distributions 
  with maximum likelihood and Bayesian methods.
Given a set of parameters describing model dynamics and a corresponding cost function,
    FAMoS performs a dynamic forward-backward model selection on a specified selection
    criterion. It also applies a non-local swap search method. Works on any cost function.
FamSKAT-RC is a family-based association kernel test for both 
    rare and common variants. This test is general and several special cases 
    are known as other methods: famSKAT, which only focuses on rare variants 
    in family-based data, SKAT, which focuses on rare variants in 
    population-based data (unrelated individuals), and SKAT-RC, which focuses 
    on both rare and common variants in population-based data. When one 
    applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes 
    famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the 
    kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one 
    applies famSKAT-RC and set the kinship matrix (fullkins) to the identity 
    matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also 
    include a small sample synthetic pedigree to demonstrate the method with.  
    For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
Computes the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters. The pathwise coordinate descent along with EM algorithm is used.  This package also includes a new graphical tool which outputs path diagram, goodness-of-fit indices and model selection criteria for each regularization parameter. The user can change the regularization parameter by manipulating scrollbars, which is helpful to find a suitable value of regularization parameter.
This package contains a collection of R functions to
        perform nonparametric analysis of covariance for regression
        curves or surfaces. Testing the equality or parallelism of
        nonparametric curves or surfaces is equivalent to analysis of
        variance (ANOVA) or analysis of covariance (ANCOVA) for
        one-sample functional data. Three different testing methods are
        available in the package, including one based on L-2 distance,
        one based on an ANOVA statistic, and one based on variance
        estimators.
Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.
Visualise sequential distributions using a range of plotting
    styles. Sequential distribution data can be input as either simulations or
    values corresponding to percentiles over time. Plots are added to
    existing graphic devices using the fan function. Users can choose from four
    different styles, including fan chart type plots, where a set of coloured
    polygon, with shadings corresponding to the percentile values are layered
    to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.
Counterparts to R string manipulation functions that account for
   the effects of ANSI text formatting control sequences.
A list of functions to download statistics from FAOSTAT (database
    of the Food and Agricultural Organization of the United Nations) and WDI
    (database of the World Bank), and to perform some harmonization operations.
Tools for detecting and summarize influential cases that
    can affect exploratory and confirmatory factor analysis models as well as
    structural equation models more generally.
Modelizations and previsions functions for
   Functional AutoRegressive processes using
   nonparametric methods: functional kernel,
   estimation of the covariance operator in
   a subspace, ...
Books are "Practical Regression and ANOVA in R" on CRAN, "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248.
Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.
Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See <http://weka.wikispaces.com/ARFF> for further info on 'ARFF' and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Note that the 'OpenML' package is currently only available from 'Github' at: <https://github.com/openml/openml-r>.
Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.
Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber's loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://github.com/jdstorey/qvalue>. For detailed description of methods and further references, see the papers on the 'FarmTest' method: Fan et al. (2017) <arXiv:1711.05386> and Zhou et al. (2017) <arXiv:1711.05381>.
The encoding of colour can be handled in many different ways, using
    different colour spaces. As different colour spaces have different uses,
    efficient conversion between these representations are important. The 
    'farver' package provides a set of functions that gives access to very fast
    colour space conversion and comparisons implemented in C++, and offers 
    speed improvements over the 'convertColor' function in the 'grDevices' 
    package.
It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.
Provides functions for pricing
	and valuating Asian Options together with tools for analyzing
    and modeling Exponential Brownian Motion (EBM).
This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.
Provides a  collection of functions 
  to manage, to investigate and to analyze data sets of financial 
  assets from different points of view.
The Fourier Amplitude Sensitivity Test (FAST) is a method to determine global sensitivities of a model on parameter changes with relatively few model runs. This package implements this sensitivity analysis method.
A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in
    Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> 
    and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.
Implements Adaboost based on C++ backend code.
             This is blazingly fast and especially useful for large, in memory data sets. 
             The package uses decision trees as weak classifiers. Once the classifiers
             have been trained, they can be used to predict new data. 
             Currently, we support only binary classification tasks.
             The package implements the Adaboost.M1 algorithm and the real
             Adaboost(SAMME.R) algorithm.
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Provides a method of recovering the precision matrix efficiently 
        and solving for the dantzig selector by applying the parametric 
        simplex method.  The computation is based on a linear optimization
        solver. It also contains a generic LP solver and a parameterized LP 
        solver using parametric simplex method.
This is a two-in-one package which provides interfaces to
        both R and 'Python'. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the 'SciPy' package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the 'Python' files, see the file INSTALL in the source distribution.
        Based on the present package, Christoph Dalitz also wrote a pure 'C++'
        interface to 'fastcluster':
        <http://informatik.hsnr.de/~dalitz/data/hclust>.
A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net.
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) DOI: <doi:10.4310/SII.2013.v6.n2.a1>.
Provides an R interface to Bob Jenkin's streaming, 
 non-cryptographic 'SpookyHash' hash algorithm for use in digest-based 
 comparisons of R objects. 'fastdigest' plugs directly into R's internal 
 serialization machinery, allowing digests of all R objects the serialize() 
 function supports, including reference-style objects via custom hooks. Speed is
 high and scales linearly by object size; memory usage is constant and 
 negligible.
Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
If there is a need to recognise edges on a raster image or a bitmap or any kind of a matrix, one can find packages
    that does only 90 degrees vectorization. Typically the nature of artefact images is linear and can be vectorized in much more
    efficient way than draw a series of 90 degrees lines. The fasteraster package does recognition of lines using only one pass.
    It also allows to calculate mass and the mass centers for the recognized zones or polygons.
Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)(<doi:10.1214/009053604000000067 >) and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library.
Provides a drop-in replacement for rasterize() from the 'raster'
   package that takes 'sf'-type objects, and is much faster. There is support
   for the main options provided by the rasterize() function, including
   setting the field used and background value, and options for 
   aggregating multi-layer rasters. Uses the scan line algorithm attributed to
   Wylie et al. (1967) <doi:10.1145/1465611.1465619>.
Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise.  See the reference: Mengyang Gu and Yanxun Xu (2017) <arXiv:1711.11501>.
Fast, numerically-stable Gauss-Hermite quadrature rules and
    utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A.
    (1994) <doi:10.2307/2337136> for a reference on these methods.
Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares 
    implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard
    against convergence issues.
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
The FastHCS algorithm of Schmitt and Vakili (2014) <doi:10.1007/s11222-015-9602-5> for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
It implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
Implementation of FastICA algorithm to perform Independent
        Component Analysis (ICA) and Projection Pursuit.
TrainFastImputation() uses training data to describe a
    multivariate normal distribution that the data approximates or
    can be transformed into approximating and stores this information
    as an object of class 'FastImputationPatterns'. FastImputation()
    function uses this 'FastImputationPatterns' object to impute (make
    a good guess at) missing data in a single line or a whole data frame
    of data.  This approximates the process used by 'Amelia'
    <http://gking.harvard.edu/amelia/> but is much faster when
    filling in values for a single line of data.
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.
Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data
    and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model
    using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing
    data merges are included. The package implements methods described in Enamorado, Fifield,
    and Imai (2017) ''Using a Probabilistic Model to Assist Merging of Large-scale Administrative
    Records'', available at <http://imai.princeton.edu/research/linkage.html>.
It is for all people who have to compute many Log-Rank-Tests and the 'survival' package is not fast enough for their purpose.
  Therefore this small package provides two functions, one for a single Log-Rank-Test and another which calculates many Log-Rank-Tests at a time using threading.
  Both runs very fast because it uses C++ code with 'Rcpp'.
Estimate spike times from calcium imaging data using an L0 penalty.
Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.
Fast implementation of a key-value store. Environments are commonly
    used as key-value stores, but every time a new key is used, it is added to
    R's global symbol table, causing a small amount of memory leakage. This can
    be problematic in cases where many different keys are used. Fastmap avoids
    this memory leak issue by implementing the map using data structures in C++.
Fast Marching Method (FMM) first developed by Sethian (1996) <http://www.pnas.org/content/93/4/1591.short>, and further extended by including a second-order approximation, the first-arrival rule, additive weights, and non-homogeneous domains following Silva and Steele (2012) <doi:10.1142/S0219525911003293> and Silva and Steele (2014) <doi:10.1016/j.jas.2014.04.021>.
Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.
This is an extremely fast implementation of a Naive Bayes classifier. This 
    package is currently the only package that supports a Bernoulli distribution, a Multinomial 
    distribution, and a Gaussian distribution, making it suitable for both binary features, 
    frequency counts, and numerical features. Another feature is the support of a mix of 
    different event models. Only numerical variables are allowed, however, categorical variables 
    can be transformed into dummies and used with the Bernoulli distribution. 
    The implementation is largely based on the paper 
    "A comparison of event models for Naive Bayes anti-spam e-mail filtering" 
    written by K.M. Schneider (2003) <doi:10.3115/1067807>. Any issues can be 
    submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics.
The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.
Finds the critical sample size ("point of stability") for a 
    correlation to stabilize in Schoenbrodt and Perugini's definition of 
    sequential stability (see <doi:10.1016/j.jrp.2013.05.009>).
Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.
'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.
Data sets and utilities to accompany
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2011), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet function.
Data sets and utilities to accompany the second edition of
    "Foundations and Applications of Statistics: an Introduction
    using R" (R Pruim, published by AMS, 2017), a text covering
    topics from probability and mathematical statistics at an advanced
    undergraduate level.  R is integrated throughout, and access to all
    the R code in the book is provided via the snippet() function.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Learning text representations and text classifiers may rely
  on the same simple and efficient approach. 'fastText' is an open-source, free, 
  lightweight library that allows users to perform both tasks.
  It transforms text into continuous vectors that can later
  be used on many language related task.
  It works on standard, generic hardware (no 'GPU' required).
  It also includes model size reduction feature.
  'fastText' original source code is available 
  at <https://github.com/facebookresearch/fastText>.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
An implementation of the fast structural filtering with L0 penalty. It includes an adaptive polynomial estimator by minimizing the least squares error with constraints on the number of breaks in their (k + 1)-st discrete derivative, for a chosen integer k >= 0. It also includes generalized structure sparsity constraint, i.e., graph trend filtering. This package is implemented via the primal dual active set algorithm, which formulates estimates and residuals as primal and dual variables, and utilizes efficient active set selection strategies based on the properties of the primal and dual variables.
Computes fast approximate Shapley values for any supervised 
    learning model. Shapley values help to explain the predictions from any black box model using ideas 
    from game theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> for details.
Functions for computing spillover measures, especially spillover
    tables and spillover indices, as well as their average, minimal, and maximal
    values.
When we do statistic work, we need to see the structure of the data.
    list.str() function will help you see the structure of the data quickly. 
    list.plot() function can help you check every variable in your dataframe.
    table_one() function will make it easy to make a baseline table including 
    difference tests. uv_linear(), uv_logit(), uv_cox(), uv_logrank() will give
    you a hand to do univariable regression analysis, while mv_linear(), 
    mv_logit() and mv_cox() will carry out multivariable regression analysis.
An interface to the 'fastText' library
	<https://github.com/facebookresearch/fastText>. The package
	can be used for text classification and to learn word vectors.
	The install folder contains the 'PATENTS' file.
	An example how to use 'fastTextR' can be found in the 'README' file.
Fast functions for timestamp
	manipulation that avoid system calls and take shortcuts
	to facilitate operations on very large data.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models
             defined by some design matrix.
Application of 'FateID' allows computation and visualization of cell fate bias for multi-lineage single cell transcriptome data. Herman, J.S., Sagar, Grün D. (2018) <DOI:10.1038/nmeth.4662>.
Kiener distributions K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.
HTTP error helpers. Methods included for general purpose HTTP 
    error handling, as well as individual methods for every HTTP status
    code, both via status code numbers as well as their descriptive names.
    Supports ability to adjust behavior to stop, message or warning.
    Includes ability to use custom whisker template to have any configuration
    of status code, short description, and verbose message. Currently 
    supports integration with 'crul', 'curl', and 'httr'.
A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos
             (http://pages.bloomsbury.com/favouritenumber).
Provides functions and datasets from the book "Forest Analytics with R".
A simple package for Flux Balance Analysis and related
    metabolic modeling techniques. Functions are provided for: parsing
    models in tabular format, converting parsed metabolic models to input
    formats for common linear programming solvers, and
    evaluating and applying gene-protein-reaction mappings. In addition, there
    are wrappers to parse a model, select a solver, find the metabolic fluxes,
    and return the results applied to the original model. Compared to other
    packages in this field, this package puts a much heavier focus on
    providing reusable components that can be used in the design of new
    implementation of new techniques, in particular those that involve large
    parameter sweeps.
Provides a collection of functions to 
    explore and to investigate basic properties of financial returns 
    and related quantities.
    The covered fields include techniques of explorative data analysis
    and the investigation of distributional properties, including
    parameter estimation and hypothesis testing. Even more there are
    several utility functions for data handling and management.
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
Normalizes the data from a file containing the raw values
        of the SNP probes of microarrray data by using the FISH probes
        and their corresponding CNs.
It implements the Nelson-Siegel and the Nelson-Siegel-Svensson
	term structures.
Wrapper functions around the Facebook Marketing 'API' to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.
This package uses time dependent Poisson regression and a record of goals scored in matches to rank teams via estimated attack and defense strengths.  The statistical model is based on Dixon and Coles (1997) Modeling Association Football Scores and Inefficiencies in the Football Betting Market, Applied Statistics, Volume 46, Issue 2, 265-280.  The package has a some webscrapers to assist in the development and updating of a match database.  If the match database contains unconnected clusters (i.e. sets of teams that have only played each other and not played teams from other sets), each cluster is ranked separately relative to the median team strength in the cluster.  The package contains functions for predicting and simulating tournaments and leagues from estimated models. The package allows fitting via the glm(), speedglm(), and glmnet() functions.  The latter allows fast and efficient fitting of very large numbers of teams.  The fitting algorithm will analyze the match data and determine which teams form a cluster (a set of teams where there is a path of matches connecting every team) and fit each cluster separately.
Implements a very fast C++ algorithm to quickly bootstrap receiver
    operating characteristics (ROC) curves and derived performance metrics,
    including the area under the curve (AUC) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of one second.
Provides a streamlined, standard evaluation-based approach to multivariate function composition. Allows for chaining commands via a forward-pipe operator, %>%.
Efficient procedures for community detection in network studies, especially for sparse networks with not very obvious community structure. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Collection of pricing by duplication methods for popular structured products ("Zertifikate").
Fatigue Crack Growth in Reliability estimates the distribution
             of material lifetime due to mechanical fatigue efforts. The FCGR
             package provides simultaneous crack growth curves fitting to 
             different specimens in materials under mechanical stress efforts. 
             Linear mixed-effects models (LME) with smoothing B-Splines 
             and the linearized Paris-Erdogan law are applied. Once defined
             the fail for a determined crack length, the distribution function
             of failure times to fatigue is obtained. The density function is
             estimated by applying nonparametric binned kernel density estimate 
             (bkde) and the kernel estimator of the distribution function (kde). 
             The results of Pinheiro and Bates method based on nonlinear 
             mixed-effects regression (nlme) can be also retrieved. The package 
             contains the crack.growth, PLOT.cg, IB.F, and Alea.A (database) functions.
Change point estimation and detection methods for functional data are implemented using dimension reduction via functional principal component analysis and a fully-functional (norm-based) method. Detecting and dating structural breaks for both dependent and independent functional samples is illustrated along with some basic functional data generating processes.
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
Provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the 'fcm' package returns a data frame of the concepts' values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.
Provides several functions to create and manipulate fuzzy
    cognitive maps. It is based on 'FCMapper' for Excel, distributed at <http://
    www.fcmappers.net/joomla/>, developed by Michael Bachhofer and Martin Wildenberg.
    Maps are inputted as adjacency matrices. Attributes of the maps and the
    equilibrium values of the concepts (including with user-defined constrained
    values) can be calculated. The maps can be graphed with a function that calls
    'igraph'. Multiple maps with shared concepts can be aggregated.
Provides a  collection of functions to 
	manage, to investigate and to analyze bivariate financial returns by  
	Copulae. Included are the families of Archemedean, Elliptical, 
	Extreme Value, and Empirical Copulae.
Dynamic prediction in functional concurrent regression with an application to child growth. Extends the pffr() function from the 'refund' package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured.  Leroux et al. (2017), Statistics in Medicine, <doi:10.1002/sim.7582>.
A fold change rank based method is presented to search for genes with changing
             expression and to detect recurrent chromosomal copy number aberrations. This 
             method may be useful for high-throughput biological data (micro-array, sequencing, ...).
             Probabilities are associated with genes or probes in the data set and there is no
             problem of multiple tests when using this method. For array-based comparative genomic
             hybridization data, segmentation results are obtained by merging the significant
             probes detected.
A set of tools for fluorescence fluctuation spectroscopy data analysis performance is provided in this package.
    It includes techniques such as single-point fluorescence correlation spectroscopy, autocorrelation and pair correlation functions, number & brightness (raster line scan) and a novel method recently developed by Hinde and co-workers, pair correlation of molecular brightness.
    A set of simulations and real experimental data is used for the examples of each function provided in this package.
    For an in-depth description of the basics behind each function here included and a detailed step-by-step guide on how to use them on your own data, please refer to the Supplementary Material file provided at (<https://github.com/RPintoC/FCSlib_Sup>).
    R.A. Migueles-Ramirez, A.G. Velasco-Felix, R. Pinto-Camara, C.D. Wood, A. Guerrero (2017, ISBN-13:978-84-942134-9-6).
    Hinde, E., Pandzic, E., Yang, Z., Ng, I. H., Jans, D. A., Bogoyevitch, M. A., Gratton, E. & Gaus, K. (2016) <doi:10.1038/ncomms11047>.
Automatically suggests a correction when a typo occurs.
FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.
These functions were developed to support functional data
        analysis as described in Ramsay, J. O. and Silverman, B. W.
        (2005) Functional Data Analysis. New York: Springer.  They were
        ported from earlier versions in Matlab and S-PLUS.  An
        introduction appears in Ramsay, J. O., Hooker, Giles, and
        Graves, Spencer (2009) Functional Data Analysis with R and
        Matlab (Springer). The package includes data sets and script
        files working many examples including all but one of the 76
        figures in this latter book.  Matlab versions of the code and
        sample analyses are no longer distributed through CRAN, as they
        were when the book was published.  For those, ftp from
        <http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/>
        There you find a set of .zip files containing the functions and
        sample analyses, as well as two .txt files giving instructions for
        installation and some additional information.
        The changes from Version 2.4.1 are fixes of bugs in density.fd and
        removal of functions create.polynomial.basis, polynompen, and 
        polynomial. These were deleted because the monomial basis
        does the same thing and because there were errors in the code.
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
An implementation of the methodology described in
    Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional
    data analysis of samples of density functions.  Densities are first
    transformed to their corresponding log quantile densities, followed by
    ordinary Functional Principal Components Analysis (FPCA).  Transformation
    modes of variation yield improved interpretation of the variability in the
    data as compared to FPCA on the densities themselves.  The standard
    fraction of variance explained (FVE) criterion commonly used for functional
    data is adapted to the transformation setting, also allowing for an
    alternative quantification of variability for density data through the
    Wasserstein metric of optimal transport.
It performs simultaneously clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.
Likelihood based analysis of 1-dimension functional data
        in a mixed-effects model framework. Matrix computation are
        approximated by semi-explicit operator equivalents with linear
        computational complexity. Markussen (2013) <doi:10.3150/11-BEJ389>.
Performs analysis of variance testing procedures for univariate and multivariate functional data (Cuesta-Albertos and Febrero-Bande (2010) <doi:10.1007/s11749-010-0185-3>, Gorecki and Smaga (2015) <doi:10.1007/s00180-015-0555-0>, Gorecki and Smaga (2017) <doi:10.1080/02664763.2016.1247791>, Zhang et al. (2018) <doi:10.1016/j.csda.2018.05.004>).
A versatile package that provides implementation of various
    methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this
    package is Functional Principal Component Analysis (FPCA), a key technique for
    functional data analysis, for sparsely or densely sampled random trajectories
    and time courses, via the Principal Analysis by Conditional Estimation
    (PACE) algorithm. This core algorithm yields covariance and mean functions,
    eigenfunctions and principal component (scores), for both functional data and
    derivatives, for both dense (functional) and sparse (longitudinal) sampling designs.
    For sparse designs, it provides fitted continuous trajectories with confidence bands,
    even for subjects with very few longitudinal observations. PACE is a viable and
    flexible alternative to random effects modeling of longitudinal data. There is also a
    Matlab version (PACE) that contains some methods not available on fdapace and vice
    versa. Please cite our package if you use it (You may run the command
    citation("fdapace") to get the citation format and bibtex entry).
    References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>;
    Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
Performs alignment, PCA, and modeling of multidimensional and
            unidimensional functions using the square-root velocity framework
            (Srivastava et al., 2011 <arXiv:1103.3817> and
            Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework
            allows for elastic analysis of functional data through phase and
            amplitude separation.
Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
Regression models for functional data, i.e., scalar-on-function,
    function-on-scalar and function-on-function regression models, are fitted
    by a component-wise gradient boosting algorithm.
Provides a variety of tools for the analysis of covariance operators including k-sample tests for equality and classification and clustering methods found in the works of Cabassi et al (2017) <doi:10.1214/17-EJS1347>, Kashlak et al (2017) <arXiv:1604.06310>, Pigoli et al (2014) <doi:10.1093/biomet/asu008>, and Panaretos et al (2010) <doi:10.1198/jasa.2010.tm09239>.
FDG copulas are a class of copulas featuring an interesting balance between flexibility and tractability. This package provides tools to construct, calculate the pairwise dependence coefficients of, simulate from, and fit FDG copulas. The acronym FDG stands for 'one-Factor with Durante Generators', as an FDG copula is a one-factor copula -- that is, the variables are independent given a latent factor -- whose linking copulas belong to the Durante class of bivariate copulas (also referred to as exchangeable Marshall-Olkin or semilinear copulas).
Provides functionality to generate a frequency 
    distribution table from a set of observations and plot the frequency
    distribution using a Pareto chart.
Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".
Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.
Forest data quality is a package that contains methods of 
    analysis of forest databases, the purpose of the analyzes is to evaluate 
    the quality of the data present in the databases focusing on the dimensions
    of consistency, pountuality and completeness. Databases can range from forest
    inventory data to growth model data. The package has methods to work with large 
    volumes of data quickly, in addition in certain analyzes it is possible to generate
    the graphs for a better understanding of the analysis and reporting of the analyzed analysis.
FDR functions for permutation-based estimators, including pi0 as well as FDR
             confidence intervals. The confidence intervals account for dependencies between
             tests by the incorporation of an overdispersion parameter, which is estimated
             from the permuted data.
It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.
Tools for FDR problems, including false discovery rate regression.
    See corresponding paper: "False discovery rate regression: application to
    neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
    C. Kelly, Matthew A. Smith, Robert E. Kass.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.
Estimate step functions via multiscale inference with controlled false discovery rate (FDR). For details see H. Li, A. Munk and H. Sieling (2016) <doi:10.1214/16-EJS1131>.
Estimates both tail area-based false 
   discovery rates (Fdr) as well as local false discovery rates (fdr) for a 
   variety of null models (p-values, z-scores, correlation coefficients,
   t-scores).  The proportion of null values and the parameters of the null 
   distribution are adaptively estimated from the data.  In addition, the package 
   contains functions for non-parametric density estimation (Grenander estimator), 
   for monotone regression (isotonic regression and antitonic regression with weights),
   for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), 
   for the half-normal and correlation distributions, and for computing
   empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
             and polygons from vector, data.frame and matrix objects for
             numerical and categorical variables.
For each feature, a score is computed that can be useful
        for feature selection. Several random subsets are sampled from
        the input data and for each random subset, various linear
        models are fitted using lars method. A score is assigned to
        each feature based on the tendency of LASSO in including that
        feature in the models.Finally, the average score and the models
        are returned as the output. The features with relatively low
        scores are recommended to be ignored because they can lead to
        overfitting of the model to the training data. Moreover, for
        each random subset, the best set of features in terms of global
        error is returned. They are useful for applying Bolasso, the
        alternative feature selection method that recommends the
        intersection of features subsets.
Provides a collection of features, decompositions, statistical
    summaries and graphics functions for the analysing tidy time series data.
    The package name 'feasts' is an acronym comprising of its key features: 
    Feature Extraction And Statistics for Time Series.
Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.
Local inferential feature significance for multivariate kernel density estimation.
Finds modelling features through a detailed analysis of model residuals using 'rpart' classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model prediction differs from the actual target variable. S. Chatterjee, A. S. Hadi (2006) <doi:10.1002/0470055464>.
Feature hashing, also called as the hashing trick, is a method to transform 
  features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. 
  Without looking up the indices in an associative array, 
  it applies a hash function to the features and uses their hash values as indices directly.
  The method of feature hashing in this package was proposed in Weinberger et al. (2009) <arXiv:0902.2206>. 
  The hashing algorithm is the murmurhash3 from the 'digest' package. 
  Please see the README in <https://github.com/wush978/FeatureHashing> for more information.
Discretely-sampled function is first smoothed.  Features
        of the smoothed function are then extracted.  Some of the key
        features include mean value, first and second derivatives,
        critical points (i.e. local maxima and minima), curvature of
        cunction at critical points, wiggliness of the function, noise
        in data, and outliers in data.
A 'reticulate'-based interface to the 'Python' module 'Featuretools'.
  The package grants functionality to interact with 'Pythons' 'Featuretools' module, which allows 
  for automated feature engineering on any data frame. Valid features and new data sets can, after
  feature synthesis, easily be extracted.
A collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.
Making the access to the Free Brazilian Repository for Open Soil Data <http://www.ufsm.br/febr/> as easy 
  as possible.
Functions and example datasets for Fechnerian scaling of discrete
  object sets.  User can compute Fechnerian distances among objects representing
  subjective dissimilarities, and other related information.  See
  package?fechner for an overview.
Functions to automate downloading geospatial data available from
    several federated data sources (mainly sources maintained by the US Federal
    government). Currently, the package enables extraction from seven datasets:
    The National Elevation Dataset digital elevation models (1 and 1/3 arc-second;
    USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic
    (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is
    led by the Natural Resources Conservation Service (NRCS) under the USDA; the
    Global Historical Climatology Network (GHCN), coordinated by National Climatic
    Data Center at NOAA; the Daymet gridded estimates of daily weather parameters 
    for North America, version 3, available from the Oak Ridge National Laboratory's
    Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; 
    and the National Land Cover Database (NLCD).
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
The Code of Federal Regulations (CFR) annual edition is the codification 
    of the general and permanent rules published in the Federal Register by the departments
    and agencies of the Federal Government of the United States of America. Simply, the 
    'fedregs' package facilitates word processing and sentiment analysis of the CFR using tidy 
    principles. Note: According to the Code of Federal Regulations XML Rendition User Guide Document: 
    "In general, there are no restrictions on re-use of information in Code of Federal Regulations
    material because U.S. Government works are not subject to copyright. OFR and GPO do not
    restrict downstream uses of Code of Federal Regulations data, except that independent providers
    should be aware that only the OFR and GPO are entitled to represent that they are the providers
    of the official versions of the Code of Federal Regulations and related Federal Register
    publications."
Downloads data from Federal 'RePORTER'
    <https://api.federalreporter.nih.gov/> using the Federal 'RePORTER' API.
    Allows the user to search job projects from different government agencies.
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Provides the function feis() to estimate fixed effects individual 
    slope (FEIS) models. The FEIS model constitutes a more general version of 
    the often-used fixed effects (FE) panel model, as implemented in the 
    package 'plm' by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. 
    In FEIS models, data are not only person "demeaned" like in conventional 
    FE models, but "detrended" by the predicted individual slope of each 
    person or group. Estimation is performed by applying least squares lm() 
    to the transformed data. For more details on FEIS models see Bruederl and 
    Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; 
    Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; 
    Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE 
    and random effects estimators against heterogeneous slopes, the package 
    also provides the functions feistest() for an artificial regression test 
    and bsfeistest() for a bootstrapped version of the Hausman test.
Provides pseudo-postfix operators and more to enhance displaying documents.
    The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects.
    The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo).
Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported.
A structured profile likelihood algorithm for the logistic fixed effects model and an approximate expectation maximization (EM) algorithm for the logistic mixed effects model. Based on He, K., Kalbfleisch, J.D., Li, Y. and Li, Y. (2013) <doi:10.1007/s10985-013-9264-6>.
Frailty Exponential Random Graph Models estimated through pseudo likelihood with frailty terms estimated using 'Stan' as per Box-Steffensmeier et. al (2017) <doi:10.7910/DVN/K3D1M2>.
    Goodness of fit for Frailty Exponential Random Graph Models is also available, with easy visualizations for comparison to fit Exponential Random Graph Models.
Data from various catalogs of astrophysical gamma-ray sources
    detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071,
    2009 June 1), on board the Fermi gamma-ray satellite. More information on
    Fermi and its data products is available from the Fermi Science Support Center
    (http://fermi.gsfc.nasa.gov/ssc/).
Wind fetch is the unobstructed length of water over which wind can
    blow from a certain direction. The wind fetch is typically calculated for many directions
    around the compass rose for a given location, which can then be incorporated 
    into a larger model (such as the InVEST coastal vulnerability model;
    <http://data.naturalcapitalproject.org/invest-releases/documentation/2_2_0/coastal_vulnerability.html>),
    or simply averaged for a reasonable measure of 
    the overall wind exposure for a specific marine location. The process of calculating
    wind fetch can be extremely time-consuming and tedious, particularly if a large
    number of fetch vectors are required at many locations. The 'fetchR' package
    calculates wind fetch and summarises the information efficiently. There are 
    also plot methods to help visualise the wind exposure at the various
    locations, and methods to output the fetch vectors to a KML file for further
    investigation.
Provides a collection of functions to evaluate
	barrier options, Asian options, binary options, currency 
    translated options, lookback options, multiple asset options
	and multiple exercise options.
Provides pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables).
Provides functions for analysing
  and modelling extreme events in financial time Series. The
  topics include: (i) data pre-processing, (ii) explorative 
  data analysis, (iii) peak over threshold modelling, (iv) block
  maxima modelling, (v) estimation of VaR and CVaR, and (vi) the
  computation of the extreme index.
Extends the out of memory vectors of 'ff' with
    statistical functions and other utilities to ease their usage.
Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.
Force field simulation of interaction of set of points.
        Very useful for placing text labels on graphs, such as
        scatterplots.
General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.
A collection of functions that allows users to retrieve 
  metadata for the Fragile Families challenge via a Web API 
  (<http://api.metadata.fragilefamilies.princeton.edu>).  
  Users can select and search metadata for relevant variables by 
  filtering on different attribute names.
An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in C++ and uses Rcpp. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic CUSUM and EWMA methods, are included.
Create, visualize, and test fast-and-frugal decision trees (FFTs). FFTs are very simple decision trees for
    binary classification problems. FFTs can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are
    robust against overfitting.
Provides a simple and efficient wrapper around the fastest
        Fourier transform in the west (FFTW) library <http://www.fftw.org/>.
Provides a wrapper for several 'FFTW' functions. This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
Bi-variate data fitting is done by two stochastic
        components: the marginal distributions and the dependency
        structure. The dependency structure is modeled through a
        copula. An algorithm was implemented considering seven families
        of copulas (Generalized Archimedean Copulas), the best fitting
        can be obtained looking all copula's options (totally positive
        of order 2 and stochastically increasing models).
This is a package for implementation of Flury-Gautschi
        algorithms.
Provides a collection of functions to 
  analyze and model heteroskedastic behavior in financial time
  series models.
Read and Parse for Fundamental Geo-Spatial Data (FGD) which downloads XML file 
    from providing site (<https://fgd.gsi.go.jp/download/menu.php>). The JPGIS format file 
    provided by FGD so that it can be handled as an R spatial object such as 'sf' and 'raster' or 'stars'.
    Supports the FGD version 4.1, and accepts fundamental items and digital elevation models.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to install and load the entire package-collection with a
    single R command, and provides convenient ways to find relevant
    documentation. Most commonly, you should not worry about the
    individual packages that make up the package-collection as you can
    access all features via this package. To learn more about ForestGEO
    visit <http://www.forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to implement analyses of plot species distributions,
    topography, demography, and biomass. It also includes a torus
    translation test to determine habitat associations of tree species as
    described by Zuleta et al. (2018) <doi:10.1007/s11104-018-3878-0>. To
    learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to plot ForestGEO data. To learn more about ForestGEO visit
    <http://www.forestgeo.si.edu/>.
To help you access, transform, analyze, and
    visualize ForestGEO data, we developed a collection of R packages
    (<https://forestgeo.github.io/fgeo/>). This package, in particular,
    helps you to easily import, filter, and modify ForestGEO data. To
    learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.
Access small example datasets from Luquillo, a
    ForestGEO site in Puerto Rico
    (<https://forestgeo.si.edu/sites/north-america/luquillo>).
Estimates a functional graphical model and a partially separable Karhunen-Loève decomposition for a multivariate Gaussian process. See Zapata J., Oh S. and Petersen A. (2019) <arXiv:1910.03134>.
Growth models and forest production require existing data
    manipulation and the creation of new data, structured from basic forest
    inventory data. The purpose of this package is provide functions to support
    these activities.
Goodness-of-fit test with multiplier or parametric
        bootstrap.
A permutation technique to explore and control for spatial autocorrelation. This package contains low level functions for performing permutations and calculating statistics as well as higher level functions. Higher level functions are an easy to use function for performing spatially restricted permutation tests and summarize and plot results.
Implement algorithms for feature grouping and selection over an undirected graph, solves problems like graph fused lasso, graph OSCAR and so on.
Rapidly create a GUI interface for a function you created by automatically creating widgets for arguments of the function. Automatically parses help routines for context-sensitive help to these arguments. The interface essentially a wrapper to some Tcl/Tk routines to both simplify and facilitate GUI creation. More advanced Tcl/Tk routines/GUI objects can be incorporated into the interface for greater customization for the more experienced.
Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.
Provides structural data for Norway. Datasets relating to maps, population in municipalities, vaccination coverage for childhood vaccines, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2019.
Functions to compare two or more survival curves with:
       a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
       b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
        Common volumetric equations (Smalian, Newton and Huber) as well
        stacking factor and form
Useful functions for fMRI preprocessing.
Routines for estimating tree fiber (tracheid) length distributions in the standing tree
           based on increment core samples. Two types of data can be used with the package, increment 
           core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani 
          Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell
          lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells)
           without being able to identify which cells are cut or if they are fines or fibres. The
          microscopy measured data consist of the observed lengths of the uncut fibres in the increment
         core. A censored version of a mixture of the fine and fiber length distributions is proposed to
         fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the
        assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers
        (fines) that at least partially appear in the increment core, being the generalized gamma and 
        the log normal densities.
Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes.  Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models.  The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>.
In remote sensing, designing a field campaign to collect ground-truth data can be a challenging task. We need to collect representative samples while accounting for issues such as budget constraints and limited accessibility created by e.g. poor infrastructure. As suggested by Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015>, this demands the establishment of best-practices to collect ground-truth data that avoid the waste of time and funds. 'fieldRS' addresses this issue by helping scientists and practitioners design field campaigns through the identification of priority sampling sites, the extraction of potential sampling plots and the conversion of plots into consistent training and validation samples that can be used in e.g. land cover classification.
For curve, surface and function fitting with an emphasis
 on splines, spatial data, geostatistics,  and spatial statistics. The major methods
 include cubic, and thin plate splines, Kriging, and compactly supported
 covariance functions for large data sets. The splines and Kriging methods are
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance function parameters by cross
 validation and also by restricted maximum likelihood. For Kriging
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple format can be used for
 spatial prediction. There are also many useful functions for plotting
 and working with spatial data as images. This package also contains
 an implementation of sparse matrix methods for large spatial data
 sets and currently requires the sparse matrix (spam) package. Use
 help(fields) to get started and for an overview.  The fields source
 code is deliberately commented and provides useful explanations of
 numerical details as a companion to the manual pages. The commented
 source code can be viewed by expanding  source code version
 and looking in the R subdirectory. The reference for fields can be generated
 by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
 of this package was supported in part by the National Science Foundation  Grant
 1417857 and the National Center for Atmospheric Research. See the Fields URL
 for a vignette on using this package and some background on spatial statistics.
Tools for random fields and bridges simulations.
A very flexible framework for building server side logic in R. The 
    framework is unopinionated when it comes to how HTTP requests and WebSocket
    messages are handled and supports all levels of app complexity; from serving
    static content to full-blown dynamic web-apps. Fiery does not hold your hand
    as much as e.g. the shiny package does, but instead sets you free to create
    your web app the way you want.
Implements a simple key-value style database where character string keys
  are associated with data values that are stored on the disk. A simple interface is provided for inserting,
  retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be
  treated much like environments and lists are already used in R. These utilities are provided to encourage
  interactive and exploratory analysis on large datasets. Three different file formats for representing the
  database are currently available and new formats can easily be incorporated by third parties for use in the
  'filehash' framework.
Simple key-value database using SQLite as the backend
Place an exclusive or shared lock on a file. It uses 'LockFile'
    on Windows and 'fcntl' locks on Unix-like systems.
Interface for working with large matrices stored in files,
        not in computer memory. Supports multiple non-character
        data types (double, integer, logical and raw) of
        various sizes (e.g. 8 and 4 byte real values).
        Access to parts of the matrix is done by indexing, 
        exactly as with usual R matrices.
        Supports very large matrices.
        Tested on multi-terabyte matrices.
        Allows for more than 2^32 rows or columns.
        Allows for quick addition of extra columns to a filematrix.
        Cross-platform as the package has R code only.
Create descriptive file names with ease. New file names are
    automatically (but optionally) time stamped and placed in date stamped
    directories. Streamline your analysis pipeline with input and output file
    names that have informative tags and proper file extensions.
Perform chunk processing or split-apply-combine on data in a
    delimited file (example: CSV) and Distributed Dataframes (DDF) across multiple
    cores of a single machine with low memory footprint. These functions are a
    convenient wrapper over the versatile package 'datadr'.
Functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for
  viewing the details (size, time created, time modified, etc.) of a folder's top level contents.
This started out as a package for file and string manipulation. 
    Since then, the 'fs' and 'strex' packages emerged, offering functionality
    previously given by this package (but it's done better in these new ones). 
    Those packages have hence almost pushed 'filesstrings' into extinction. 
    However, it still has a small number of unique, handy file manipulation 
    functions which can be seen in the vignette. 
    One example is a function to remove spaces from all file names in a 
    directory.
A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) <doi:10.1007/BF01441146>. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.
Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.
Edit vectors to fill missing values, based on the vector itself.
Provides a collection of utility functions 
    to download and manage data sets from the Internet or from other 
    sources.
Generate regression results tables and plots in final 
    format for publication. Explore models and export directly to PDF 
    and 'Word' using 'RMarkdown'.
Functions for financial analysis and financial modeling, 
             including batch graphs generation, beta calculation, 
             descriptive statistics, annuity calculation, bond pricing 
             and financial data download.
Infrastructure for defining meta-data and
    relationships for financial instruments.
Contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam, and some topics in the 'Models for Financial Economics' exam.
This package accomplishes two tasks: a) it classifies
        implicit trading activity from quotes in OTC markets using the
        algorithm of Lee and Ready (1991); b) based on information for
        trade initiation, the package computes the probability of
        informed trading of Easley and O'Hara (1987).
A programmatic interface to the 'Finnish Biodiversity Information
    Facility' ('FinBIF') API (<https://api.laji.fi>). 'FinBIF' aggregates
    Finnish biodiversity data from multiple sources in a single open access
    portal for researchers, citizen scientists, industry and government.
    'FinBIF' allows users of biodiversity information to find, access, combine
    and visualise data on Finnish plants, animals and microorganisms. The
    'finbif' package makes the publicly available data in 'FinBIF' easily
    accessible to programmers. Biodiversity information is available on taxonomy
    and taxon occurrence. Occurrence data can be filtered by taxon, time,
    location and other variables. The data accessed are conveniently
    preformatted for subsequent analyses.
Package for time value of money calculation, time series analysis and computational finance.
Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple
    and Archives. Functionality includes reading and parsing all the
    files in a Darwin Core Archive, including the datasets and metadata;
    read and parse simple Darwin Core files; and validation of Darwin
    Core Archives.
Estimation and regularization for covariance matrix of asset
    returns. For covariance matrix estimation, three major types of factor
    models are included: macroeconomic factor model, fundamental factor model and
    statistical factor model. For covariance matrix regularization, four regularized
    estimators are included: banding, tapering, hard-thresholding and soft-
    thresholding. The tuning parameters of these regularized estimators are selected
    via cross-validation.
Find all root(s) of the equation,including complex
        roots;Find root(s) of the equation by dichotomy.Besides,in
        dichotomy, more than one interval can be given at a time.
The heterogeneous treatment effect estimation procedure 
        proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.  
        The proposed method is applicable, for
        example, when selecting a small number of most (or least)
        efficacious treatments from a large number of alternative
        treatments as well as when identifying subsets of the
        population who benefit (or are harmed by) a treatment of
        interest. The method adapts the Support Vector Machine
        classifier by placing separate LASSO constraints over the
        pre-treatment parameters and causal heterogeneity parameters of
        interest. This allows for the qualitative distinction between
        causal and other parameters, thereby making the variable
        selection suitable for the exploration of causal heterogeneity. 	
        The package also contains a class of functions, CausalANOVA, 
        which estimates the average marginal interaction effects (AMIEs)
        by a regularized ANOVA as proposed by Egami and Imai (2019)<DOI:10.1080/01621459.2018.1476246>. 
        It contains a variety of regularization techniques to facilitate 
	analysis of large factorial experiments.
Package designed to find an acceptable python binary.
Scans all directories and subdirectories of a path for code snippets, R scripts,
    R Markdown, PDF or text files containing a specific pattern.  Files found can be copied to a new folder.
A tool to explore wide data sets, by detecting, ranking
    and plotting groups of statistically dependent columns.
Statistical tool set for population genetics. The package provides following functions: 1) empirical Bayes estimator of Fst and other measures of genetic differentiation, 2) regression analysis of environmental effects on genetic differentiation using bootstrap method, 3) interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.
Functions to manipulate binary fingerprints
 of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
 which is internally represented a vector of integers, such
 that each element represents the position in the fingerprint that is set to 1.
 The bitwise logical functions in R are overridden so that they can be used directly
 with 'fingerprint' objects. A number of distance metrics are also
 available (many contributed by Michael Fadock). Fingerprints 
 can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
 can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
 handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.
Quantifies the provenance of the sediments in a catchment or study area. Based on a comprehensive characterization of the sediment sources and the end sediment mixtures a mixing model algorithm is applied to the sediment mixtures in order to estimate the relative contribution of each potential source. The package includes several statistical methods such as Kruskal-Wallis test, discriminant function analysis ('DFA'), principal component plot ('PCA') to select the optimal subset of tracer properties. The variability within each sediment source is also considered to estimate the statistical distribution of the sources contribution.
Use Fingertips charts to recreate the visualisations 
    that are displayed on the Fingertips website (<http://fingertips.phe.org.uk/>).
Fingertips (<http://fingertips.phe.org.uk/>) contains data for many indicators of public health in England. The underlying data is now more easily accessible by making use of the API.
In the Cramér–Lundberg risk process perturbed by a Wiener
    process, this packages provides approximations to the probability of
    ruin within a finite time horizon.  Currently, there are three methods
    implemented: The first one uses saddlepoint approximation (two
    variants are provided), the second one uses importance sampling and
    the third one is based on the simulation of a dual process.  This last
    method is not very accurate and only given here for completeness.
Download and display company financial data from the U.S. Securities
    and Exchange Commission's EDGAR database. It contains a suite of functions with
    web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR 
    in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
    for more information.
R companion to Tsay (2005)
 Analysis of Financial Time Series, second edition (Wiley).
 Includes data sets, functions and script files
 required to work some of the examples.  Version 0.3-x
 includes R objects for all data files used in the text
 and script files to recreate most of the analyses in
 chapters 1-3 and 9 plus parts of chapters 4 and 11.
The Brazilian vehicle purchase pricing table is provided by 
  the Institute of Economic Research Foundation (Fipe) and used in purchase 
  negotiations according to region, vehicle’s conservation, color, accessories 
  or any other factor that might influence the demand and supply for a specific 
  vehicle. For more on the data themselves and web access, please see 
  <https://www.fipe.org.br/en-us/home/>.
The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets.
    The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.
Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <DOI:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <DOI:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.
Proposes non-parametric estimates of the Fisher information measure and the 
              Shannon entropy power. The state-of-the-art studies related to the Fisher-Shannon 
              measures, with new analytical formulas for positive unimodal skewed distributions 
              are presented in Guignard et al. <arXiv:1912.02452>. A 'python' version of this 
              work is available on 'github' and 'PyPi' ('FiShPy').
A collection of four datasets 
    based around the population dynamics of migratory fish. Datasets 
    contain both basic size information on a per fish basis, as well as
    otolith data that contains a per day record of fish growth history.
    All data in this package was collected by the author, from 
    2015-2016, in the Wellington region of New Zealand.
The FisherEM algorithm, proposed by Bouveyron & Brunet (201) <doi:10.1007/s11222-011-9249-9>,
        is an efficient method for the clustering of high-dimensional data. FisherEM models and 
        clusters the data in a discriminative and low-dimensional latent subspace. It also provides
        a low-dimensional representation of the clustered data. A sparse version of Fisher-EM
        algorithm is also provided.
fisheyeR provides tools for creating Interactive Data
        Visualizations by implementing ideas from Furnas, Munzner,
        Costa and Venturini.
FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.
Dataset of 302 measurements of 11 fish species to accompany the
    manuscript "Length-weight relationships of six freshwater fish species
    from lake Kirkkojarvi, Finland".
Functions for applying a wide range of fisheries stock assessment methods.
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs.
Functions to predict fish movement parameters plotting leptokurtic fish dispersal kernels (see Radinger and Wolter, 2014: Patterns and predictors of fish dispersal in rivers. Fish and Fisheries. 15:456-473.)
Calculates metabolic rate of fish and other aquatic organisms measured using
             an intermittent-flow respirometry approach. The tool is used to run a set of graphical
             QC tests of raw respirometry data, correct it for background respiration and chamber
             effect, filter and extract target values of absolute and mass-specific metabolic rate.
             Experimental design should include background respiration tests and measuring of one
             or more metabolic rate traits. The package allows a user to import raw respirometry
             data obtained from 'AquaResp' (free software), 'AutoResp' ('LoligoSystems'), 'OxyView'
             ('PreSens'), 'Pyro Oxygen Logger' ('PyroScience') and 'Q-box Aqua' ('QubitSystems').
An interface to the Fish Tree of Life API to download taxonomies,
    phylogenies, fossil calibrations, and diversification rate information for
    ray-finned fishes.
Implementation of color palettes based on fish species.
Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. 
  This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software 'FisPro' <https://www.fispro.org>. 
  'FisPro' allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system.
Provides functionality for constructing
    statistical models of transcriptomic dynamics in field conditions.
    It further offers the function to predict expression of a gene given 
    the attributes of samples and meteorological data. Nagano, A. J., Sato, 
    Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and 
    Izawa, T. (2012). <doi:10.1016/j.cell.2012.10.048>. Iwayama, K., Aisaka, Y., 
    Kutsuna, N., and Nagano, A. J. (2017). <doi:10.1093/bioinformatics/btx049>.
The fit.models function and its associated methods (coefficients,
  print, summary, plot, etc.) were originally provided in the robust package to
  compare robustly and classically fitted model objects. The aim of the fit.models
  package is to separate this fitted model object comparison functionality from
  the robust package and to extend it to support fitting methods (e.g., classical,
  robust, Bayesian, regularized, etc.) more generally.
Comprehensive model building function for identification,
        estimation and diagnostic checking for AR and subset AR models.
        Two types of subset AR models are supported.  One family of
        subset AR models, denoted by ARp, is formed by taking subet of
        the original AR coefficients and in the other, denoted by ARz,
        subsets of the partial autocorrelations are used. The main
        advantage of the ARz model is its applicability to very large
        order models.
Implements fast maximum likelihood algorithm for fitting ARMA time series. Uses S3 methods print, summary, fitted, residuals. Fast exact Gaussian ARMA simulation.
Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official
    API, but instead uses the API that the web dashboard uses to generate the graphs
    displayed on the dashboard after login at <http://www.fitbit.com>.
A pure R package for decoding activity files written in the FIT ("Flexible and Interoperable Data Transfer") format. A format that is fast becoming the standard for recording running and cycling data. Details of the FIT protocol can be found at <https://www.thisisant.com/resources/fit>.
Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available. See e.g. Casella & Berger (2002). Statistical inference. Pacific Grove.
Fits Density Ratio Classes to elicited
        probability-quantile points or intervals.
Robust estimation methods for the mean vector and covariance matrix 
    from data (possibly containing NAs) under multivariate heavy-tailed 
    distributions such as angular Gaussian, Cauchy, and Student's t. Additionally,
    a factor model structure can be specified for the covariance matrix.
    The package is based on the papers:
    Y. Sun, P. Babu, and D. P. Palomar (2014). Regularized Tyler’s Scatter 
    Estimator: Existence, Uniqueness, and Algorithms. IEEE Trans. on Signal 
    Processing, vol. 62, no. 19, pp. 5143-5156. <doi:10.1109/TSP.2014.2348944>.
    Y. Sun, P. Babu, and D. P. Palomar (2015). Regularized Robust Estimation of 
    Mean and Covariance Matrix Under Heavy-Tailed Distributions. IEEE Trans. on 
    Signal Processing, vol. 63, no. 12, pp. 3096-3109. <doi:10.1109/SAM.2014.6882356>.
    Chuanhai Liu and Donald B. Rubin (1995).
    ML estimation of the t-distribution using EM and its extensions, ECM and ECME.
    Statistica Sinica (5), pp. 19-39.
    <https:www3.stat.sinica.edu.tw/statistica/oldpdf/A5n12.pdf>.
    R. Zhou, J. Liu, S. Kumar, and D. P. Palomar (2015).
    Robust factor analysis parameter estimation.
    Lecture Notes in Computer Science (LNCS). <arXiv:1909.12530>.
Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>), also Journal article to use the package(<doi:10.21105/joss.01505>).
Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.
Genotyping assays for bi-allelic markers (e.g. SNPs) produce
	signal intensities for the two alleles. 'fitPoly' assigns genotypes 
	(allele dosages) to a collection of polyploid samples based on these
	signal intensities. 'fitPoly' replaces the older package 'fitTetra' that was
	limited (a.o.) to only tetraploid populations whereas 'fitPoly' accepts any
	ploidy level. Reference: Voorrips RE, Gort G, Vosman B (2011)
	<doi:10.1186/1471-2105-12-172>.
Utilities to read and write files in the FITS (Flexible
  Image Transport System) format, a standard format in astronomy (see
  e.g. <https://en.wikipedia.org/wiki/FITS> for more information).
  Present low-level routines allow: reading, parsing, and modifying
  FITS headers; reading FITS images (multi-dimensional arrays);
  reading FITS binary and ASCII tables; and writing FITS images
  (multi-dimensional arrays).  Higher-level functions allow: reading
  files composed of one or more headers and a single (perhaps
  multidimensional) image or single table; reading tables into
  data frames; generating vectors for image array axes; scaling and
  writing images as 16-bit integers.  Known incompletenesses are
  reading random group extensions, as well as
  bit, complex, and array descriptor data types in binary tables.
Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a 'shiny' app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.
Package fitTetra contains three functions that can be used
        to assign genotypes to a collection of tetraploid samples based
        on bialleleic marker assays. Functions fitTetra (to fit several
        models for one marker from the data and select the best
        fitting) or saveMarkerModels (calls fitTetra for multiple
        markers and saves the results to files) will probably be the
        most convenient to use. Function CodomMarker offers more
        control and fits one specified model for a given marker.
Extends the fitdist() (from 'fitdistrplus') adding the Anderson-Darling ad.test() (from 'ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from 'stats' package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from 'mclust').
Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.
An easy package for scraping and processing Australia Rules Football (AFL)
    data. 'fitzRoy' provides a range of functions for accessing publicly available data 
    from 'AFL Tables' <https://afltables.com>, 'Footy Wire' <https://www.footywire.com> and
    'The Squiggle' <https://squiggle.com.au>. Further functions allow for easy processing, 
    cleaning and transformation of this data into formats that can be used for analysis.
Datasets and code published by the data journalism website 
    'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>. 
    Note that while we received guidance from editors at 'FiveThirtyEight', this 
    package is not officially published by 'FiveThirtyEight'.
For functions that take and return vectors (or scalars), this package provides 8 algorithms for finding fixed point vectors (vectors for which the inputs and outputs to the function are the same vector). These algorithms include Anderson (1965) acceleration <doi:10.1145/321296.321305>, epsilon extrapolation methods (Wynn 1962 <doi:10.2307/2004051>) and minimal polynomial methods (Cabay and Jackson 1976 <doi:10.1137/0713060>).
Distribution functions and test for over-representation of short
    distances in the Liland distribution. Simulation functions are included for
    comparison.
An R client for the "fixer.io" currency conversion and exchange 
  rate API. The API requires registration and some features are only available 
  on paid accounts. The full API documentation is available at 
  <https://fixer.io/documentation>.
Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial.
    The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://wwwen.uni.lu/content/download/110162/1299525/file/2018_13>.
    Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
Several generalized / directional Fixed Sequence Multiple Testing
    Procedures (FSMTPs) are developed for testing a sequence of pre-ordered
    hypotheses while controlling the FWER, FDR and Directional Error (mdFWER).
    All three FWER controlling generalized FSMTPs are designed under arbitrary
    dependence, which allow any number of acceptances. Two FDR controlling
    generalized FSMTPs are respectively designed under arbitrary dependence and
    independence, which allow more but a given number of acceptances. Two mdFWER
    controlling directional FSMTPs are respectively designed under arbitrary
    dependence and independence, which can also make directional decisions based
    on the signs of the test statistics. The main functions for each proposed
    generalized / directional FSMTPs are designed to calculate adjusted p-values
    and critical values, respectively. For users' convenience, the functions also
    provide the output option for printing decision rules.
An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. 
    It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start 
    and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives 
    interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm
    themselves.
This is a fast and flexible implementation of the Kalman
        filter, which can deal with NAs. It is entirely written in C
        and relies fully on linear algebra subroutines contained in
        BLAS and LAPACK. Due to the speed of the filter, the fitting of
        high-dimensional linear state space models to large datasets
        becomes possible. This package also contains a plot function
        for the visualization of the state vector and graphical
        diagnostics of the residuals.
Implements the method of Hofmeyr, D.P. (2019) <10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations.
  Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods
  for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected
  covariates.
Contains tools and features, which can be used for an Exploratory
    Landscape Analysis (ELA) of single-objective continuous optimization problems.
    Those features are able to quantify rather complex properties, such as the
    global structure, separability, etc., of the optimization problems.
Three methods are implemented in R to facilitate the aggregations of flags in official statistics.  
            From the underlying flags the highest in the hierarchy, the most frequent, or with the highest total weight
            is propagated to the flag(s) for EU or other aggregates. Below there are some reference documents for the topic: 
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_v2_1.docx>,
            <https://sdmx.org/wp-content/uploads/CL_CONF_STATUS_1_2_2018.docx>,
            <http://ec.europa.eu/eurostat/data/database/information>,
            <http://www.oecd.org/sdd/33869551.pdf>,
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.pdf>.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.
The 'FLAME' (Fast Large-scale Almost Matching Exactly) package implements the
    matching algorithm in Roy, Rudin, Volfovsky, and Wang (2017) <arXiv:1707.06315>. 'FLAME' 
    performs matching of treatment and control units in the potential outcomes framework 
    for large categorical datasets. 'FLAME' creates matches that include as many covariates 
    as possible, and sequentially drops covariates that are less useful based on a match 
    quality measure. Match quality combines two important elements – it considers predictive 
    power from machine learning on a hold out training set, and a balancing factor to ensure 
    that it does not remove a covariate that would ruin overlap between treatment and 
    control groups. Currently the 'FLAME' package applies to categorical data, and 
    provides two approaches for implementation - bit vectors and database management systems 
    (e.g., 'PostgreSQL', 'SQLite'). For data that has been adequately processed and fits in memory, 
    bit vectors should be applied. For extremely large data that does not fit in memory, database 
    systems should be applied.
Provides a variety of original and flexible user-friendly 
    statistical latent variable models for the simultaneous clustering and 
    segmentation of heterogeneous functional data (i.e time series, or more 
    generally longitudinal data, fitted by unsupervised algorithms, including 
    EM algorithms. Functional Latent Data Models for Clustering heterogeneous 
    curves ('FLaMingos') are originally introduced and written in 'Matlab' by
    Faicel Chamroukhi 
    <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. 
    The references are mainly the following ones.
    Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>.
    Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>.
    Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). <doi:10.1109/IJCNN.2011.6033590>.
    Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>.
    Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>.
    Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>.
    Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>.
    Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.
Tools for fluctuations analysis of mutant cells counts.
Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.
Variable selection algorithm for functional linear regression with scalar response variable and mixed scalar/functional predictors.
Fast implementation of hierarchical clustering
Shed light on black box machine learning models by
    the help of model performance, permutation variable importance (Fisher
    et al. (2018) <arxiv:1801.01489>), ICE profiles, partial dependence
    (Friedman J. H. (2001) <doi:10.1214/aos/1013203451>), accumulated
    local effects (Apley D. W. (2016) <arXiv:1612.08468>), further effects
    plots, interaction strength, and variable contribution breakdown
    (approximate SHAP) for single observations (Gosiewska and Biecek
    (2019) <arxiv:1903.11420>). All tools are implemented to work with
    case weights and allow for stratified analysis. Furthermore, multiple
    flashlights can be combined and analyzed together.
Contingency Tables are a pain to work with when you want to run regressions.
  This package takes them, flattens them into a long data frame, so you can more easily analyse them!
  As well, you can calculate other related statistics. All of this is done so in a 'tidy' manner,
  so it should tie in nicely with 'tidyverse' series of packages.
On import, the XML information is converted to a dataframe that reflects the hierarchical XML structure. Intuitive functions allow to navigate within this transparent XML data structure (without any knowledge of 'XPath'). 'flatXML' also provides tools to extract data from the XML into a flat dataframe that can be used to perform statistical operations.
The main function kcca implements a general framework for
  k-centroids cluster analysis supporting arbitrary distance measures
  and centroid computation. Further cluster methods include hard
  competitive learning, neural gas, and QT clustering. There are
  numerous visualization methods for cluster results (neighborhood
  graphs, convex cluster hulls, barcharts of centroids, ...), and
  bootstrap methods for the analysis of cluster stability.
Format for converting an R Markdown document to a grid oriented
  dashboard. The dashboard flexibly adapts the size of it's components to the
  containing web page.
Provides tools to work with the Flexible Dirichlet
    distribution. The main features are an E-M algorithm for computing the maximum
    likelihood estimate of the parameter vector and a function based on conditional
    bootstrap to estimate its asymptotic variance-covariance matrix. It contains
    also functions to plot graphs, to generate random observations and to handle
    compositional data.
Standard generalized additive models assume a response function, 
    which induces an assumption on the shape of the distribution of the 
    response. However, miss-specifying the response function results in biased 
    estimates. Therefore in Spiegel et al. (2017) 
    <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function
    jointly with the covariate effects. This package provides the underlying 
    functions to estimate these generalized additive models with flexible 
    response functions. The estimation is based on an iterative algorithm. In 
    the outer loop the response function is estimated, while in the inner loop 
    the covariate effects are determined. For the response function a strictly
    monotone P-spline is used while the covariate effects are estimated based on
    a modified Fisher-Scoring algorithm. Overall the estimation relies on the 
    'mgcv'-package.
Application of the filtered monotonic polynomial (FMP) item response
    model to flexibly fit item response models. The package includes tools that
    allow the item response model to be build on any monotonic transformation of
    the latent trait metric, as described by Feuerstahler (2016) <http://hdl.handle.net/11299/182267>.
A general framework for finite mixtures of regression
  models using the EM algorithm is implemented. The E-step and all
  data handling are provided, while the M-step can be supplied by the
  user to easily define new models. Existing drivers implement
  mixtures of standard linear models, generalized linear models and
  model-based clustering.
The fitting of mixtures of generalized nonlinear models is implemented as an extension of the existing package 'flexmix'.
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.
Estimation of flexible parametric models for survival data.
Package for parametric relative survival analyses. It allows to model non-linear and 
        non-proportional effects using splines (B-spline and truncated power basis). It also includes 
        both non proportional and non linear effects of 
			Remontet, L. et al. (2007) <DOI:10.1002/sim.2656> and 
			Mahboubi, A. et al. (2011) <DOI:10.1002/sim.4208>.
An easy way to conduct flexible scan.
    Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile.
    A table with formal style and a map with clusters are included in the result report.
    The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
Flexible parametric models for time-to-event data,
    including the Royston-Parmar spline model, generalized gamma and
    generalized F distributions.  Any user-defined parametric
    distribution can be fitted, given at least an R function defining
    the probability density or hazard. There are also tools for
    fitting and predicting from fully parametric multi-state models.
Flexible parametric mixture and non-mixture cure models for time-to-event data.
Create pretty tables for 'HTML', 'Microsoft Word' and 'Microsoft PowerPoint' documents. 
  Functions are provided to let users create tables, modify and format their content. 
  It extends package 'officer' that does not contain any feature for customized tabular reporting 
  and can be used within R markdown documents.
Provides an interface to the Flickr API <https://www.flickr.com/services/api/>
      and allows R users to download data on Flickr.
Functions to create and manipulate 
    FIFO (First In First Out), LIFO (Last In First Out), and NINO (Not In or Never Out) 
    stacks in R.
Utility functions for creating flight plans for unmanned aerial vehicles (UAV), specially for the Litchi Hub platform. It calculates the flight and camera settings based on the camera specifications, exporting the flight plan CSV format ready to import into Litchi Hub.
Spatio-temporal locations of an animal are computed 
    from annotated data with a hidden Markov  model via particle
    filter algorithm. The package is relatively robust to varying
    degrees of shading.
    The hidden Markov model is described in Movement Ecology (Rakhimberdiev et al., 2015) <doi:10.1186/s40462-015-0062-5>,
    general package description is in the Methods in Ecology and Evolution (Rakhimberdiev et al., 2017) <doi:10.1111/2041-210X.12765>
    and package accuracy assessed in the Journal of Avian Biology (Rakhimberdiev et al. 2016) <doi:10.1111/jav.00891>.
It implements many univariate and multivariate permutation (and
    rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear
    models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney,
    Kruskal-Wallis), Sign test and Mc Nemar. Test on Linear Models are
    performed also in presence of covariates (i.e. nuisance parameters).
    The permutation and the rotation methods to get the null distribution of
    the test statistics are available. It also implements methods for
    multiplicity control such as Westfall & Young minP procedure and Closed
    Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed
    effects in mixed effects models.
The lipid scrambling activity of protein extracts and purified
    scramblases is often determined using a fluorescence-based assay involving
    many manual steps. flippant offers an integrated solution for the analysis
    and publication-grade graphical presentation of dithionite scramblase
    assays, as well as a platform for review, dissemination and extension of the
    strategies it employs. The package's name derives from a play on the fact
    that lipid scrambling is also sometimes referred to as 'flipping'.
Provides robust tests for testing in GLMs,
    by sign-flipping score contributions.
    The tests are often robust against overdispersion,
    heteroscedasticity and, in some cases,
    ignored nuisance variables.
    See Hemerik and Goeman (2017) <doi:10.1007/s11749-017-0571-1>.
Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV).  Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample.  Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
R comes with a suite of utilities for linear algebra with "numeric"
    (double precision) vectors/matrices. However, sometimes single precision (or
    less!) is more than enough for a particular task.  This package extends R's
    linear algebra facilities to include 32-bit float (single precision) data.
    Float vectors/matrices have half the precision of their "numeric"-type
    counterparts but are generally faster to numerically operate on, for a
    performance vs accuracy trade-off.  The internal representation is an S4
    class, which allows us to keep the syntax identical to that of base R's.
    Interaction between floats and base types for binary operators is generally
    possible; in these cases, type promotion always defaults to the higher
    precision.  The package ships with copies of the single precision 'BLAS' and
    'LAPACK', which are automatically built in the event they are not available
    on the system.
Converts files to and from flobs. 
    A flob is a file that was 
    read into binary in integer-mode as little endian, 
    saved as the single element of a named list (where the name is the name 
    of the original file) and then serialized before being coerced into a blob.
    Flobs are useful for writing and reading files to and from databases.
Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.
Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations).
	For details see Kinsvater et al. (2017) <arXiv:1701.06455>.
A gridded model to facilitate cross-scale flooding prediction under heavy precipitation events. The model also allows the consideration of current urban stormwater systems and their future developments in order to help explore potential adaptation strategies against climate change. Reference: Wang, X., G. Huang, and J. Liu (2014) <doi:10.1002/2014JD022564>.
Tools to quickly compile taxonomic and distribution data from
    the Brazilian Flora 2020.
Creates and assesses a list of possible networks solved using 'LIM' (Linear Inverse Modelling) (Soetaert, Karline, and Dick Van Oevelen (2009) <doi:10.1007/s10021-009-9297-6>) and restructuring this list, enabling ENA (Ecological Network Analysis) to be performed on the flow network in R package 'enaR' (Borrett, Stuart R., and Matthew K. Lau (2014) <doi:10.1111/2041-210X.12282>).
Uses multidimensional contingency tables to calculate ecological diversity indices for gated populations.
Flowering is an important life history trait of flowering plants. It has been mainly analyzed with respect to flowering onset and duration of flowering. This tools provide some functions to compute the temporal distribution of an flowering individual related to other population members. fCV() measures the temporal variation in flowering. RIind() measures the rank order of flowering for individual plants within a population. SI(), SI2(), SI3(), and SI4() calculate flowering synchrony with different methods.
Flow field forecasting draws information from an interpolated flow field of the observed time series to incrementally build a forecast.
This framework allows you to design and implement complex
    pipelines, and deploy them on your institution's computing cluster. This has
    been built keeping in mind the needs of bioinformatics workflows. However, it is
    easily extendable to any field where a series of steps (shell commands) are to
    be executed in a (work)flow.
An application to calculate the daily environmental costs of river flow regulation by dams based on García de Jalon et al. 2017 <doi:10.1007/s11269-017-1663-0>.
Selections on flow matrices, statistics on selected flows, map and
    graph visualisations.
Screens daily streamflow time series for temporal trends and
    change-points. This package has been primarily developed for assessing the
    quality of daily streamflow time series. It also contains tools for plotting
    and calculating many different streamflow metrics. The package can be used to
    produce summary screening plots showing change-points and significant temporal
    trends for high flow, low flow, and/or baseflow statistics, or it can be used
    to perform more detailed hydrological time series analyses. The package was
    designed for screening daily streamflow time series from Water Survey Canada
    and the United States Geological Survey but will also work with streamflow time
    series from many other agencies.
Implements a path algorithm for the Fused Lasso Signal Approximator. 
    For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>.
Specialized solvers for combinatorial optimization problems in the Subset Sum family. These solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value sets with predefined subset sum errors, and (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The latter decomposes the problem in a novel approach, and the multi-threaded framework offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. Package updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) an option of mapping real-domain problems to the integer domain with user-controlled precision loss, and those integers are further zipped non-uniformly in 64-bit buffers. Arithmetic on compressed integers is done by bit-manipulation and the design has virtually zero speed lag relative to normal integers arithmetic. The consequent reduction in dimensionality may yield substantial acceleration. Compilation with g++ '-Ofast' is recommended. See package vignette (<arXiv:1612.04484v3>) for details. Functions prefixed with 'aux' (auxiliary) are or will be implementations of existing foundational or cutting-edge algorithms for solving optimization problems of interest.
Functions to estimate the mortality attributable to 
    influenza and temperature, using distributed-lag nonlinear models
    (DLNMs), as first implemented in 
    Lytras et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.14.1800118>.
    Full descriptions of underlying DLNM methodology in Gasparrini et al. 
    <doi:10.1002/sim.3940> (DLNMs), 
    <doi:10.1186/1471-2288-14-55> (attributable risk from DLNMs) and 
    <doi:10.1002/sim.5471> (multivariate meta-analysis).
Use spectrophotometry measurements performed on insects as a way to infer pathogens 
      virulence. Insect movements cause fluctuations in fluorescence signal, and functions are 
      provided to estimate when the insect has died as the moment when variance in autofluorescence 
      signal drops to zero. The package provides functions to obtain this estimate together with 
      functions to import spectrophotometry data from a Biotek microplate reader. Details of the method 
      are given in Parthuisot et al. (2018) <doi:10.1101/297929>.
Contains data sets from Bernard Flury (1997) A First
        Course in Multivariate Statistics, Springer NY
A model for leaf fluorescence, reflectance and transmittance spectra. It implements
    the model introduced by Vilfan et al. (2016) <DOI:10.1016/j.rse.2016.09.017>. Fluspect-B
    calculates the emission of ChlF on both the illuminated and shaded side of the leaf.
    Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter 
    and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF 
    quantum efficiency for the two photosystems, PS-I and PS-II.
Functions for the calculation of greenhouse gas flux rates 
	from closed chamber concentration measurements. The package follows 
	a modular concept: Fluxes can be calculated in just two simple steps 
	or in several steps if more control in details is wanted. Additionally 
	plot and preparation functions as well as functions for modelling
	gpp and reco are provided.
Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.
Perform input, output of files in R from data sources like Google Cloud Storage ('GCS') <https://cloud.google.com/storage/>, Amazon Web Services ('AWS S3') <https://aws.amazon.com/s3> or local drive.
All data sets from "Forecasting: methods and applications" by
             Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.
Implements basic financial market objects like currencies, currency
  pairs, interest rates and interest rate indices. You will be able to use
  Benchmark instances of these objects which have been defined using their most
  common conventions or those defined by International Swap Dealer Association
  (ISDA, <https://www.isda.org>) legal documentation.
Generate cost effective minimally changed run sequences 
              for symmetrical as well as asymmetrical factorial 
              designs.
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC)
         framework for implementing Metropolis-Hastings algorithm in a modular way
         allowing users to specify automatic convergence checker, personalized
         transition kernels, and out-of-the-box multiple MCMC chains using
         parallel computing. Most of the methods implemented in this package can
         be found in Brooks et al. (2011, ISBN 9781420079425).
Implements common date calculations relevant for specifying
  the economic nature of financial market contracts that are typically defined
  by International Swap Dealer Association (ISDA, <http://www2.isda.org>) legal
  documentation. This includes methods to check whether dates are business
  days in certain locales, functions to adjust and shift dates and time length
  (or day counter) calculations.
Provides functions to help in fitting models to data, to
  perform Monte Carlo, sensitivity and identifiability analysis. It is
  intended to work with models be written as a set of differential
  equations that are solved either by an integration routine from
  package 'deSolve', or a steady-state solver from package
  'rootSolve'. However, the methods can also be used with other types of
  functions.
Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.
Functions that support stable prediction and classification with radiomics data through factor-analytic modeling. For details, see Peeters et al. (2019) <arXiv:1903.11696>.
Contains R-functions to perform an fMRI analysis as described in
             Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>,
             Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>,
             Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Methods for performing fMRI quality assurance (QA) measurements of
  test objects. Heavily  based on the fBIRN procedures detailed by Friedman and 
  Glover (2006) <doi:10.1002/jmri.20583>.
Provides parameter estimation as well as variable selection in
    Finite Mixture of Accelerated Failure Time Regression and Finite
    Mixture of Regression Models.
    Furthermore, this package provides Ridge Regression and Elastic Net.
Several utility functions for the book entitled 
	"Practices of Medical and Health Data Analysis using R"
	(Pearson Education Japan, 2007) with Japanese demographic
	data and some demographic analysis related functions.
Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.
This package implements some basic procedures for dealing
        with log maximally skew stable distributions, which are also
        called finite moment log stable distributions.
Provides a  collection of functions 
	to manage, to investigate and to analyze bivariate and multivariate 
	data sets of financial returns.
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
        including KNN classification, regression and information measures are implemented.
Provides a collection of functions for testing various aspects of
	univariate time series including independence and neglected
	nonlinearities. Further provides functions to investigate the chaotic
	behavior of time series processes and to simulate different types of chaotic
	time series maps.
foba is a package that implements forward, backward, and foba sparse learning algorithms for ridge regression, described in the paper "Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations".
Feature Ordering by Conditional Independence (FOCI) is a variable selection algorithm based on the measure of conditional dependence.
    For more information, see the paper: Azadkia and Chatterjee (2019),"A simple measure of conditional dependence" <arXiv:1910.12327>.
Takes a distance matrix and plots it as an 
    interactive graph. One point is focused at the center of the graph,
    around which all other points are plotted in their exact distances as
    given in the distance matrix. All other non-focus points are plotted 
    as best as possible in relation to one another. Double click on any 
    point to choose a new focus point, and hover over points to see their
    ID labels. If color label categories are given, hover over colors in 
    the legend to highlight only those points and click on colors to 
    highlight multiple groups. For more information on the rationale 
    and mathematical background, as well as an interactive introduction,
    see <https://lea-urpa.github.io/focusedMDS.html>.
The CRAN check results and where your package stands in the
    CRAN submission queue in your R terminal.
Fits continuous-time random walk and correlated random walk state-space models to filter animal tracking data ('Argos', processed light-level 'geolocation', 'GPS'). Template Model Builder ('TMB') is used for fast estimation. The 'Argos' data can be: (older) least squares-based locations; (newer) Kalman filter-based locations with error ellipse information; or a mixture of both. The models estimate two sets of location states corresponding to: 1) each observation, which are (usually) irregularly timed; and 2) user-specified time intervals (regular or irregular). Latent variable models are provided to estimate move persistence along tracks as an index of behaviour. 'Jonsen I', 'McMahon CR', 'Patterson TA', 'Auger-Methe M', 'Harcourt R', 'Hindell MA', 'Bestley S' (2019) Movement responses to environment: fast inference of variation among southern elephant seals with a mixed effects model. Ecology 100:e02566 <doi:10.1002/ecy.2566>.
If you find yourself working on multiple different projects in R, you'll want a 
	series of folders pointing to raw data, processed data, plot results, intermediate table 
	outputs, etc. This package makes it easier to do that by providing a quick and easy way 
	to create and use functions for project-level directories.
Provides fonts licensed under the 'Bitstream Vera Fonts'
    license for the 'fontquiver' package.
Computer Modern font for use with extrafont package
Provides 'ggplot2' themes based on the 'Hind' fonts.
  'Hind' is an open source 'typeface' supporting the 'Devanagari' and Latin scripts.
  Developed explicitly for use in User Interface design, the 'Hind' font family includes five styles.
  More information about the font can be found at <https://fonts.google.com/specimen/Hind>.
A placeholder for the Liberation fontset intended for the
    `fontquiver` package. This fontset covers the 12 combinations of
    families (sans, serif, mono) and faces (plain, bold, italic, bold
    italic) supported in R graphics devices.
Provides 'ggplot2' themes based on the 'M+' fonts.
  The 'M+' fonts are a font family under a free license. The font family provides
  multilingual glyphs. The fonts provide 'Kana', over 5,000 'Kanji', Basic Latin,
  Latin-1 Supplement, Latin Extended-A, and 'IPA' Extensions glyphs. Most of the Greek,
  Cyrillic, Vietnamese, and extended glyphs and symbols are included too.
  So the fonts are in conformity with ISO-8859-1, 2, 3, 4, 5, 7, 9, 10, 13, 14, 15, 16,
  Windows-1252, T1, and VISCII encoding.
  More information about the fonts can be found at <http://mplus-fonts.osdn.jp/about-en.html>.
Provides a set of fonts with permissive licences. This is
    useful when you want to avoid system fonts to make sure your
    outputs are reproducible.
Displays a weighted undirected food graph from an adjacency matrix.
    Can perform confidence-interval bootstrap inference with mutual information
    or maximal information coefficient.
    Based on my Master 1 internship at the Bordeaux Population Health center.
    References : Reshef et al. (2011) <doi:10.1126/science.1205438>,
    Meyer et al. (2008) <doi:10.1186/1471-2105-9-461>,
    Liu et al. (2016) <doi:10.1371/journal.pone.0158247>.
Calculates twelve commonly-used, basic measures of food
        web network structure from binary, predator-prey matrices:
        species richness, connectance, total number of links, link
        density, number of trophic positions, predator:prey ratio, and
        fraction of carnivores, herbivores, top species and
        intermediate species.  Employs food web language in the code
        and output, translates between a couple of common food web
        formats, can handle food webs consisting of multiple levels,
        and can automate the analysis for a large number of webs. The
        program produces 3-dimensional graphs of high quality that can
        be customized by the user.
Provides functionality for manipulating functions and translating them
    in metaprogramming.
Provides a collection of functions to 
    valuate basic options. This includes the generalized
	Black-Scholes option, options on futures and options on
	commodity futures.
SHE, FORAM Index and ABC Method analyses and custom plot
        functions for community data.
Helpers for reordering factor levels (including
    moving specified levels to front, ordering by first appearance,
    reversing, and randomly shuffling), and tools for modifying factor
    levels (including collapsing rare levels into other, 'anonymising',
    and manually 'recoding').
Support for the foreach looping construct.  Foreach is an
        idiom that allows for iterating over elements in a collection,
        without the use of an explicit loop counter.  This package in
        particular is intended to be used for its return value, rather
        than for its side effects.  In that sense, it is similar to the
        standard lapply function, but doesn't require the evaluation
        of a function.  Using foreach without side effects also
        facilitates executing the loop in parallel.
Implementation of Forecastable Component Analysis ('ForeCA'),
    including main algorithms and auxiliary function (summary, plotting, etc.) to
    apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension
    reduction (DR) technique for temporally dependent signals. Contrary to other
    popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency
    explicitly into account and searches for the most ''forecastable'' signal.
    The measure of forecastability is based on the Shannon entropy of the spectral
    density of the transformed signal.
Methods and tools for displaying and analysing
             univariate time series forecasts including exponential smoothing
             via state space models and automatic ARIMA modelling.
Provides geometric- and regression-based forecast
    combination methods under a unified user interface for the packages 'ForecastCombinations'
    and 'GeomComb'. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity). For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>,
    Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>, 
    and Clemen RT (1989). <doi:10.1016/0169-2070(89)90012-5>.
Aim: Supports the most frequently used methods to combine forecasts. Among others: Simple average, Ordinary Least Squares, Least Absolute Deviation, Constrained Least Squares, Variance-based, Best Individual model, Complete subset regressions and Information-theoretic (information criteria based).
Create modular models.  Quickly prototype models whose input includes (multiple) time series data.  Create pieces of model use cases separately, and swap out particular models as desired. Create modeling competitions, data processing pipelines, and re-useable models.
Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights
    based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>),
    or cross-validated weights. Cross validation for time series data with user-supplied models
    and forecasting functions is also supported to evaluate model accuracy.
The purpose of 'forecastML' is to simplify the process of multi-step-ahead direct forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.
Methods to compute linear h-step ahead prediction coefficients based
    on localised and iterated Yule-Walker estimates and empirical mean squared
    and absolute prediction errors for the resulting predictors. Also, functions
    to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time
    series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics,
    forthcoming. Preprint <arXiv:1611.04460>.
Routines for forecasting univariate time series using Theta Models. Contains several cross-validation routines.
Reading and writing data stored by some versions of
	'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
	and for reading and writing some 'dBase' files.
The statistical evaluation of DNA mixtures, DNA profile
        match probability
Statistical methods and simulation tools for the
        interpretation of forensic DNA mixtures
A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) <doi:10.1029/2012EO410001> and Prudhomme et al. (2010) <doi:10.1016/j.jhydrol.2010.06.043>). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) <doi:10.1002/2015WR018253>) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) <doi:10.1016/j.jhydrol.2016.03.025>). It incorporates a number of stochastic weather models to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances.
Metrics of two Essential Biodiversity Variables: forest extents and forest fragmentation indices (O'connor et al., 2015) <doi:10.1002/rse2.4> are computed by processing Global Forest Change data (Hansen et al., 2013) <doi:10.1126/science.1244693>. The Forest Change Data are extracted using either Geographic Administrative Units, see <https://gadm.org/>, or user-defined polygons. Most of the procedures can be understood implementing two functions: FCPolygon() and EBVmetric().
Approximate false positive rate control in selection frequency for
    random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>.
    Methods for calculating the selection frequency threshold at false positive rates
    and selection frequency false positive rate feature selection.
Developed for the following tasks. I) Computing the probability density function, cumulative distribution function, random generation, and estimating the parameters of the eleven mixture models including mixture of Birnbaum-Saunders, BurrXII, Chen, F, Frechet, gamma, Gompertz, log-logistic, log-normal, Lomax, and Weibull. II) Point estimation of the parameters of two- and three-parameter Weibull distributions. In the case of two-parameter, twelve methods consist of generalized least square type 1, generalized least square type 2, L-moment, maximum likelihood, logarithmic moment, moment, percentile, rank correlation, least square, weighted maximum likelihood, U-statistic, weighted least square are used and investigated methods for the three-parameter case are: maximum likelihood, modified moment type 1, modified moment type 2, modified moment type 3, modified maximum likelihood type 1, modified maximum likelihood type 2, modified maximum likelihood type 3, modified maximum likelihood type 4, moment, maximum product spacing, T-L moment, and weighted maximum likelihood. III) The Bayesian estimators of the three-parameter Weibull distribution developed by Green et al. (1994) <doi:10.2307/2533217>. IV) Estimating parameters of the three-parameter Birnbaum-Saunders, generalized exponential, and Weibull distributions fitted to grouped data using three methods including approximated maximum likelihood, expectation maximization, and maximum likelihood. V) Estimating the parameters of the gamma, log-normal, and Weibull mixture models fitted to the grouped data through the EM algorithm, VI) Estimating parameters of the non-linear growth curve fitted to the height-diameter observation, and VII) estimating parameters, computing probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced by Venturini et al. (2008) <doi:10.1214/07-AOAS156>.
Form visualizations of high dimensional mapping structures of random forests and feature contributions.
Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps.
Assessing forest ecosystem health is an effective way for forest
    resource management.The national forest health evaluation system at the forest 
    stand level using analytic hierarchy process, has a high application value 
    and practical significance. The package can effectively and easily realize the 
    total assessment process, and help foresters to further assess and management 
    forest resources.
Extensive global and small-area estimation procedures for multiphase
    forest inventories under the design-based Monte-Carlo approach are provided.
    The implementation includes estimators for simple and cluster sampling
    published by Daniel Mandallaz in 2007 (<DOI:10.1201/9781584889779>),
    2013 (<DOI:10.1139/cjfr-2012-0381>, <DOI:10.1139/cjfr-2013-0181>,
    <DOI:10.1139/cjfr-2013-0449>, <DOI:10.3929/ethz-a-009990020>)
    and 2016 (<DOI:10.3929/ethz-a-010579388>). It provides point estimates,
    their external- and design-based variances as well as confidence intervals.
    The procedures have also been optimized for the use of remote sensing data
    as auxiliary information.
Set of functions for processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and non linear grouped data fitting, and statistical tests.
Produces forest plots using 'ggplot2' from models produced by functions
    such as stats::lm(), stats::glm() and survival::coxph().
A forest plot that allows for
    multiple confidence intervals per row,
    custom fonts for each text element,
    custom confidence intervals,
    text mixed with expressions, and more.
    The aim is to extend the use of forest plots beyond meta-analyses.
    This is a more general version of the original 'rmeta' package's forestplot()
    function and relies heavily on the 'grid' package.
Provides functions that calculates common types of splitting
    criteria used in random forests for classification problems, as well as 
    functions that make predictions based on a single tree or a Forest-R.K. model; 
    the package also provides functions to generate importance plot for a 
    Forest-R.K. model, as well as the 2D multidimensional-scaling plot of 
    data points that are colour coded by their predicted class types by the 
    Forest-R.K. model. This package is based on: 
    Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) 
    "Forest-R.K.: A New Random Forest Induction Method", 
    Fourth International Conference on Intelligent Computing,
    September 2008, Shanghai, China, pp.430-437.
In recent years, there has been considerable interest in a group of neighborhood-based structural parameters that properly express the spatial structure characteristics of tree populations and forest communities and have strong operability for guiding forestry practices.the 'forestSAS' package provide more important information and allow us to better understand and analyze the fine-scale spatial structure of tree populations and stand structure.
Provides tools for analyzing remotely sensed forest data, including functions for detecting treetops from canopy models, outlining tree crowns and generating spatial statistics.
Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class 
    Discrete Weibull Models.This package is based on Fader and Hardie (2007) 
    <doi:10.1002/dir.20074> and Fader and Hardie et al. (2018) <doi:10.1016/j.intmar.2018.01.002>.
Helper functions with a consistent interface to coerce and verify
    the types and shapes of values for input checking.
Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm
This package provides estimates of tree volume and biomass from
    Italian NFI models
Implemented, addition, subtracking, multiplication,
        division in formal series rings of any number of variables
        (except division is only to 3 variables). Also are available
        "[" "[<-" operators.
Provides a function tidy_source() to format R source code. Spaces
    and indent will be added to the code automatically, and comments will be
    preserved under certain conditions, so that R code will be more
    human-readable and tidy. There is also a Shiny app as a user interface in
    this package (see tidy_app()).
Provides functions to create formattable vectors and data frames.
    'Formattable' vectors are printed with text formatting, and formattable
    data frames are printed with multiple types of formatting in HTML
    to improve the readability of data presented in tabular form rendered in
    web pages.
Infrastructure for extended formulas with multiple parts on the
             right-hand side and/or multiple responses on the left-hand side
	     (see <DOI:10.18637/jss.v034.i01>).
These utilities facilitate the programmatic manipulations of
    formulas, expressions, calls, assignments and other R language objects. 
    These objects all share the same structure: a left-hand side, operator and 
    right-hand side. This packages provides methods for accessing and 
    modifying this structures as well as extracting and replacing names and 
    symbols from these objects.
Many statistical models and analyses in R are implemented through formula objects. The formulaic package creates a unified approach for programmatically and dynamically generating formula objects. Users may specify the outcome and inputs of a model directly, search for variables to include based upon naming patterns, incorporate interactions, and identify variables to exclude. A wide range of quality checks are implemented to identify issues such as misspecified variables, duplication, a lack of contrast in the inputs, and a large number of levels in categorical data.  Variables that do not meet these quality checks can be automatically excluded from the model.  These issues are documented and reported in a manner that provides greater accountability and useful information to guide an investigation of the data.
Automatically generates wrappers for modelling functions
    that accept data as a data matrix X and a data vector y and produces
    a wrapper that allows users to specify input data with a formula and a data frame.
    In addition to generating formula interfaces, users may also generated wrapper S3
    generics.
A collection of fortunes from the R community.
Robust analysis using forward search in linear and
        generalized linear regression models, as described in Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer.
Anonymized data from surveys conducted by Forwards <https://forwards.github.io/>, the R Foundation task force on women and other under-represented groups. Currently, a single data set of responses to a survey of attendees at useR! 2016 <https://www.r-project.org/useR-2016/>, the R user conference held at Stanford University, Stanford, California, USA, June 27 - June 30 2016.
Forward Search analysis of time series regressions. Implements the asymptotic theory developed in Johansen and Nielsen (2013, 2014).
A set of analytical tools useful in analysing ecological
        and geographical data sets, both ancient and modern. The
        package includes functions for estimating species richness
        (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta
        diversity, species area curves and geographic distances and
        areas.
Simulating taxonomy and fossil data on phylogenetic trees under mechanistic
    models of speciation, preservation and sampling.
The Fourier Transform Textural Ordination method 
  uses a principal component analysis on radially averaged
  two dimensional Fourier spectra to characterize image texture.
The four-gamete test is based on the infinite-sites model which assumes that the probability of the same mutation occurring twice (recurrent or parallel mutations) and the probability of a mutation back to the original state (reverse mutations) are close to zero. Without these types of mutations, the only explanation for observing the four dilocus genotypes (example below) is recombination (Hudson and Kaplan 1985, Genetics 111:147-164). Thus, the presence of all four gametes is also called phylogenetic incompatibility.
Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation.
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> 
    four parameter IRT model with lower and upper asymptotes using Bayesian
    formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
A game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.
Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.
Various methods for clustering and cluster validation.
  Fixed point clustering. Linear regression clustering. Clustering by 
  merging Gaussian mixture components. Symmetric 
  and asymmetric discriminant projections for visualisation of the 
  separation of groupings. Cluster validation statistics
  for distance based clustering including corrected Rand index. 
  Standardisation of cluster validation statistics by random clusterings and 
  comparison between many clustering methods and numbers of clusters based on
  this.  
  Cluster-wise cluster stability assessment. Methods for estimation of 
  the number of clusters: Calinski-Harabasz, Tibshirani and Walther's 
  prediction strength, Fang and Wang's bootstrap stability. 
  Gaussian/multinomial mixture fitting for mixed 
  continuous/categorical variables. Variable-wise statistics for cluster
  interpretation. DBSCAN clustering. Interface functions for many 
  clustering methods implemented in R, including estimating the number of
  clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
  mixtures. For an overview see package?fpc.
A geometric approach to MLE for functional principal
        components
Compute the two dimension functional principal component scores for a series of two dimension images.
Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.
Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    'The R Inferno' <http://www.burns-stat.com/pages/Tutor/R_inferno.pdf>).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. Factor PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets.
Tools to help text files importation. It can return 
 the number of lines; print the first and last lines; convert 
 encoding. Operations are made without reading the entire file 
 before starting, resulting in good performances with large files. 
 This package provides an alternative to a simple use of the 
 'head', 'tail', 'wc' and 'iconv' programs that are not always 
 available on machine where R is installed.
Given the values of sampled units and selection probabilities 
             the desraj function  in the package computes the estimated value of the total
             as well as estimated variance.
Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and boxplots.
Algorithms for detection of outliers based on frequent pattern
    mining. Such algorithms follow the paradigm: if an instance contains more frequent patterns, 
    it means that this data instance is unlikely to be an anomaly (He Zengyou, Xu Xiaofei, Huang 
    Zhexue Joshua, Deng Shengchun (2005) <doi:10.2298/CSIS0501103H>).
    The package implements a list of existing state of the art algorithms as well as other published 
    approaches: FPI, WFPI, FPOF, FPCOF, LFPOF, MFPOF, WCFPOF and WFPOF.
A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles.
  The 'fpop'  package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017
  <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated 
  in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error 
  for a penalty linear in the number of changepoints.
Provides a collection
	of functions to optimize portfolios and to analyze them from
    different points of view.
Returns the noncentrality parameter of the noncentral F
        distribution if probability of type I and type II error,
        degrees of freedom of the numerator and the denominator are
        given.  It may be useful for computing minimal detectable
        differences for general ANOVA models.  This program is
        documented in the paper of A. Baharev, S. Kemeny, On the
        computation of the noncentral F and noncentral beta
        distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
        the book "Forecasting: principles and practice" by Rob J
        Hyndman and George Athanasopoulos. All packages required to run
        the examples are also loaded.
All data sets required for the examples and exercises 
  in the book "Forecasting: principles and practice" 
  by Rob J Hyndman and George Athanasopoulos <https://OTexts.org/fpp2/>. 
  All packages required to run the examples are also loaded.
All data sets required for the examples and exercises in the book
    "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos
    <http://OTexts.org/fpp3/>.  All packages required to run the examples are also
    loaded.
Efficient approximation of first-passage-time densities for diffusion processes based on the First-Passage-Time Location (FPTL) function.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, see Filzmoser and Viertl (2004) <doi:10.1007/s001840300269>, (2) testing fuzzy hypotheses based on crisp data, see Parchami et al. (2010) <doi:10.1007/s00362-008-0133-4>, and (3) testing fuzzy hypotheses based on fuzzy data, see Parchami et al. (2012) <doi:10.1007/s00362-010-0353-2>. In all cases, the fuzziness of data or / and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Maximum likelihood estimation of the parameters of a fractionally
   differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989);
   including inference and basic methods.  Some alternative algorithms to estimate "H".
Functions for fitting data to a quiescent growth model,
        i.e. a growth process that involves members of the population
        who stop dividing or propagating.
Stochastic fractal and deterministic chaotic time series
        analysis.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data.
The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) <doi:10.1002/sim.6497>.
The basic principle driving fractal generation of time
        series is that data is generated iteratively based on
        increasing levels of resolution.  The initial series is defined
        by a so-called initiator pattern and then generators are used
        to replace each segment of the initial pattern. Regular,
        repeatable patterns can be produced by using the same seed and
        generators.  By using a set of generators, non-repeatable time
        series can be produced.  This technique is the basis of the
        fractal time series process in this package.
This is the package which can help you turn
        numeric,dataframe,matrix into fraction form.
The main function of this package allows numerical vector objects to
  be displayed with their values in vulgar fractional form.  This is convenient if
  patterns can then be more easily detected.  In some cases replacing the components
  of a numeric vector by a rational approximation can also be expected to remove
  some component of round-off error.  The main functions form a re-implementation
  of the functions 'fractions' and 'rational' of the MASS package, but using a
  radically improved programming strategy.
Implements and extends the fragility index calculation for
    dichotomous results as described in Walsh, Srinathan, McAuley,
    Mrkobrada, Levine, Ribic, Molnar, Dattani, Burke, Guyatt,
    Thabane, Walter, Pogue, and Devereaux (2014)
    <DOI:10.1016/j.jclinepi.2013.10.019>.
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).
Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.
Implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.
Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.
Tools to support sensible statistics for functional response analysis.
Generation of theoretical size distributions of framboidal or sunflower pyrite. 
        The growth mechanisms used are surface and supply controlled and dependent
        or independent of size. The algorithms are fully described in the published work 
        in Mineralogy and Petrology journal:
        "Theoretical growth of framboidal and sunflower pyrite using the R-package frambgrowth"
        The authors Merinero, R., and Cardenes, V. (2018).
        <DOI:10.1007/s00710-017-0535-x>.
Point and interval estimation in dual frame surveys. In contrast
    to classic sampling theory, where only one sampling frame is considered,
    dual frame methodology assumes that there are two frames available for
    sampling and that, overall, they cover the entire target population. Then,
    two probability samples (one from each frame) are drawn and information
    collected is suitably combined to get estimators of the parameter of
    interest.
With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.
Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.
Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.
An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. 
    FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
    representing the reasoning of human experts in a set of IF-THEN rules, to
    handle real-life problems in, e.g., control, prediction and inference, data
    mining, bioinformatics data processing, and robotics. FRBSs are also known
    as fuzzy inference systems and fuzzy models. During the modeling of an
    FRBS, there are two important steps that need to be conducted: structure
    identification and parameter estimation. Nowadays, there exists a wide
    variety of algorithms to generate fuzzy IF-THEN rules automatically from
    numerical data, covering both steps. Approaches that have been used in the
    past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
    methods, genetic algorithms, squares methods, etc. Furthermore, in this
    version we provide a universal framework named 'frbsPMML', which is adopted
    from the Predictive Model Markup Language (PMML), for representing FRBS
    models. PMML is an XML-based language to provide a standard for describing
    models produced by data mining and machine learning algorithms. Therefore,
    we are allowed to export and import an FRBS model to/from 'frbsPMML'.
    Finally, this package aims to implement the most widely used standard
    procedures, thus offering a standard package for FRBS modeling to the R
    community.
This package implements the functions associated with Fast
        Regularized Canonical Correlation Analysis.
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 heterogeneous data.  In this package, we introduce Fréchet trees and Fréchet random forests, which allow to manage data for which input and output variables are curves. To this end, a new way of splitting the nodes of trees is introduced and the prediction procedures of trees and forests are generalized.
An R client for the 'Federal Reserve Economic Data' ('FRED') API
  <https://api.stlouisfed.org>.  Functions to retrieve economic time series and 
  other data from 'FRED'.
The free algebra in R; multivariate polynomials with non-commuting indeterminates.
Provides functionality for manipulating elements of the free group (juxtaposition is represented by a plus) including inversion, multiplication by a scalar, group-theoretic power operation, and Tietze forms.  The package is fully vectorized.
Algorithms for fitting free-knot splines for data with one independent variable and one dependent variable.  Four free-knot spline algorithms are provided for the case where the number of knots is known in advance.  A knot-search algorithm is provided for the case where the number of knots is not known in advance.  In addition, methods are available to compute the fitted values, the residuals, and the coefficients of the splines, and to plot the results, along with a method to summarize the results.
Provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
A collections of useful statistical functions used in Columbia
    course W4240/W4400.
Wrapper functions that interface with 'Freesurfer' 
    <https://surfer.nmr.mgh.harvard.edu/>, a powerful and 
    commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with
    'Freesurfer' completely in R, where you pass R objects of class 'nifti',
    implemented by package 'oro.nifti', and the function executes an 'Freesurfer'
    command and returns an R object of class 'nifti' or necessary output.
Provides functions to read and write data from neuroimaging files in 'FreeSurfer' <http://freesurfer.net/> binary formats. This includes the following file formats: 1) MGH/MGZ format files, which can contain multi-dimensional images or other data. Typically they contain time-series of three-dimensional brain scans acquired by magnetic resonance imaging (MRI). They can also contain vertex-wise measures of surface morphometry data. The MGH format is named after the Massachusetts General Hospital, and the MGZ format is a compressed version of the same format. 2) 'FreeSurfer' morphometry data files in binary 'curv' format. These contain vertex-wise surface measures, i.e., one scalar value for each vertex of a brain surface mesh. These are typically values like the cortical thickness or brain surface area at each vertex. 3) Annotation file format. This contains a brain surface parcellation derived from a cortical atlas. 4) Surface file format. Contains a brain surface mesh, given by a list of vertices and a list of faces.
Unlike other tools that dynamically link to the 'Cairo'
    stack, 'freetypeharfbuzz' is statically linked to specific
    versions of the 'FreeType' and 'harfbuzz' libraries (2.9 and 1.7.6
    respectively). This ensures deterministic computation of text box
    extents for situations where reproducible results are crucial (for
    instance unit tests of graphics).
A collection of functions for linear and non-linear regression modelling. 
	It implements a wrapper for several regression models available in the base and
	contributed packages of R.
Signal compression methods for functional regression. It includes various function-on-function (FOF) regression models such as the linear FOF model with functional response and both scalar and functional predictors for a small number of functional predictors, linear FOF models with a large number of functional predictors, linear FOF model for spiky data, stepwise selection for FOF models with two-way interactions, and nonlinear FOF models. It also includes scalar-on-function regression models with single (SOF) or multivariate (mSOF) scalar response variable, and SOF model for spiky data.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
Generates a frequency distribution. The frequency
    distribution includes raw frequencies, percentages in each category, and
    cumulative frequencies. The frequency distribution can be stored as a data
    frame.
Implementation of dynamic principal component
    analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. 
    These frequency domain methods for dimensionality reduction of multivariate time series
    were introduced by David Brillinger in his book Time Series (1974). We follow implementation
    guidelines as described in Hormann, Kidzinski and Hallin (2016),
    Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
Implementations of functional dynamic principle components analysis. Related graphic tools and frequency domain methods.
  These methods directly use multivariate dynamic principal components implementation,
  following the guidelines from Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.
New approaches to parallel coordinates plots for
   multivariate data visualization, including applications to clustering,
   outlier hunting and regression diagnostics.  Includes general functions
   for multivariate nonparametric density and regression estimation, 
   using parallel computation.
Tools for generating an informative type of line graph, the frequency profile, 
    which allows single behaviors, multiple behaviors, or the specific behavioral patterns 
    of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
Generate 'SPSS'/'SAS' styled frequency tables. Frequency tables are 
    generated with variable and value label attributes where applicable with optional
    html output to quickly examine datasets.
Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
Implement frequent-directions algorithm for efficient matrix sketching. 
  (Edo Liberty (2013) <doi:10.1145/2487575.2487623>).
Contains a set of utilities for building and testing statistical models (linear, logistic,ordinal or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
Customize 'Bootstrap' and 'Bootswatch' themes, like colors, fonts, grid layout, 
  to use in 'Shiny' applications, 'rmarkdown' documents and 'flexdashboard'.
Regular and non-regular Fractional Factorial 2-level designs 
        can be created. Furthermore, analysis tools for Fractional
        Factorial designs with 2-level factors are offered (main
        effects and interaction plots for all factors simultaneously,
        cube plot for looking at the simultaneous effects of three
        factors, full or half normal plot, alias structure in a more
        readable format than with the built-in function alias).
This package provides catalogues of resolution IV regular
        fractional factorial designs in 128 runs for up to 33 2-level
        factors. The catalogues are complete, excluding resolution IV
        designs without 5-letter words, because these do not add value
        for a search for clear designs. The previous package version
        1.0 with complete catalogues up to 24 runs (24 runs and a
        namespace added later) can be downloaded from the authors
        website.
Fixed Rank Kriging is a tool for spatial/spatio-temporal modelling
    and prediction with large datasets. The approach, discussed in Cressie and
    Johannesson (2008) <DOI:10.1111/j.1467-9868.2007.00633.x>, decomposes the field, 
    and hence the covariance function, using a fixed set of n basis functions, 
    where n is typically much smaller than the number of data points (or polygons) m. 
    The method naturally allows for non-stationary, anisotropic covariance functions 
    and the use of observations with varying support (with known error variance). The 
    projected field is a key building block of the Spatial Random Effects (SRE) model, 
    on which this package is based. The package FRK provides helper functions to model, 
    fit, and predict using an SRE with relative ease.
When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.
Estimation and specification analysis of one- and two-part fractional regression models and calculation of partial effects.
Estimation and specification analysis of fractional regression models with neglected heterogeneity and/or endogenous covariates.
Estimation of panel data regression models for fractional responses.
A collection of R and C++ functions to work with the
        generalized hyperbolic distribution, related distributions and
        their applications in financial risk management and
        quantitative analysis.
Fast, numerically robust computation of weighted moments via 'Rcpp'. 
   Supports computation on vectors and matrices, and Monoidal append of moments. 
   Moments and cumulants over running fixed length windows can be computed, 
   as well as over time-based windows.
   Moment computations are via a generalization of Welford's method, as described
   by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.
Maximum Likelihood Estimation of
   Stochastic Frontier Production and Cost Functions.
   Two specifications are available:
   the error components specification with time-varying efficiencies
   (Battese and Coelli, 1992, <doi:10.1007/BF00158774>)
   and a model specification in which the firm effects are directly 
   influenced by a number of variables (Battese and Coelli, 1995,
   <doi:10.1007/BF01205442>).
It calculates the alpha-quantile and order-m efficiency score in multi-dimension and computes several summaries and representation of the associated frontiers in 2d and 3d.
A compilation of empirical methods used by farmers and agronomic engineers to predict the minimum temperature to detect a frost night. These functions use variables such as environmental temperature, relative humidity, and dew point. See <http://sedici.unlp.edu.ar/handle/10915/72102> <http://www.fao.org/docrep/008/y7223e/y7223e0b.htm#bm11.8> for details.
An unofficial R client to MET Norway's 
    'Frost' API <https://frost.met.no/index2.html> to retrieve data as data 
    frames. The 'Frost' API, and the underlying data, is made available by the
    Norwegian Meteorological Institute (MET Norway). The data and products are 
    distributed under the Norwegian License for Open Data 2.0 (NLOD) <https://data.norge.no/nlod/en/2.0>
    and Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.
A cross-platform interface to file system operations, built on
  top of the 'libuv' C library.
A variety of simple fish stock assessment methods.
    Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.
The datasets to support the Fish Stock Assessment ('FSA') package.
Provides high-level access to 'FreeSurfer' <http://freesurfer.net/> neuroimaging data on the level of subjects and groups. Load morphometry data, surfaces and brain parcellations based on atlases. Mask data using labels, load data for specific atlas regions only, and visualize the results directly in 'R'.
Automated feature selection using variety of models
        provided by 'caret' package.
        This work was funded by Poland-Singapore bilateral cooperation
        project no 2/3/POL-SIN/2012.
Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis
    (FSDA)' which is a comprehensive and computationally efficient
    software package for robust statistics in regression, multivariate
    and categorical data analysis. The current R version implements tools
    for regression: (forward search, S- and MM-estimation, least trimmed
    squares (LTS) and least median of squares (LMS)). The distinctive
    feature of our regression package relies in the possibility of
    monitoring the statistics of interest as function of breakdown point,
    efficiency or subset size, depending on the estimator. This is
    accompanied by a rich set of graphical features, such as dynamic
    brushing, linking, particularly useful for exploratory data analysis.
Functions for selecting attributes from a given dataset. Attribute
    subset selection is the process of identifying and removing as much of the
    irrelevant and redundant information as possible.
'Rcpp' (free of 'Java'/'Weka') implementation of 'FSelector' entropy-based feature selection 
 algorithms based on an MDL discretization (Fayyad U. M., Irani K. B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning.
 In 13'th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93), pages 1022-1029, Chambery, France, 1993.) <https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf>
 with a sparse matrix support. It is also equipped with a parallel backend.
Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from <http://sourceforge.net/projects/formscanner/>. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of a few subjects.
Feature subset selection algorithms modularized in search algorithms and measure utilities. Full list and more information available at <https://dicits.ugr.es/software/FSinR/>.
Performs fast detection of interactions in large-scale data using the
    method of random intersection trees introduced in
    Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. 
    The algorithm finds potentially high-order interactions in high-dimensional binary
    two-class classification data, without requiring lower order interactions
    to be informative.  The search is particularly fast when the matrices of
    predictors are sparse.  It can also be used to perform market basket analysis
    when supplied with a single binary data matrix.  Here it will find collections
    of columns which for many rows contain all 1's.
Wrapper functions that interface with 'FSL' 
    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/>, a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with 'FSL'
    completely in R, where you pass R objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' if desired.
Filling large gaps in low or uncorrelated multivariate time series uses a new fuzzy weighted similarity measure. It contains all required functions to create large missing consecutive values within time series and then fill these gaps, according to the paper Phan et al. (2018), <DOI:10.1155/2018/9095683>. Performance indicators are also provided to compare similarity between two univariate signals (incomplete signal and imputed signal).
Fuzzy set ordination is a multivariate analysis used in ecology to
    relate the composition of samples to possible explanatory variables.  While
    differing in theory and method, in practice, the use is similar to 'constrained
    ordination.'  The package contains plotting and summary functions as well as
    the analyses.
Social Relations Analysis with roles ("Family SRM") are computed,
    using a structural equation modeling approach. Groups ranging from three members
    up to an unlimited number of members are supported and the mean structure can
    be computed. Means and variances can be compared between different groups of
    families and between roles.
An optimizer of Fused-Sparse Structural Equation Models, which is 
 the state of the art jointly fused sparse maximum likelihood function 
 for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 
 <doi:10.1101/466623>).
Multithreaded serialization of compressed data frames using the
    'fst' format. The 'fst' format allows for random access of stored data and
    compression with the LZ4 and ZSTD compressors created by Yann Collet. The ZSTD
    compression library is owned by Facebook Inc.
Has two functions to help with calculating feature selection stability. 'Lump' is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length.
    'ASM' is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection.
    The calculation for 'asm' uses the Adjusted Stability Measure proposed in: 'Lustgarten', 'Gopalakrishnan', & 'Visweswaran' (2009)<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815476/>.
A program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.
Functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.
A wrapper for the API of the Danish Parliament. It makes it 
    possible to get data from the API easily into a data frame. Learn more at 
    <http://www.ft.dk/dokumenter/aabne_data>.
This package was developed partially with funding from the
        NIH Training Program in Biomolecular Technology
        (2-T32-GM08799).
The package contains R-functions to perform the methods in
        nonparametric regression and density estimation, described in
        Davies, P. L. and Kovac, A. (2001) Local Extremes, Runs,
        Strings and Multiresolution (with discussion) Annals of
        Statistics. 29. p1-65 Davies, P. L. and Kovac, A. (2004)
        Densities, Spectral Densities and Modality Annals of
        Statistics. Annals of Statistics. 32. p1093-1136 Kovac, A.
        (2006) Smooth functions and local extreme values. Computational
        Statistics and Data Analysis (to appear) D\"umbgen, L. and
        Kovac, A. (2006) Extensions of smoothing via taut strings
        Davies, P. L. (1995) Data features. Statistica Neerlandica
        49,185-245.
A collection of functions for trading and rebalancing financial
	instruments. It implements various technical indicators to analyse time series such
	as moving averages or stochastic oscillators.
Fast operations for time series objects.
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
Functions for estimating spectral density operator of functional
    time series (FTS) and comparing the spectral density operator of two
    functional time series, in a way that allows detection of differences of
    the spectral density operator in frequencies and along the curve length.
Lognormal models have broad applications in various research areas such as economics, actuarial science, biology, environmental science and psychology. The estimation problem in lognormal models has been extensively studied. This R package 'fuel' implements thirty-eight the existing and newly proposed estimators. See Zhang, F., and Gou, J. (2020), A unified framework for estimation in lognormal models, Technical report.
Fuel economy data from the EPA, 1985-2015, conveniently
    packaged for consumption by R users.
This is an evolutionary algorithm for fuzzy systems, a
        genetic algorithm is used to construct a fuzzy system able to
        fit the given training data.  This fuzzy system can then be
        used as a prediction model, it's composed of fuzzy logic rules
        that provide a good lingustic representation.
As in music, a fugue statistic repeats a theme in small variations.  Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes.  The main functions in the package are sen() and senCI().  For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.
We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016) <doi:10.1002/ece3.1943>.
Provides a single interface to many sources of full text
    'scholarly' data, including 'Biomed Central', Public Library of
    Science, 'Pubmed Central', 'eLife', 'F1000Research', 'PeerJ',
    'Pensoft', 'Hindawi', 'arXiv' 'preprints', and more. Functionality
    included for searching for articles, downloading full or partial
    text, downloading supplementary materials, converting to various
    data formats.
This is a collection of R games and other funny stuff, such as the
    classic Mine sweeper and sliding puzzles.
A machine learning based approach for fungal species identification using barcode sequence data. The multi-class random forest model has been used for prediction purpose, where the gap-pair compositional feature was used to encode the barcode sequence data. The encoded dataset was used as input for prediction purpose. Though this approach has been developed for fungal species identification in particular, can be used for other species identification as well.
A set of functions, some subset of which I use in every .R file I write. Examples are table2(), which adds useful functionalities to base table (sorting, built-in proportion argument, etc.); lyx.xtable(), which converts xtable() output to a format more easily copy-pasted into LyX; pdf2(), which writes a plot to file while also displaying it in the RStudio plot window; and abbr_to_colClass(), which is a much more concise way of feeding many types to a colClass argument in a data reader.
Statistical hypothesis testing methods for
 inferring model-free functional dependency using asymptotic
 chi-squared or exact distributions. Functional test
 statistics are asymmetric and functionally optimal, unique
 from other related statistics. Tests in this package reveal
 evidence for causality based on the
 causality-by-functionality principle. They include
 asymptotic functional chi-squared tests
 ('Zhang & Song' 2013) <arXiv:1311.2707> and an exact
 functional test ('Zhong & Song' 2019)
 <doi:10.1109/TCBB.2018.2809743>. The normalized functional
 chi-squared test was used by Best Performer 'NMSUSongLab'
 in HPN-DREAM (DREAM8) Breast Cancer Network Inference
 Challenges ('Hill et al' 2016) <doi:10.1038/nmeth.3773>. A
 function index ('Zhong & Song' in press) ('Kumar et al' 2018)
 <doi:10.1109/BIBM.2018.8621502> derived from the
 functional test statistic offers a new effect size measure
 for the strength of functional dependency, a better
 alternative to conditional entropy in many aspects. For
 continuous data, these tests offer an advantage over
 regression analysis when a parametric functional form
 cannot be assumed; for categorical data, they provide a
 novel means to assess directional dependency not possible
 with symmetrical Pearson's chi-squared or Fisher's exact
 tests.
FunCluster performs a functional analysis of microarray
        expression data based on Gene Ontology & KEGG functional
        annotations. From expression data and functional annotations
        FunCluster builds classes of putatively co-regulated biological
        processes through a specially designed clustering procedure.
Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers).  Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank.  FuncMap should be useful for comparing packages at a high level for their overall design.  Plus, it's just plain fun.  The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package.  Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.
Curry, Compose, and other higher-order functions
R package providing functions to perform gene and gene set network inference.
Extends functional programming in R by
    providing support to the usual higher order functional
    suspects (Map, Reduce, Filter, etc.).
Unified framework to cluster functional data according to one of
    seven models. All models are based on the projection of the curves onto a basis.
    The main function funcit() calls wrapper functions for the existing algorithms,
    so that input parameters are the same. A list is returned with each entry
    representing the same or extended output for the corresponding method. Method
    specific as well as general visualization tools are available.
S4 classes for univariate and multivariate functional data with
    utility functions.
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>.
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights.
    Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices:
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <DOI:10.1080/00273171.2016.1178566>.
    Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF)
    covariance matrix of standardized regression coefficients: theoretical extensions
    and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>.
    Waller, N. G.  (2018).  Direct Schmid-Leiman transformations and rank-deficient loadings matrices.  Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.
The funHDDC algorithm  allows to cluster functional univariate (Bouveyron and Jacques, 2011, <doi:10.1007/s11634-011-0095-6>) or multivariate data (Schmutz et al., 2018) by modeling each group within a specific functional subspace.
Similar to base's unique function, only optimized for working with 
    data frames, especially those that contain date-time columns.
Provides four addons for analyzing trends and
    unit roots in financial time series: (i) functions for the density
    and probability of the augmented Dickey-Fuller Test, (ii) functions 
    for the density and probability of MacKinnon's unit root test 
    statistics, (iii) reimplementations for the ADF and MacKinnon
    Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's
    unit root test suite.
The funLBM algorithm allows to simultaneously cluster the rows and the columns of a data matrix where each entry of the matrix is a function or a time series.
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
Time-based joins to analyze sequence of events, 
    both in memory and out of memory. after_join() joins two 
    tables of events, while funnel_start() and funnel_step() 
    join events in the same table. With the type argument, you
    can switch between different funnel types, like first-first
    and last-firstafter.
An implementation of the Spiegelhalter (2005) <doi:10.1002/sim.1970> Funnel plots  for reporting standardised ratios, with overdispersion adjustment.
A set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.
High-order functions for data manipulation : sort or group data, given one
    or more auxiliary functions. Functions are inspired by other pure
    functional programming languages ('Haskell' mainly). The package also 
    provides built-in function operators for creating compact anonymous
    functions, as well as the possibility to use the 'purrr' package syntax.
A small utility which wraps Rscript and provides access to all R
    functions from the shell.
Computes functional rarity indices as proposed by Violle et al.
    (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed
    using both regional and local information. Functional Rarity combines both
    the functional aspect of rarity as well as the extent aspect of rarity.
    'funrar' is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.
Performs functional regression, and some related
    approaches, for intensive longitudinal data (see the book by Walls & Schafer, 
    2006, Models for Intensive Longitudinal Data, Oxford) when such data is not
    necessarily observed on an equally spaced grid of times.  The
    approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
    Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but
    with some modifications to make it more feasible to use with long rather
    than wide, non-rectangular longitudinal datasets with unequal and
    potentially random measurement times.  It also allows easy plotting of the
    correlation between the smoothed covariate and the outcome as a function of
    time, which can add additional insights on how to interpret a functional
    regression.  Additionally, it also provides several permutation tests for
    the significance of the functional predictor.  The heuristic interpretation
    of ``time'' is used to describe the index of the functional predictor, but
    the same methods can equally be used for another unidimensional continuous
    index, such as space along a north-south axis.  The development of this
    package was part of a research project supported by Award R03 CA171809-01
    from the National Cancer Institute and Award P50 DA010075 from the National
    Institute on Drug Abuse. The content is solely the responsibility of the
    authors and does not necessarily represent the official views of the
    National Institute on Drug Abuse, the National Cancer Institute, or the
    National Institutes of Health.
Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.
Includes non-parametric estimators and tests for time series analysis. The functions are to test for presence of possibly non-monotonic trends and for synchronism of trends in multiple time series, using modern bootstrap techniques and robust non-parametric difference-based estimators.
Contains four main functions (i.e., four pieces of furniture):
    table1() which produces a well-formatted table of descriptive statistics common as Table 1
    in research articles, tableC() which produces a well-formatted table of correlations,
    tableF() which provides frequency counts, and washer() which 
    is helpful in cleaning up the data. These furniture-themed functions are designed 
    to simplify common tasks in quantitative analysis. Other data summary and cleaning tools 
    are also available.
Implementations of the family of map() functions from 'purrr' that can be resolved using any 'future'-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.
Provides methods fusedest_normal() and fusedest_logit() for carrying out block splitting algorithms for fused penalty estimation. For details, please see Tso-Jung Yen (2019) <doi.10618600.2019.1660178>.
Enables high-dimensional penalized regression across heterogeneous 
    subgroups. Fusion penalties are used to share information about the linear 
    parameters across subgroups. The underlying model is described 
    in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.
Provides the Big Merge Tracker and COSCI algorithms for convex clustering and 
    feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and 
    T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.
The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.
This utility package provides polymorphism over common operations and is now subsumed by lambda.tools.
Provides a simple yet powerful logging utility. Based loosely on
    log4j, futile.logger takes advantage of R idioms to make logging a
    convenient and easy to use replacement for cat and print statements.
A collection of functions for manipulating matrices and generating
    ensembles of random matrices. Used primarily to identify the cutoff point
    for the noise portion of the eigenvalue spectrum.
A scoped options management framework. Used in other packages.
Provides dispatching implementations suitable for
        functional programming paradigms. The framework provides a
        mechanism for attaching guards to functions similar to Erlang,
        while also providing the safety of assertions reminiscent of
        Eiffel.
Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate, offering the option to specify a robust mixture prior distribution, is used for generating future data (see the vignette for details). Inference can be restricted to participants who received treatment according to the protocol and are observed to be at risk for the endpoint at a specified timepoint. Plotting functions are provided for graphical display of results.
The purpose of this package is to provide a lightweight and
    unified Future API for sequential and parallel processing of R
    expression via futures.  The simplest way to evaluate an expression
    in parallel is to use `x %<-% { expression }` with `plan(multiprocess)`.
    This package implements sequential, multicore, multisession, and
    cluster futures.  With these, R expressions can be evaluated on the
    local machine, in parallel a set of local machines, or distributed
    on a mix of local and remote machines.
    Extensions to this package implement additional backends for
    processing futures via compute cluster schedulers etc.
    Because of its unified API, there is no need to modify any code in order
    switch from sequential on the local machine to, say, distributed
    processing on a remote compute cluster.
    Another strength of this package is that global variables and functions
    are automatically identified and exported as needed, making it
    straightforward to tweak existing code to make use of futures.
Implementations of apply(), by(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.  These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework.
Implementation of the Future API on top of the 'BatchJobs' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.
    NOTE: The 'BatchJobs' package is deprecated in favor of the 'batchtools'
    package. Because of this, it is recommended to use the 'future.batchtools'
    package instead of this package.
Implementation of the Future API on top of the 'batchtools' package.
    This allows you to process futures, as defined by the 'future' package,
    in parallel out of the box, not only on your local machine or ad-hoc
    cluster of machines, but also via high-performance compute ('HPC') job
    schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS',
    e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.
Implementation of the Future API on top of the 'callr' package.  This allows you to process futures, as defined by the 'future' package, in parallel out of the box, on your local (Linux, macOS, Windows, ...) machine.  Contrary to backends relying on the 'parallel' package (e.g. 'future::multisession'), the 'callr' backend provided here can run more than 125 parallel R processes.
Inputs a directory of climate projection files and, for each,
    identifies and characterizes heat waves for specified study locations. The
    definition used to identify heat waves can be customized. Heat wave
    characterizations include several metrics of heat wave length, intensity,
    and timing in the year. The heat waves that are identified can be
    explored using a function to apply user-created functions across all
    generated heat wave files.This work was supported in part by grants from
    the National Institute of Environmental Health Sciences (R00ES022631), the
    National Science Foundation (1331399), and the Colorado State University
    Vice President for Research.
Test function arguments with a wide array of inputs, and produce
  reports summarizing messages, warnings, errors, and returned values.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Calculation of AHP (Analytic Hierarchy Process -
    <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>)
    with classic and fuzzy weights based on Saaty's pairwise
    comparison method for determination of weights.
Exact calculation of fuzzy decision rules for multiple
	testing. Choose to control FDR (false discovery rate) using the
	Benjamini and Hochberg method, or FWER (family wise error rate)
	using the Bonferroni method. Kulinsakaya and Lewin (2007).
Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
    See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
    for further details.
Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.
Provides methods to solve Fuzzy Linear Programming Problems with
             fuzzy constraints (following different approaches proposed by 
             Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy
             technological matrix.
Implementation of several MCDM methods for fuzzy data (triangular
    fuzzy numbers) for decision making problems. The methods that are implemented in
    this package are Fuzzy TOPSIS (with two normalization procedures), Fuzzy VIKOR,
    Fuzzy Multi-MOORA and Fuzzy WASPAS. In addition, function MetaRanking() calculates
    a new ranking from the sum of the rankings calculated, as well as an aggregated ranking.
S4 classes and methods
    to deal with fuzzy numbers. They allow for computing any arithmetic
    operations (e.g., by using the Zadeh extension principle),
    performing approximation of arbitrary fuzzy numbers by trapezoidal
    and piecewise linear ones, preparing plots for publications, computing
    possibility and necessity values for comparisons, etc.
One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.
Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic.
    This toolkit includes with graphical user interface (GUI) and an adaptive neuro-
    fuzzy inference system (ANFIS). This toolkit is a continuation from the previous
    package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA)
    and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham.
Does fuzzy tests and confidence intervals (following Geyer
    and Meeden, Statistical Science, 2005, <doi:10.1214/088342305000000340>)
    for sign test and Wilcoxon signed rank and rank sum tests.
Estimators for fuzzy linear regression. The functions estimate parameters of 
    fuzzy linear regression models with crisp or fuzzy independent variables (triangular 
    fuzzy numbers are supported). Implements multiple methods for parameter estimation and 
    algebraic operations with triangular fuzzy numbers. Includes functions for 
    summarising, printing and plotting the model fit. Calculates predictions from the 
    model and total error of fit. 
    Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>,
	Hung & Yang (2006) <doi:10.1016/j.fss.2006.08.004>,
	Lee & Tanaka (1999) <doi:10.15807/jorsj.42.98>,
	Nasrabadi, Nasrabadi & Nasrabady (2005) <doi:10.1016/j.amc.2004.02.008>,
	Tanaka, Hayashi & Watada (1989) <doi:10.1016/0377-2217(89)90431-1>,
	Zeng, Feng & Li (2017) <doi:10.1016/j.asoc.2016.09.029>.
Functions to calculate fuzzy versions of species' occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis and prevalence-independent favourability GLM), and pair-wise fuzzy similarity (based on fuzzy versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model comparison (overlap and fuzzy similarity, loss or gain), and for data preparation, such as obtaining unique abbreviations of species names, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, assessing the false discovery rate, or analysing and dealing with multicollinearity among variables. Includes also sample datasets for providing practical examples.
An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
        from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley 
        in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities.
The aim of the package is to provide some basic functions
 for doing statistics with trapezoidal fuzzy numbers. In particular,
  the package contains several functions for simulating trapezoidal 
  fuzzy numbers, as well as for calculating some central tendency 
  measures (mean and two types of median), some scale measures 
  (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and 
  one diversity index and one inequality index. Moreover, 
  functions for calculating the 1-norm distance, the mid/spr 
  distance and the (phi,theta)-wabl/ldev/rdev distance between 
  fuzzy numbers are included, and a function to calculate the 
  value phi-wabl given a sample of trapezoidal fuzzy numbers.
A custom framework for working with Type 1 Fuzzy Logic,
        produced by the University of Nottingham IMA Group.
Fuzzy string matching implementation of the 'fuzzywuzzy' <https://github.com/seatgeek/fuzzywuzzy> 'python' package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.
A simple method
    to select the best model or best subset of variables using
    different types of data (binary, Gaussian or Poisson) and
    applying it in different contexts (parametric or non-parametric).
Provides three functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
Exchange rate regression and structural change tools
             for estimating, testing, dating, and monitoring
	     (de facto) exchange rate regimes.
An R6 implementation for calculating features from grouped data.
    The output will be one row for each group.
    This functionality is often needed in the feature extraction process of machine learning problems.
    Very large datasets are supported, since data is only read into RAM when needed.
    Calculation can be done in parallel and the process can be monitored. 
    Global error handling is supported.
    Results are available in one final dataframe.
In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June
     of the following calendar year. As such, many databases need to represent and 
     validate financial years efficiently. While the use of integer years with a convention that  
     they represent the year ending is common, it may lead to ambiguity with calendar years.
     On the other hand, string representations may be too inefficient and do not easily admit
     arithmetic operations. This package tries to make validation of financial years quicker while
     retaining clarity.
Create and maintain delayed-data packages (ddp's).  Data stored in
  a ddp are available on demand, but do not take up memory until requested.
  You attach a ddp with g.data.attach(), then read from it and assign to it in
  a manner similar to S-PLUS, except that you must run g.data.save() to
  actually commit to disk.
G1DBN performs DBN inference using 1st order conditional
        dependencies.
For a given metabolic network, this package finds the gaps (metabolites not produced or not consumed in any other reaction), and fills it from the stoichiometric reactions of a reference metabolic reconstruction using a weighting function. Also the option to download all the set of gene-associated stoichiometric reactions for a specific organism from the KEGG database <http://www.genome.jp/kegg/> is available.
Full descriptive statistics, physical description of sediment,
            metric or phi sieves.
R interface for 'g3-lollipop' JavaScript library.
    Visualize genetic mutation data using an interactive lollipop diagram in RStudio or your browser.
Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e. a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.
Scale abbreviation using Genetic Algorithms that maximally capture the variance in the original data.
Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.
The genetic algorithm can be used directly to find the similarity of users and more effectively to increase the efficiency of the collaborative filtering method.
  By identifying the nearest neighbors to the active user, before the genetic algorithm, and by identifying suitable starting points, an effective method for user-based collaborative filtering method has been developed.
  This package uses an optimization algorithm (continuous genetic algorithm) to directly find the optimal similarities between active users (users for whom current recommendations are made) and others.
  First, by determining the nearest neighbor and their number, the number of genes in a chromosome is determined. Each gene represents the neighbor's similarity to the active user.
  By estimating the starting points of the genetic algorithm, it quickly converges to the optimal solutions.
  The positive point is the independence of the genetic algorithm on the number of data that for big data is an effective help in solving the problem.
This package analyses complex ANOVA models with any
        combination of orthogonal/nested and fixed/random factors, as
        described by Underwood (1997). There are two restrictions: (i)
        data must be balanced; (ii) fixed nested factors are not
        allowed. Homogeneity of variances is checked using Cochran's C
        test and 'a posteriori' comparisons of means are done using
        Student-Newman-Keuls (SNK) procedure.
Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).
In this package we consider Gaussian Diffusion processes and smooth thresholds. After evaluating the mean of the process to check the subthreshold regimen hypothesis, the FPT density function is reconstructed via the numerical quadrature of the integral equation in (Buonocore 1987); first passage times are also generated by the method in (Buonocore 2014) and results are compared. The timestep of the simulations can iteratively be refined. User should provide the functional form for the drift and the infinitesimal variance in the script 'userfunc.R' and for the threshold in the script 'userthresh.R'. All the parameters required by the implementation are to be set in the script 'userparam.R'. Example scripts for common drifts and thresholds are given.
Manipulate, assemble, export <http://www.gadm.org> maps. Create 'choropleth', 'isopleth', dots plot, proportional dots,
   dot-density and more.
A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).
Tools for using genetic markers, stable isotope data, and habitat
    suitability data to calculate posterior probabilities of breeding origin of
    migrating birds.
Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators.
  H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.
Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.  The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11.
GAIPE implements graphical representation of accuracy in
        parameter estimation (AIPE) on RMSEA for sample size planning
        in structural equation modeling. Sample sizes suggested by
        RMSEA with AIPE method and power analysis approach can also be
        obtained separately using the provided functions.
Build multivariate predictive models from large datasets
	having far larger number of features than samples such as in 
	functional genomics datasets.
	Trevino and Falciani (2006) <doi:10.1093/bioinformatics/btl074>.
Includes the ga.lts() function that estimates
        LTS (Least Trimmed Squares) parameters using genetic algorithms
        and C-steps. ga.lts() constructs a genetic algorithm to form a
        basic subset and iterates C-steps as defined in Rousseeuw and
        van-Driessen (2006) to calculate the cost value of the LTS
        criterion. OLS (Ordinary Least Squares) regression is known to
        be sensitive to outliers. A single outlying observation can
        change the values of estimated parameters. LTS is a resistant
        estimator even the number of outliers is up to half of the
        data. This package is for estimating the LTS parameters with
        lower bias and variance in a reasonable time. Version >=1.3 
        includes the function medmad for fast outlier detection in
        linear regression.
Functions for fitting and working with generalized
		additive models, as described in chapter 7 of "Statistical Models in
		S" (Chambers and Hastie (eds), 1991), and "Generalized Additive
		Models" (Hastie and Tibshirani, 1990).
An evolutionary approach to performing hard partitional clustering. The algorithm uses genetic operators guided by information about the quality of individual partitions. The method looks for the best barycenters/centroids configuration (encoded as real-value) to maximize or minimize one of the given clustering validation criteria: Silhouette, Dunn Index, C-Index or Calinski-Harabasz Index. As many other clustering algorithms, 'gama' asks for k: a fixed a priori established number of partitions. If the user does not know the best value for k, the algorithm estimates it by using one of two user-specified options: minimum or broad. The first method uses an approximation of the second derivative of a set of points to automatically detect the maximum curvature (the 'elbow') in the within-cluster sum of squares error (WCSSE) graph. The second method estimates the best k value through majority voting of 24 indices. One of the major advantages of 'gama' is to introduce a bias to detect partitions which attend a particular criterion. References: Scrucca, L. (2013) <doi:10.18637/jss.v053.i04>; CHARRAD, Malika et al. (2014) <doi:10.18637/jss.v061.i06>; Tsagris M, Papadakis M. (2018) <doi:10.7287/peerj.preprints.26605v1>; Kaufman, L., & Rousseeuw, P. (1990, ISBN:0-47 1-73578-7).
Data sets and scripts used in the book 'Generalized Additive 
             Models: An Introduction with R', Wood (2006,2017) CRC.
Fits unimodal and multimodal gambin distributions to species-abundance distributions
    from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. 
    'gambin' is short for 'gamma-binomial'. The main function is fit_abundances(), which estimates 
    the 'alpha' parameter(s) of the gambin distribution using maximum likelihood. Functions are 
    also provided to generate the gambin distribution and for calculating likelihood statistics.
This package provides routines for fitting generalized
        linear and and generalized additive models by likelihood based
        boosting, using penalized B-splines
Boosting models for fitting generalized additive models for
  location, shape and scale ('GAMLSS') to potentially high dimensional
  data.
Provides features to use function gamboost() from  package mboost for estimation of multistate models
Functions and data are provided that support a course that emphasizes statistical
  issues of inference and generalizability.  Attention is restricted to a relatively small
  number of methods, often (misleadingly, in my view) referred to as algorithms.
Implementation of various inference and simulation tools to
    apply generalized additive models to bivariate dependence structures and
    non-simplified vine copulas.
Implements the GAMbag, GAMrsm and GAMens ensemble
    classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles
    implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601>
    , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.
Implementation of the characteristic palettes from the TV show 'Game of Thrones'.
Provides estimation and analysis functions for
    strategic statistical models.
Finds adaptive strategies for sequential symmetric 
    games using a genetic algorithm. Currently, any symmetric two by two matrix
    is allowed, and strategies can remember the history of an opponent's play
    from the previous three rounds of moves in iterated interactions between
    players. The genetic algorithm returns a list of adaptive strategies given
    payoffs, and the mean fitness of strategies in each generation.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization, <arXiv:1308.5623>.
Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Interface for extra smooth functions including tensor products, neural networks and decision trees.
This is an add-on package to GAMLSS. The purpose of this
        package is to allow users to fit interval response variables in
        GAMLSS models. The main function gen.cens() generates a
        censored version of an existing GAMLSS family distribution.
This is an add on package to 'GAMLSS'. The main purpose of this package is generating and fitting inflated distributions at any desired point (0, 1, 2, ...). The function gen.Kinf() generates K-inflated version of an existing discrete 'GAMLSS' family distribution.
Data used as examples to demonstrate GAMLSS models.
Demos for smoothing and gamlss.family distributions.
A set of distributions  which can be used  for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed  distributions.  Extra distributions can be created, by transforming, any continuous distribution defined on the real line,  to  a distribution defined on ranges 0 to infinity  or  0 to 1,  by using a ''log'' or a ''logit' transformation respectively.
This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.
The main purpose of this package is to allow fitting of
        mixture distributions with GAMLSS models.
This is an add on package to GAMLSS. It allows one extra
        method for fitting GAMLSS models. The main function nlgamlss()
        can fit any parametric (up to four parameter) GAMLSS
        distribution.
It allows us to fit Gaussian Markov Random Field within the
    Generalized Additive Models for Location Scale and Shape algorithms.
This is an add on package to GAMLSS. The purpose of this
        package is to allow users to defined truncated distributions in
        GAMLSS models. The main function gen.trun() generates truncated
        version of an existing GAMLSS family distribution.
Extra functions for GAMLSS and others models.
Density, distribution function, quantile function and random generation for the bimodal skew symmetric normal distribution of Hassan and El-Bassiouni (2016) <doi:10.1080/03610926.2014.882950>.
Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', using 'lme4' for estimation.
To compare nonlinear curves and surface estimations between groups using semiparametric methods for cross-sectional and longitudinal dataset.
Uses a slice sampling-based Markov chain Monte Carlo to
        conduct Bayesian fitting and inference for generalized additive
        mixed models.  Generalized linear mixed models and generalized 
        additive models are also handled as special cases of generalized
        additive mixed models. The methodology and software is described 
        in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand
        Journal of Statistics, 60, 279-330 <DOI:10.1111/ANZS.12241>.
Robust regression via gamma-divergence with L1, elastic net and ridge.
To calculate the relative risk (RR) for the generalized additive model.
Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.
Functions for querying the 'Google Analytics' core reporting,
    real-time, multi-channel funnel and management APIs, as well as the 'Google
    Tag Manager' (GTM) API. Write methods are also provided for the management
    and GTM APIs so that you can change tag, property or view settings, for
    example. Define reporting queries using natural R expressions instead of
    being concerned as much about API technical intricacies like query syntax,
    character code escaping, and API limitations.
This package implements a network-based gene weighting
        algorithm for pathways, as well as a gene-weighted gene set
        analysis approach for microarray data pathway analysis.
This is a dataset package for GANPA, which implements a
        network-based gene weighting approach to pathway analysis. This
        package includes data useful for GANPA, such as a functional
        association network, pathways, an expression dataset and
        multi-subunit proteins.
It is designed as an integrated package for genetic data
        analysis of both population and family data. Currently, it
        contains functions for sample size calculations of both
        population-based and family-based designs, probability of
        familial disease aggregation, kinship calculation, statistics
        in linkage analysis, and association analysis involving genetic
        markers including haplotype analysis with or without environmental
        covariates.
Datasets associated with the 'gap' package. Currently,
             it includes an example data for regional association
             plot (CDKN), an example data for a genomewide association
             meta-analysis (OPG), Manhattan plots with (hr1420, mhtdata)
             and without (w4) gene annotations.
Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in {Martinez-de-Pison} (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.
Tools to fill missing values in satellite data and to develop new
    gap-fill algorithms. The methods are tailored to data (images) observed
    at equally-spaced points in time. The package is illustrated with MODIS
    NDVI data.
The gap encodes the distance between clusters and improves
    interpretation of cluster heatmaps. The gaps can be of the same
    distance based on a height threshold to cut the dendrogram. Another
    option is to vary the size of gaps based on the distance between
    clusters.
An excerpt of the data available at Gapminder.org. For each of 142
    countries, the package provides values for life expectancy, GDP per capita,
    and population, every five years, from 1952 to 2007.
Provides utilities for working with Google APIs
    <https://developers.google.com/apis-explorer>.  This includes
    functions and classes for handling common credential types and for
    preparing, executing, and processing HTTP requests.
Organize a so-called ragged array as generalized arrays, which
	is simply an array with sub-dimensions denoting the subdivision of
	dimensions (grouping of members within dimensions).
	By the margins (names of dimensions and sub-dimensions) in generalized
	arrays, operators and utility functions provided in this package
	automatically match the margins,
	doing map-reduce style parallel computation
	along margins.  Generalized arrays are also cooperative to R's native
	functions that work on simple arrays.
Simulate, estimate and forecast using univariate and multivariate GAS models 
  as described in Ardia et al. (2019) <doi:10.18637/jss.v088.i06>.
Provides a genetic algorithm for finding variable
    subsets in high dimensional data with high prediction performance. The
    genetic algorithm can use ordinary least squares (OLS) regression models or
    partial least squares (PLS) regression models to evaluate the prediction
    power of variable subsets. By supporting different cross-validation
    schemes, the user can fine-tune the tradeoff between speed and quality of
    the solution.
Functions for greenhouse gas flux calculation from chamber
    measurements.
Fits gastric emptying time series from MRI or scintigraphic measurements
   using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with 
   Stan; computes derived parameters such as t50 and AUC.
Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al <doi:10.1159/000488519>.
Allows user to choose/gate a region on the plot and returns points
    within it.
Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel (not for Windows), and 'Rcpp'
 has been used to speed up calculations. Other R packages that perform
 similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.
Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is  zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets.  Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "A Model-free Approach to Linear Least Squares Regression with Exact  Probabilities and Applications to Covariate Selection", 2019, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633v4>.
A collection difference measures for multivariate Gaussian
        probability density functions, such as the Euclidea mean, the
        Mahalanobis distance, the Kullback-Leibler divergence, the
        J-Coefficient, the Minkowski L2-distance, the Chi-square
        divergence and the Hellinger Coefficient.
Display a random fact about Carl Friedrich Gauss
 based the on collection curated by Mike Cavers via the
 <http://gaussfacts.com> site.
Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.
A collection of functions to perform Gaussian quadrature
        with different weight functions corresponding to the orthogonal
        polynomials in package orthopolynom.  Examples verify the
        orthogonality and inner products of the polynomials.
Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.
A collection of algorithms and functions 
  for fitting data to a generalized lambda distribution 
  via moment matching methods, and generalized 
  bootstrapping.
Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.