Implementation of the BC3NET algorithm for gene regulatory network inference (de Matos Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from Large-Scale Gene Expression Data, PLoS ONE 7(3): e33624).
Underlying support functions for RcmdrPlugin.BCA and a
companion to the book Customer and Business Analytics: Applied
Data Mining for Business Decision Making Using R by Daniel S.
Putler and Robert E. Krider
Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.
Tools for Dating Business Cycles using Harding-Pagan (Quarterly Bry-Boschan) method and various plotting features.
Diagnostic tools based on two-way
anova and median-polish residual plots for Bicluster output
obtained from packages; "biclust" by Kaiser et al.(2008),"isa2"
by Csardi et al. (2010) and "fabia" by Hochreiter et al.
(2010). Moreover, It provides visualization tools for bicluster
output and corresponding non-bicluster rows- or columns
outcomes.  It has also extended the idea of Kaiser et al.(2008)
which is, extracting bicluster output in a text format, by
adding two bicluster methods from the fabia and isa2 R
packages.
Function to estimates taxonomic compositions from biomarker data, using a Bayesian approach.
Produces an economic evaluation of a Bayesian model in the form of MCMC simulations. Given suitable variables of cost and effectiveness / utility for two or more interventions, This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis.
Implementation of the Bayesian Causal Effect Estimation algorithm,
a data-driven method for the estimation of the causal effect of an exposure
on a continuous outcome. For more details, see Talbot et al. (2015) DOI:10.1515/jci-2014-0035.
Implements a full Bayesian cost-effectiveness analysis in the case where the cost variable is characterised by structural zeros. The package implements the Gamma, log-Normal and Normal models for the cost variable and the Gamma, Beta, Bernoulli and Normal models for the measure of clinical effectiveness.
Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); Age-depth modelling as per the algorithm of Haslett and Parnell (2008); Relative sea level rate estimation incorporating time uncertainty in polynomial regression models; and non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the Oxcal function SUM). The package includes a vignette which can be accessed via vignette(‘Bchron’).
This package takes pollen/time data from lake cores and produces a Bayesian posterior distribution of palaeoclimate from that location after fitting a non-linear non-Gaussian state-space model.
Builds a dendrogram using log posterior as a natural distance defined by the model and meanwhile waits the clustering variables. It is also capable to computing equivalent Bayesian discrimination probabilities. The adopted method suites small sample large dimension setting. The model parameter estimation maybe difficult, depending on data structure and the chosen distribution family.
Provides an implementation of the Barry and Hartigan (1993) product partition model for the normal errors change point problem using Markov Chain Monte Carlo.  It also extends the methodology to regression models on a connected graph (Wang and Emerson, 2015); this allows estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented.
The Behavioral Change Point Analysis (BCPA) is a method of
identifying hidden shifts in the underlying parameters of a time series,
developed specifically to be applied to animal movement data which is
irregularly sampled.  The method is based on: E.
Gurarie, R. Andrews and K. Laidre A novel method for identifying
behavioural changes in animal movement data (2009) Ecology Letters 12:5
395-408.
A Bayesian approach to detect mean shifts in AR(1) time series while accommodating metadata (if available). In addition, a linear trend component is allowed.
Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.
Methods for advanced analysis of B cell receptor repertoire data, like gene usage, mutations, clones and diversity and their visualisation.
Implements a wide variety of one and two-parameter Bayesian CRM
designs. The program can run interactively, allowing the user to enter outcomes
after each cohort has been recruited, or via simulation to assess operating
characteristics.
An R interface to the OpenBSD 'blowfish' password hashing algorithm,
as described in "A Future-Adaptable Password Scheme" by Niels Provos. The
implementation is derived from the 'py-bcrypt' module for Python which is a
wrapper for the OpenBSD implementation.
Methods for choosing the rank of an SVD approximation via cross
validation.  The package provides both Gabriel-style "block"
holdouts and Wold-style "speckled" holdouts.  It also includes an
implementation of the SVDImpute algorithm.  For more information about
Bi-cross-validation, see Owen & Perry's 2009 AoAS article
(at http://arxiv.org/abs/0908.2062) and Perry's 2009 PhD thesis
(at http://arxiv.org/abs/0909.3052).
Functions for density estimation based on grouped (or pre-binned)
data.
A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
Provides statistical tools for Bayesian structure learning in undirected graphical models with either continuous or discrete variables.
Analyze differences among time series curves with Oleson et al's modified p-value technique.
Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.
Provides a continuous date scale, omitting weekends and holidays.
This is a special case of sparse matrices, used by coxme
Biodiversity data visualizations using R would be helpful to
understand completeness of biodiversity inventory, extent of geographical,
taxonomic and temporal coverage, gaps and biases in data.
The package bdynsys for panel/longitudinal data combines methods to model
changes in up to four indicators over times as a function of the indicators
themselves and up to three predictors using ordinary differential equations
(ODEs) with polynomial terms that allow to model complex and nonlinear
effects. A Bayesian model selection approach is implemented. The package
provides also visualisation tools to plot phase portraits of the dynamic
system, showing the complex co-evolution of two indicators over time with the
possibility to highlight trajectories for specified entities (e.g. countries,
individuals). Furthermore the visualisation tools allow for making
predictions of the trajectories of specified entities with respect to the
indicators.
This package contains functions to fit the filtering model
of Forcheh et al., (2012) which is used to derive the
intra-cluster correlation (ICC). Model fitting is done using
the modified version of the ``MLM.beadarray" function of Kim
and Lin (2011).
Imports bead-summary data from Illumina scanner.
Pre-processes using a suite of optional normalizations and
transformations. Clusters and automatically calls genotypes,
critically able to handle markers in duplicated regions of the
genome (multisite variants; MSVs). Interactive clustering if
needed. MSVs with variation in both paralogs may be resolved
and mapped to their respective chromosomes. Quality control
including pedigree checking and visual assessment of clusters.
Too large data-sets are handled by working on smaller subsets
of the data in sequence.
Plots univariate comparison graphs, an alternative to
boxplot/stripchart/violin plot.
Computing age specific nest survival rates based on a Bayesian hierarchical model. This package can handle unknown nest age, irregular visiting schedule and nest-specific covariates.
provides functions that allow users to quantify the relative
contributions of geographic and ecological distances to empirical patterns of genetic
differentiation on a landscape.  Specifically, we use a custom Markov chain
Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the
inference model, as well as functions for performing MCMC diagnosis and assessing
model adequacy.
A matrix-like data structure that allows for efficient,
convenient, and scalable subsetting of binary genotype/phenotype files
generated by PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/), the whole
genome association analysis toolset, without loading the entire file into
memory.
Genomic region processing using tools such as bedtools, bedops and tabix.
The sole function of this package is beep(), with the purpose to
make it easy to play notification sounds on whatever platform you are on.
It is intended to be useful, for example, if you are running a long analysis
in the background and want to know when it is ready.
The bee swarm plot is a one-dimensional scatter plot like "stripchart", but with closely-packed, non-overlapping points.
Full implementation of the 28 distributions introduced as
benchmarks for nonparametric density estimation by Berlinet and
Devroye (1994). Includes densities, cdfs, quantile functions
and generators for samples as well as additional information on
features of the densities. Also contains the 4 histogram
densities used in Rozenholc/Mildenberger/Gather (2010).
The benchmark package provides a toolbox for setup, execution
and analysis of benchmark experiments. Main focus is the analysis of
data accumulating during the execution -- one primary objective is the
statistical correct computation of the candidate algorithms' order.
Methods for frontier
analysis, Data Envelopment Analysis (DEA), under different
technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+),
and using different efficiency measures (input based, output based,
hyperbolic graph, additive, super, and directional efficiency). Peers
and slacks are available, partial price information can be included,
and optimal cost, revenue and profit can be calculated. Evaluation of
mergers is also supported.  Methods for graphing the technology sets
are also included. There is also support comparative methods based
on Stochastic Frontier Analyses (SFA). In general, the methods can be
used to solve not only standard models, but also many other model
variants. It complements the book, Bogetoft and Otto,
Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of
course also be used as a stand-alone package.
The Benford Analysis package provides tools that make it easier to
validate data using Benford's Law.
Several specialized statistical tests and support functions
for determining if numerical data could conform to Benford's law.
Included are two main interfaces for fitting and diagnosing
bent-cable regressions for autoregressive time-series data or
independent data (time series or otherwise): 'bentcable.ar()' and
'bentcable.dev.plot()'. Some components in the package can also be
used as stand-alone functions. The bent cable
(linear-quadratic-linear) generalizes the broken stick
(linear-linear), which is also handled by this package. Version 0.2
corrects a glitch in the computation of confidence intervals for the
CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear
in Version 0.2.3 and up. Version 0.3.0 improves robustness of the
error-message producing mechanism. It is the author's intention to
distribute any future updates via GitHub.
Tool for analysing benthos data. It estimates several quality
indices like the total abundance of species, species richness,
Margalef's d, AZTI Marine Biotic Index (AMBI), and the BEQI-2 index.
Furthermore, additional (optional) features are provided that enhance data
preprocessing: (1) genus to species conversion, i.e.,taxa counts at the
taxonomic genus level can optionally be converted to the species level and
(2) pooling: small samples are combined to bigger samples with a
standardized size to (a) meet the data requirements of the AMBI,
(b) generate comparable species richness values and
(c) give a higher benthos signal to noise ratio.
The functions in this package remove batch effects from
microarrary normalized data. The expression levels of the genes
are represented in a matrix where rows correspond to
independent samples and columns to genes (variables). The
batches are represented by categorical variables (objects of
class factor). When further covariates of interest are
available they can be used to remove efficiently the batch
effects and adjust the data.
Set of tools to analyse Bayesian exponential random graph models
This package provides easy access to http://daten.berlin.de
It allows you to search through the data catalogue and to download
the data directly from within R.
Draw horizontal histograms, color scatterpoints by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, use the unit hydrograph in a linear storage cascade, convert lists to data.frames, fit multiple functions by regression (power, reciprocal, exponential, logarithmic, polynomial, rational).
Bessel Function Computations for complex and real numbers;
notably interfacing TOMS 644; approximations for large arguments,
experiments, etc.
An alternative to t-tests, producing posterior estimates
for group means and standard deviations and their differences and
effect sizes.
Best subset glm using AIC, BIC, EBIC, BICq or Cross-Validation. For the normal case, the 'leaps' is used. Otherwise, a slower exhaustive search. The 'xtable' package is needed for vignette 'SimExperimentBICq.Rnw' accompanying this package.
To detecting rare variants for quantitative traits using
nuclear families, the linear combination methods are proposed
using the estimated regression coefficients from the multiple
regression and regularized regression as the weights.
Measures of beta-diversity in networks, and easy visualization of
why two networks are different.
betapart allows computing pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.
Permutational method to incorporate taxonomic uncertainty
and some functions to assess its effects on parameters of some
widely used multivariate methods in ecology
Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions.
In addition to maximum likelihood regression (for both mean and precision of a beta-distributed
response), bias-corrected and bias-reduced estimation as well as finite mixture models and
recursive partitioning for beta regressions are provided.
Computes standardized beta coefficients and corresponding
standard errors for the following models:
linear regression models with numerical covariates only,
linear regression models with numerical and factorial covariates,
weighted linear regression models,
all these linear regression models with interaction terms, and
robust linear regression models with numerical covariates only.
Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).
The sample size according to the Bethel's procedure.
The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.
This package provides model fitting for
several Bayesian factor models including Gaussian,
ordinal probit, mixed and semiparametric Gaussian
copula factor models under a range of priors.
BFAST integrates the decomposition of time series into trend,
seasonal, and remainder components with methods for detecting
and characterizing abrupt changes within the trend and seasonal
components. BFAST can be used to analyze different types of
satellite image time series and can be applied to other disciplines
dealing with seasonal or non-seasonal time series, such as hydrology,
climatology, and econometrics. The algorithm can be extended to
label detected changes with information on the parameters of the
fitted piecewise linear models. BFAST monitoring functionality is added
based on a paper that has been submitted to Remote Sensing of Environment.
BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST-type models.
BFAST approach is flexible approach that handles missing data without interpolation.
Furthermore now different models can be used to fit the time series data and detect structural changes (breaks).
The package implements the Bayesian paradigm for fractional
polynomial models under the assumption of normally distributed error terms.
Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.
Implementation of Bayesian estimation in generalized linear models following Gamerman (1997).
Two partially supervised mixture modeling methods:
soft-label and belief-based modeling are implemented.
For completeness, we equipped the package also with the
functionality of unsupervised, semi- and fully supervised
mixture modeling.  The package can be applied also to selection
of the best-fitting from a set of models with different
component numbers or constraints on their structures.
For detailed introduction see:
Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
Tiuryn (2012), The R Package bgmm: Mixture Modeling with
Uncertain Knowledge, Journal of Statistical Software.
Computes the hazard rate estimate as described by Nieto-Barajas and Walker (2002) and Nieto-Barajas (2003).
Implement an efficient block Gibbs sampler with incomplete
data from a multinomial distribution taking values from the k
categories 1,2,...,k, where data are assumed to miss at random
and each missing datum belongs to one and only one of m
distinct non-empty proper subsets A1, A2,..., Am of 1,2,...,k
and the k categories are labelled such that only consecutive
A's may overlap.
Boost provides free peer-reviewed portable C++ source
libraries.  A large part of Boost is provided as C++ template code
which is resolved entirely at compile-time without linking.  This
package aims to provide the most useful subset of Boost libraries
for template use among CRAN package. By placing these libraries in
this package, we offer a more efficient distribution system for CRAN
as replication of this code in the sources of other packages is
avoided.

As of release 1.60, the following Boost libraries are included:
algorithm any bind circular_buffer concept config container
date_time detail dynamic_bitset exception filesystem foreach
functional fusion geometry graph heap integer interprocess
intrusive io iostreams iterator math move mpl multiprcecision
numeric pending phoenix preprocessor random range smart_ptr
spirit tuple type_trains typeof unordered utility uuid.
Functions for MLE, MCMC, CIs (originally in Fortran)
Functions and data sets reproducing some examples in
Box, Hunter and Hunter II.  Useful for statistical design
of experiments, especially factorial experiments.
Bias correction of second order of the maximum likelihood
estimators of the parameters of the beta regression model.
Statistical models of biased sampling in the form of
univariate and multivariate noncentral hypergeometric distributions,
including Wallenius' noncentral hypergeometric distribution and
Fisher's noncentral hypergeometric distribution
(also called extended hypergeometric distribution).
See vignette("UrnTheory") for explanation of these distributions.
The main function biclust provides several algorithms to
find biclusters in two-dimensional data: Cheng and Church,
Spectral, Plaid Model, Xmotifs and Bimax. In addition, the
package provides methods for data preprocessing (normalization
and discretisation), visualisation, and validation of bicluster
solutions.
An S3 class with a method for calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
This package makes global and multiple inferences for
given bi- and trifactorial clinical trial designs using
bootstrap methods and a classical approach.
Contains tools for survey statistics (especially in educational
assessment) for datasets with replication designs (jackknife,
bootstrap, replicate weights). Descriptive statistics, linear
and logistic regression, path models for manifest variables
with measurement error correction and two-level hierarchical
regressions for weighted samples are included. Statistical
inference can be conducted for multiply imputed datasets and
nested multiply imputed datasets.
This package is developed by BIFIE (Federal Institute for
Educational Research, Innovation and Development of the Austrian
School System; Salzburg, Austria).
This package provides arithmetic functions for R matrix and big.matrix objects.
Extend the 'bigmemory' package with various analytics.
Functions 'bigkmeans' and 'binit' may also be used with native R objects.
For 'tapply'-like functions, the bigtabulate package may also be helpful.
For linear algebra support, see 'bigalgebra'.  For mutex (locking) support
for advanced shared-memory usage, see 'synchronicity'.
The big data package is a collection of scalable methods
for large-scale data analysis.
Data sets and functions for chi-squared Hardy-Weinberg and case-
control association tests of highly polymorphic genetic data [e.g., human
leukocyte antigen (HLA) data]. Performs association tests at multiple levels of
polymorphism (haplotype, locus and HLA amino-acids). Combines rare variants to
a common class to account for sparse cells in tables as described by Hollenbach
JA, Mack SJ, Thomson G, Gourraud PA. 'Analytical methods for disease association
studies with immunogenetic data.' Methods Mol Biol. 2012;882:245-66.
Distributes Gaussian process calculations across nodes
in a distributed memory setting, using Rmpi. The bigGP class
provides high-level methods for maximum likelihood with normal data,
prediction, calculation of uncertainty (i.e., posterior covariance
calculations), and simulation of realizations. In addition, bigGP
provides an API for basic matrix calculations with distributed
covariance matrices, including Cholesky decomposition, back/forwardsolve,
crossproduct, and matrix multiplication.
Least-angle regression, lasso and stepwise regression for
numeric datasets in which the number of observations is greater
than the number of predictors.  The functions can be used with
the ff library to accomodate datasets that are too large to be
held in memory.
Regression for data too large to fit in memory
Create, store, access, and manipulate massive matrices.
Matrices are allocated to shared memory and may use memory-mapped
files.  Packages 'biganalytics', 'bigtabulate', 'synchronicity', and
'bigalgebra' provide advanced functionality.
This package provides a shared resource interface for the bigmemory and synchronicity packages.
The 'bigml' package contains bindings for the BigML API.
The package includes methods that provide straightforward access
to basic API functionality, as well as methods that accommodate
idiomatic R data types and concepts.
Adds wrappers to add functionality for big.matrix objects (see the bigmemory project).
This allows fast scalable principle components analysis (PCA), or singular value decomposition (SVD).
There are also functions for transposing, using multicore 'apply' functionality, data importing
and for compact display of big.matrix objects. Most functions also work for standard matrices if
RAM is sufficient.
Easily talk to Google's BigQuery database from R.
The package fits large-scale (generalized) ridge regression for various distributions of response. The shrinkage parameters (lambdas) can be pre-specified or estimated using an internal update routine (fitting a heteroscedastic effects model, or HEM). It gives possibility to shrink any subset of parameters in the model. It has special computational advantage for the cases when the number of shrinkage parameters exceeds the number of observations. For example, the package is very useful for fitting large-scale omics data, such as high-throughput genotype data (genomics), gene expression data (transcriptomics), metabolomics data, etc.
Fits smoothing spline regression models using scalable algorithms designed for large samples. Five marginal spline types are supported: cubic, different cubic, cubic periodic, cubic thin-plate, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
Extend the bigmemory package with 'table', 'tapply', and 'split' support for 'big.matrix' objects.  The functions may also be used with native R matrices for improving speed and memory-efficiency.
This package is trying to implement Top Scoring Pair based
methods for classification including LDCA, TSP-tree, TSP-random
forest and TSP gradient boosting algorithm.
Performs logistic regression for binary longitudinal
data, allowing for serial dependence among observations from a given
individual and a random intercept term. Estimation is via maximization
of the exact likelihood of a suitably defined model. Missing values and
unbalanced data are allowed, with some restrictions.
A power calculator for Genome-wide association studies
(GWAs) with combined gold (error-free) and silver (erroneous)
phenotyping per McDavid A, Crane PK, Newton KM, Crosslin DR, et
al. (2011)
Estimates non-Gaussian mixture models of case-control data. The four types of models supported are binormal, two component constrained, two component unconstrained, and four component.  The most general model is the four component model, under which both cases and controls are distributed according to a mixture of two unimodal distributions.  In the four component model, the two component distributions of the control mixture may be distinct from the two components of the case mixture distribution.  In the two component unconstrained model, the components of the control and case mixtures are the same; however the mixture probabilities may differ for cases and controls.  In the two component constrained model, all controls are distributed according to one of the two components while cases follow a mixture distribution of the two components.  In the binormal model, cases and controls are distributed according to distinct unimodal distributions.  These models assume that Box-Cox transformed case and control data with a common lambda parameter are distributed according to Gaussian mixture distributions.   Model parameters are estimated using the expectation-maximization (EM) algorithm.  Likelihood ratio test comparison of nested models can be performed using the lr.test function. AUC and PAUC values can be computed for the model-based and empirical ROC curves using the auc and pauc functions, respectively. The model-based and empirical ROC curves can be graphed using the roc.plot function. Finally, the model-based density estimates can be visualized by plotting a model object created with the bimixt.model function.
Provides methods for the binarization of one-dimensional data and some visualization functions.
Modeling under- and over-dispersed binary data using extended Poisson process models (EPPM).
Convert to binary numbers (Base2). Shift, rotate, summary. Based on logical vector.
The "binda" package implements functions for multi-class
discriminant analysis using binary predictors, for corresponding
variable selection, and for dichotomizing continuous data.
Generation of correlated artificial binary data.
Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored.  We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results.  We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.
This package provides tools to analyze binary graph objects.
This package provides methods for estimation and
hypothesis testing of proportions in group testing designs. It
involves methods for estimating a proportion in a single
population (assuming sensitivity and specificity 1 in designs
with equal group sizes), as well as hypothesis tests and
functions for experimental design for this situation. For
estimating one proportion or the difference of proportions, a
number of confidence interval methods are included, which can
deal with various different pool sizes. Further, regression
methods are implemented for simple pooling and matrix pooling
designs.
Binomial Haar-Fisz transforms for Gaussianization
Asymptotic simultaneous confidence intervals for comparison of many treatments with one control,
for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals.
Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size
iteration based on approximate any-pair power.
Exact conditional maximum test for many-to-one comparisons to a control.
Generation of multiple binary and continuous non-normal variables simultaneously
given the marginal characteristics and association structure based on the methodology
proposed by Demirtas et al. (2012).
Generating multiple binary and normal variables
simultaneously given marginal characteristics and association
structure based on the methodology proposed by Demirtas and
Doganay (2012).
Constructs confidence intervals on the probability of
success in a binomial experiment via several parameterizations
Includes functions for working with taxonomic data,
including functions for combining, separating, and filtering
taxonomic groups by any rank or name. Allows standard (SE)
and non-standard evaluation (NSE).
Binomial random numbers are generated via the perfect
sampling algorithm. At each iteration dual markov chains are
generated and coalescence is checked. In case coalescence
occurs, the resulting number is outputted. In case not, then
the algorithm is restarted from T(t)=2*T(t) until coalescence
occurs.
The R package contains different MCMC schemes to estimate the regression coefficients of a binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler, an auxiliary mixture sampler and a hybrid auxiliary mixture (HAM) sampler. All sampling procedures are based on algorithms using data augmentation, where the regression coefficients are estimated by rewriting the logit model as a latent variable model called difference random utility model (dRUM).
A suite of functions to compute confidence intervals and necessary
sample sizes for the parameter p of the Bernoulli B(p)
distribution under simple random sampling or under pooled
sampling. Such computations are e.g. of interest when investigating
the incidence or prevalence in populations.
The package contains functions to compute coverage probabilities and
coverage coefficients of the provided confidence intervals
procedures. Sample size calculations are based on expected length.
This package provides a range of diagnostic methods for
binomial regression models.
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions.
Implementation of algorithms for cutting numerical values
exhibiting a potentially highly skewed distribution into evenly distributed
groups (bins). This functionality can be applied for binning discrete
values, such as counts, as well as for discretization of continuous values,
for example, during generation of features used in machine learning
algorithms.
For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.
Utilities to process, organize and explore protein structure, sequence and dynamics data.  Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data.  In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data.  Please refer to the URLs below for more information.
Contains functions for the data analysis with the emphasis on biological data, including several algorithms for feature ranking, feature selection, classification
algorithms with the embedded validation procedures.
The functions can deal with numerical as well as with nominal features. Includes also the functions for calculation
of feature AUC (Area Under the ROC Curve) and HUM (hypervolume under manifold) values and construction 2D- and 3D- ROC curves.
Biocomb provides the calculation of Area Above the RCC (AAC) values and construction of Relative Cost Curves
(RCC) to estimate the classifier performance under unequal misclassification costs problem.
Biocomb has the special function to deal with missing values, including different imputing schemes.
The Biodem package provides a number of functions for Biodemographic analysis.
Graphical User Interface (via the R-Commander) and utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package.
Multilevel data frames of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in functions modelFrame and ringApply.
Functions for working with biogas data. Both low- and high-level functions are included for carrying out common tasks for analysis of biogas and related data. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to (possibly user-defined) standard temperature and pressure. Gas composition, cumulative production, or other variables can be interpolated to a specified time. Cumulative biogas and methane production (and rates) can be calculated from individual biogas volume (or reactor mass) and biogas composition measurements for any number of reactors. With cumulative methane production data and data on reactor contents, biochemical methane potential (BMP) can be calculated and summarized, including subtraction of the inoculum contribution and normalization by substrate mass. Cumulative production and production rates can be summarized in several different ways (e.g., omitting normalization) using the same function. Lastly, biogas and methane production can be predicted from substrate composition and additional, optional data.
Tools for extraction and analysis of various
n-grams (k-mers) derived from biological sequences (proteins
or nucleic acids). Uses QuiPT (quick permutation test) for fast
feature-filtering to deal with the dimensionality  of the n-gram data.
Prediction and adjustment to experimental data of microbial
inactivation. Several models available in the literature are implemented.
Imports benthic count data, reformats this data, and
computes environmental inferences from this data.
This is an R package for interfacing with the
BIOM format. This package includes basic tools for
reading biom-format files, accessing and subsetting
data tables from a biom object (which is more complex
than a single table), as well as limited support for
writing a biom-object back to a biom-format file. The
design of this API is intended to match the python API
and other tools included with the biom-format project,
but with a decidedly "R flavor" that should be familiar
to R users. This includes S4 classes and methods, as
well as extensions of common core functions/methods.
Variable selection methods are provided for several classification methods: the lasso/elastic net, PCLDA, PLSDA, and several t-tests. Two approaches for selecting cutoffs can be used, one based on the stability of model coefficients under perturbation, and the other on higher criticism.
Performing biological data retrieval and functional annotation with R.
Functions for species distribution modeling, calibration and evaluation, ensemble of models.
Provides utilities to facilitate import, export and computation with the
BIOM (Biological Observation Matrix) format (http://biom-format.org).
Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
Utilities and functions to investigate the relation
between biomolecular structures, their interactions, and the
evolutionary information revealed in sequence alignments of
these molecules.
bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
networks.
Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions
are defined.
For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and
solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA.
For hybrid deterministic/stochastic,
it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
bioPN algorithms are developed in C to achieve adequate performance.
The bioRxiv preprint server (http://www.biorxiv.org) is a website where scientists can post preprints of scholarly texts in biology. Users can search and download PDFs in bulk from the preprint server. The text of abstracts are stored as raw text within R, and PDFs can easily be saved and imported for text mining with packages such as 'tm'.
Bios2mds is primarily dedicated to the analysis of
biological sequences by metric MultiDimensional Scaling with
projection of supplementary data. It contains functions for
reading multiple sequence alignment files, calculating distance
matrices, performing metric multidimensional scaling and
visualizing results.
Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.
This packages provides datasets and functions for the book "Initiation à la Statistique avec R", Dunod, 2ed, 2014.
Tools designed to perform and work with cluster analysis (including Tocher's algorithm),
discriminant analysis and path analysis (standard and under collinearity), as well as some
useful miscellaneous tools for dealing with sample size and optimum plot size calculations.
Mantel's permutation test can be found in this package. A new approach for calculating its
power is implemented. biotools also contains the new tests for genetic covariance components.
Bipartite provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in ecological webs. It focuses on webs consisting of only two trophic levels, e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the webs topology.
A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot.
Provides a GUI with which users can construct and interact
with biplots.
Bayesian parameter estimation for (partially observed)
two-dimensional diffusions.
R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to http://www.tandfonline.com/doi/full/10.1080/03078698.2014.933053.
This is a compilation of functions that I found useful to make. It currently includes a unit of measurement conversion function, a Q10 calculator for temperature dependence of chemical and biological rates, and some miscellaneous wrapper functions to make R code shorter and faster to write.
Tools to find bad commits with git bisect. See
https://github.com/wch/bisectr for examples and test script
templates.
Enables the user to infer potential synthetic lethal relationships
by analysing relationships between bimodally distributed gene pairs in big
gene expression datasets.  Enables the user to visualise these candidate
synthetic lethal relationships.
Provides functions for fitting Bayesian monotonic regression models to data.
bitmapped vectors of booleans (no NAs),
coercion from and to logicals, integers and integer subscripts;
fast boolean operators and fast summary statistics.
With 'bit' vectors you can store true binary booleans {FALSE,TRUE} at the
expense of 1 bit only, on a 32 bit architecture this means factor 32 less
RAM and ~ factor 32 more speed on boolean operations. Due to overhead of
R calls, actual speed gain depends on the size of the vector: expect gains
for vectors of size > 10000 elements. Even for one-time boolean operations
it can pay-off to convert to bit, the pay-off is obvious, when such
components are used more than once.
Reading from and writing to bit is approximately as fast as accessing
standard logicals - mostly due to R's time for memory allocation. The package
allows to work with pre-allocated memory for return values by calling .Call()
directly: when evaluating the speed of C-access with pre-allocated vector
memory, coping from bit to logical requires only 70% of the time for copying
from logical to logical; and copying from logical to bit comes at a
performance penalty of 150%. the package now contains further classes for
representing logical selections: 'bitwhich' for very skewed selections and
'ri' for selecting ranges of values for chunked processing. All three index
classes can be used for subsetting 'ff' objects (ff-2.1-0 and higher).
Package 'bit64' provides serializable S3 atomic 64bit (signed) integers
that can be used in vectors, matrices, arrays and data.frames. Methods are
available for coercion from and to logicals, integers, doubles, characters
and factors as well as many elementwise and summary functions. Many fast
algorithmic operations such as 'match' and 'order' support interactive data
exploration and manipulation and optionally leverage caching.
Functions for bitwise operations on integer vectors.
Provides methods for the binarization and trinarization of one-dimensional data and some visualization functions.
Parameter estimation of bivariate distribution functions
modeled as a Archimedean copula function. The input data may contain
values from right censored. Used marginal distributions are two-parameter.
Methods for density, distribution, survival, random sample generation.
Implements sample size calculations for bivariate random
intercept regression model that are described in Comulada and
Weiss (2010)
This is a port of the WTC MATLAB package written by Aslak Grinsted
and the wavelet program written by Christopher Torrence and Gibert P.
Compo. This package can be used to perform univariate and bivariate
(cross-wavelet, wavelet coherence, wavelet clustering) analyses.
Compute multivariate location, scale, and correlation
estimates based on Tukey's biweight M-estimator.
Business days calculations based on a list of holidays and
nonworking weekdays. Quite useful for fixed income and derivatives pricing.
Fast and accurate calculation of Blaker's binomial confidence limits (and some related stuff).
Bland-Altman Plots using either base graphics or ggplot2,
augmented with confidence intervals, with detailed return values and
a sunflowerplot option for data with ties.
A wrapper around the 'Blat' command line SMTP mailer for Windows.
'Blat' is public domain software, but be sure to read the license before use.
It can be found at the Blat website http://www.blat.net.
An integrated set of tools to calculate and analyze Blau statuses quantifying social distance between individuals belonging to organizations. Relational (network) data may be incorporated for additional analyses.
Fit a variety of Bayesian latent variable models, including confirmatory
factor analysis, structural equation models, and latent growth curve models.
An implementation of the Black-Litterman Model and Atilio
Meucci's copula opinion pooling framework.
Tools for assessing exotic species' contributions to
landscape homogeneity using average pairwise Jaccard similarity
and an analytical approximation derived in Harris et al. (2011,
"Occupancy is nine-tenths of the law," The American
Naturalist). Also includes a randomization method for assessing
sources of model error.
Function for drawing the coastline of the British Isles
This package is an extension to the "ergm" package which implements the block ergms.
Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.
Maximum a posteriori estimation for linear and generalized
linear mixed-effects models in a Bayesian setting. Extends
'lme4' by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker.
Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.
Simultaneous clustering of rows and columns, usually designated by
biclustering, co-clustering or block clustering, is an important technique
in two way data analysis. It consists of estimating a mixture model which
takes into account the block clustering problem on both the individual and
variables sets. The blockcluster package provides a bridge between the C++
core library and the R statistical computing environment. This package
allows to co-cluster binary, contingency, continuous and categorical
data-sets. It also provides utility functions to visualize the results.
This package may be useful for various applications in fields of Data
mining,Information retrieval, Biology, computer vision and many more. More
information about the project and comprehensive tutorial can be found on
the link mentioned in URL.
Some elementary matrix algebra tools are implemented to manage
block matrices or partitioned matrix, i.e. "matrix of matrices"
(http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here
defined as a new S3 object. In this package, some methods for "matrix"
object are rewritten for "blockmatrix" object. New methods are implemented.
This package was created to solve equation systems with block matrices for
the analysis of environmental vector time series .
Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Creates strings that show a text message in 8 by 8 block
letters
The package is primarly ment as an implementation of
Generalized blockmodeling for valued networks. In addition,
measurese of similarity or dissimilarity based on structural
equivalence and regular equivalence (REGE algorithem) can be
computed and partitioned matrices can be ploted.
Latent and Stochastic Block Model estimation by a Variational EM algorithm.
Various probability distribution are provided (Bernoulli,
Poisson...), with or without covariates.
Create randomizations for block random clinical trials.
Can also produce a pdf file of randomization cards.
Nested block designs for unstructured treatment sets where
blocks can be repeatedly nested and treatments can have different levels of
replication. Blocks are optimized hierarchically from the top down with each
set of nested blocks optimized within the levels of any existing higher-level
blocks. Block sizes are equal if the number of blocks exactly divides the number
of plots, otherwise they differ by at most one plot. The design output is a
data table giving a randomised allocation of treatments to blocks together with
a plan table showing treatments in blocks and a set of blocks-by-treatments
incidence matrices, one for each blocks stratum.
Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units.  Maximum, minimum, or an allowable range of differences between units on one variable can be set.  Randomly assign units to treatment conditions.  Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files.
Provides tools for making statistical comparisons with distance-function based permutation tests developed by P. W. Mielke, Jr. and colleagues at Colorado State University (Mielke, P. W. & Berry, K. J. Permutation Methods: A Distance Function Approach (Springer, New York, 2001)) and for testing parameters estimated in linear models with permutation procedures developed by B. S. Cade and colleagues at the Fort Collins Science Center, U. S. Geological Survey.
Optimize a function with constraints via a transformation of the
Lagrangian.
Allows users to request data for one or multiple series through the U.S. Bureau of Labor Statistics API.  Users provide parameters as specified in http://www.bls.gov/developers/api_signature.htm and the function returns a JSON string.
Package for Bayesian model averaging and variable selection for linear models,
generalized linear models and survival models (cox
regression).
Benchmark dose analysis for continuous and quantal
dose-response data.
Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models.
Provides a collection of functions for conducting meta-analyses under Bayesian context in R. The package includes functions for computing various effect size or outcome measures (e.g. odds ratios, mean difference and incidence rate ratio) for different types of data based on MCMC simulations. Users are allowed to fit fixed- and random-effects models with different priors to the data. Meta-regression can be carried out if effects of additional covariates are observed. Furthermore, the package provides functions for creating posterior distribution plots and forest plot to display main model output. Traceplots and some other diagnostic plots are also available for assessing model fit and performance.
The BMhyd package analyzes the phenotypic evolution of species of hybrid origin on a phylogenetic network. This package can detect the hybrid vigor effect, a burst of variation at formation, and the relative portion of heritability from its parents. Parameters are estimated by maximum likelihood. Users need to enter a comparative data set, a phylogeny, and information on gene flow leading to hybrids.
This is a bare-bones implementation of sampling algorithms
for a variety of Bayesian stick-breaking (marginally DP)
mixture models, including particle learning and Gibbs sampling
for static DP mixtures, particle learning for dynamic BAR
stick-breaking, and DP mixture regression.  The software is
designed to be easy to customize to suit different situations
and for experimentation with stick-breaking models.  Since
particles are repeatedly copied, it is not an especially
efficient implementation.
MCMC diagnostic package that contains tools to diagnose
convergence as well as to evaluate sensitivity studies,
Includes summary functions which output mean, median,
95percentCI, Gelman & Rubin diagnostics and the Hellinger
distance based diagnostics, Also contains functions to
determine when an MCMC chain has converged via Hellinger
distance, A function is also provided to compare outputs from
identically dimensioned chains for determining sensitivy to
prior distribution assumptions
This package implements approximate and exact methods for
pairwise binary markov models. The exact method uses an
implementation of the junction tree algorithm for binary
graphical models. For more details see the help files
Reads Windows BMP format images. Currently limited to 8 bit
greyscale images and 24,32 bit (A)RGB images. Pure R implementation without
external dependencies.
Bundle methods for minimization of convex and non-convex risk
under L1 or L2 regularization. Implements the algorithm proposed by Teo et
al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR
2012). The package comes with lot of loss functions for machine learning
which make it powerful for big data analysis. The applications includes:
structured prediction, linear SVM, multi-class SVM, f-beta optimization,
ROC optimization, ordinal regression, quantile regression,
epsilon insensitive regression, least mean square, logistic regression,
least absolute deviation regression (see package examples), etc... all with
L1 and L2 regularization.
Provides two Bayesian models for detecting the association between rare variants and traits that can be continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect and is dedicated to twin design while it can also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty information and can be applied to either independent or family samples. Furthermore, it deals with continuous, binary and ordinal traits.
Bayesian model averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, flexible and hyper-g priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison.
Implements state-of-the art algorithms for learning discrete Bayesian network classifiers from data, as well as functions for using these classifiers for prediction, assessing their predictive performance, and inspecting their properties.
Data generator based on Bayesian network model
Bayesian network structure learning, parameter learning and
inference.
This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
for discrete, Gaussian and conditional Gaussian networks, along with many
score functions and conditional independence tests.
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are
also implemented.
Some utility functions (model comparison and manipulation, random data
generation, arc orientation testing, simple and advanced plots) are
included, as well as support for parameter estimation (maximum likelihood
and Bayesian) and inference, conditional probability queries and
cross-validation. Development snapshots with the latest bugfixes are
available from www.bnlearn.com.
Implementation of Bayesian estimation in normal heteroscedastic nonlinear regression Models following Cepeda-Cuervo, (2001).
Modeling mixtures with normalized random measures by a Ferguson-Klass type algorithm for posterior normalized random measures.
Implements the Bayesian nonparametric monotonic regression
method described in Bornkamp & Ickstadt (2009), Biometrics, 65,
198-205.
Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).
Markov chain Monte Carlo algorithms for non- and semi-parametric models.
Bayesian Network Structure Learning from Data with Missing Values.
The package implements the Silander-Myllymaki complete search,
the Max-Min Hill-climbing heuristic search, and the Structural
Expectation-Maximization algorithm. Available scoring functions are
BDeu, AIC, BIC. The package also implements methods for generating and using
bootstrap samples, imputed data, inference.
A menu-driven program and library of functions for carrying out
convergence diagnostics and statistical and graphical analysis of Markov
chain Monte Carlo sampling output.
This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.
An implementation of three statistical tests for identification of COG (Cluster of Orthologous Groups) that are over represented among genes that show differential expression under conditions. It also provides tabular and graphical summaries of the results for easy visualisation and presentation.
Generic Extraction of main text content from HTML files; removal
of ads, sidebars and headers using the boilerpipe
(http://code.google.com/p/boilerpipe/) Java library. The
extraction heuristics from boilerpipe show a robust performance for a wide
range of web site templates.
The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial design for finding
the maximum tolerated dose (MTD). It can be used to design both single-agent and
drug-combination trials. The BOIN design is motivated by the top priority and concern of
clinicians when testing a new drug, which is to effectively treat patients and minimize the chance
of exposing them to subtherapeutic or overly toxic doses. The prominent advantage of the
BOIN design is that it achieves simplicity and superior performance at the same time.
The BOIN design is algorithm-based and can be implemented in a simple way similar to the
traditional 3+3 design. The BOIN design yields an average performance that is comparable to
that of the continual reassessment method (CRM, one of the best model-based designs) in terms of
selecting the MTD, but has a substantially lower risk of assigning patients to subtherapeutic
or overly toxic doses.
A programmatic interface to the Web Service methods provided by
Bold Systems for genetic barcode data. Functions include methods for
searching by sequences by taxonomic names, ids, collectors, and
institutions; as well as a function for searching for specimens, and
downloading trace files.
A set of R functions and data sets for the book
Introduction to Bayesian Statistics, Bolstad, W.M. (2007), John
Wiley & Sons ISBN 0-471-27020-2
A set of R functions and data sets for the book
Understanding Computational Bayesian Statistics, Bolstad, W.M.
(2009), John Wiley & Sons ISBN 978-0470046098
This package implements a
partial-observability procedure for testing Boolean
hypotheses that generalizes the binary response GLM as
outlined in Braumoeller (2003).
Provides methods to reconstruct and generate synchronous,
asynchronous, probabilistic and temporal Boolean networks, and to
analyze and visualize attractors in Boolean networks.
A C++ library for Bayesian modeling, with an emphasis on
Markov chain Monte Carlo.  Although boom contains a few R utilities
(mainly plotting functions), its primary purpose is to install the
BOOM C++ library on your system so that other packages can link
against it.
Spike and slab regression a la McCulloch and George (1997).
boostr provides a modular framework that return the focus of
ensemble learning back to 'learning' (instead of programming).
This package contains functionality to select a subsample
of a genotyped cohort e.g. from a GWAS that is preferential for
resequencing under the assumtion that causal variants share a
haplotype with the risk allele of associated variants. The
subsample is selected such that is contains risk alleles at
maximum frequency for all SNPs specified. Phentoypes can also
be included as additional variables to obtain a higher fraction
of extreme phenotypes. An arbitrary number of SNPs and/or
phentoypes can be specified for enrichment in a single
subsample.
Functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Applications" by A. C. Davison and
D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Calculate robust measures of effect sizes using the bootstrap.
Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.
Bootstrap standard errors on various network estimation routines, such as EBICglasso from the qgraph package and IsingFit from the IsingFit package.
Bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series
Calculation of Bootstrapped Response and Correlation
Functions for Use in Dendroclimatology
This package provides a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.
Bootstrap for testing the hypothesis that the spectral
densities of a number m, m>=2, not necessarily independent time
series are equal
Applicable to any classification problem with more than 2 classes. It relies on bootstrap subsamplings of sPLS-DA and provides tools to select the most stable variables (defined as the ones consistently selected over the bootstrap subsamplings) and to predict the class of test samples.
Model selection by bootstrapping the stepAIC() procedure.
Software (bootstrap, cross-validation, jackknife) and data
for the book "An Introduction to the Bootstrap" by B. Efron and
R. Tibshirani, 1993, Chapman and Hall.
_____________________________________________________________
This package is primarily provided for projects already based
on it, and for support of the book.  New projects should
preferentially use the recommended package "boot".
Implements fast, exact bootstrap Principal Component Analysis and
Singular Value Decompositions for high dimensional data, as described in
<http://arxiv.org/abs/1405.0922>. For data matrices that are too large to operate
on in memory, users can input objects with class 'ff' (see the 'ff'
package), where the actual data is stored on disk. In response, this
package will implement a block matrix algebra procedure for calculating the
principal components (PCs) and bootstrap PCs. Depending on options set by
the user, the 'parallel' package can be used to parallelize the calculation of
the bootstrap PCs.
Used to create bootstrap tolerance levels for the Kolmogorov-Smirnov (KS) statistic, the area under receiver operator characteristic curve (AUROC) statistic, and the Gini coefficient for each score cutoff. Also provides a bootstrap alternative to the Vasicek test.
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via JAGS. Three types of models may be fitted: 1) With explanatory variables only, boral fits independent column GLMs to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
An all-relevant feature selection wrapper algorithm.
It finds relevant features by comparing original attributes'
importance with importance achievable at random, estimated
using their permuted copies.
BOSS uses parameter estimates obtained without genotype to boost standard one-step approximations, and precomputes as much as possible without genotype (using boss.set) to minimize effort required.
Sort sequence from genbank. Retrieve sequence information
from genbank (designed for viruses sequences and retrieve
information such as isolation date and host). BLAST sequence
and accession numbers. Detect group of sequences presenting
phylogeography signal. Read PDB file (protein 3D structure
file).
This package is a collection of R functions implemented
from published and available analytic solutions for the
One-Dimensional Boussinesq Equation (ground-water). In
particular, the function "beq.lin" is the analytic solution of
the linearized form of Boussinesq Equation between two
different head-based boundary (Dirichlet) conditions;
"beq.song" is the non-linear power-series analytic solution of
the motion of a wetting front over a dry bedrock (Song at al,
2007, see complete reference on function documentation).
Bugs/comments/questions/collaboration of any kind are warmly
welcomed.
Correlation chart of two set (x and y) of data.
Using Quartiles with boxplot style.
Visualize the effect of factor.
An R interface for the remote file hosting service 'Box'
(https://www.box.com/). In addition to uploading and downloading files, this
package includes functions which mirror base R operations for local files,
(e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well as 'git'
style functions for entire directories (e.g. box_fetch(), box_push()).
Implements biplot (2d and 3d) of multivariate data based
on principal components analysis and diagnostic tools of the quality of the reduction.
Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time. Especially important for small sample sizes or heavily censored data. Includes mid-p options.
bPeaks is a simple approach to identify transcription factor binding sites from ChIP-seq data. Our general philosophy is to provide an easy-to-use tool, well-adapted for small eukaryotic genomes (< 20 Mb). bPeaks uses a combination of 4 cutoffs (T1, T2, T3 and T4) to mimic "good peak" properties as described by biologists who visually inspect the ChIP-seq data on a genome browser. For yeast genomes, bPeaks calculates the proportion of peaks that fall in promoter sequences. These peaks are good candidates as transcription factor binding sites.
Nonparametric multivariate kernel density \
estimation using a back-projected kernel.
QTL mapping toolkit for inbred crosses and recombinant
inbred lines. Includes maximum likelihood and Bayesian tools.
Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.
Blind users do not have access to the graphical output from R without printing the content of graph windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R. This includes the text descriptions of graphs, convenience functions to replace the functionality offered in many GUI front ends, and experimental functionality for optimising graphical content to prepare it for embossing as tactile images.
A set of tools for performing graph theory analysis of brain MRI
data. It is best suited to data from a Freesurfer analysis (cortical
thickness, volumes, local gyrification index, surface area), but also works
with e.g., tractography data from FSL. It contains a graphical user
interface for graph visualization and data exploration.
This includes functions for creating 3D and 4D images using WebGL, RGL, and JavaScript Commands.  This package relies on the X ToolKit (XTK, https://github.com/xtk/X#readme).
This package computes the correlation matrix for each
scale of a wavelet decomposition, namely the one performed by
the R package waveslim (Whitcher, 2000). An hypothesis test is
applied to each entry of one matrix in order to construct an
adjacency matrix of a graph. The graph obtained is finally
analysed using the small-world theory (Watts and Strogatz,
1998) and using the computation of efficiency (Latora, 2001),
tested using simulated attacks. The brainwaver project is
complementary to the camba project for brain-data
preprocessing. A collection of scripts (with a makefile) is
avalaible to download along with the brainwaver package, see
information on the webpage mentioned below.
Estimates geometry of SICM pipette tips by fitting a physical model to recorded breakage-current data.
Species richness estimation is an important problem in biodiversity analysis. This package provides methods for total species richness estimation (observed plus unobserved) and a method for modelling total diversity with covariates. breakaway() estimates total (observed plus unobserved) species richness. Microbial diversity datasets are characterized by a large number of rare species and a small number of highly abundant species. The class of models implemented by breakaway() is flexible enough to model both these features. breakaway_nof1() implements a similar procedure however does not require a singleton count. betta() provides a method for modelling total diversity with covariates in a way that accounts for its estimated nature and thus accounts for unobserved taxa.
Implements the cross-entropy (CE) method, which is a model based stochastic optimization technique to estimate both the number and their corresponding locations of break-points in biological sequences of continuous and discrete measurements as described in Priyadarshana and Sofronov (2014, 2012a, 2012b).
A collection of functions to analyse, visualize and interpret wind data
and to calculate the potential energy production of wind turbines.
brew implements a templating framework for mixing text and
R code for report generation. brew template syntax is similar
to PHP, Ruby's erb module, Java Server Pages, and Python's psp
module.
Retrieves and parses graduate admissions survey data from the Grad Cafe website (http://thegradcafe.com).
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
Decomposes the empirical Brier score into reliability, resolution and uncertainty. Two different estimators for the components are provided: The original estimators proposed by Murphy (1974), and the bias-corrected estimators proposed by Ferro and Fricker (2012). Sampling variances of all the components are estimated. This package applies only to probabilistic predictions of binary events.
Fit Bayesian generalized linear mixed models using Stan for full
Bayesian inference.
Bayesian regularization for feed-forward neural networks
Handles very large numbers in R.  Real numbers are held
using their natural logarithms, plus a logical flag indicating
sign.  The package includes a vignette that gives a
step-by-step introduction to using S4 methods.
Convert statistical analysis objects from R into tidy data frames,
so that they can more easily be combined, reshaped and otherwise processed
with tools like 'dplyr', 'tidyr' and 'ggplot2'. The package provides three S3
generics: tidy, which summarizes a model's statistical findings such as
coefficients of a regression; augment, which adds columns to the original
data such as predictions, residuals and cluster assignments; and glance,
which provides a one-row summary of model-level statistics.
A lossless compressed data format that uses a combination of the LZ77
algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip) but
offers more dense compression.
Implementation of the Bayesian inference for the two independent Poisson samples model, using the semi-conjugate family of prior distributions.
Includes methods for fetching 'phylogenies' from a variety
of sources, currently includes 'Phylomatic'
(http://phylodiversity.net/phylomatic/), with more in the future.
Fully-interactive R interface to the 'OpenBUGS' software for Bayesian analysis using MCMC sampling.  Runs natively and stably in 32-bit R under Windows.  Versions running on Linux and on 64-bit R under Windows are in "beta" status and less efficient.
Collection of functions, data sets and code examples
for evaluations of field trials with the objective of equivalence assessment.
Data sets for book "Basic Statistics and Data Analysis" by
Larry J. Kitchens
The integration of Bayesian variable and sparse group variable selection approaches for regression models.
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed.
Bayes screening and model discrimination follow-up designs.
Bayesian inference on the (discrete) power spectrum of time series.
Some functions for nonparametric and semiparametric
Bayesian models for random effects meta-analysis
This package models the quantile process as a function of
predictors.
Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function,
moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter
Birnbaum-Saunders model based on Skew-Normal Distribution.
Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.
Functional gradient descent algorithm for a variety of convex and nonconvex loss functions, for both classical and robust regression and classification problems. HingeBoost is implemented for binary and multi-class classification, with unequal misclassification costs for binary case. The algorithm can fit linear and nonlinear classifiers.
Time series regression using dynamic linear models fit using MCMC.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs.
Trend filtering uses the generalized
lasso framework to fit an adaptive polynomial of degree k to
estimate the function f_0 at each input x_i in the model: y_i =
f_0(x_i) + epsilon_i, for i = 1, ..., n, and epsilon_i
is sub-Gaussian with E(epsilon_i) = 0.  Bayesian trend filtering adapts
the genlasso framework to a fully Bayesian hierarchical model, estimating
the penalty parameter lambda within a tractable Gibbs sampler.
Performs 'BTLLasso', a method to model heterogeneity in paired comparison data. Subject-specific covariates are allowed to have an influence on the attractivity/strength of the objects. An L1 penalty on the pairwise differences between the object-specific parameters allows for both clustering of objects with regard to covariates and elimination of irrelevant covariates.
BTSPAS provides advanced Bayesian methods to estimate
abundance and run-timing from temporally-stratified
Petersen mark-recapture experiments. Methods include
hierarchical modelling of the capture probabilities
and spline smoothing of the daily run size. This version
uses JAGS to sample from the posterior distribution.
This package contains functions for data preparation, parameter estimation, scoring, and plotting for the BG/BB, BG/NBD and Pareto/NBD models.
Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data.
A suite of functions for finance, including the estimation
of variance matrices via a statistical factor model or
Ledoit-Wolf shrinkage.
corner, genetic optimization, permutation tests
An implementation of Jon Kleinberg's burst detection algorithm.  Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
This package implements a simple alternative to the
traditional Venn diagram. It depicts each overlap as a separate
bubble with area proportional to the overlap size. Relation of
the bubbles to input sets is shown by their their arrangement.
An R interface to the Stark-Parker implementation of an
algorithm for bounded-variable least squares
Functions that solve boundary value problems ('BVP') of systems of ordinary
differential equations ('ODE') and differential algebraic equations ('DAE').
The functions provide an interface to the FORTRAN functions
'twpbvpC', 'colnew/colsys', and an R-implementation of the shooting method.
The functions in this package focus on analyzing
case-control association studies involving a group of genetic
variants.  In particular, we are interested in modeling the
outcome variable as a function of a multivariate genetic
profile using Bayesian model uncertainty and variable selection
techniques.  The package incorporates functions to analyze data
sets involving common variants as well as extensions to model
rare variants via the Bayesian Risk Index (BRI) as well as
haplotypes.  Finally, the package also allows the incorporation
of external biological information to inform the marginal
inclusion probabilities via the iBMU.
Designed for whole-genome regressions using Bayesian models fitted via Gibbs sampling with optional resampling techniques and polygenic term with reduced dimensionality.
c060 provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models
This package allows inferring gene regulatory networks
with direct physical interactions from microarray expression
data using C3NET.
C5.0 decision trees and rule-based models for pattern recognition.
A package for computation and visualization of simple, multiple and joint correspondence analysis.
Performs correspondence analysis on a two-way contingency
table and produces bootstrap-based elliptical confidence
regions around the projected coordinates for the category
points. Includes routines to plot the results in a variety of
styles. Also reports the standard numerical output for
correspondence analysis.
Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, and the like.
This package performs Hansen's (1995) Covariate-Augmented
Dickey-Fuller (CADF) test. The only required argument is y, the
Tx1 time series to be tested. If no stationary covariate X is
passed to the procedure, then an ordinary ADF test is
performed. The p-values of the test are computed using a
procedure proposed in Costantini, Lupi and Popp (2007),
illustrated in Lupi (2009).
Provides functions for the estimation of the conditional Akaike
information in generalized mixed-effects models fitted with (g)lmer
form lme4.
Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.
This device uses Cairo and GTK to draw to the screen,
file (png, svg, pdf, and ps) or memory (arbitrary GdkDrawable
or Cairo context). The screen device may be embedded into RGtk2
interfaces and supports all interactive features of other graphics
devices, including getGraphicsEvent().
Count all common subsequences between 2 string sequences, with
items separated by the same delimiter. The first string input is a length-
one vector, the second string input can be a vector or list containing
multiple strings. The algorithm differentiates between the more strict
definition of subsequence, where a common subsequence cannot be separated
by any other items, from its looser counterpart, where a common subsequence
can be interrupted by other items. For example, q-w is a common subsequence
of q-w-e-r and q-e-w-r on the looser definition, but not the more strict
definition. calACSLoose Algorithm from Wang, H. All common subsequences
(2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.
Contains a greedy algorithm for coarse approximation linear function.
Functions to impute using Random Forest under Full Conditional Specifications (Multivariate Imputation by Chained Equations). The CALIBER programme is funded by the Wellcome Trust (086091/Z/08/Z) and the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (RP-PG-0407-10314). The author is supported by a Wellcome Trust Clinical Research Training Fellowship (0938/30/Z/10/Z).
Package for drawing calibrated scales with tick marks on (non-orthogonal)
variable vectors in scatterplots and biplots.
Performs Bayesian calibration of computer models as per
Kennedy and O'Hagan 2001.  The package includes routines to find the
hyperparameters and parameters; see the help page for stage1() for a
worked example using the toy dataset.  A tutorial is provided in the
calex.Rnw vignette; and a suite of especially simple one dimensional
examples appears in inst/doc/one.dim/.
A multi-array post-processing method of allele-specific copy-number estimates (ASCNs).
The code takes an n x p data matrix and fits a Causal Additive Model (CAM) for estimating the causal structure of the underlying process. The output is a p x p adjacency matrix (a one in entry (i,j) indicates an edge from i to j). Details of the algorithm can be found in: P. Bühlmann, J. Peters, J. Ernest: "CAM: Causal Additive Models, high-dimensional order search and penalized regression", Annals of Statistics 42:2526-2556, 2014.
Tools for the analysis of finite semiparametric mixtures.
These are useful when data is heterogeneous, e.g. in
pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
(flexible support size) and EM algorithms (fixed support size)
are provided for univariate and bivariate data.
The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.
Management of and data extraction from camera trap photographs in wildlife studies. The package provides a workflow for storing and sorting camera trap photographs, computes record databases and detection/non-detection matrices for occupancy and spatial capture-recapture analyses with great flexibility. In addition, it provides simple mapping functions (number of species, number of independent species detections by station) and can visualise activity data.
This is a package with functions for timing copy number changes using estimates of mutational allele frequency from resequencing of tumor samples.
Functions for computing and visualizing
generalized canonical discriminant analyses and canonical correlation analysis
for a multivariate linear model.
Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA'
design and is equivalent to canonical correlation analysis between a set of quantitative
response variables and a set of dummy variables coded from the factor variable.
The 'candisc' package generalizes this to higher-way 'MANOVA' designs
for all factors in a multivariate linear model,
computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D)
visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are
now provided for canonical correlation analysis when all predictors are quantitative.
A statistical framework and computational procedure for identifying the sub-populations within a tumor, determining the mutation profiles of each subpopulation, and inferring the tumor's phylogenetic history. The input are variant allele frequencies (VAFs) of somatic single nucleotide alterations (SNAs) along with allele-specific coverage ratios between the tumor and matched normal sample for somatic copy number alterations (CNAs). These quantities can be directly taken from the output of existing software. Canopy provides a general mathematical framework for pooling data across samples and sites to infer the underlying parameters. For SNAs that fall within CNA regions, Canopy infers their temporal ordering and resolves their phase.  When there are multiple evolutionary configurations consistent with the data, Canopy outputs all configurations along with their confidence assessment.
Extract CANSIM (Statistics Canada) tables and transform them into readily usable data in panel (wide) format. It can also extract more than one table at a time and produce the resulting merge by time period and geographical region.
This package combines complementary information across multiple related phenotypes to infer directed epistatic interactions between genetic markers. The analysis in this package can be applied to a variety of engineered and natural populations.
Functions for performing phylogenetic comparative analyses.
Quantitative analysis to support companion animal population
management.
Provides a method for automatically numbering figures,
tables, or other objects.  Captions can be displayed in full, or as citations.
This is especially useful for adding figures and tables to R markdown
documents without having to numbering them manually.
Get text from images of text using Captricity Optical Character
Recognition (OCR) API. Captricity allows you to get text from handwritten
forms --- think surveys --- and other structured paper documents. And it can
output data in form a delimited file keeping field information intact. For more
information, read https://shreddr.captricity.com/developer/overview/.
Calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.
Fits models from Miller et al. 2005 to estimate population
sizes from natural populations. Several models are implemented.
Package also includes functions to perform a likelihood ratio
test to choose between models, perform parametric bootstrapping
to obtain confidence intervals and multiple functions to
simulate data.
Functions and Datasets to Accompany J. Fox and S. Weisberg,
An R Companion to Applied Regression, Second Edition, Sage, 2011.
Implements a class of spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (McMC) simulation. The response variable can be binomial, Gaussian or Poisson. The spatial autocorrelation is modelled by  a set of random effects, which are assigned a conditional autoregressive (CAR) prior distribution. A number of different CAR priors are available for the random effects, and full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development is supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1.
Three spatial data sets from Scotland used in the vignette accompanying the CARBayes package. These include a lip cancer data set for all of Scotland, and property price and respiratory hospitalisation data sets from the Greater Glasgow and Clyde health board.
Implements a class of spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (McMC) simulation. The response variable can be binomial, Gaussian or Poisson. The spatio-temporal autocorrelation is modelled by  random effects,  which are assigned conditional autoregressive (CAR) prior distributions. A number of different random effects structures are available, and full details are given in the vignette accompanying this package and the references below. The creation of this package was supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1 and the Medical Research Council (MRC) grant MR/L022184/1.
The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
Identification of cardinal dates
(begin, time of maximum, end of mass developments)
in ecological time series using fitted Weibull functions.
Includes 100 mixed-effects model structures describing the relationship between drug concentration and QT interval, heart rate/pulse rate or blood pressure. Given an exposure-response dataset, the tool fits each model structure to the observed data.
Implements the regression approach
of Zuber and Strimmer (2011) "High-dimensional regression and variable
selection using CAR scores" SAGMB 10: 34.
CAR scores measure the correlation between the response and the
Mahalanobis-decorrelated  predictors.  The squared CAR score is a
natural measure of variable importance and provides a canonical
ordering of variables. This package provides functions for estimating
CAR scores, for variable selection using CAR scores, and for estimating
corresponding regression coefficients. Both shrinkage as well as
empirical estimators are available.
The R package CARE1, the first part of the program CARE
(Capture-Recapture) in
http://chao.stat.nthu.edu.tw/softwareCE.html, can be used to
analyze epidemiological data via sample coverage approach (Chao
et al. 2001a). Based on the input of records from several
incomplete lists (or samples) of individuals, the R package
CARE1 provides output of population size estimate and related
statistics.
Misc functions for training and plotting classification and
regression models.
Functions for creating ensembles of caret models: caretList,
caretEnsemble, and caretStack.  caretList is a convenience function for
fitting multiple caret::train models to the same dataset. caretEnsemble will
make a linear combination of these models using greedy forward selection,
and caretStack will make linear or non-linear combinations of these models,
using a caret::train model as a meta-model.
This is a package for estimating the population size of
migratory caribou herds based on large scale aggregations
monitored by radio telemetry. It implements the methodology
found in the article by Rivest et al. (1998) about caribou
abundance estimation. It also includes a function based on the
Lincoln-Petersen Index as applied to radio telemetry data by
White and Garrott (1990).
Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions.
Functions to calculate and plot ecological quality ratios (EQR) as specified by Ballesteros et al. 2007.
The caroline R library contains dozens of functions useful
for: database migration (dbWriteTable2), database style joins &
aggregation (nerge, groupBy & bestBy), data structure
conversion (nv, tab2df), legend table making (sstable &
leghead), plot annotation (labsegs & mvlabs), data
visualization (violins, pies & raPlot), character string
manipulation (m & pad), file I/O (write.delim), batch scripting
and more.  The package's greatest
contributions lie in the database style merge, aggregation and
interface functions as well as in it's extensive use and
propagation of row, column and vector names in most functions.
CRISPR-Analyzer for pooled CRISPR screens (caRpools) provides an end-to-end analysis of CRISPR screens including quality control, hit candidate analysis, visualization and automated report generation using R markdown. Needs MAGeCK (http://sourceforge.net/p/mageck/wiki/Home/), bowtie2 for all functions. CRISPR (clustered regularly interspaced short palindromic repeats) is a method to perform genome editing. See <https://en.wikipedia.org/wiki/CRISPR> for more information on
CRISPR.
This package fits Bayesian conditional autoregressive models for spatial and spatiotemporal data on a lattice.  It uses OpenCL kernels running on GPUs to perform rejection sampling to obtain independent samples from the joint posterior distribution of model parameters.
Create and integrate maps in your R workflow. This package allows
various cartographic representations: proportional symbols, chroropleth,
typology, flows, discontinuities... It also proposes some additional useful
features: cartographic palettes, layout (scale, north arrow, title...), labels,
legends, access to cartographic API...
Functions, data sets and exercises solutions for the book 'Séries temporelles avec R - Méthodes et cas' (Yves Aragon, Springer, 2011). For all chapters, a vignette is available with some additional material and exercises solutions.
This package allows users to identify similar cases for qualitative case studies using statistical matching methods.
Analysis of categorical-variable with missing values
This R-package contains examples from the book "Regression for Categorical Data", Tutz 2011, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 77 types of models of increasing complexity, and 56 likelihood models for the data.
Provides several methods for factor analysis in high dimension (both n,p >> 1) and methods to adjust for possible confounders in multiple hypothesis testing.
Gives methods to create a catenary object and then plot it and get
properties of it. Can construct from parameters or endpoints. Also can get
catenary fitted to data.
A multi-factor dimensionality reduction based forward selection method for genetic association mapping.
Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models.
Functions designed to simulate data that conform to basic
unidimensional IRT models (for now 3-parameter binary response models
and graded response models) along with Post-Hoc CAT simulations of
those models with various item selection methods, ability estimation
methods, and termination criteria.
Structure learning and parameter estimation of discrete Bayesian networks using likelihood-based criteria.  Exhaustive search for fixed node orders and stochastic search of optimal orders via simulated annealing algorithm are implemented.
Contains several basic utility functions including: moving
(rolling, running) window statistic functions, read/write for
GIF and ENVI binary files, fast calculation of AUC, LogitBoost
classifier, base64 encoder/decoder, round-off-error-free sum
and cumsum, etc.
Generation of response patterns under dichotomous and polytomous computerized adaptive testing (CAT) framework.
`ctab' creates (multiway) percentage tables. `sqtab'
contains a set of functions for estimating models for square
tables such as quasi-independence, symmetry, uniform
association. Examples show how to use these models in a
loglinear model using glm or in a multinomial logistic model
using mlogit or clogit
Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments.
Functions for identification and transportation of causal effects.  Provides a conditional causal effect identification algorithm (IDC) by Ilya Shpitser and Judea Pearl, and algorithms for z-identifiability, transportability, z-transportability and meta-transportability of causal effects by Elias Bareinboim and Judea Pearl.  All of the previously mentioned algorithms are based on a causal effect identification algorithm by Jin Tian.
Estimate causal effects of one variable on another, currently for
binary data only. Methods include instrumental variable bounds, adjustment by a
given covariate set, adjustment by an induced covariate set using a variation of
the PC algorithm, and an effect bounding method (the Witness Protection Program)
based on covariate adjustment with observable independence constraints.
This package implements various estimators for average
treatment effects---an inverse probability weighted (IPW)
estimator, an augmented inverse probability weighted (AIPW)
estimator, and a standard regression estimator---that make use
of generalized additive models for the treatment assignment
model and/or outcome model.
The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
The Causata package provides utilities for
extracting data from the Causata application, training binary classification
models, and exporting models as PMML for scoring.
Provides six variants of two-way correspondence analysis (ca):
simple ca, singly ordered ca, doubly ordered ca, non-symmetrical ca,
singly ordered non symmetrical ca, and doubly ordered non symmetrical
ca.
Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.
Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014; JRSSB). The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking.  The package also implements several extensions of the CBPS beyond the cross-sectional, binary treatment setting.  The current version implements the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models (Imai and Ratkovic, 2014), treatments with three- and four-valued treatment variables, continuous-valued treatments (Fong, Hazlett, and Imai, 2015), and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates.
The package provide a set of functions that extend the
cancor function with new numerical and graphical outputs. It
also include a regularized extension of the cannonical
correlation analysis to deal with datasets with more variables
than observations.
Variational Bayesian algorithms for learning canonical correlation analysis (CCA), inter-battery factor analysis (IBFA), and group factor analysis (GFA). Inference with several random initializations can be run with the functions CCAexperiment() and GFAexperiment().
Canonical correlation analysis and maximum correlation via
projection pursuit, as well as fast implementations of correlation
estimators, with a focus on robust and nonparametric methods.
Class Cover Catch Digraphs, neighborhood graphs, and
relatives.
ccChooser can be used to developing and evaluation of core
collections for germplasm collections (entire collection). This
package used to develop a core collection for biological
resources like genbanks. A core collection is defined as a
sample of accessions that represent, with the lowest possible
level of redundancy, the genetic diversity (the richness of
gene or genotype categories) of the entire collection. The
establishing a core collection that represents genetic
diversity of the entire collection with minimum loss of its
original diversity and minimum redundancies is an important
problem for gene-banks curators and crop breeders. ccChooser
establish core collection base on phenotypic data (agronomic,
morphological, phenological).
Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <http://cvxopt.org> for more information).
Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. The estimation approaches implemented are variance components and U-statistics approaches.
This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. DOI: http://dx.doi.org/10.1016/j.envsoft.2014.01.010
Functions for estimating and simulating the family of the
CC-GARCH models.
Contains a function, also called cchs, that calculates Estimator III of Borgan et al (2000), Exposure-stratified Case-cohort Designs. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
Convex Clustering methods, including K-means algorithm,
On-line Update algorithm (Hard Competitive Learning) and Neural Gas
algorithm (Soft Competitive Learning), and calculation of several
indexes for finding the number of clusters in a data set.
Classification method that classifies a sample according
to the class with the maximum mean (or any other function of)
correlation between the test and training samples with known
classes.
Tools to simulate networks based on Congruence Class models.
Significance tests for canonical correlation analysis,
including asymptotic tests and a Monte Carlo method
Tests of association between SNPs or pairs of SNPs and binary phenotypes, in case-control / case-population / case-control-population studies.
A user-friendly GUI for the application of Cultural Consensus Theory (CCT) models and methods to consensus data. The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.
Solves the electromagnetic problem of coupled-dipoles (scattering
and absorption by a cluster of subwavelength particles in arbitrary 3D
configuration) by setting up the interaction matrix and solving the linear
system for multiple incident fields. Functions are provided for linear
polarisation as well as circular polarisation with full angular averaging
(optical activity). Retardation is included in the dipole-dipole
interaction.
A constant database is a data structure created by Daniel
J. Bernstein in his cdb package. Its format consists on a
sequence of (key,value)-pairs. This R package replicates the
basic utilities for reading (cdbget) and writing (cdbdump)
constant databases.
The U.S. Centers for Disease Control (CDC) maintains a portal
<http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html> for
accessing state, regional and national influenza statistics. The Flash
interface makes it difficult and time-consuming to select and retrieve
influenza data. This package provides functions to access the data provided
by the portal's underlying API.
Gives conditional distance correlation and performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data. The conditional distance correlation is a novel conditional dependence measurement of two random variables given a third variable. The conditional distance correlation sure independence screening is used for screening variables in ultrahigh dimensional setting.
Employs a two-parameter family of
distributions for modelling random variables on the (0, 1) interval by
applying the cumulative distribution function (cdf) of one parent
distribution to the quantile function of another.
This package proposes a statistical downscaling method for
cumulative distribution functions (CDF), as well as the
computation of the Cram\`er-von Mises statistics U, and the
Kolmogorov-Smirnov statistics KS.
Coordinate Descent Algorithms for Lasso Penalized L1, L2,
and Logistic Regression
Functions for cognitive diagnosis modeling
and multidimensional item response modeling for dichotomous
and polytomous data. This package enables the estimation of the
DINA and DINO model, the multiple group (polytomous) GDINA model,
the multiple choice DINA model, the general diagnostic model (GDM),
the multidimensional linear compensatory item response model and
the structured latent class model (SLCA).
Components of Canadian Credit Aggregates and Monetary Aggregates with continuity adjustments.
This is an implementation of constrained dual scaling for detecting response
styles in categorical data, including utility functions. The procedure involves adding
additional columns to the data matrix representing the boundaries between the rating categories.
The resulting matrix is then doubled and analyzed by dual scaling. One-dimensional solutions are
sought which provide optimal scores for the rating categories. These optimal scores are
constrained to follow monotone quadratic splines. Clusters are introduced within which the
response styles can vary. The type of response style present in a cluster can be diagnosed
from the optimal scores for said cluster, and this can be used to construct an
imputed version of the data set which adjusts for response styles.
Functions for statistical inference of canonical vine (C-vine)
and D-vine copulas. Tools for bivariate exploratory data analysis and for bivariate
as well as vine copula selection are provided. Models can be estimated
either sequentially or by joint maximum likelihood estimation.
Sampling algorithms and plotting methods are also included.
Data is assumed to lie in the unit hypercube (so-called copula
data).
Cross-Entropy Clustering (CEC) divides the data into Gaussian type clusters. It performs the automatic reduction of unnecessary clusters, while at the same time allows the simultaneous use of various type Gaussian mixture models.
This package is a wrapper for the C implementation of the 25 benchmark functions for the CEC 2005 Special Session on Real-Parameter Optimization. The original C code by Santosh Tiwari and related documentation are available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm.
This package provides R wrappers for the C implementation of 28 benchmark functions defined for the  Special Session and Competition on Real-Parameter Single Objective Optimization at CEC-2013. The focus of this package is to provide an open-source and multi-platform implementation of the CEC2013 benchmark functions, in order to make easier for researchers to test the performance of new optimization algorithms in a reproducible way. The original C code (Windows only) was provided by Jane Jing Liang, while GNU/Linux comments were made by Janez Brest. This package was gently authorised for publication on CRAN by Ponnuthurai Nagaratnam Suganthan. The official documentation is available at http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian).
Model building, surrogate model
based optimization and Efficient Global Optimization in combinatorial
or mixed search spaces.
Contains a number of common astronomy conversion routines, particularly the HMS and degrees schemes, which can be fiddly to convert between on mass due to the textural nature of the former. It allows users to coordinate match datasets quickly. It also contains functions for various cosmological calculations.
Helper functions to work with spreadsheets and the "A1:D10" style
of cell range specification.
This package is an object-oriented implementation of one-dimensional cellular automata. It supports many of the features offered by Mathematica, including elementary rules, user-defined rules, radii, user-defined seeding, and plotting.
This package implements a methodology for using cell
volume distributions to estimate cell growth rates and division
times that is described in the paper entitled "Cell Volume
Distributions Reveal Cell Growth Rates and Division Times", by
Michael Halter, John T. Elliott, Joseph B. Hubbard, Alessandro
Tona and Anne L. Plant, which is in press in the Journal of
Theoretical Biology.  In order to reproduce the analysis used
to obtain Table 1 in the paper, execute the command
"example(fitVolDist)".
Implementation of the Coarsened Exact Matching algorithm.
Conditional expectation manifolds are an approach to compute principal curves and surfaces.
Fit censored linear regression models where the random errors follow a finite mixture of Normal or Student-t distributions.
Estimation of censored regression (Tobit) models
with cross-section and panel data
Fits univariate censored linear regression model under Normal or Student-t distribution
Use the US Census API to collect summary data tables
for SF1 and ACS datasets at arbitrary geographies.
Optimization solver based on the Cross-Entropy method.
Use pathway topology information to assign weight to
pathway nodes.
Functions and Data to support context epp.
This package provides the iterated RMCD method of Cerioli (2010)
for multivariate outlier detection via robust Mahalanobis distances. It also
provides the finite-sample RMCD method discussed in the paper, as well as
the methods provided in Hardin and Rocke (2005) and Green and Martin (2014).
An implementation of the covariance estimation method
proposed in Chi and Lange (2014), "Stable estimation of a covariance matrix guided by nuclear norm penalties,"
Computational Statistics and Data Analysis 80:117-128.
Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++.
Estimate quantiles using formula (18) from
http://www.jaschke-net.de/papers/CoFi.pdf (Yaschke; 2001)
This project provides a group of new functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS) at various time scales: the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System. Some functions have two versions, table and raster based.
cg is comprehensive data analysis software, and stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research &  development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.
A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors with or without shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The cone information criterion (CIC) may be used to select the best combination of variables and shapes. This package depends on the R package coneproj.
The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.
Provides a basic set of R functions for querying the Cancer
Genomics Data Server (CGDS), hosted by the Computational Biology Center at
Memorial-Sloan-Kettering Cancer Center (MSKCC).
Efficient procedures for fitting an entire regression
sequences with different model types.
Functions to analyze microarray comparative genome
hybridization data using the Smith-Waterman algorithm
Spatial smoothing and hot spot detection using the fused
lasso regression
cghseg is an R package dedicated to the analysis of CGH
profiles using segmentation models.
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
A set of tools the author has found useful for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. Several other tools mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' .
Various statistical methods and models which are
typically used for the estimation of outstanding claims reserves
in general insurance, including those to estimate the claims
development result as required under Solvency II.
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. It's called the online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identifying structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++. For customizations or interest in other statistical methodologies for web data analysis please contact <davide.altomare@gmail.com>.
This package provides functions to compute Marcus, Marcus-Levich-Jortner or Landau-Zener charge transfer rates. These rates can then be used to perform kinetic Monte Carlo simulations to estimate charge carrier mobilities in molecular materials. The preparation of this package was supported by the the Fondazione Cariplo (PLENOS project, ref. 2011-0349).
CHAT is a collection of tools developed for tumor subclonality analysis using high density DNA SNP array data and sequencing data. The pipeline consists of four major compartments: 1) tumor aneuploid genome proportion (AGP) calculation and ploidy estimation. 2) segment-specific AGP calculation and absolute copy number estimation for somatic CNAs. 3) cancer cell fraction correction for somatic SNVs in clonal or subclonal sCNA regions. 4) number of subclones estimation using Dirichlet process prior followed by MCMC approach.
A compilation of historical through contemporary climate
measurements scraped from the Environment Canada Website
Including tools for scraping data, creating metadata and
formating temperature files.
Contains methods for creating multivariate Chebyshev
approximation of functions on a hypercube. Some methods for
non-Chebyshev grids are also provided.
A set of functions to calculate check digits according to
various algorithms and to verify whether a string ends in a
valid check digit
Tests and assertions to perform frequent argument checks. A
substantial part of the package was written in C to minimize any worries
about execution time overhead.
The goal of checkpoint is to solve the problem of package
reproducibility in R. Specifically, checkpoint allows you to install
packages as they existed on CRAN on a specific snapshot date as if you had
a CRAN time machine.  To achieve reproducibility, the checkpoint() function
installs the packages required or called by your project and scripts to a
local library exactly as they existed at the specified point in time. Only
those packages are available to your project, thereby avoiding any package
updates that came later and may have altered your results. In this way,
anyone using checkpoint's checkpoint() can ensure the reproducibility of
your scripts or projects at any time. To create the snapshot archives, once
a day (at midnight UTC) Revolution Analytics refreshes the Austria CRAN mirror, on the "Managed
R Archived Network" server (http://mran.revolutionanalytics.com/).
Immediately after completion of the rsync mirror process, the process takes a
snapshot, thus creating the archive. Snapshot archives exist starting from
2014-09-17.
Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
Simple functions for plotting linear
calibration functions and estimating standard errors for measurements
according to the Handbook of Chemometrics and Qualimetrics: Part A
by Massart et al. There are also functions estimating the limit
of detection (LOD) and limit of quantification (LOQ).
The functions work on model objects from - optionally weighted - linear
regression (lm) or robust linear regression ('rlm' from the 'MASS' package).
This package is the R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009)
Functions and scripts used in the book "Chemometrics with R - Multivariate Data Analysis in the Natural Sciences and Life Sciences" by Ron Wehrens, Springer (2011).
The package provides data sets used in the book
"Chemometrics with R - Multivariate Data Analysis in the
Natural Sciences and Life Sciences" by Ron Wehrens, Springer
(2011).
This package introduces a software tool for the design of
synthetic experiments in machine olfaction. The generator of gas sensor
signals can be used in applications related to test and benchmarking of
signal processing methods, neuromorphic simulations in machine olfaction
and educational tools. A so-called virtual sensor array features unlimited
number of sensors, support of multicomponent gas mixtures and full
parametric control of the noise in the system. The artificial (virtual)
sensor array is inspired from a reference database of seventeen polymeric
sensors with concentration profiles for three analytes.
A collection of functions for top-down exploratory data analysis of spectral data obtained via nuclear magnetic resonance (NMR), infrared (IR) or Raman spectroscopy.  Includes functions for plotting and inspecting spectra, peak alignment, hierarchical cluster analysis (HCA), principal components analysis (PCA), model-based clustering and statistical total correlation spectroscopy (STOCSY) analysis.  Robust methods appropriate for this type of high-dimensional data are available.  ChemoSpec is designed with metabolomics data sets in mind, where the samples fall into groups such as treatment and control.  Graphical output is formatted consistently for publication quality plots.  ChemoSpec is intended to be very user friendly and help you get usable results quickly.  A vignette covering typical operations is available.
Provides an alternative approach to multiple testing
by calculating a simultaneous upper confidence bounds for the
number of true null hypotheses among any subset of the hypotheses of interest.
Some of the functions in this package are optionally enhanced by the 'gurobi'
software and its accompanying R package. For their installation, please follow the
instructions at www.gurobi.com and http://www.gurobi.com/documentation, respectively.
Calculation of standard deviation scores from different
growth standards
The phenology of plants (i.e. the timing of their annual life phases) depends on climatic cues. For temperate trees and many other plants, spring phases, such as leaf emergence and flowering, have been found to result from the effects of both cool (chilling) conditions and heat. Fruit tree scientists (pomologists) have developed some metrics to quantify chilling and heat. 'chillR' contains functions for processing temperature records into chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often considered the most promising, but they are difficult to calculate. This package makes it easy. 'chillR' also contains procedures for conducting a PLS analysis relating phenological dates (e.g. bloom dates) to either mean temperatures or mean chill and heat accumulation rates, based on long-term weather and phenology records.
A collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems (VideoScan HCU, capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used.
Change point regression models are also called two-phase regression, break-point regression, split-point regression structural change models and threshold regression models. Hypothesis testing in change point logistic regression with or without interaction terms. Several options are provided for testing in models with interaction, including a maximum of likelihood ratios test that determines p-value through Monte Carlo. Estimation under change point model is also included, but less developed at this point.
Functions and data sets to support chemical thermodynamic modeling in biochemistry
and low-temperature geochemistry. The features include calculation of the standard molal
thermodynamic properties and chemical affinities of reactions involving minerals and/or
biomolecules; a database of thermodynamic properties of aqueous, crystalline and gaseous
species; amino acid group additivity for the standard molal thermodynamic properties of
neutral and ionized proteins; use of the revised Helgeson-Kirkham-Flowers equations of state
for aqueous species; construction of equilibrium activity diagrams as a function of
temperature, pressure, and chemical activities or fugacities of basis species.
This package consists of functions to design DCMs and other types of choice
studies (including MaxDiff and other tradeoffs)
Implements an MCMC algorithm to estimate a hierarchical
multinomial logit model with a normal heterogeneity
distribution. The algorithm uses a hybrid Gibbs Sampler with a
random walk metropolis step for the MNL coefficients for each
unit. Dependent variable may be discrete or continuous.
Independent variables may be discrete or continuous with
optional order constraints. Means of the distribution of
heterogeneity can optionally be modeled as a linear function of
unit characteristics variables.
Choplump Tests are Permutation Tests for Comparing Two Groups with Some Positive but Many Zero Responses
Resampling is a standard step in particle filtering and in
sequential Monte Carlo. This package implements the chopthin resampler, which
keeps a bound on the ratio between the largest and the smallest weights after
resampling.
Maximum likelihood estimation in respondent driven samples.
Choropleths are thematic maps where geographic regions, such as
states, are colored according to some metric, such as the number of people
who live in that state. This package simplifies this process by 1.
Providing ready-made functions for creating choropleths of common maps. 2.
Providing data and API connections to interesting data sources for making
choropleths. 3. Providing a framework for creating choropleths from
arbitrary shapefiles. 4. Overlaying those maps over reference maps from
Google Maps. Please see the vignettes for more details.
Contains an administrative-level-1 map of the world.
Administrative-level-1 is the generic term for the largest sub-national
subdivision of a country. This package was created for use with the
choroplethr package.
Contains 3 maps. 1) US States 2) US Counties 3) Countries of the
world.
A programmatic interface to the Chromosome Counts Database
(http://ccdb.tau.ac.il/). This package is part of the rOpenSci suite
(http://ropensci.org)
chromoR provides users with a statistical pipeline for analysing chromosomal interactions data (Hi-C data).It combines wavelet methods and a Bayesian approach for correction (bias and noise) and comparison (detecting significant changes between Hi-C maps) of Hi-C contact maps.In addition, it also support detection of change points in 1D Hi-C contact profiles.
Chronological objects which can handle dates and times.
Functions for use in perturbing data prior to use of nonparametric smoothers
and clustering.
Text data can be processed chunkwise using 'dplyr' commands. These are recorded and executed per data chunk,
so large files can be processed with limited memory using the 'LaF' package.
Generative models for complex networks with conditionally independent dyadic structure. Now supports directed arcs!
Estimate of cumulative incidence function in two samples. Provide weighted summary statistics based on various methods and weights.
Many experiments in neuroscience involve randomized and fast stimulation while the continuous outcome measures respond at much slower time scale, for example event-related fMRI.  This package provide valid statistical tools with causal interpretation under these challenging settings, without imposing model assumptions.
This package provides functions to compute a method for identifying the instar of Curculionid larvae from the observed distribution of the headcapsule size of mature larvae.
Detecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms or epistatic interactions, especially high-order epistatic interactions, are important topics in bioinformatics because of their significant mathematical and computational challenges. We present CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for detecting, visualizing, and analyzing high-order epistatic interactions by introducing virtual vertices into the construction of a hypergraph. CINOEDV was developed as an alternative to existing software to build a global picture of epistatic interactions and unexpected high-order epistatic interactions, which might provide useful clues for understanding the underlying genetic architecture of complex diseases.
This package contains functions for fitting circumplex
structural models for correlation matrices (with negative
correlation) by the method of maximum likelihood.
Circular layout is an efficient way for the visualization of huge
amounts of information. Here the circlize package provides an implementation
of circular layout generation in R as well as an enhancement of available
software. The flexibility of this package is based on the usage of low-level
graphics functions such that self-defined high-level graphics can be easily
implemented by users for specific purposes. Together with the seamless
connection between the powerful computational and visual environment in R,
circlize gives users more convenience and freedom to design figures for
better understanding complex patterns behind multi-dimensional data.
Functions for the analysis of circular data using
distributions based on Nonnegative Trigonometric Sums (NNTS).
Functions for calculation of densities and distributions, the
estimation of parameters, plotting and more
Detecting of outliers in circular-circular regression models, modifying its and estimating of models parameters.
Circular Statistics, from "Topics in circular Statistics"
(2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Jointly model the accuracy of cognitive responses and item choices within a bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.
A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.
Supports quantitative
research in scientometrics and bibliometrics. Provides
various tools for preprocessing bibliographic
data retrieved, e.g., from Elsevier's SciVerse Scopus,
computing bibliometric impact of individuals,
or modeling many phenomena encountered in the social sciences.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Breast Cancer Molecular Subtypes described in Guedj et al 2012.
This package implements the approach to assign tumor gene expression dataset to the 6 CIT Colon Cancer Molecular Subtypes described in Marisa et al 2013.
Input: a csv-file for each database table and a
controlfile describing relations between tables. Output: An
extended ER diagram
An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Client for 'CKAN' 'API' (http://ckan.org/). Includes interface
to 'CKAN' 'APIs' for search, list, show for packages, organizations, and
resources. In addition, provides an interface to the 'datastore' 'API'.
A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.
This package implements in C++/Rcpp various cladogenesis-related calculations that are slow in pure R.  These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events.  Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state).  This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue.  C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations.  CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it!  Type: citation(package="cladoRcpp") to get the citation information.
Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.
Get description of images from Clarifai API. For more information,
see http://clarifai.com. Clarifai uses a large deep learning cloud to come
up with descriptive labels of the things in an image. It also provides how
confident it is about each of the labels.
Various functions for classification, including k-nearest
neighbour, Learning Vector Quantization and Self-Organizing Maps.
Construct directed graphs of S4 class hierarchies and
visualize them.  In general, these graphs typically are DAGs (directed
acyclic graphs), often simple trees in practice.
Given $p$-dimensional training data containing
$d$ groups (the design space), a classification
algorithm (classifier) predicts which group new data
belongs to.  Generally the input to these algorithms is
high dimensional, and the boundaries between groups
will be high dimensional and perhaps curvilinear or
multi-faceted. This package implements methods for
understanding the division of space between the groups.
IRT classification uses the probability that candidates of
a given ability, will answer correctly questions of a specified
difficulty to calculate the probability of their achieving
every possible score in a test. Due to the IRT assumption of
conditional independence (that is every answer given is assumed
to depend only on the latent trait being measured) the
probability of candidates achieving these potential scores can
be expressed by multiplication of probabilities for item
responses for a given ability. Once the true score and the
probabilities of achieving all other scores have been
determined for a candidate the probability of their score lying
in the same category as that of their true score
(classification accuracy), or the probability of consistent
classification in a category over administrations
(classification consistency), can be calculated.
Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.
A collection of functions for the creation and application of highly optimised, robustly evaluated ensembles of support vector machines (SVMs). The package takes care of training individual SVM classifiers using a fast parallel heuristic algorithm, and combines individual classifiers into ensembles. Robust metrics of classification performance are offered by bootstrap resampling and permutation testing.
Provides a set of utility tools to inspect spatial objects, facilitate
handling and reporting of topology errors and geometry validity issues.
Finally, it provides a geometry cleaner that will fix all geometry problems,
and eliminate (at least reduce) the likelihood of having issues when doing
spatial data processing.
Implements an empirical Bayes approach for simultaneous variable clustering and regression. This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma DB et al. - 2013).
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006). This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012).
Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.
A set of tools to read, analyze and write lists of click sequences on websites (i.e., clickstream). A click can be represented by a number, character or string. Clickstreams can be modeled as zero- (only computes occurrence probabilities), first- or higher-order Markov chains.
CliFlo is a web portal to the New Zealand National Climate
Database and provides public access (via subscription) to around 6,500
various climate stations. Collating and manipulating data from CliFlo
(hence clifro) and importing into R for further analysis, exploration and
visualisation is now straightforward and coherent. The user is required to
have an internet connection, and a current CliFlo subscription (free) if
data from stations, other than the public Reefton electronic weather
station, is sought.
Classification of climate according to Koeppen - Geiger, of aridity
indices, of continentality indices, of water balance after Thornthwaite, of
viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy,
Bagnouls-Gaussen.
PCIC's implementation of Climdex routines for computation of
extreme climate indices.
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.
Absolute homogeneity tests SNHT absolute 1-breaks, 1-break,
SD different from 1, 2-breaks, Buishand, Pettitt, von Neumann ratio and
ratio-rank, Worsley, and Craddock, Relative homogeneity tests SNHT
absolute 1-breaks, 1-break SD different from 1, 2-breaks, Peterson
and Easterling, and Vincent, Differences in scale between two groups Siegel–Tukey,
Create reference time series mean, weights/correlation, finding outliers Grubbs,
ESD, MAD, Tietjen Moore, Hampel, etc.
Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response.
Utilities to make your clinical collaborations easier if not fun.
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 system
clipboards of Windows, OS X, and Linux.
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.
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
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.
CLUE is an R package for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms.
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.
Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data.
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.
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.
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.
This package enables researchers to calculate power for cluster-randomized crossover trials by employing a simulation-based approach.  A particular study design is specified, with fixed sample sizes for all clusters and an assumed treatment effect, and the empirical power for that study design is calculated by simulating hypothetical datasets.
A function for validating microarry clusters via
reproducibility
Calculate p-values and confidence intervals using cluster-adjusted t-statistics (based on Ibragimov and Muller 2010, Journal of Business and Economic Statistics 28(4)), pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller 2008, Review of Economics and Statistics 90(3)). Procedures are included for use with GLM, ivreg, plm (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, data generation (typical and non-typical data), HINoV method,	replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorial and symbolic interval-valued data).
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.
Functions which allow to integrate geographical constraints in Ward hierarchical clustering. Geographical maps of typologies obtained can be displayed with the use of shapefiles.
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.
An implementation of a variable selection procedure in clustering by mixture of multinomial models for discrete data. Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
Cluster analysis of a set of variables. Variables can be
quantitative, qualitative or a mixture of both.
A collection of methods for joint dimension reduction and
clustering
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. 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 function which implements variable selection methodology for model-based clustering which 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.
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.
Calculation and plot of the stepwise Cronbach-Mesbah Curve
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.
Quite extensive package for the estimation of marginal models for categorical data.
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 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.
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, and Fine JP and
Gray RJ (1999), A proportional hazards model for the subdistribution
of a competing risk, JASA, 94:496-509.
A collection of useful helper routines developed by
students of the Center for the 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 observing of the climate
system by providing Essential Climate Variables related to the energy and water
cycle of the atmosphere (www.cmsaf.eu). It is a joint cooperation of seven
National Meteorological and Hydrological Services, including the Deutscher
Wetterdienst (DWD).
The cmsaf R-package provides a small collection of R-functions, which are
inspired by the Climate Data Operators (cdo). This gives the opportunity to
analyse and manipulate CM SAF data without the need of installing cdo.
The cmsaf R-package is tested for CM SAF netcdf data, which are structured
in three-dimensional arrays (longitude, latitude, time) on a rectangular grid.
Layered CM SAF data have to be converted with the provided `levbox_mergetime`
function. The cmsaf R-package functions have only minor checks for deviations
from the recommended data structure, and give only few specific error messages.
Thus, there is no warranty of accurate results.
A Windows binary file and scripts for an easy application of the functions are
provided at the CM SAF homepage (http://www.cmsaf.eu/EN/Products/Tools/Tools_node.html).
Various utilities for the complex multivariate Gaussian distribution.
Provides functions for performing Coincidence Analysis (CNA).
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.
Contains functions for computing the nonparametric maximum
likelihood estimate of a log-concave density function from
univariate observations. The log-density estimate is always a
piecewise linear function.
Methods for assigning copy number states and breakpoints in resequencing experiments of prokaryotic organisms.
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.
The CNVassoc package carries out analysis of common
Copy Number Variants (CNVs) and imputed Single Nucleotide
Polymorphisms (SNPs) in population-based studies.
It includes tools for estimating association under a series
of study designs (case-control, cohort, etc), using several
dependent variables (class status, censored data, counts)
as response, adjusting for covariates and considering
various inheritance models. Moreover, it is possible to
perform epistasis studies with pairs of CNVs or imputed SNPs.
It has been optimized in order to make feasible the analyses
of Genome Wide Association studies (GWAs) with hundreds of
thousands of genetic variants (CNVs / imputed SNPs). Also,
it incorporates functions for inferring copy number (CNV
genotype calling). Various classes and methods for generic
functions (print, summary, plot, anova, ...) have been
created to facilitate the analysis.
This package contains example data sets with Copy Number Variants and imputed SNPs to be used by CNVassoc package.
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.
This package contains 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.
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.
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 at
http://comparingcorrelations.org. A plugin for the R GUI and
IDE RKWard is included. Please install RKWard from
https://rkward.kde.org to use this feature. The respective R
package 'rkward' cannot be installed directly from a
repository, as it is a part of RKWard.
Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix
to another data matrix. More specifically, CoCA maximises the weighted covariance
between the weighted averaged species scores of one community and the weighted averaged species
scores of another community. CoCA attempts to find patterns that are common to both communities.
This package provides functions to statistically compare two or
more alpha coefficients based on either dependent or independent groups of
individuals.
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.
Markov chain Monte Carlo burn-in based on "bridge" statistics, in the way of coda::heidel.diag, but including non asymptotic tabulated statistics.
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.
Computation of Multiscale Codependence Analysis and spatial eigenvector maps, as an additional feature.
A toolbox of ecological community dynamics metrics that are explicitly
temporal. Functions fall into two categories: temporal diversity indices and community
stability metrics. The diversity indices are temporal analogs to traditional diversity
indices such as richness and rank-abundance curves. Specifically, functions are provided
to calculate species turnover, mean rank shifts, and lags in community similarity between
time points. The community stability metrics calculate overall stability and patterns
of species covariance and synchrony over time.
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 a model object
Simulate species occurrence and abundances (counts) along
gradients.
species coexistence modeling under asymmetric dispersal
and fluctuating source-sink dynamics;testing the proportion of
coexistence scenarios driven by neutral and niche processes
Generate cofeature (feature by sample) matrices. The package
utilizies ggplot2::geom_tile to generate the matrix allowing for easy
additions from the base matrix.
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.
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.
Performs regression analysis for longitudinal count
data using a self-decomposable probability distribution,
a random intercept term is also allowed. Estimation is via maximization
of the exact likelihood of a suitably defined model. Missing values and
unbalanced data are allowed.
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.
Performs the colocalisation tests described in Plagnol et al
(2009), Wallace et al (2013) and Giambartolomei et al (2013).
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.
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.
This function takes a hierarchical cluster-class object and a factor describing the groups as arguments and generates colorful dendrograms in which leaves belonging to different groups are identified by colors.
Different methods to generate a color palette based on a specified base color and a number of colors that should be created.
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.
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.
Provides methods for the import/export and automated analysis of concept maps.
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). Finally, package saves 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 wetlab experimental platforms
for example fluostar, automated robots etc. One needs to call a single function only
to perform all analysis (examples are provided with sample data).
Suite of R functions for combination of probabilities using a game-theoretic method.
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.
The Combinatory Method (s) to Construct a series of PBIB designs and we give their associated U-type design.
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.
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.
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
The CommonMark specification defines a rationalized version of markdown
syntax. This package uses the libcmark 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.
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.
Facilitates optimal clustering of a data set. Provides a framework to run a wide range of clustering algorithms to determine the optimal number (k) of clusters in the data. Then analyzes the cluster assignments from each clustering algorithm to identify samples that repeatedly classify to the same group. We call these 'core clusters', providing a basis for later class discovery.
The CommunityCorrelogram package is designed for the geostatistical analysis of ecological community datasets with either a spatial or temporal distance component.
Comparison of two ROC curves throughout the methodology propose 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),
'RFCI' (really fast causal inference)
(all from package 'pcalg') and regression.
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. Perform 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 Nucleotids 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
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): 10.1002/sim.4422.
Functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations
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.
The package contains a function (which uses a similar interface to
the `glm' function) for the fitting of a Conway-Maxwell-Poisson GLM. There
are also various methods for analysis of the model fit. The package also
contains functions for the Conway-Maxwell-Poisson distribution in a similar
interface to functions `dpois', `ppois' and `rpois'. The functions are
generally quick, since the workhorse functions are written in C++ (thanks
to the Rcpp package).
Performs the complementary hierarchical clustering procedure and returns X' (the expected residual matrix) and a vector of the relative gene importances.
Contains several functions to enhance approaches to the Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps.
Evaluate complex integrals along a disc or a straight line.
Evaluate higher-order derivatives by employing the Cauchy integral formula.
Evaluate residues.
Search for simple poles given a function.
Evaluate limits.
Provides functionality to perform robust regression
on compositional data. To get information on the distribution of the
estimates, various bootstrapping methods are implemented for the
compositional as well as for standard robust regression models, to provide
a 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 a CMP regression model to count data. The code provides functions for model estimation, dispersion testing, and diagnostics.
The package 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 Aitchison and Pawlowsky-Glahn.
Calculate regression coefficients and their standard deviations under the Cox proportional hazard model with a large number of covariates. A shrinkage type estimation based on the compound covariate method (Emura, Chen & Chen, 2012 PLoS ONE) and a univariate gene selection method under dependent censoring (Emura & Chen, 2014 Statistical Methods in Medical Research) are implemented. Algorithms for simulating gene expressions in these references are also given.
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.
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).
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.
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.
Higher order likelihood-based inference for logistic and
loglinear models
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.
Conditional density estimation with mixtures for
heavy-tailed distributions
Computes conditional multivariate normal probabilities, random deviates and densities.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems.
This small library contains a series of simple tools for
constructing and manipulating confounded and fractional
factorial designs.
Functions for estimating and reporting multiyear 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.
Implementation of conformal prediction using caret models for classification and regression
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'.
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)
Conjoint is a simple package that implements a conjoint
analysis method to measure the preferences.
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.
Converts 'LaTeX' files (with extension '.tex') generated by R Sweave using package 'knitr' to Rich Text Format files (with extension '.rtf'). Rich Text Format files can be read and written by most word processors.
ConnMatTools collects several different methods for analyzing and
working with connectivity matrices 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.
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 the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete.
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.
Fits mixtures of multivariate contaminated normal distributions
(with eigen-decomposed scale matrices) via the expectation conditional-
maximization algorithm under a clustering or classification paradigm.
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.
Contrast methods, in the style of the Design package,
for fit objects produced by the lm, glm, gls, and geese functions.
Nonparametric two-sample procedure for comparing the median survival time.
Converts between the Date class and d/m/y for several
calendars, including Persian, Islamic, and Hebrew
This package provides a way to investigate various modes of convergence of random variables.
Quantifies and assesses the significance of convergent evolution.
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.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
A logical workflow for the reading, processing, and visualization
of data obtained from the Union Biometrica Complex Object Parametric
Analyzer and Sorter (COPAS) platform large-particle flow cytometers and a
powerful suite of functions for the rapid processing and analysis of large
high-throughput screening data sets. It combines the speed of dplyr with
the elegance of ggplot2 to make analysis of COPAS data fast and painless.
Extensive functions for bivariate copula (bicopula) computations and related mathematical operations oriented around the oft cited and fundamental bicopula theory described by Nelsen (2006), Joe (2014), and other selected works. The lower, upper, product, and a few select other bicopula are implemented. Individual copula support is deliberately limited to keep the package focused on an educational API to major bicopula theory. Expressions for an arbitrary bicopula include the diagonal, survival copula, the dual of a copula, co-copula, and numerical bicopula density. Level curves (sets) as well as horizontal and vertical sections and derivatives of such sections also are supported. The numerical derivatives of a bicopula are provided as well as inverses; simulation by the conditional distribution method thus are supported. Composition, convex combination, and products of bicopula are provided.  Copula properties include Kendall Function and L-moments thereof, Kendall Tau, Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer-Wolff Sigma, tail dependency and order, and skewness. Evaluators of positively/negatively quadrant dependency, left-tail decreasing, and right-tail increasing are available. Kullback-Leibler divergence, Vuong's procedure, and L-comoments for copula inference are available. Quantile and median regressions for V with respect to U and U with respect to V are available. Empirical copulas (EC) are supported.
Provides tools for fitting the copCAR regression model for discrete areal data. Three types of estimation are supported: continuous extension, composite marginal likelihood, and distributional transform.
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.
Classes (S4) of commonly used elliptical, Archimedean, extreme
value and some more copula families.  Methods for density, distribution,
random number generation, bivariate dependence measures, perspective and
contour plots.  Fitting copula models including variance estimates.
Independence and serial (univariate and multivariate) independence tests,
and other copula related tests. Empirical copula and multivariate CDF.
Goodness-of-fit tests for copulas based on multipliers, the parametric
bootstrap with several transformation options.
Merged former package 'nacopula' for nested Archimedean copulas: Efficient
sampling algorithms, various estimators, goodness-of-fit tests and related
tools and special functions.
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.
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.
Estimation and statistical process control are performed under copula-based time-series models.
This R-packages presents a bivariate, copula-based model
for the joint distribution of a pair of continuous and discrete
random variables. The two marginal random variables are modeled
via generalized linear models, and their joint distribution
(represented by a parametric copula family) is estimated using
maximum-likelihood techniques.
It has functions to implement the copula mixed models for bivariate and trivariate meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos, A.K. (2015a,b).
CopyDetect contains several IRT and non-IRT based statistical indices proposed in the literature for detecting answer copying on multiple-choice examinations. CopyDetect includes the indices that have been shown as effective and reliable based on the simulation studies. CopyDetect provides results for the Omega index (Wollack, 1996), Generalized Binomial Test (van der Linden & Sotaridona, 2006), K index (Holland, 1996), K1 and K2 indices (Sotaridona & Meijer, 2002), and S1 and S2 indices (Sotaridona & Meijer, 2003).
The 450k arrays are frequently used in the epigenetic studies, the copy number calling from the 450k data is possible but it faces some difficulties in cancer samples regarding the determination of the copy number status due to the false sample centering and baseline shifting. Without solving this issue the CN calling will be inaccurate. CopyNumber450kCancer-package was designed to correct the baseline in cancer samples using the Maximum Density Peak Estimation (MDPE) method.
The main advantages for CopyNumber450kCancer-package are: Fast (few seconds per sample), high accuracy rate, in-sample correction, no input parameters needed, low computer sources required, and adaptable for 450k-similar technologies.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Generate high-dimensional correlated count random
variables with a prespecified Pearson correlation.
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.
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.
This is a suite of machine learning algorithms written in C++ with R
interface. It contains several machine learning model learning techniques in
classification and regression, for example 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 ExplainPrediction package.
The 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 example to discretize
numeric attributes.
Its additional feature is OrdEval algorithm and its visualization 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.
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.
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.
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 Sch\"afer and Strimmer (2005) and
Opgen-Rhein and Strimmer (2007).  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 and data sets for the statistical
analysis of corpus frequency data, used in the SIGIL statistics
course.
This package 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.
Linear regression based on a recursive structural equation model
(explicit multiples correlations) found by a MCMC algorithm. It permits to face
highly correlated covariates. Variable selection is included (by lasso,
elastic net, etc.). It also provides some graphical tools for basic
statistics (Boxplot with confidence intervals, etc) and regression trees.
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.
Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
Calculates correlation of variables and displays the results graphically.
Included panel functions can display points, shading, ellipses, and correlation
values with confidence intervals.
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.
The corrplot package is a graphical display of a
correlation matrix, confidence interval. It also contains some
algorithms to do matrix reordering.
Statistical summary of Structure output.
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.
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.
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.
User-friendly interfaces to perform fast and reliable photometric
redshift estimation. The code makes use of generalized linear models and
can adopt gamma or inverse gaussian families, either from a frequentist or
a Bayesian perspective. The code additionally provides a Shiny application
providing a simple user interface.
This package aims at the easy production of descriptive and
bivariate statistics tables in HTML. It can load files in csv, xls/xlsx
formats, and read labels and formats from SAS or plain text files. It is
intended to be usable by absolute R beginners. Ce paquet permet la
production facile de tableaux HTML descriptifs et comparatifs, meme pour
les debutants sous R.
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.
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.
Implements cointegration/cotrending rank selection
algorithm in Guo and Shintani(2011). Paper: "Consistant
Cotrending rank selection when both stochastic and nonlinear
deterministic trends are present", Preprint, Feb 2011.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2-nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
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)
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.
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.
We implement a collection of techniques for estimating covariance matrices.
Covariance matrices can be built using missing data. Stambaugh Estimation and
FMMC methods can be used to construct such matrices. Covariance matrices can
be built by denoising or shrinking the eigenvalues of a sample covariance
matrix. Such techniques work by exploiting the tools in Random Matrix Theory
to analyse the distribution of eigenvalues. Covariance matrices can also
be built assuming that data has many underlying regimes. Each regime is
allowed to follow a Dynamic Conditional Correlation model. Robust covariance
matrices can be constructed by multivariate cleaning and smoothing of noisy data.
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 the tests.  It is an indirect measure of
test quality. 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 for robust covariance estimation
by the nearest neighbor variance estimation (NNVE) method of
Wang and Raftery (2002,JASA)
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.
This package computes covariance test for the lasso.
Some helpful extensions and modifications to the 'ggplot2'
library. In particular, this package makes it easy to combine multiple
'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc.,
as is often required for scientific publications. The package also provides
a streamlined and clean theme that is used in the Wilke lab, hence the
package name, which stands for Claus O. Wilke's plot library.
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
Fits Cox-type models based on interval-censored data from a survival
or illness-death process.
Cox proportional hazards models containing Gaussian random
effects, also known as frailty models.
Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty, and their adaptive forms, such as adaptive lasso and net adjusting for signs of linked coefficients. Moreover, it treats the number of non-zero coefficients as another tuning parameter and simultaneously selects with the regularization parameter \code{lambda}. In addition, it fits a varying coefficient Cox model by kernel smoothing, incorporated with the aforementioned penalties. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.
Cox regression with Firth's penalized likelihood
This package implements weighted estimation in Cox regression as proposed by
Schemper, Wakounig and Heinze (Statistics in Medicine, 2009). 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.
Additionally estimation of nonlinear effects using fractional polynomials similar to the
MFP algorithm (Royston, Sauerbrei, 2008) is provided. This feature can also be used to
estimate the interaction of a covariate with a nonlinear function of time.
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
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.
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
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.
Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP).
At its core, the package has a Gibbs Sampler that allows running univariate linear
mixed models that have both, sparse and dense design matrices. The parallel sampling method
in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for
a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models.
In addition, the package offers parallelized functions for common tasks like genome-wide
association studies and cross validation in a memory efficient way.
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.
This package computes the maximum likelihood estimator (MLE) of the shape-constrained hazard baseline and the effect parameters in the Cox proportional hazards model under IID sampling.  We assume that the data are continuous and allow for right censoring.  The function 'find.shapeMLE' allows for four different shape constraints: increasing, decreasing, unimodal, and u-shaped.
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.
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 developped 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 developped by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
Estimates the conditional probability function of a competing event, and fits, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function
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) 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. (2013, Econometric Reviews). 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).
This package contains R-functions to perform power
calculation in a group sequential clinical trial with censored
survival data and possibly unequal patient allocation between
treatment and control groups. The fuctions can also be used to
determine the study duration in a clinical trial with censored
survival data as the sum of the accrual duration, which
determines the sample size in a traditional sense, and the
follow-up duration, which more or less controls the number of
events to be observed. This package also contains R functions
and methods to display the computed results.
R functions for cosmological research.
The main functions are similar to the python library, cosmolopy.
Using a sampling-based approach (either sequential importance sampling or explicit Monte Carlo), this package can be used to perform a probabilistic damage tolerance for aircraft structures. It can model a single crack, or two simultaneously growing fatigue cracks (the so-called continuing damage problem). With a single crack, multiple types of future repairs are possible.
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.
'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/metacran/cranlogs.app> for the raw 'API'.
Various R tools for http://crantastic.org/
The (C)orrelated (RA)ndom (W)alk (L)ibrary of
R functions was designed for fitting continuous-time
correlated random walk (CTCRW) models with time indexed
covariates. 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.
A set of functions for computing the CreditMetrics risk model
Provides tools for pricing credit default swaps using
C code for the International Swaps and Derivatives
Association (ISDA) CDS Standard Model. See
<http://www.cdsmodel.com/cdsmodel/documentation.html>
for more information about the model and
<http://www.cdsmodel.com/cdsmodel/cds-disclaimer.html>
for license details for the C code.
It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.
This package implements modeling and computational tools for
conditional random fields model (CRF) as well as other probabilistic
undirected graphical models of discrete data with pairwise and unary
potentials.
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.
This package fits discrete mixtures of Zero-Inflated
Poisson (ZIP) models for analyzing criminal trajectories.
Statistical Methods for Crime Series Linkage. This package provides
code for criminal case linkage, crime series identification, crime series
clustering, and suspect identification.
CRM simulator for Phase I Clinical Trials
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.
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.
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.
Package Crossover provides different crossover designs from combinatorial or search algorithms as well as from literature and a GUI to access them.
This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using
the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
Contains generic functions for performing
cross validation and for computing diagnostic errors.
Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
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.
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:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).
Provides the implementation of analytical and graphical approaches for checking the assumptions of the Fine and Gray model.
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).
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.
Functions for reading in and manipulating CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 data.
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Performs cryptographic randomness tests on a sequence of random integers or bits. Most of the included tests 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. Additionally, it includes functions for the calculation of greatest common divisor, the Stirling numbers of the second kind, critical value of the topological binary test, and base conversions from base 2 to 10 and vice versa.
Monte Carlo conditional inference for the parameters of a
linear nonnormal regression model
cSFM is a method to model skewed functional data when considering covariates via a copula-based approach.
Package for CShapes, a GIS dataset of country boundaries (1946-2015). Includes functions for data extraction and the computation of distance matrices and -lists.
This package contains functions for likelihood and
posterior analysis of conditionally specified logistic
regression models.  All calculus and simulation is done in
compiled FORTRAN.
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.
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.
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).
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.
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), this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic movement models to animal tracking data.
Functions to Fit Continuous Time Autoregressive Models with the Kalman Filter.
An easily accessible continuous (and discrete) time dynamic
modelling package for panel and time series data, reliant upon the OpenMx.
package (http://openmx.psyc.virginia.edu/) for computation. Most dynamic
modelling approaches to longitudinal data rely on the assumption that time
intervals between observations are consistent. When this assumption is
adhered to, the data gathering process is necessarily limited to a specific
schedule, and when broken, the resulting parameter estimates may be biased
and reduced in power. Continuous time models are conceptually similar to
vector autoregressive models (thus also the latent change models popularised
in a structural equation modelling context), however by explicitly including
the length of time between observations, continuous time models are freed
from the assumption that measurement intervals are consistent. This allows:
data to be gathered irregularly; the elimination of noise and bias due to
varying measurement intervals; parsimonious structures for complex dynamics.
The application of such a model in this SEM framework allows full-information
maximum-likelihood estimates for both N = 1 and N > 1 cases, multiple measured
indicators per latent process, and the flexibility to incorporate additional
elements, including individual heterogeneity in the latent process and
manifest intercepts, and time dependent and independent exogenous covariates.
Furthermore, due to the SEM implementation we are able to estimate a random
effects model where the impact of time dependent and time independent predictors
can be assessed simultaneously, but without the classic problems of random
effects models assuming no covariance between unit level effects and predictors.
Interactive shiny application for running classical test theory (item analysis).
Server-side and client-side tools for task views
to CRAN-style repositories
Estimating and testing models for ordinal data within the family of
CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions).
Adaptive multivariate integration over hypercubes
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 (2009).
Regression modeling using rules with added instance-based corrections
Compute Unified Device Architecture (CUDA) is a software
platform for massively parallel high-performance computing on
NVIDIA GPUs. This package provides a CUDA implementation of a
Bayesian multilevel model for the analysis of brain fMRI data.
A fMRI data set consists of time series of volume data in 4D
space. Typically, volumes are collected as slices of 64 x 64
voxels. Analysis of fMRI data often relies on fitting linear
regression models at each voxel of the brain. The volume of the
data to be processed, and the type of statistical analysis to
perform in fMRI analysis, call for high-performance computing
strategies. In this package, the CUDA programming model uses a
separate thread for fitting a linear regression model at each
voxel in parallel. The global statistical model implements a
Gibbs Sampler for hierarchical linear models with a normal
prior. This model has been proposed by Rossi, Allenby and
McCulloch in `Bayesian Statistics and Marketing', Chapter 3,
and is referred to as `rhierLinearModel' in the R-package
bayesm. A notebook equipped with a NVIDIA `GeForce 8400M GS'
card having Compute Capability 1.1 has been used in the tests.
The data sets used in the package's examples are available in
the separate package cudaBayesregData.
FMRI data sets used in the examples of "cudaBayesreg".
Data sets have been separated from the main package
"cudaBayesreg" for convenience.
Reconstruct individual-level values from aggregate-level
summaries.
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.
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.
Test Equality of Curves with Homoscedastic or
Heteroscedastic Errors.
Filtering, also known as gating, of
flow cytometry samples using the curvHDR method.
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.
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts.
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.
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
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
The package uses majorization minimization by coordinate
descent (MMCD) algorithm to compute the solution surface for
concave penalized logistic regression model.  The SCAD and MCP
(default) are two concave penalties considered in this
implementation.  For the MCP penalty, the package also provides
the local linear approximation by coordinate descant (LLA-CD)
and adaptive rescaling algorithms for computing the solutions.
The package also provides a Lasso-concave hybrid penalty for
fast variable selection. The hybrid penalty applies the concave
penalty only to the variables selected by the Lasso.  For all
the implemented methods, the solution surface is computed along
kappa, which is a more smooth fit for the logistic model.
Tuning parameter selection method by k-fold cross-validated
area under ROC curve (CV-AUC) is implemented as well.
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.
This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST 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 underperforming 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.
Miscellaneous useful or interesting functions. Some parameters of functions may have changed, so beware!
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
extend cxxfunction by saving the dynamic shared objects
for reusing across R sessions
A suite of functions for analysing cycling data.
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.
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.
Calculates ecological diversity indices for a microbial
community
The relationship between statistical dependency and causality lies
at the heart of all statistical approaches to causal inference. The D2C
package implements a supervised machine learning approach to infer the
existence of a directed causal link between two variables in multivariate
settings with n>2 variables. The approach relies on the asymmetry of some
conditional (in)dependence relations between the members of the Markov
blankets of two variables causally connected. The D2C algorithm predicts
the existence of a direct causal link between two variables in a
multivariate setting by (i) creating a set of of features of the
relationship based on asymmetric descriptors of the multivariate dependency
and (ii) using a classifier to learn a mapping between the features and the
presence of a causal link
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.
Two sample test based on Wasserstein metric. This is motivated from detection of differential DNA-methylation sites based on underlying distributions.
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. !!!
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 biological
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.
The three-way data consists of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides functional methods (principal component analysis, multidimensional scaling, 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.
A document 'daeDesignRandomization.pdf', available in the doc subdirectory of the
installation directory for 'dae', describes the use of the package for generating randomized
layouts for experiments. The ANOVA functions facilitate the extraction of information when
the 'Error' function has been used in the call to 'aov'.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Diff, patch and merge for data frames. Document changes in data sets and use them to apply patches.
Changes to data can be made visible by using render_diff. The V8 package is used to
wrap the 'daff.js' JavaScript library which is included in the package.
Data and miscellanea to support the book "Introduction to
Data analysis with R for Forensic Scientists", Curran, J.M.
2010 CRC Press ISBN: 978-1-4200-8826-7
dagbag is a set of methods that learn DAGs via bootstrap aggregating.
Integrates the information from multiple linkage maps to
create a consensus directed graph, which is then linearized to
produce a consensus map.
Functions to draw, manipulate, evaluate directed
acyclic graphs and simulate corresponding data.
Several functions for evaluating the accuracy of
classification models. The package provides the following
performance measures: repeated k-fold cross-validation,
0.632 and 0.632+ bootstrap estimation of the misclassification rate,
sensitivity, specificity and AUC. If an application is
computationally intensive, parallel execution can be used
to reduce the computational effort.
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.
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.
The DALY Calculator is a free, open-source Graphical User
Interface (GUI) for stochastic disability-adjusted life year
(DALY) calculation.
This package contains a miscellaneous set of functions I use in my teaching either at UWM or the ICPSR Summer Program.  Broadly, the functions help with presentation and interpretation of GLMs.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
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.
This package contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis
The darch package is built on the basis of the code from G. E.
Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
nets). This package is for generating neural networks with many layers (deep
architectures) and train them with the method introduced by the publications
"A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero,
Y. W. Teh; doi: 10.1162/neco.2006.18.7.1527) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov; doi: 10.1126/science.1127647). This method includes a pre
training with the contrastive divergence method published by G.E Hinton
(2002; doi: 10.1162/089976602760128018) and a fine tuning with common known
training algorithms like backpropagation or conjugate gradients.
Additionally, supervised fine-tuning can be enhanced with maxout and
dropout, two recently developed techniques to improve fine-tuning for deep
learning.
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.
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 an interface to the C++ FILEVECTOR library
facilitating analysis using large (giga- to tera-bytes) matrices.
Matrix storage is organized in a way that either columns or rows
are quickly accessible. DatABEL is primarily aimed to support
genome-wide association analyses e.g. using GenABEL, MixABEL and
ProbABEL.
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.
Tools for combining and cleaning data sets, particularly
with grouped and time series data.
dataframes2xls writes data frames to xls files. It
supports multiple sheets and basic formatting.
Our method automatically generates 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 import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
from a given directory into R data frames.
datamap utilizes variable bindings and objects of class
"UserDefinedDatabase" to provide a simple mapping system to
foreign objects. Maps can be used as environments or attached
to the search path, and changes to either are persistent.
Mapped foreign objects are fetched in real-time and are never
cached by the mapping system.
Provides an S4 infrastructure for unified handling
of internal datasets and web based data sources. The package is
currently in beta; things may break, change or go away without
warning.
Post-GFC 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.
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 the
National Water Information System (NWIS) tools. Both EPA and USGS water
quality data are obtained from the Water Quality Portal.
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.
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 and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and use the utility
methods to traverse the tree in various orders. Aggregate, cumulate, print, convert to and from data.frame,
and apply functions to your tree data. Useful for decision trees, machine learning, finance, conversion
from and to JSON, and many other applications.
Support functions for packages VBmix, semisupKernelPCA,
and patchPlot.
Tools for deciphering the contents of
unknown objects or environments from within the terminal,
a problem often encountered when working with unfamiliar packages or debugging complex functions.
If working in xterm256 or ANSI terminals the output is coloured by default
to improve readability (e.g. the standard Ubuntu terminal).
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 collection of functions accompanying the book "Data Analysis in Vegetation Ecology", Second edition. 2013, Wiley-Blackwell, Chichester
Various utilities for the Davies distribution and the
generalized lambda 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.
Fits Bayesian additive regression trees (BART) 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
This package provides methods and examples to support a
method of Gene Set Analysis (GSA).  DBGSA is a novel
distance-based gene set enrichment analysis method. We consider
that, the distance between 2 groups with different phenotype by
focusing on the gene expression should be larger, if a certain
gene functional set was significantly associated with a
particular phenotype.
A database interface (DBI) 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.
This package 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).
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, including classical ones (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.
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 and the LOF (local outlier factor) algorithm. The implementations uses 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.
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.
Performs differential network analysis to infer disease specific gene networks.
Functions for analyzing dichotomous choice contingent valuation (CV) data. It provides
functions for estimating parametric and nonparametric models for single- and double-bounded
CV data.
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.
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.
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.
Sequential and parallel MCMC support
for JAGS, WinBUGS and OpenBUGS.
A set of functions for the detection of spatial clusters
of disease using count data. Bootstrap is used to estimate
sampling distributions of statistics.
S4 classes around infrastructure
provided by the dclone package to make package
development with data cloning for hierarchical
models easy as a breeze.
Analysis of dichotomous response data to obtain attribute profile
estimates for respondents using Diagnostic Classification Model (DCM) and
Markov Chain Monte Carlo (MCMC) method. The estimation procedure uses a
loglinear cognitive diagnostic modeling (LDCM) framework that allows for
the estimation of a host of DCMs such as NIDO, NIDA, NC-RUM etc.
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). 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.
Implements maximum likelihood methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent.
See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, doi: 10.1098/rspb.2011.1439.
Also implements maximum likelihood methods to detect various types of key innovations in the light of diversity-dependence.
See Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, doi: 10.1086/667574.
Finally, DDD contains a function to simulate the diversity-dependent process.
A wrapper for the Duke Analytics model deployment API. See <http://www.dukeanalytics.com/products/dukedeployR.php> for more details.
The package 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.
This package provides useful functions for various DDI (Data Documentation Initiative) related outputs.
Provides distributed data structures and simplifies
distributed computing in R.
Bayesian networks with continuous and/or discrete
variables can be learned and compared from data.
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.
Debugger for R functions, with code display, graceful
error recovery, line-numbered conditional breakpoints, access
to exit code, flow control, and full keyboard input.
Provides functions for retrieving energy statistics from
the United Kingdom Department of Energy and Climate Change and
related data sources.  The current version focuses on total final
energy consumption statistics at the local authority, MSOA, and
LSOA geographies.  Methods for calculating the generation mix of
grid electricity and its associated carbon intensity are also
provided.
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.
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 and Clustered Expected Value of Perfect Information.
The probabilistic calculations are done via Monte Carlo
simulations. This Monte Carlo functionality can be used on its own.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
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.
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.
Added functionality for Deducer. This package includes
additional dialogs for calculating distribution function
values, cluster analysis and more.
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.
Implement some deep learning architectures and neural network
algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
Numerical quadrature of functions of one variable over a finite or infinite interval
with double exponential formulas.
Detection of differentially expressed genes between two
distinct groups of samples.
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 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.
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.
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.
The deltaPlotR package proposes an implementation of 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.
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.
Implementation of the DEMI method for the analysis of high-density microarray data.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
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.
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.
Offers faster manipulation of dendrogram objects in
R. A dendrogram object in R is a list structure with attributes in its
nodes and leaves. Working with dendrogram objects often require a function
to recursively go through all (or most) element in the list object.
Naturally, such function are rather slow in R, but can become much faster
thanks to 'Rcpp'.
This package 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.
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.
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.
An implementation of the clustering algorithm described by Alex
Rodriguez and Alessandro Laio (Science, 2014 vol. 344), along with tools to
inspect and visualize the results.
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).
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
An implementation of the jDE variant of the Differential Evolution
stochastic algorithm for global optimization of nonlinear programming problems.
Suppose that one can observe bivariate random variables (X, Y) only when X<=Y holds. Data (Xj, Yj), subject to Xj<=Yj, for all j=1,...,n, are called truncated data. Parametric approach (Emura & Konno 2012 Stat Papers), semiparametric approach (Chaieb et al. 2006 Biometrika; Emura et al. 2011 Sinica; Emura & Murotani 2015 TEST), nonparametric maximum likelihood approach (Emura & Wang 2012 JMVA), and regression approach (Emura & Wang 2015 AISM) are implemented for statistical inference on (X, Y), when X and Y are dependent. Also included is truncated data on the number of deaths at each year (1963-1980) for Japanese male centenarians (Emura & Murotani 2015 TEST).
Fit (multigroup) mixtures of latent 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: https://r-forge.r-project.org/R/?group_id=156
Fit latent (hidden) Markov models on mixed categorical and continuous (time series)
data, otherwise known as dependent mixture models
Depth functions methodology applied to multivariate
analysis. Besides allowing calculation of depth values and
depth-based location estimators, the package includes functions
for 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 nonparametric 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.
Offers a special data structure called a 'deque'
(pronounced like 'deck'), which is a list-like structure.  However,
unlike R's list structure, data put into a 'deque' is not necessarily
stored contiguously, making insertions and deletions at the front/end
of the structure much faster.  The implementation here is new and
uses a doubly linked list, and whence does not rely on R's
environments.  To avoid unnecessary data copying, most 'deque'
operations are performed via side-effects.
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.
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.
Produce publication quality graphics from output of GGobi's
describe display plugin
Allows users to quickly and easily describe data using
common descriptive statistics.
A collection of 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.
Deseasonalize daily or monthly time series.
The package provides R scripts for designing genetical
genomics experiments.
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 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.
Several estimators of the diagonal elements of a sparse precision
(inverse covariance) matrix from a sample of Gaussian vectors for a
given matrix of estimated marginal regression coefficients.
To install package 'gurobi', instructions at
http://user.gurobi.com/download/gurobi-optimizer and
http://www.gurobi.com/documentation/6.0/refman/r_api_overview.html.
A function for plotting maps of agricultural field experiments that
are laid out in grids.
The package implements models to analyze
site occupancy and count data models with detection error.
The package development was supported by the
Alberta Biodiversity Monitoring Institute (ABMI)
and the Boreal Avian Modelling (BAM) Project.
Allows users to quickly and easily detect data containing
Personally Identifiable Information (PII) through convenience functions.
Solvers and testset for stiff and nonstiff differential equations, and
differential algebraic equations
DetMCD is a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, M., Rousseeuw, P.J. and Verdonck, T. (2012),"A deterministic algorithm for robust location and scatter", Journal of   Computational and Graphical Statistics, Volume 21, Number 3, Pages 618--637.
Simple GUI to perform some standard tree-ring analyses.
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.
Output graphics to EMF (enhanced metafile).
Data sets and sample analyses from Jay L. Devore (2008),
"Probability and Statistics for Engineering and the Sciences
(7th ed)", Thomson.
Collection of package development tools.
It handles numerics, characters, factors, and logicals.
Phase I/II adaptive dose-finding design for combination
studies. Several methods are proposed depending on the type of
combinations: (1) the combination of two cytotoxic agents, and (2)
combination of a molecularly targeted agent with a cytotoxic agent.
This package 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) Differential Functioning of Items and Tests (DFIT) analyses. It includes
functions to use the Monte Carlo Item Parameter Replication  approach
(Oshima, Raju, & Nanda, 2006) for obtaining the associated statistical
significance tests cut-off points. They may also be used for a priori and
post-hoc power calculations.
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" (in review).
Derivative-Free optimization algorithms. These algorithms
do not require gradient information. More importantly, they can
be used to solve non-smooth optimization problems.
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.
dglars implements the differential geometric extension
of the least angle regression method for logistic and Poisson
regression models. To compute the implicitly defined solution
curve, two different algorithms are implemented, i.e. the
predictor-corrector method and the cyclic coordinate descent
method.
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.
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.
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.
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.
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, ...
Support for the books "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. Springer.
Includes demo(flowchart), demo(plotmat), demo(plotweb)
Create graph diagrams and flowcharts using R.
The R package provides utilities to estimate and make inference on two summary measures for diagnostic tests when there are three ordinal groups ----volume under ROC surface (VUS) and the extended Youden index, assuming that diagnostic markers increase monotonically and stochastically with disease severity. Point estimates, variance, confidence interval and optimal cut-points both under the normal assumption and also the non-parametric method(s) will be provided for the summary measures. Statistical tests are implemented to compare multiple diagnostic tests and two diagnostic test.  Sample size is calculated to estimate the summary measures within a user-specified margin of error for future study planning.
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.
Expected Improvement. EGO algorithm. Multipoint EI and parallelized versions of EGO. Criteria and algorithms for Noisy Kriging-based Optimization , including AEI, AKG, EQI, and EI with plugin.
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.
Physical constants. Gold, silver and glass permittivities,
together with spline interpolation functions.
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.
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 and examples from the book Solving Differential
Equations in R by Karline Soetaert, Jeff R Cash and Francesca Mazzia.
Springer, 2012.
Metrics of difference for comparing 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 R interface to the 'codediff' JavaScript library (a copy of which is included in the package).
Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.
Residual-based baseline identification and peak decomposition for x-ray diffractograms as introduced in Davies/Gather/Mergel/Meise/Mildenberger (2008).
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.
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.
A package to perform DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many covariates and also metric covariates.
Collection of traditional methods to detect differential item functioning among dichotomously scored items.
Item focused recursive partitioning for simultaneous selection of
items and variables that induce Differential Item Functioning (DIF) in Item Response models.
Implementation of a function 'digest()' for the creation
of hash digests of arbitrary R objects (using the md5, sha-1, sha-256,
crc32, xxhash and murmurhash algorithms) permitting easy comparison of R
language objects, as well as a function 'hmac()' to create hash-based
message authentication code.

The md5 algorithm by Ron Rivest is specified in RFC 1321, the sha-1
and sha-256 algorithms are specified in FIPS-180-1 and FIPS-180-2,
and the crc32 algorithm is described in
ftp://ftp.rocksoft.com/cliens/rocksoft/papers/crc_v3.txt.

For md5, sha-1, sha-256 and aes, this package uses small standalone
implementations that were provided by Christophe Devine. For crc32, code
from the zlib library is used. For sha-512, an implementation by Aaron
D. Gifford is used. For xxHash, the implementation by Yann Collet is used.
For murmurhash, an implementation by Shane Day is used.

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.
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.
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).
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.
Implements the clustering method in Fu, Russell, Bray and
Tavare: Bayesian clustering with the Dirichlet-process prior.
Estimates the number of clusters from the data.  Data are
replicated time series or replicated measurements from multiple
experimental conditions.
A collection of R functions for directional data analysis.
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.
Direction analysis is a set of tools designed to integrate multiple experimental treatments for analyzing pathways and kinases from microarray, RNA-seq, proteomics, or phosphoproteomics data.
Implements Dirichlet regression models in R.
Estimate parameters in Dirichlet-Multinomial and compute
profile log-likelihoods.
Discrete smoothing of probability mass function is performed using three discrete associated kernels: Dirac Discrete Uniform (DiracDU), Binomial and Discrete Triangular. Two automatic bandwidth selection procedures are implemented: the cross-validation method for the three kernels and the local Bayesian approach for Binomial kernel. Note that DiracDU is used for categorical data, Binomial kernel is appropriate for count data with small or moderate sample sizes, and Discrete Triangular kernel is recommended for count data with large sample sizes.
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).
DiscML performs rate estimation using maximum likelihood with the
options to correct for unobservable data, to implement a Gamma-distribution
for rate variation, and to estimate the prior root probabilities from the
empirical data.
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.
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.
'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.
Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites.
An implementation of two exact algorithms for testing the
Hausdorff and simplex dispersion orderings
Disparity filter is a network reduction algorithm to extract the backbone structure of both directed and undirected weighted networks. Disparity filter can reduce the network without destroying the multi-scale nature of the network. The algorithm has been developed by 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).
A package containing R functions for displaying data and
results from high-throughput screening experiments.
Description: Functions for modelling dispersion in GLM.
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.
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.
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. Web applications are provided
(via 'shiny') for the implemented methods to help in designing and deploying the
computations.
An R package that is developed for constructing confidence
regions without the need to know the sampling distribution of
bivariate data.
Some very simple method functions for confidence interval calculation and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.
Geodesic distance between phylogenetic trees and
associated functions.
S4 Classes and Methods for distributions
provides documentation in form of a common vignette to packages distr, distrEx,
distrMod, distrSim, distrTEst, distrTeach, and distrEllipse
Distribution (S4-)classes for elliptically contoured distributions (based on
package distr)
Extensions of package distr and some additional functionality
This package contains utilities 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.
Object oriented implementation of probability models based on packages 'distr' and
'distrEx'
Estimation for a multinomial logistic regression factorized into independent  Poisson log regressions.  See the textir package for applications in multinomial inverse regression analysis of text.
(S4-)distribution classes based on package distr for distributions from packages
fBasics and fGarch
Simulation (S4-)classes based on package distr
Evaluation (S4-)classes based on package distr for evaluating procedures
(estimators/tests) at data/simulation in a unified way.
Provides tools for quality checks of georeferenced plant
species accessions.
R-package DivE contains functions for the DivE estimator (Laydon, D. et al., Quantification of HTLV-1 clonality and TCR diversity, PLOS Comput. Biol. 2014). 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 natural 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.
Weir and Cockerham's 1984 F-statistics are also calculated.
Various plotting features are also provided, as well as Chi-square tests of
genetic heterogeneity are also provided.
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.
There are two functions in this package, which can be used
together to estimate the Shannon's Diversity index at different
levels of sample size. A Monte-Carlo procedure is used to
re-sample a given observation at each level of sampling. The
expectation being that the mean of the re-sampling will
approach Shannon's diversity index at that sample level.
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). Some fortran code has been included to
the original dixon2002 function of the ecespa package to
improve speed.
Implements various decision support tools related to the new product development.
Subroutines include combinatorial search (all possible subset regression), productivity evaluation using distance measures: DDF (directional distance function), DEA (data envelopment analysis), SBM (slack-based measure), and SF (shortage function), benchmarking, risk analysis, technology adoption model, new product target setting, etc.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
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.
dlmodeler is a set of user-friendly functions to simplify the state-space modelling, fitting, analysis and forecasting of Generalized Dynamic Linear Models (DLMs). It includes functions to name and extract individual components of a DLM, build classical seasonal time-series models (monthly, quarterly, yearly, etc. with calendar adjustments) and provides a unified interface compatible with other state-space packages including: dlm, FKF and KFAS.
Simulation testing and implementation of data-limited fishery stock assessment methods.
The package dlnm contains functions to specify and run distributed lag linear and non-linear models.
Dynamic model averaging for binary and continuous
outcomes.
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.
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.
A backward selection procedure called delete or merge
regressors (DMR) combines deleting continuous variables with
merging levels of factors. The method assumes greedy search
among linear models with set of constraints of two types:
either a parameter for a continuous variable is set to zero or
parameters corresponding to two levels of a factor are
compared. DMR is a stepwise regression procedure, where in each
step a new constraint is added according to ranking of the
hypotheses based on squared t-statistics. As a result a nested
family of linear models is obtained and the final decision is
made according to minimization of the generalized information
criterion (GIC, default BIC). The main function of the package
is DMR, which is based on hierarchical clustering. Moreover,
other functions for extensions of DMR method are given, such as
stepDMR which is based on recalculation of t-statistics in each
step and function DMR4glm for generalized linear models.
This package includes functions and data accompanying the book
"Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Package for conducting differential network analysis from
microarray data.
Methods for studying forensic DNA profiling, in particular familial database searches.
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().
There lacks an open-source tool for high-throughput omics data in an integrative manner in terms of network, evolution and ontology. To meet this need, dnet intends to analyse the biological network whose nodes/genes are associated with digitised information such as expression levels across samples. To help make sense of identified 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 semantic similarity calculation between ontology terms (and genes), and network affinity calculation using Random Walk with Restart; both can be done via high-performance parallel computing. In summary, dnet aims to deliver an eye-intuitive tool with rich visuals but less inputs.
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.
Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains
The facilities can roughly be grouped as:

1) Facilities for groupwise computations of summary statistics and
other 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.
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.
Production of R packages from tagged comments introduced within the code
and a minimum of additional documentation files.
Microsoft Word docx files provide an XML structure that is fairly
straightforward to navigate, especially when it applies to Word tables. The
docxtractr package provides tools to determine table count/structure and
extract/clean tables from Microsoft Word docx documents.
Various sampling plans are able to be compared using evaluations of their OC, AOQ, ATI etc.
Package DoE.base creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Additionally, it provides utility functions for the class design, which is also used by other packages for designed experiments.
This package creates various kinds of designs for
(industrial) experiments. It 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.
Provides a parallel backend for the %dopar% function using
the multicore functionality of the parallel package.
The package can calculate ADI (Average Dominance Index) and can build social network graphs with dual directions, can build a Music Notation Graph
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.dominoup.com> for more information).
Provides a parallel backend for the %dopar% function using
the Rmpi package.
Provides a parallel backend for the %dopar% function using
the parallel package.
A Redis parallel backend for the %dopar% function
This package provides functions to perform
reproducible parallel foreach loops, using independent
random streams as generated by L'Ecuyer's combined
multiple-recursive generator [L'Ecuyer (1999)]. 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.
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.
Provides a parallel backend for the %dopar% function using
Luke Tierney's snow package.
It estimates a dose-response relation from either a single or
multiple summarized data. The trend estimation takes into account the
correlation among sets of log relative risks and use it to efficiently
estimate the dose-response relation. To obtain a pooled functional
relation, the study-specific trends are combined according to principles of
multivariate random-effects meta-analysis.
A small package containing helper utilities for creating function
for computing statistics.
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.
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.
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 estimator for the univariate hazard (hazard rate) and bandwidth parameter selection using the do-validation method and the standard least squares cross-validation method.
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.
A set of functions that downscales species occupancy at
coarse grain sizes to predict species occupancy at fine grain sizes.
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.
Analysis, visualisation and simulation of digital polymerase chain
reaction (dPCR).
fits the primal graphical lasso, via one-at-a-time
block-coordinate descent.
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.
The package implements a Dirichlet Process Mixture (DPM)
model for clustering and image segmentation.  The DPM model is
a Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components.  The code implements conjugate models with
normal structure (conjugate normal-normal DP mixture model).
The package's applications are oriented towards the
classification of magnetic resonance images according to tissue
type or region of interest.
Create, install, and summarise data packages that follow
the Open Knowledge Foundation's Data Package Protocol.
This package contains functions to perform inference via
simulation from the posterior distributions for Bayesian
nonparametric and semiparametric models. Although the name of
the package was motivated by the Dirichlet Process prior, the
package considers and will consider other priors on functional
spaces. So far, DPpackage includes models considering Dirichlet
Processes, Dependent Dirichlet Processes, Dependent Poisson-
Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
Trees, Linear Dependent Tailfree Processes, Mixtures of
Triangular distributions, Random Bernstein polynomials priors
and Dependent Bernstein Polynomials.  The package also includes
models considering Penalized B-Splines. Currently the package
includes semiparametric models for marginal and conditional
density estimation, ROC curve analysis, interval censored data,
binary regression models, generalized linear mixed models, IRT
type models, and generalized additive models. The package also
contains functions to compute Pseudo-Bayes factors for model
comparison, and to elicitate the precision parameter of the
Dirichlet Process. To maximize computational efficiency, the
actual sampling for each model is done in compiled FORTRAN. The
functions return objects which can be subsequently analyzed
with functions provided in the coda package.
Data preprocessing techniques for classification. Functions for normalization, handling of missing values,discretization, outlier detection, feature selection, and data visualization are included.
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.
Creation and Use of R Repositories via two 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.
Graphical display of R expression, showing the
interpretation of an expression by R and the various kind of R
data structure. The steps of the interpretation of an
expression are obtained through the parsed tree.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
A somewhat outdated package of basic and easy-to-use functions for
fitting dose-response curves to continuous dose-response data, calculating some
(eco)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 lognormal 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 RODBC 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.
This is a collection of functions for discontinuous regression
analysis and image processing.
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.
Likelihood-based marginal regression and association
modelling for repeated, or otherwise clustered, categorical
responses using dependence ratio as a measure of the
association
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.
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.
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').
The package performs various analyzes of descriptive statistics, including correlations
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.
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.
Fits inhomogeneous Poisson process spatial models
to line transect sampling data and provides estimate of
abundance within a region.
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 pre-generated using Distance software) 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.
This package allows you to make basic probability assignments on a set of
possibilities (events) and combine these events with Dempster's rule of combination.
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.
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'.
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.
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.
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.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.
This package provides functions for 1D and 2D Discrete
Cosine Transform (DCT), Discrete Sine Transform (DST) and
Discrete Hartley Transform (DHT).
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.
Time series clustering using different techniques related to the
Dynamic Time Warping distance and its corresponding lower bounds. Additionally,
an implementation of k-Shape clustering is available, and the package can be
easily extended with custom distance measures and centroid definitions.
Provides a Time-Weighted Dynamic Time Warping (TWDTW) algorithm to measure similarity between two temporal sequences. This adaptation of the classical Dynamic Time Warping (DTW) algorithm is flexible to compare events that have a strong time dependency, such as phenological stages of cropland systems and tropical forests. This package provides methods for visualization of minimum cost paths, time series alignment, and time intervals classification.
Functions to analyze multiple choice data using Dual Scaling
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).
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.
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.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Hurst exponent estimation of a fractional Brownian motion
by using discrete variations methods in presence of outliers
and/or an additive noise
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.
Provides access to The Dataverse Network APIs via the RCurl package, enabling access to archived data (and metadata), and the ability to create and manipulate studies in a user's dataverse(s).
Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.
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.
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, its, 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.
The package is aimed at dynamically visualizing probability
distributions and their moments and all the commonly used distributions are
included.
Interactive graphical tool for manipulating graphs
Contains methods for detection of clusters in hierarchical clustering dendrograms.
a native R implementation and enhancement of Dynamic TOPMODEL, Beven and Freers' (2001) extension to the semi-distributed hydrological model TOPMODEL (Beven and Kirkby, 1979).
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.
Computes dynamical correlation estimates and percentile
bootstrap confidence intervals for pairs of longitudinal
responses, including consideration of lags and derivatives.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
Demonstrate the results of an lm(), glm() or coxph() model object as a dynamic nomogram that can be displayed in an 'RStudio' panel or web browser.
The dynpred package contains functions for dynamic prediction in survival analysis.
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 simulations and graphical depictions of autoregressive
relationships.
Functions to fit time-varying coefficient models for interval
censored and right censored survival data. Three major
approaches are implemented: 1) Bayesian Cox model with
time-independent, time-varying or dynamic coefficients for
right censored and interval censored data; 2) Spline based
time-varying coefficient Cox model for right censored data;
3) Transformation model with time-varying coefficients for
right censored data using estimating equations.
A comprehensive toolkit for estimating Dynamic Treatment Regimes. Available methods include Interactive Q-Learning, Q-Learning, and value-search methods based on Augmented Inverse Probability Weighted estimators and Inverse Probability Weighted estimators.
Functions for latent class analysis, short time Fourier
transform, fuzzy clustering, support vector machines,
shortest path computation, bagged clustering, naive Bayes
classifier, ...
Plots of the empirical attainment function for two objectives.
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". (The term "MARS" is trademarked and thus not used in
the name of the package.)
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.
Perform analysis of variance and other important complementary analyzes. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.
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.
The package fit and plot some nonlinear models
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.
This is a pipelining tool that facilitates
evaluation and visualisation of stratified genome-wide
association meta-analyses (GWAMAs) results data. It
provides (i) statistical methods to test and to account
for between-strata difference and to clump genome-wide
results into independent loci and (ii) extended graphical
features (e.g., Manhattan, Miami and QQ plots) tailored
for stratified GWAMA results.
Set of tools to simplify application of atomic forecast verification
metrics for (comparative) verification of ensemble forecasts to large datasets.
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; Luce, 1959), elimination-by-aspects (EBA) models
(Tversky, 1972), and preference tree (Pretree) models (Tversky
& Sattath, 1979).
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.
This package carries out 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.
This package is used to 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 significant 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 Detection Grouping Effects.
This package performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc
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.
An implementation of the univariate elliptic distribution
and elliptic option pricing model. It provides detailed functionality
and data sets for the distribution and modelling.
Especially, it contains functions for the computation of
density, probability, quantile, fitting procedures, option prices,
volatility smile. It also comes with sample financial data, and plotting routines.
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".
Functions to update datasets in Ecdat and to create,
manipulate, plot and analyze those and similar datasets.
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.
We implement the Bayesian and likelihood methods proposed
in Imai, Lu, and Strauss (2008, 2011) 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 provides access to more than 5 million georeferenced specimen records from the University of California, Berkeley's Natural History Museums.
Geostatistical tools for analyzing spatial patterns in
population biology. Easy integration of information from multiple sources
with "ecogen" objects.
This package 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.
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.
Estimating individual-level covariate-outcome associations
using aggregate data ("ecological inference") or a combination of
aggregate and individual-level data ("hierarchical related regression").
Provides an R interface to the EcoData Retriever
(http://ecodataretriever.org/) via the EcoData Retriever's
command line interface. The EcoData Retriever automates the
tasks of finding, downloading, and cleaning ecological datasets,
and then stores them in a local database.
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.
R package for the support of spatial analyses and modelling of species niches and distributions, written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) & Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.
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.
A package for simulations of classical ecological models as a learning resource.
Implements various procedures for finding
multiple change-points through the use of U-statistics.
The procedures do not make any distributional assumptions
other than the existence of certain absolute moments. Hierarchical
and exact search methods are included. All methods return the
set of estimated change-points as well as other summary information.
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.
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.
An implementation of maximum entropy sampling for spatial
data is provided. An exact branch-and-bound algorithm as well as greedy and
dual greedy heuristics are included.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
EDGAR is the Electronic Data Gathering, Analysis, and Retrieval system which performs automated collection, validation, indexing, acceptance, and forwarding of submissions by companies and others who are required by law to file forms with the U.S. Securities and Exchange Commission (SEC). This package downloads EDGAR quarterly master index, daily master index, filings from SEC.org site and do sentiment analysis of 10-K statements.
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.
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 integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach in which 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.
Optimized DMR analysis based on bimodal normal distribution
model and cost function for regional methylation analysis optimization.
It captures the regional methylation modification by taking the spatial
distribution of CpGs into account for the enrichment DNA methylation sequencing
data so as to optimize the definition of the empirical regions. Combined with
the dependent adjustment for regional p-value combination and DMR annotation.
The library contains R-functions to estimate the effective
dimension reduction space in multi-index regression models.
This package comes to illustrate the articles "A graphical
tool for selecting the number of slices and the dimension of
the model in SIR and SAVE approaches" and "Comparison of sliced
inverse regression approaches for underdetermined cases"
Package with tools for classification of electroencephalography (EEG) data. Feature extraction techniques such as Fourier Transform and Continuous Wavelet Transform (CWT) are available. Support Vector Machines (SVM) can be used to classify the extracted features. An algorithm using Analysis of Variance (ANOVA), False Discovery Rate (FDR), and SVM is available to feature selection. Additionally, the package contains functions to plot data and features.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for plotting (a) EEG caps, (b) single- and multi-channel EEG time courses, and (c) EEG spatial maps. Also includes smoothing and Independent Component Analysis functions for EEG data analysis, and a function for 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 to calculate EEM metrics and preprocess EEM for PARAFAC analysis.
Collection of convenience functions to make working with
administrative records easier and more consistent. Includes functions to
clean strings, identify cutpoints, and quickly combine shapefiles and
dataframes for plotting. Includes four alternative themes for ggplot2. Also
includes three example datasets of administrative education records for
learning how to process records with errors.
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.
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.
Graphical and tabular effect displays, e.g., of interactions, for
various statistical models with linear predictors.
Functions to test for changes of a response to a stimulus grouping relative
to a background or baseline response.
The package provides functions to visualize regression models with categorical response. The effects of the covariates are plotted with star plots in order to allow for an optical impression of the fitted model.
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.
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.
Functions for assigning Clarke or Parkes (Consensus) error grid
zones to blood glucose values, and for plotting both types of error grids.
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.
An implementation of hierarchical models
for faecal egg count data to assess anthelmintic
efficacy. Bayesian inference is done via MCMC sampling.
A small tool for Social Network Analysis, dealing with
ego-centric network measures, including Burt's effective size
and aggregate constraint and an import code suitable for a
large number of adjacency matrices. A free web application is
also available on http://www.egonet.associazionerospo.org
Statistics and graphics for streamflow history,
water quality trends, and the statistical modeling algorithm: Weighted
Regressions on Time, Discharge, and Season (WRTDS).
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.
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. AFT regression for left truncated and
right censored data. Binary and Poisson regression for
clustered data, fixed and random effects with bootstrapping.
Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman eta l. 1993 JVS) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API.
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.
This package estimates the parameters of 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 accomodated, 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.
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 RxC 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.
EIVE performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression.
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.
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.
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' (http://elasticsearch.org), 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 'APIs' for
'indices', documents, nodes, clusters, an interface to the cat 'API', and more.
This package 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.
This is a bizzare 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 charactaristics 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.
Useful when reading the book above mentioned, in the
documentation referred to as `the book'.
Correlation chart of two set (x and y) of data.
Using Quantiles.  Visualize the effect of factor.
This package 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.
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 SLFN ( Single
Hidden-layer Feedforward Neural Networks ) using the ELM
algorithm. 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.
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.
Allows calculating global scores for characteristics of visual stimuli. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses  preference for one stimulus over the other. 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.
Calculate Elo ratings as means to describe animal dominance hierarchies
elrm implements a Markov Chain Monte Carlo algorithm to
approximate exact conditional inference for logistic regression
models. Exact conditional inference is based on the
distribution of the sufficient statistics for the parameters of
interest given the sufficient statistics for the remaining
nuisance parameters. Using model formula notation, users
specify a logistic model and model terms of interest for exact
inference.
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.
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.
Unsupervised, multivariate, clustering algorithm yielding a meaningful binary clustering taking into account the uncertainty in the data. A specific constructor for trajectory movement analysis yields behavioural annotation of the tracks based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator.
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
evolutionary Monte Carlo methods for clustering,
temperature ladder construction and placement. For mclust see
http://cran.r-project.org/web/packages/mclust/LICENSE
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.
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).
Package providing calculation of Earth Mover's Distance
(EMD).
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
A toolbox for designing and evaluating predictive models with
resampling methods. The aim of this package is to provide a simple and
efficient general framework for working with any type of prediction
problem, be it classification, regression or survival analysis, that is
easy to extend and adapt to your specific setting. Some commonly used
methods for classification, regression and survival analysis are included.
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.
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.
This package provides a set of functions for convenient end-member modelling analysis of grain-size data (EMMAgeo).
This package includes functions to read and write to an
EMME/2 databank
For forming contrasts in the mixed effects for mixtures of linear mixed models fitted to the gene profiles.
EM algorithm for Mixture of Multivariate Skew Normal/T Distributions
Functions to fit finite mixture of unrestricted multivariate skew t (FM-uMST) model, random sample generation, discriminant analysis, 2D and 3D contour plots
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 R package for fixation and saccade detection in eye movement recordings. Eye movements consist of alternating saccades and fixations. 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).
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.
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.
This package 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
working example is given in the help page for function `interpolant()',
which should be the first point of reference.
Contains logic for the data-driven optimization of annotations via minimization of the entropy of variable group members over discrete variable clusters.
Provides algorithms for the analysis of ecological networks.
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.
This R package implements the statistical model proposed by Bullock, Imai, and Shapiro (2011; Political Analysis) 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 the Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
E-statistics (energy) tests and statistics for comparing
distributions: multivariate normality, multivariate distance
components and k-sample test for equal distributions,
hierarchical clustering by e-distances, multivariate
independence tests, distance correlation, goodness-of-fit
tests. Energy- statistics concept based on a generalization of
Newton's potential energy is due to Gabor J. Szekely.
Allow numbers to be presented in an English language
version, one, two, three, ...
Datasets from Nelson, Coffin and Copeland "Introductory
Statistics for Engineering Experimentation" (Elsevier, 2003)
with sample code.
The company 'Enigma' (https://enigma.io) holds many public 'datasets' from
governments, companies, universities, and organizations. 'Enigma' provides an
'API' for data, 'metadata', and statistics on each of the 'datasets'. 'enigma' is
a client to interact with the 'Enigma' 'API', including getting the data
and 'metadata' for 'datasets' in 'Enigma', as well as collecting statistics on
'datasets'. In addition, you can download a 'gzipped' 'csv' file of a 'dataset'
if you want the whole 'dataset'. An 'API' key from 'Enigma' is required to use
'enigma'.
The package allows to perform the Ecological Niche Factor Analysis, calculate habitat suitability maps and classify the habitat in suitability classes. Computations are executed in a throw-away GRASS environment from R in order to be able to perform analysis with large data sets.
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).
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.
Ensemble partial least squares regression, a unified
framework for feature selection, outlier detection, and ensemble learning.
An R package for joint statistical modelling of ChIP-seq data, accounting for technical/biological replicates, multiple conditions and different ChIP efficiencies of the individual experiments.
These programs are used for calculating enrichment
factors, drawing enrichment curves to evaluate virtual
screening approaches.
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, and K-Nearest Neighbors. 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.
This package 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.
This package 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.
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.
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.
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.
Sequential partitioning, clustering and peak detection of
centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
data plot.
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.
Plot environmental conditions with a high number of species, create boxplots with a range of environmental variables under different environmental conditions selected by the user, list species under the different environmental conditions selected by the user and create a summary of the environmental conditions, using an output file of ModestR (www.ipez.es/ModestR).
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).
A collection of nice plotting functions directly from a
data.frame with limited customisation possibilities.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Reads water network simulation data in Epanet's
text-based '.inp' and '.rpt' formats into R.  Basic summary information and plots
are also available. 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 main functions compute the expectation propagation approximation of a Bayesian probit/logit models with Gaussian prior. More information can be found in Chopin and Ridgway (2015). More models and priors should follow.
Functions for demographic and epidemiological analysis in
the Lexis diagram, i.e. register and cohort follow-up data, in
particular representation and manipulation of multistate data - the
Lexis suite of functions, which includes interfaces to mstate, etm
and cmprsk packages.
Also contains functions for Age-Period-Cohort modeling and a
function for interval censored data and some useful functions for
tabulation and plotting, as well some epidemiological datasets.
This package 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.
Hierarchical Bayesian models for use in disease
freedom and disease prevalence studies, designed
with epidemiological applications in mind. The models
themselves are in the spirit of those presented in Branscum
et al. (2006) (see package documentation for full
reference). The helper functions and methods were designed
to make implementation and processing of the complex model
output relatively simple in application.
Routines for epidemiological contact tracing
and visualisation of network of contacts.
Package for data exploration and result presentation.
Full 'epicalc' package with data management functions is available
at the author's repository.
Mathematical models of infectious diseases in humans and animals.
Both, deterministic and stochastic models can be simulated and plotted.
This package provides tools to quantify transmissibility
throughout an epidemic from the analysis of time series of
incidence.
Provides flexible model fitting used in epidemiological data analysis
by a unified model specification, along with some data manipulation functions.
This package covers fitting of variety models including Cox regression models,
linear regression models, Poisson regression models, logistic models and
others whose likelihood is expressed in negative binomial, gamma and Weibull
distributions.
Tools for simulating mathematical models of infectious disease.
Epidemic model classes include deterministic compartmental models,
stochastic individual contact models, and stochastic network models.
Disease types include SI, SIR, and SIS epidemics with and without
demography, with tools available for expansion to model complex epidemic
processes.
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.
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.
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.
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.
EpiTools: R Package for Epidemiologic Data and Graphics
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.
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 reference and the manual page of `lassoshooting'.
The package performs analysis of polynomial regression in simple designs with quantitative treatments
Computation of the EQL for a given family of variance
functions, Saddlepoint-approximations and related auxiliary
functions (e.g. Hermite polynomials)
Transitioning from EQS to R for structural equation modeling (SEM)
is made easier with a set of functions to convert .out files into R code.
The EQS output can be converted into lavaan syntax and run in the R
environment.  Other functions parse descriptive statistics and the covariance matrix
from an EQS .out file.  A heat map plot of a covariance matrix is also
included.
Analysis of experimental crosses to identify genes (called
quantitative trait loci, QTLs) contributing to variation in
quantitative traits.
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.
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.
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.
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 (ERGM). "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.
Integrates graphlet statistics based model terms for use in exponential-family random graph models ('ergm') as part of the 'statnet' suite of packages.
Data for use with the Sage Introduction to Exponential
Random Graph Modeling text by Jenine K. Harris.  Network data
set consists of 1283 local health departments and the
communication links among them along with several attributes.
A template package to demonstrate the use of user-specified statistics for use in "ergm" models as part of the "statnet" suite of packages.
The eRm package 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-Löf-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, various ICC and related plots, automated stepwise item elimination, simulation module for various binary data matrices.  An eRm platform is provided at R-forge (see URL).
The functions provided in the package ERP are designed for the significance analysis of ERP data in a linear model framework. The possible procedures are either the collection of FDR or FWER controlling methods available in the generic function p.adjust, the same collection combined with a factor modeling of the time dependence among tests (see Sheu, Perthame, Lee and Causeur, 2016) and the Guthrie and Buchwald (1991) test.
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.
Implementation of the Edge Selection Algorithm
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).
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.
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++.
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.
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.
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.
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.
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.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
Estimation of the components of an ETAS 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.
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.
Matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space
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.
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.
Calculations for DNA mixtures accounting for possibly inbred pedigrees (simulations with conditioning, LR). Calculation of exact p-values.
Frequentist and Bayesian single arm trial design and sample size software.
Designs cover one and two binary endpoints with both single and multi-stage
methodology. The research leading to these results has received funding from the
European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement
number 278742 (Eurosarc).
Tools to download data from the Eurostat database
http://ec.europa.eu/eurostat/ together with search and manipulation
utilities.
Goodness-of-fit tests for selection of r in the r-largest order
statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
peaks-over-threshold approach. 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.
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.
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.
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.
Compare lists (from 2 to infinite) and plot the results in a Venn diagram if (N<=4) with regulation details. It allows to produce a complete annotated file, merging the annotations of the compared lists. It is also possible to compute an overlaps table to show the overlaps proportions of all the couples of lists and draw proportional Venn diagrams.
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
Implementation of short and long term event study
methodology
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.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
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.
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.
An implementation of 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.
Performs unconditional exact tests and power calculations for 2x2 contingency
tables. Unconditional exact tests are often more powerful than conditional exact tests and asymptotic tests.
Calculates Fisher's exact test, Blaker's exact test, or the exact McNemar's test with appropriate matching confidence intervals. Provides power and sample size calculations. Also gives melded confidence intervals for the binomial case.
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.
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.
ExactPath implements an algorithm for exact LASSO
solution. Two methods are provided to print and visualize the
whole solution paths. Use ?ExactPath to see an introduction.
Packages ncvreg and lars are required so that their data sets
can be used in examples.
Computes exact conditional p-values and quantiles using an
implementation of the Shift-Algorithm by Streitberg & Roehmel.
Automatic generation of exams based on exercises in Sweave (R/LaTeX) or R/Markdown format,
including multiple-choice questions and arithmetic problems. Exams can be produced in various formats,
including PDF, HTML, Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1, ARSnova, and TCExam.
In addition to fully customizable PDF exams, a standardized PDF format is provided that can be
printed, scanned, and automatically evaluated.
Tools for constructing confidence regions for exceedance regions
and contour lines.
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()'. 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.
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.
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.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
Package for analysis of simple experimental designs (CRD,
RBD and LSD), experiments in double factorial schemes (in CRD
and RBD), experiments in a split plot in time schemes (in CRD
and RBD), experiments in double factorial schemes with an
additional treatment (in CRD and RBD), experiments in triple
factorial scheme (in CRD and RBD) and experiments in triple
factorial schemes with an additional treatment (in CRD and
RBD), performing the analysis of variance and means comparison
by fitting regression models until the third power
(quantitative treatments) or by a multiple comparison test,
Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott,
Duncan test, t test (LSD) and Bonferroni t test (protected LSD)
- for qualitative treatments.
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.
Expectile and quantile regression of models with nonlinear effects
e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes
as well as boosting; also supplies expectiles for common distributions.
The package provides various statistical methods for
designing and analyzing randomized experiments. One main
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, 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.
Package contains methods to generate explanations for individual predictions of
classification and regression models. Weighted averages of individual explanations form explanation of the whole model.
The package extends 'CORElearn' package, but other prediction models can also be explained using a wrapper.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Computation of the matrix exponential, logarithm, and related quantities.
An R-interface to the Fortran package Expokit.
ExPosition is for descriptive (i.e., fixed-effects) multivariate analysis with the singular value decomposition.
Calculates the density dependent likelihood of a phylogenetic tree. It takes branching and sampling times as an argument and integrates the likelihood function over the whole tree.
Tools and data to accompany Schlicht, Valcu and Kempenaers "Spatial patterns of extra-pair paternity: beyond paternity gains and losses"
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by
Hyndman, Koehler, Ord and Snyder (Springer, 2008).
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.
The package provides tools for botanists, plant taxonomists,
curators of plant genebanks and perhaps other biological collections.
A consistent, unified and extensible
framework for estimation of parameters for probability distributions, including
parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.
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.
This package tests for differences in minor allele
frequency between groups and is based on an extra-binomial
variation model for pooled sequencing data.
Categorical Data Analysis and Visualization.
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
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.
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.
Functions for performing extreme value analysis.
Detect outliers in one-dimensional data.
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.
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.
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.
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.
ezsim provides a handy way to run simulation and examine its result
Functions that can fulfill the gap between the outcomes of 'dplyr' and a print-ready summary table.
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.
Multivariate exploration of a data table with factorial
analysis and cluster methods.
Exploratory data analysis methods such as principal component methods and clustering.
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.
Perform factorial analysis with a menu and draw graphs interactively thanks to FactoMineR and a Shiny application.
Provides functions to score the Functional Assessment of Cancer
Therapy (FACT) and Functional Assessment of Chronic Illness Therapy (FACIT)
family of patient-reported outcome (PRO) measures.  The questionnaires
themselves can be downloaded from www.FACIT.org.  For most of the FACIT
questionnaires, FACIT.org provides scoring syntax for use with commercial
statistical software (SAS and SPSS).  The FACTscorer R package is intended
to serve as a free, reliable alternative for those without access to SAS or
SPSS.  Additionally, it will allow R users to both score and analyze the
FACT and FACIT scales in R, avoiding the time-consuming and and error-prone
process of transferring data back-and-forth between statistical software.
Finally, use of the FACTscorer package will prevent many sources of scoring
error common when using SAS and/or SPSS syntax (e.g., copy-paste errors and
other accidental modifications to the syntax).
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 adecorrelation 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.
This package estimates factor analysis models using a
genetic algorithm, which permits a general mechanism for
restricted optimization with arbitrary restrictions that are
chosen at run time with the help of a GUI. Importantly,
inequality restrictions can be imposed on functions of multiple
parameters, which provides a new avenues for testing and
generating theories with factor analysis models. This package
also includes an entirely new estimator of the common factor
analysis model called semi-exploratory factor analysis, which
is a general alternative to exploratory and confirmatory factor
analysis. Finally, this package integrates a lot of other
packages that estimate sample covariance matrices and thus
provides a lot of alternatives to the traditional sample
covariance calculation. Note that you need to have the Gtk run
time library installed on your system to use this package; see
the URL below for detailed installation instructions. Most
users would only need to understand the first twenty-four pages
of the PDF manual.
It is used for systematic analysis of decisions based on attributes and its levels.
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.
A value is falsy if it is NULL, FALSE, the empty string,
zero, or an empty vector or list. Other values are truthy. The new
%&&% and %||% operators work with falsy and truthy values and
can be used for concise conditional execution.
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.
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.
An R package "fanc" 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.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the RStudio IDE with RStudio's package 'manipulate' loaded. RStudio is freely available (www.rstudio.org), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only.
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.
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 and "Extending the Linear Model with R" published by CRC press in December 2005.
Environment for teaching "Financial Engineering and
Computational Finance"
Allow numbers to be presented in an persian language
version
Environment for teaching
"Financial Engineering and Computational Finance".
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.
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.
An efficient method of recovering precision matrices
by applying the parametric simplex method is provided in this package.
The computation is based on a linear optimization solver.
It also contains a generic Linear Programming solver and a
parameterized Linear Programming solver based on the 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.
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.
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.
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). Please see an eprint at http://arxiv.org/pdf/1507.06055.pdf for more details.
The FastHCS algorithm of Schmitt and Vakili (2014) 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. The FastImputation
function uses this FastImputationPatterns object to impute (make
a good guess at) missing data in a single line or a whole dataframe
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.
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.
The package implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators.
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.
The FastPCS algorithm of Vakili and Schmitt (2014) for robust estimation of multivariate location and scatter and multivariate outliers detection.
Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.
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.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
Functions for computing spillover measures, especially
spillover tables and spillover indices, as well as their
average, minimal, and maximal values.
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.
Kiener distributions K1, K2, K3 and K4 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. Also, power hyperbolas and power hyperbolic functions. Some functions
introduced in v1.2-0 were suppressed.
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".
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 graphical models when
the variables are assumed to satisfy a given ordering. The
approach used is based on non-local parameter priors and thus
it is suitable for learning sparse graphs. The algorithm is
implemented in C++ using the open-source library Armadillo.
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.
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) 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.
Currently the calculation of 100000 bootstrap replicates for 500 observations
takes about one second.
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.
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
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 an interface to kernel routines from the FCNN C++ library.
FCNN is based on a completely new Artificial Neural Network representation that
offers unmatched efficiency, modularity, and extensibility. FCNN4R provides
standard teaching (backpropagation, Rprop, simulated annealing, stochastic
gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon),
but it is first and foremost an efficient computational engine. Users can
easily implement their algorithms by taking advantage of fast gradient computing
routines, as well as network reconstruction functionality (removing weights
and redundant neurons, reordering inputs, merging networks). Networks can be
exported to C functions in order to integrate them into virtually any software
solution.
A fold change rank ordering statistics method is presented to search for
differentially expressed genes and to detect recurrent chromosomal copy number aberrations.
This method may be useful for high-throughput biological data (microarray, sequencing, ...).
Probabilities are associated with genes/probes in the dataset and there is no problem of
multiple tests when using this method. For aCGH data, segmentation results are obtained by
merging the significant probes detected.
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.
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.
Functions to support functional data analysis using the square root velocity framework.
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.
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.
Regression models for functional data, i.e. scalar-on-function,
function-on-scalar and function-on-function regression models are fitted
using a component-wise gradient boosting algorithm.
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).
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 p-values (and test statistics) with discrete (and heterogeneous) null distributions.
This package implements the more powerful weighted generalized FDR procedure that adapts to these two features of the discrete paradigm for multiple testing.
The package takes in the original data set rather than the p-values in order to carry out the adjustments needed for multiple testing in this paradigm. The methodology applies also to multiple testing where the null p-values are uniformly distributed.
The package implements the method for three 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,
(c) the exact negative binomial test on if two independent negative binomial distributions with the same size parameter have the same means.
It depends on the R packages ``MCMCpack'' to use its function ``dnoncenhypergeom'' for hypergeometric distributions, and edgeR to uses its normalization techniques for data that follow negative binomial distributions.
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.
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.
Local inferential feature significance for multivariate kernel density estimation.
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).
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.
Functions and example datasets for Fechnerian scaling of
discrete object sets.  It computes 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 allows for retrieval of five 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; and the International Tree Ring Data Bank. Additional
data sources are in the works, including global DEM resources (ETOPO1, ETOPO5,
ETOPO30, SRTM), global soils (HWSD), MODIS satellite data products, the National
Atlas (US), Natural Earth, PRISM, and WorldClim.
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
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.
Efficient estimation of fixed-effect maximum likelihood models with, possibly, non-linear right hand sides.
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/).
Collection of pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables)
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.
This package performs general linear modeling with
multiple responses (MANCOVA).  An overall p-value for each
model term is calculated by the 50-50 MANOVA method, which
handles collinear responses.  Rotation testing is used to
compute adjusted single response p-values according to
familywise error rates and false discovery rates.
Provides a simple and efficient wrapper around the fastest
Fourier transform in the west (FFTW) library.
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 FFTW3. 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.
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.
Exact MLE and Whittle MLE estimation for power law decay models.
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-sensative help to these arguments. The interface
essentially a wrapper to some tcltk routines to both simplify
and facilitate GUI creation. More advanced tcltk routines/GUI
objects can be incorporated into the interface for greater
customization for the more experienced.
R function to plot high quality, elegant heatmap using 'ggplot2' graphics .
Some of the important features of this package are, coloring of row/column side tree with
respect to the number of user defined cuts in the cluster, add annotations to both columns
and rows, option to input annotation palette for tree and column annotations and multiple
parameters to modify aesthetics (style, color, font) of texts in the plot.
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.
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 curve, surface and function
fitting with an emphasis on splines, spatial data and spatial
statistics. The major methods include cubic, and thin plate splines,
Kriging and compact covariances for large data sets. The splines and
Kriging methods are supporting by functions that can determine the
smoothing parameter (nugget and sill variance) by cross validation
and also by restricted maximum likelihood. For Kriging there is
an easy to function that also estimates the correlation scale (range).
A major feature is that
any covariance function implemented in R and following a simple fields
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 in addition to the
manual pages. The commented source code can be viewed by expanding the
source code .tar.gz file and looking in R subdirectory.
Tools for random fields and bridges simulations.
A collection of functions that assist in data
manipulation analysis and plotting.
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
Interface for working with large matrices stored in files, not in computer memory.
Supports multiple data types (double, integer, logical and raw) of different sizes (e.g. 4, 2, or 1 byte integers).
Access to parts of the matrix is done by indexing, exactly as with usual R matrices.
Supports very large matrices (tested on 1 terabyte matrix), allowing for more than 2^32 rows or columns.
Cross-platform as the package has R code only, no C/C++.
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.
Time value of money, cash flows and other financial
functions.
Infrastructure for defining meta-data and
relationships for financial instruments.
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).
Package for time value of money calculation, time series analysis and computational finance.
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).
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 the function, INT, which estimates
the average marginal treatment effect, the average treatment
combination effect, and the average marginal treatment interaction
effect proposed by Egami and Imai (2015).
Creates models from all combinations of a list of variables and sorts by minimum IC (information criterion).
Package designed to find an acceptable python binary.
This package contains 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.
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.
R companion to Tsay (2005)
Analysis of Financial Time Series, 2nd ed. (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 FisherEM package provides an efficient algorithm for
the unsupervised classification of high-dimensional data. This
FisherEM algorithm 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.
Fishery science methods and models from published literature and contributions from colleagues.
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (GL)Ms.
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.)
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 www.fitbit.com, the website that contains data
for the popular fitness tracker. 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 www.fitbit.com.
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.
Fits Density Ratio Classes to elicited
probability-quantile points or intervals
Utilities to read and write files in the FITS (Flexible
Image Transport System) format, a standard format in astronomy.
Present low-level routines allow: reading, parsing, and modifying
FITS headers; reading FITS images (multi-dimensional arrays);
reading FITS binary 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 bintable; reading bintables into
data frames; generating vectors for image array axes; scaling and
writing images as 16-bit integers.  Known incompletenesses are
reading FITS ASCII table and random group extensions, as well as
bit, complex, and array descriptor data types in binary tables.
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.
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.
Contains tools and features, which can be used for an exploratory
landscape analysis of continuous optimization problems. Those are able to
quantify rather complex properties, such as the global structure,
separability, etc., of continuous optimization problems.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2014). Fused Lasso Additive Model. arXiv preprint arXiv:1409.5391.
The package "flare" provides 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.
Fast implementation of hierarchical clustering
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.
Allows for maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.
FlexMix implements a general framework for finite
mixtures of regression models using the EM algorithm.
FlexMix provides the E-step and all data handling, 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.
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.
Estimation of flexible parametric models for survival data.
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.
FLIM fits linear models for the observed increments in a longitudinal dataset, and imputes missing values according to the models.
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 McNemar. 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.
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.
Tools to quickly compile taxonomic and distribution data from
the Brazilian Flora Checklist.
Concatenates some 'flowWorkspace', 'flowCore', 'vegan' and 'gdata' packages functionalities to import 'FlowJo' workspaces and calculates ecological diversity indices for gated populations, based on two dimensions cytograms.
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.
Selections on flow matrices, statistics on selected flows, map and graph visualisations.
This package implements a path algorithm for the Fused
Lasso Signal Approximator. For more details see the help files
Solve an approximate Subset Sum Problem via a recursive compression algorithm
Contains data sets from Bernard Flury (1997) A First
Course in Multivariate Statistics, Springer NY
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.
All data sets from "Forecasting: methods and applications"
by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
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.
The package contains R-functions to perform an fmri analysis
as described in
K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny,
Analysing fMRI experiments with structure adaptive smoothing procedures,
NeuroImage, 33:55-62 (2006),
J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation
for statistical parametric mapping, NeuroImage, 52:515-523 (2010)
and
K. Tabelow, J. Polzehl, Statistical Parametric Maps for
Functional MRI Experiments in {R}: The Package {fmri}},
Journal of Statistical Software, 44(11):1--21 (2011).
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.
This package implements some basic procedures for dealing
with log maximally skew stable distributions, which are also
called finite moment log stable distributions.
This package computes posterior residual variances to be
used in the denominator of a moderated t-statistic from a
linear model analysis of microarray data.  It is an extension
of the moderated t-statistic original proposed by Smyth (2004).
LOESS local regression and empirical Bayesian method are used
to estimate gene specific prior degrees of freedom and prior
variance based on average gene intensity level. The posterior
residual variance in the denominator is a weighted average of
prior and residual variance and the weights are prior degrees
of freedom and residual variance degrees of freedom. The
degrees of freedom of the moderated t-statistic is simply the
sum of prior and residual variance degrees of freedom.
Environment for teaching
"Financial Engineering and Computational Finance"
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
including KNN classification, regression and information measures are implemented.
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".
Computer Modern font for use with extrafont package
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.
Environment for teaching
"Financial Engineering and Computational Finance".
Pricing and Evaluating Basic Options.
SHE, FORAM Index and ABC Method analyses and custom plot
functions for community data.
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.
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).
Routines for forecasting univariate time series using Theta Method and Optimised Theta Method (Fioruci et al, 2015). Contains several cross-validation routines.
The implemented algorithm performs a floating-point genetic algorithm search with a statistical forecasting operator that generates offspring which probably will be generated in future generations. Use of this operator enhances the search capabilities of floating-point genetic algorithms because offspring generated by usual genetic operators rapidly forecasted before performing more generations.
Functions for reading and writing data stored by some versions of
Epi Info, Minitab, S, SAS, SPSS, Stata, Systat and 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
Enables users to form appropriate visualizations of high dimensional mapping structures of random forests.
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.
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 markdown
to improve the readability of data presented in tabular form rendered as
web pages.
Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side.
These utilities facilitate the programmatic manipulations of
formulas, expressions, calls, names, symbols and other objects.
Forward search approach to robust analysis in linear and
generalized linear regression models.
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.
Estimate Barton & Lord's four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2015).
A game for two players: Who gets first four in a row
(horizontal, vertical or diagonal) wins. Published by Milton
Bradley, designed by Howard Wexler and Ned Strongin.
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.
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
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. Facto 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 allows clustering of high dimensional data sets.
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.
Efficient approximation of first-passage-time densities for diffusion processes based on the First-Passage-Time Location (FPTL) function.
Maximum likelihood estimation of the parameters of a
fractionally differenced ARIMA(p,d,q) model (Haslett and
Raftery, Appl.Statistics, 1989).
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 randomized Sierpinski Carpet which is built on [p-p-p-q]-model. Thereby, for three times simulation with p-value and once with q-value is assumed. Hence these parameters are estimated and displayed.
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.
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file and contains 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).
The frailtyHL package implements the h-likelihood
estimation procedures for frailty models. The package fits
Cox's proportional hazards models with random effects (or
frailties). For the frailty distribution lognormal and gamma
are allowed. The h-likelihood uses the Laplace approximation
when the numerical integration is intractable, giving a
statistically efficient estimation in frailty models.
The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package:
1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied.
2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope).
3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects.
4) Joint frailty models in the context of joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed.
5) Joint general frailty models in the context of a joint modelling for recurrent events with terminal event data with two independent frailty terms.
6) Multivariate joint frailty models for two types of recurrent events and a terminal event.
7) Joint models for longitudinal data and a terminal event.
8) Trivariate joint models for longitudinal data, recurrent events and a terminal event.
Prediction values are available. Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty 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.
A package for functional response analysis.
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'. The data sets used in the
book are contained in this package.
This package performs robust inference based on applying
Fast and Robust Bootstrap on robust estimators. Available
methods are multivariate regression, PCA and Hotelling tests.
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.
This package is 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.
The package FreeSortR 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.
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.
Methods for the analysis of multivariate time series using frequency domain techniques. Implementations of dynamic principle components analysis (DPCA) and estimators of operators in lagged regression. Examples of usage in functional data analysis setup.
A frequency moving average plot (MAP) is estimated from a
multinomial data and a continuous covariate. The frequency MAP
is a moving average estimate of category frequencies, where
frequency means and posterior bounds are estimated. Comparisons
of two frequency MAPs as well as odds ratios can be plotted.
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.
The frequency of a particular data value is the number of times it
occurs. A frequency table is a table of values with their corresponding
frequencies. Frequency weights are integer numbers that indicate how many
cases each case represents. This package provides some functions to work
with such type of collected data.
Contains a set of utilities for building and testing formula-based models (linear, logistic or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
This package creates regular and non-regular Fractional
Factorial designs. 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). The
package is still under development. While much of the intended
functionality is already available, some changes and
improvements are still to be expected. Suggestions are welcome.
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.
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.
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)
and a model specification in which the firm effects are directly
influenced by a number of variables (Battese and Coelli, 1995).
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.
Functions to perform a variety of simple fish stock assessment
methods. Detailed vignettes are available on the fishR website listed below.
The datasets to support the Fish Stock Assessment (FSA) package.
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.
This package provides 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.
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, the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of one or a few persons.
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) Random Intersection Trees''.  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.
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.
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.
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.
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.
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 used in and outside of R.
This is a collection of R games and other funny stuff,
such as the classical Mine sweeper and sliding puzzles.
Statistical hypothesis testing methods for non-parametric functional dependencies based on asymptotic chi-square and exact statistics. These tests were designed to reveal causality based on evidence from functional dependencies. They include an asymptotic non-constant functional chi-square test, an exact functional test, and a comparative functional chi-square test. The normalized functional chi-square test was used by the Best Performer NMSUSongLab in DREAM8 Breast Cancer Network Inference Challenges. These tests offer an advantage over regression analysis when a parametric functional form cannot be assumed.
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.
This packages proposes a model-based clustering algorithm for
multivariate functional data. The parametric mixture model, based on the
assumption of normality of the principal components resulting from a
multivariate functional PCA, is estimated by an EM-like algorithm. The main
advantage of the proposed algorithm is its ability to take into account the
dependence among curves.
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.
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Functions for computing fungible coefficients and Monte Carlo data.
The package provides the funHDDC algorithm (Bouveyron & Jacques, 2011) which allows to cluster functional data by modeling each group within a specific functional subspace.
A small utility which wraps Rscript and provides access to all R
functions from the shell.
This package performs functional regression, and some related
approaches, for intensive longitudinal data (see Walls & Schafer, 2006)
which 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) 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.
Includes non-parametric estimators and tests for time series analysis. The functions allow 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.
Efficient procedures for community detection in network studies, especially for sparse networks. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
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
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.
A Future API for R is provided. In programming, a future is an
abstraction for a value that may be available at some point in the future.
The state of a future can either be unresolved or resolved. As soon as it is
resolved, the value is available. Futures are useful constructs in for instance
concurrent evaluation, e.g. multicore parallel processing and distributed
processing on compute clusters. The purpose of this package is to provide a
lightweight interface for using futures in R. Functions 'future()' and 'value()'
exist for creating futures and requesting their values. An infix assignment
operator '%<=%' exists for creating futures whose values are accessible by the
assigned variables (as promises). This package implements the synchronous "lazy"
and "eager" futures, and the asynchronous "multicore" future (not on Windows).
Additional types of futures are provided by other packages enhancing this
package.
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).
Methods to solve Fuzzy Linear Programming Problems with
fuzzy constraints (by Verdegay, Zimmermann, Werner, Tanaka),
fuzzy costs (multiobjective, interval arithmetic, stratified piecewise reduction,
defuzzification-based), and fuzzy technological matrix.
S4 classes and methods
to deal with fuzzy numbers. With them you can compute any arithmetic
operations (e.g. by using the Zadeh extension principle),
perform approximation of arbitrary FNs by trapezoidal and piecewise
linear FNs, prepare plots of FNs for publications, calculate
possibility and necessity values for comparisons, etc.
Does fuzzy tests and confidence intervals (following Geyer
and Meeden, Statistical Science, 2005) for sign test and Wilcoxon
signed rank and rank sum tests.
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.
A custom framework for working with Type 1 Fuzzy Logic,
produced by the University of Nottingham IMA Group.
fwdmsa performs the Forward Search for Mokken scale
analysis. It detects outliers, it produces several types of
diagnostic plots.
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).
This package provides three new 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.
G1DBN performs DBN inference using 1st order conditional
dependencies.
Full descriptive statistics, physical description of sediment,
metric or phi sieves.
An R package for optimization using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. 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. GAs can be run sequentially or in parallel.
This is a Genetic Algorithm package for the determination
of the stratum boundaries and sample sizes of each stratum in
stratified sampling
The GAabbreviate uses Genetic Algorithms as an optimization tool to create abbreviated forms of lengthy questionnaires (or other measures) that maximally capture the variance in the original data of the long form of the measure.
Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.
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.
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.
This package constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.
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.
This package 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
included 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).
Data sets and scripts used in the book "Generalized
Additive Models: An Introduction with R", Wood (2006) CRC.
Fits the gambin distribution to species-abundance distributions from
ecological data. 'gambin' is short for 'gamma-binomial'. The main function is
fitGambin, which estimates the 'alpha' parameter 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.
Provides estimation and analysis functions for
strategic statistical models.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
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 (2015), One-Step Estimator Paths for Concave Regularization, http://arxiv.org/abs/1308.5623.
Functions for fitting, displaying and checking GAMLSS Models.
Interfaces 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.
Demos for smoothing and gamlss.family distributions.
The different distributions used for the response variables in GAMLSS
modelling.
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 Markov Random Field (MRF) within the GAMLSS 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
Fit generalized additive mixed models via a version of
mgcv's gamm function, using lme4 for estimation.
This package performs gamma regression, where both mean and shape parameters follows lineal regression structures.
Uses a slice sampling-based Markov chain Monte Carlo to
conduct Bayesian fitting and inference for generalized additive
mixed models (GAMM).  Generalized linear mixed models and
generalized additive models are also handled as special cases
of GAMM.
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.
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.
Performs a Genetic Algorithm Optimization, given a
real-based or permutation-based function and the associated
search space.
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.
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.
The functions included are used to obtain initial authentication with Google Analytics as well as simple and organized data retrieval from the API. Allows for retrieval from multiple profiles at once.
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.
Manipulation of genetic data (SNPs), computation of Genetic Relationship Matrix, Linkage Disequilibrium, etc. Efficient algorithms for Linear Mixed Model (AIREML, diagonalisation trick).
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.
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.
This package collects 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.
An implementation of extensions to Freund and
Schapire's AdaBoost algorithm and Friedman's gradient boosting
machine. Includes regression methods for least squares,
absolute loss, t-distribution loss, quantile regression,
logistic, multinomial logistic, Poisson, Cox proportional
hazards partial likelihood, AdaBoost exponential loss,
Huberized hinge loss, and Learning to Rank measures
(LambdaMart).
Writes SAS code to get predicted values from every tree of a gbm.object.
Provides utilities for processing Rd objects and files.
Extract argument descriptions and other parts of the help pages
of functions.
Many inherited biases and effects exists in RNA-seq due to both biological and technical effects. We observed the biological variance of testing target transcripts can influence the yield of sequencing reads, which might indicate a resource competition existing in RNA-seq. We developed this package to capture the bias depending on local sequence and perform the correction of this type of bias by borrowing information from spike-in measurement.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
GPU/CPU Benchmarking on Debian-package based systems
This package benchmarks performance of a few standard linear algebra
operations (such as a matrix product and QR, SVD and LU decompositions)
across a number of different BLAS libraries as well as a GPU implementation.

To do so, it takes advantage of the ability to 'plug and play' different
BLAS implementations easily on a Debian and/or Ubuntu system.  The current
version supports
- reference blas (refblas) which are unaccelerated as a baseline
- Atlas which are tuned but typically configure single-threaded
- Atlas39 which are tuned and configured for multi-threaded mode
- Goto Blas which are accelerated and multithreaded
- Intel MKL which are a commercial accelerated and multithreaded version.
As for GPU computing, we use the CRAN package
- gputools

For Goto Blas, the gotoblas2-helper script from the ISM in Tokyo can be
used. For Intel MKL we use the Revolution R packages from Ubuntu 9.10.
Contains the Global Charcoal database data. Data include charcoal
series (age, depth, charcoal quantity, associated units and methods) and
informations on sedimentary sites (localisation, depositional environment, biome, etc.).
This package implements a generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the elastic net penalized least squares, the elastic net penalized SVM with the squared hinge loss and the elastic net penalized logistic regression.
Orders panels in scatterplot matrices and parallel
coordinate displays by some merit index. Package contains
various indices of merit, ordering functions, and enhanced
versions of pairs and parcoord which color panels according to
their merit level.
Likelihood inference in Gaussian copula marginal
regression models.
Estimates a sparse inverse covariance matrix from a convex
pseudo-likelihood function with L1 penalty
This package contains data sets used in the book "R
Graphics Cookbook" by Winston Chang, published by O'Reilly
Media.
Analyze the default risk of credit portfolios. Commonly known models,
like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
The portfolio loss distribution can be achieved either by simulation or analytically
in case of the classic CreditRisk+ model. Models are only implemented to respect losses
caused by defaults, i.e. migration risk is not included. The package structure is kept
flexible especially with respect to distributional assumptions in order to quantify the
sensitivity of risk figures with respect to several assumptions. Therefore the package
can be used to determine the credit risk of a given portfolio as well as to quantify
model sensitivities.
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
Wrappers for the Geospatial Data Abstraction Library (GDAL)
Utilities.
Various R programming tools for data manipulation, including:
- medical unit conversions ('ConvertMedUnits', 'MedUnits'),
- combining objects ('bindData', 'cbindX', 'combine', 'interleave'),
- character vector operations ('centerText', 'startsWith', 'trim'),
- factor manipulation ('levels', 'reorder.factor', 'mapLevels'),
- obtaining information about R objects ('object.size', 'elem', 'env',
'humanReadable', 'is.what', 'll', 'keep', 'ls.funs',
'Args','nPairs', 'nobs'),
- manipulating MS-Excel formatted files ('read.xls',
'installXLSXsupport', 'sheetCount', 'xlsFormats'),
- generating fixed-width format files ('write.fwf'),
- extricating components of date & time objects ('getYear', 'getMonth',
'getDay', 'getHour', 'getMin', 'getSec'),
- operations on columns of data frames  ('matchcols', 'rename.vars'),
- matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'),
- operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'),
- operations on data frames ('frameApply', 'wideByFactor'),
- value of last evaluated expression ('ans'), and
- wrapper for 'sample' that ensures consistent behavior for both
scalar and vector arguments ('resample').
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.
This package contains functions for 'specific' MCA (Multiple Correspondence Analysis),
'class specific' MCA, computing and plotting structuring factors and concentration ellipses,
'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions
for the translation of logit models coefficients into percentages (forthcoming), weighted contingency tables and an association
measure - i.e. Percentages of Maximum Deviation from Independence (PEM).
The GDELT data set is over 60 GB now and growing 100 MB a month.
The number of source articles has increased over time and unevenly across
countries. This package makes it easy to download a subset of that data,
then normalize that data to facilitate valid time series analysis.
Diffusion anisotropy has been used to characterize
white matter neuronal pathways in the human brain, and infer global
connectivity in the central nervous system. The package implements
algorithms to estimate and visualize the orientation of neuronal
pathways in model-free methods (q-space imaging methods).
For estimating fibre orientations two methods have been
implemented.  One method implements fibre orientation detection
through local maxima extraction. A second more robust method
is based on directional statistical clustering of ODF voxel data.
Fibre orientations in multiple fibre voxels are estimated using
a mixture of von Mises-Fisher (vMF) distributions. This statistical
estimation procedure is used to resolve crossing fibre
configurations.
Reconstruction of orientation distribution function (ODF)
profiles may be performed using the standard generalized
q-sampling imaging (GQI) approach, Garyfallidis' GQI (GQI2)
approach, or Aganj's variant of the Q-ball imaging (CSA-QBI)
approach. Procedures for the visualization of RGB-maps,
line-maps and glyph-maps of real diffusion magnetic resonance
imaging (dMRI) data-sets are included in the package.
Calculate distances and routes on geographic grids.
A toolkit with functions to fit, plot, and summarize Generalized Dissimilarity Models.
Useful tools for writing vector graphics devices.
Implements common geostatistical methods in a clean,
straightforward, efficient manner. A quasi reboot of the SpatialTools R package.
GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting.  The "M" in the name "geeM" is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R.
Generalized estimating equations solver for parameters in
mean, scale, and correlation structures, through mean link,
scale link, and correlation link. Can also handle clustered
categorical responses.
Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.
Functions to compute generalized eigenvalues and eigenvectors,
the generalized Schur decomposition and
the generalized Singular Value Decomposition of a matrix pair,
using Lapack routines.
Methods for fitting macroevolutionary models to phylogenetic trees.
Implements several extensions of the elastic net regularization scheme. These extensions include individual feature penalties for the L1 term, feature-feature penalties for the L2 term, as well as translation coefficients for the latter.
Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.
Network meta-analyses (mixed treatment comparisons) in the Bayesian
framework using JAGS. Includes methods to assess heterogeneity and
inconsistency, and a number of standard visualizations.
An R package providing the Java JAR for the gemtc package
a package for genome-wide association analysis between
quantitative or binary traits and single-nucleotide
polymorphisms (SNPs).
GenABEL.data package consists of a data set used by GenABEL functions
R based genetic algorithm for binary and floating point
chromosomes.
genasis package contains methods for air pollution assessment. Concerned on persistent organic pollutants, the package allows to compute trends of their concentrations, compare different datasets and estimate relations between values from active and passive air samplers.
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Implementation of the genetic class level association testing (GenCAT) method from SNP level association data. Refer to: "Qian, J., Reed, E., Nunez, S., Qu, L., Reilly, M., Foulkes, A. (2015) Testing class-level genetic associations using single-element summary statistics. (Under review).".
Set of functions to create datasets using a correlation matrix.
Encodes gender based on names and dates of birth using historical
datasets. By using these datasets instead of lists of male and female names,
this package is able to more accurately guess the gender of a name, and it
is able to report the probability that a name was male or female.
Utilizes the 'genderize.io' API to predict gender from first
names extracted from a text vector. The accuracy of prediction could be
controlled by two parameters: counts of a first name in the database and
probability of prediction.
Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
Functions and analytics for GENEA-compatible accelerometer
data into R objects. See topic 'GENEAread' for an introduction
to the package.
The GeneCycle package implements the approaches of Wichert
et al. (2004), Ahdesmaki et al. (2005) and Ahdesmaki et al.
(2007) for detecting periodically expressed genes from gene
expression time series data.
This package implements several generalized F-statistics.
The current version includes a generalized F-statistic based on
the flexible isotonic/monotonic regression or order restricted
hypothesis testing.
GeneFeST is a genome scan method to detect outlier loci (genes) which are under balancing or directional selection.
Stochastic simulation and MCMC inference of structure from genetic data.
"geneListPie" package is for mapping a gene list to
function categories defined in GOSlim or Kegg. The results can
be plotted as a pie chart to provide a quick view of the genes
distribution of the gene list among the function categories.
The gene list must contain a list of gene symbols. The package
contains a set of pre-processed gene sets obtained from Gene
Ontology and MSigDB including human, mouse, rat and yeast. To
provide a high level concise view, only GO slim and kegg are
provided. The gene sets are regulared updated. User can also
use customized gene sets. User can use the R Pie() or Pie3D()
function for plotting the pie chart. Users can also choose to
output the gene function mapping results and use external
software such as Excel(R) for ploting.
Analyzes gene expression
(time series) data with focus on the inference of gene networks.
In particular, GeneNet implements the methods of Schaefer and
Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
for learning large-scale gene association networks (including
assignment of putative directions).
Functions for Genetic Epi Methods Developed at MSKCC
This package provides functions for the hyperbolic and
related distributions. Density, distribution and quantile
functions and random number generation are provided for the
hyperbolic distribution, the generalized hyperbolic
distribution, the generalized inverse Gaussian distribution and
the skew-Laplace distribution. Additional functionality is
provided for the hyperbolic distribution, normal inverse
Gaussian distribution and generalized inverse Gaussian
distribution, including fitting of these distributions to data.
Linear models with hyperbolic errors may be fitted using
hyperblmFit.
Perform the Blinder-Oaxaca decomposition for generalized linear
model with bootstrapped standard errors. The twofold and threefold
decomposition are given, even the generalized linear model output in each group.
Allows users to quickly and easily generate fake data containing
Personally Identifiable Information (PII) through convenience functions.
GeneReg is an R package for inferring time delay gene
regulatory network using time course gene expression profiles.
The main idea of time delay linear model is to fit a linear
regression model using a set of putative regulators to estimate
the transcription pattern of a specific target gene.
A tool for finding an ensemble gene-signature by a steepest ascending algorithm partially supervised by survival time data.
Classes and methods for handling genetic data. Includes
classes to represent genotypes and haplotypes at single markers
up to multiple markers on multiple chromosomes. Function
include allele frequencies, flagging homo/heterozygotes,
flagging carriers of certain alleles, estimating and testing
for Hardy-Weinberg disequilibrium, estimating and testing for
linkage disequilibrium, ...
Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
A loose collection of tools for the analysis of gene expression and genotype data, currently with main focus on eQTL and MDR analysis.
Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').
This package computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations.
Implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.
This is a genMOSS package with additional datafile preprocessing functions. Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. The MOSS algorithm is a Bayesian variable selection procedure that is applicable to GWAS data. It identifies combinations of the best predictive SNPs associated with the response. It also performs a hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of SNPs. This package also includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.
Draws gene or genome maps and comparisons between these, in a
publication-grade manner. Starting from simple, common files, it will
draw postscript or pdf files that can be sent as such to journals.
A gaussian copula based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.
genpathmox provides a very interesting solution for handling
segmentation variables in complex statistical methodology. It contains en
extended version of the PATHMOX algorithm in the context of partial least
square path modeling (Sanchez, 2009) including the F-block test (to detect
the responsible latent endogenous equations of the difference), the
F-coefficient (to detect the path coefficients responsible of the
difference) and the invariance test (to realize a comparison between the
sub-models' latent variables). Furthermore, the package contains a
generalized version of the PATHMOX algorithm to approach different
methodologies: linear regression and least absolute regression models.
The genridge package introduces generalizations of the standard univariate
ridge trace plot used in ridge regression and related methods.  These graphical methods
show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
predictors.
Performs search for global minimum of a very complex non-linear objective function with a very large number of optima.
Generalized ensemble methods allowing arbitrary underlying
models to be used. Currently only bagging is supported.
Generation of survival data with one (binary)
time-dependent covariate.  Generation of survival data arising
from a progressive illness-death model.
Defines window or bin boundaries for the analysis of genomic data.
Boundaries are based on the inflection points of a cubic smoothing spline
fitted to the raw data. Along with defining boundaries, a technique to
evaluate results obtained from unequally-sized windows is provided.
Applications are particularly pertinent for, though not limited to, genome
scans for selection based on variability between populations (e.g. using
Wright's fixations index, Fst, which measures variability in subpopulations
relative to the total population).
Used by Hafro staff to draw maps showing the distribution of
fishing intensity and catches, and of survey data for Icelandic fish stocks.
Potentially useful for others.
Functions to fit geostatistical data. The data can be
continuous, binary or count data and the models implemented are
flexible. Conjugate priors are assumed on some parameters while
inference on the other parameters can be done through a full
Bayesian analysis of by empirical Bayes methods.
Make geographic box plot as detailed in Willmott et al. (2007).
Wrapper for Nokia's HERE geocoding API. See http://here.com/ for
more information on HERE and https://developer.here.com/geocoder for more
information on the HERE geocoding API.
This package provides a variety of functions to analyze and model geostatistical count data with generalized linear spatial models, including
1) simulate and visualize the data;
2) posterior sampling with robust MCMC algorithms (in serial or parallel way);
3) perform prediction for unsampled locations;
4) conduct Bayesian model checking procedure to evaluate the goodness of fitting;
5) conduct transformed residual checking procedure.
In the package, seamlessly embedded C++ programs and parallel computing techniques are implemented to speed up the computing processes.
Given expression data this package calculate a multivariate
geometrical characterization of the differential expression and can also
perform gene-set enrichment.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Kriging based methods are used for predicting functional data
(curves) with spatial dependence.
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Convert data to 'geoJSON' or 'topoJSON' from various R classes,
including vectors, lists, data frames, shape files, and spatial
classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos',
but rather aims to be a high level client to simplify conversions of data
from and to 'geoJSON' and 'topoJSON'.
Processes gridded datasets found on the
U.S. Geological Survey Geo Data Portal web application or elsewhere,
using a web-enabled workflow that eliminates the need to download and store
large datasets that are reliably hosted on the Internet. The package provides
access to several data subset and summarization algorithms that are available
on remote web processing servers.
Provides basic functions for global
positioning based on light intensity measurements over time.
Positioning process includes the determination of sun events, a
discrimination of residency and movement periods, the
calibration of period-specific data and, finally, the
calculation of positions.
Set of routines for making Map Projections (forward and inverse), Topographic Maps, Perspective plots, Geological Maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Set of data for use in package GEOmap.  Includes world
map, USA map, Coso map, Japan Map, ETOPO5
Makes the qhull library (www.qhull.org)
available in R, in a similar manner as in Octave and MATLAB. Qhull
computes convex hulls, Delaunay triangulations, halfspace
intersections about a point, Voronoi diagrams, furthest-site
Delaunay triangulations, and furthest-site Voronoi diagrams. It
runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the
Quickhull algorithm for computing the convex hull. Qhull does not
support constrained Delaunay triangulations, or mesh generation of
non-convex objects, but the package does include some R functions
that allow for this. Currently the package only gives access to
Delaunay triangulation and convex hull computation.
Network visualization in the 'ggplot2' framework. Network
functionality is provided in a single 'ggplot2' layer by calling the geom 'net'.
Layouts are calculated using the 'sna' package, example networks are included.
Read, manipulate, and digitize landmark data, generate shape
variables via Procrustes analysis for points, curves and surfaces, perform
shape analyses, and provide graphical depictions of shapes and patterns of
shape variation.
Code for querying the web service at www.geonames.org
Codes for analyzing various problems of Geophysics, Continuum Mechanics and  Mogi Models.
Geostatistical analysis including traditional, likelihood-based and Bayesian methods.
Functions for inference in generalised linear spatial models. The posterior and predictive inference is based on Markov chain Monte Carlo methods. Package geoRglm is an extension to the package geoR, which must be installed first.
The georob package provides functions for efficiently fitting
linear models with spatially correlated errors by robust and
Gaussian (Restricted) Maximum Likelihood and for computing robust
and customary point and block external-drift kriging predictions,
along with utility functions for variogram modelling
in ad-hoc geostatistical analyses, model building, model evaluation
by cross-validation and for unbiased back-transformation of kriging
predictions of log-transformed data.
Function for adding the geological timescale to bivariate plots.
Generates distance matrices from shape files and represents spatially weighted multilevel analysis results (see 'spacom')
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis
functions (optimization, prediction and cross-validation), summary
statistics from cross-validation, pocket plot, and design of
optimal sampling networks through sequential and simultaneous
points methods.
Spatio-temporal: Inverse Distance Weighting (IDW) and radial basis functions; optimization, prediction, summary statistics from leave-one-out cross-validation, adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
Geostatistical modelling facilities using Raster and SpatialPoints objects are provided.  Non-Gaussian models are fit using INLA, and Gaussian geostatistical models use Maximum Likelihood Estimation.
It analyzes raster maps and other information as input/output
files from the Hydrological Distributed Model GEOtop. It contains functions
and methods to import maps and other keywords from geotop.inpts file. Some
examples with simulation cases of GEOtop 2.0 are presented in the package.
Any information about the GEOtop Distributed Hydrological Model source code
is available on http://code.google.com/p/geotop/ or
https://github.com/se27xx/GEOtop. Technical details about the model are
available in Endrizzi et al, 2014
(http://www.geosci-model-dev.net/7/2831/2014/gmd-7-2831-2014.html).
GeoXp is a tool for researchers in spatial statistics,
spatial econometrics, geography, ecology etc allowing to link
dynamically statistical plots with elementary maps. This
coupling consists in the fact that the selection of a zone on
the map results in the automatic highlighting of the
corresponding points on the statistical graph or reversely the
selection of a portion of the graph results in the automatic
highlighting of the corresponding points on the map. GeoXp
includes tools from different areas of spatial statistics
including geostatistics as well as spatial econometrics and
point processes. Besides elementary plots like boxplots,
histograms or simple scatterplot, GeoXp also couples with maps
Moran scatterplots, variogram cloud, Lorentz Curves,...In order
to make the most of the multidimensionality of the data, GeoXp
includes some dimension reduction techniques such as PCA.
The package allows geometric objects defined in geozoo to be
displayed in the R package tourr
Estimation and diagnosis of the convergence of Generalized
Exponential Random Graph Models (GERGM) via Gibbs sampling or Metropolis
Hastings with exponential down weighting.
Provides programmatic access to the GESIS - Leibniz-Institute for
the Social Sciences Data Catalogue/Datenbestandkatalog (DBK), which
maintains a large repository of data sets related to the social sciences.
The Gene Expression State Transformation (GESTr) models
the states of expression of genes across a compendium of
samples in order to provide a universal scale of gene
expression for all genes. TranSAM is a modification of the SAM
approach designed to utilise GESTr-transformed gene expression
data.
Hydrologic models often require users to collect and format input meteorological data. This package contains functions for sourcing, formatting, and
editing meteorological data for hydrologic modeling purposes.
Package designed to be used with Rscript to write
``#!'' shebang scripts that accept short and long flags/options.
Many users will prefer using instead the packages optparse or argparse
which add extra features like automatically generated help option and usage,
support for default values, positional argument support, etc.
This is yet another command-line argument parser which wraps the
powerful Perl module Getopt::Long and with some adaptation for easier use
in R. It also provides a simple way for variable interpolation in R.
The GetR package calculates Guttman error trees, which can
be used to find homogeneous subgroups regarding Guttman errors.
Automated multi-path General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.
Getting to the Bottom accompanies the "Getting to
the Bottom" optimization methods series at Statisticsviews.com.  It
contains data and code to reproduce the examples in the articles.
Implements a flexible nonlinear modelling framework for nonstationary
generalized extreme value analysis in hydroclimatology.
Package for simulation and estimation of ARMA-GARCH/APARCH models with GEV and stable distributions.
Perform statistical tests to unveil genomic clusters,
produces garphical interpretations of the statistical results
in pdf files, perform a Gene Ontology analysis and produces
graphic results in pdf files
The gfcanalysis package supports analyses using the Global
Forest Change dataset released by Hansen et al. gfcanalysis was
written for the Tropical Ecology Assessment and Monitoring (TEAM) Network
(http://www.teamnetwork.org). For additional details on the Global Forest
Change dataset, see: Hansen, M. et al. 2013. "High-Resolution Global Maps
of 21st-Century Forest Cover Change." Science 342 (15 November): 850-53.
The forest change data and more information on the product is available at
http://earthenginepartners.appspot.com.
Implemented are the Wald-type statistic,
a permuted version thereof as well as the ANOVA-type statistic
for general factorial designs, even with non-normal error terms
and/or heteroscedastic variances, for crossed designs with an
arbitrary number of factors and nested designs with up to three factors.
The R package \href{http://docs.ggplot2.org/current/}{ggplot2}
is a plotting system based on the grammar of graphics. \href{https://
ggobi.github.io/ggally}{GGally} extends ggplot2 by adding several functions to
reduce the complexity of combining geoms with transformed data. Some of these
functions include a pairwise plot matrix, a scatterplot plot matrix, a parallel
coordinates plot, a survival plot, and several functions to plot networks.
This is a set of tools for dendrograms and
tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
clearly separate data from the presentation.
Unfortunately the plot method for dendrograms plots
directly to a plot device without exposing the data.
The 'ggdendro' package resolves this by making available
functions that extract the dendrogram plot data. The package
provides implementations for tree, rpart, as well as diana and agnes
cluster diagrams.
Create biplots for GGE (genotype plus genotype-by-environment) and
GGB (genotype plus genotype-by-block-of-environments) models.
A GUI with which to construct and interact with GGE biplots.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Uses the Grammar of Graphics.
Collection of functions and layers to enhance 'ggplot2'. The main
function is ggMarginal(), which can be used to add marginal
histograms/boxplots/density plots to 'ggplot2' scatterplots.
Unified plotting tools for statistics commonly used, such as GLM,
time series, PCA families, clustering and survival analysis. The package offers
a single plotting interface for these analysis results and plots in a unified
style using 'ggplot2'.
A tool to process and analyse data collected with wearable raw acceleration sensors. The package has been developed and tested for binary data from GENEActiv and GENEA devices and .csv-export data from  Actigraph devices. These devices are currently widely used in research on human daily physical activity.
This package implements a unified algorithm, blockwise-majorization-decent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM.
Functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.
A collection of functions to visualize spatial data and models
on top of static maps from various online sources (e.g Google Maps and Stamen
Maps). It includes tools common to those tasks, including functions for
geolocation and routing.
Tools for assessing and diagnosing convergence of
Markov Chain Monte Carlo simulations, as well as for graphically display
results from full MCMC analysis. The package also facilitates the graphical
interpretation of models by providing flexible functions to plot the
results against observed variables.
Graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
Create hammock plots, parallel sets, and common angle plots
with ggplot2.
An implementation of the grammar of graphics in R. It combines the
advantages of both base and lattice graphics: conditioning and shared axes
are handled automatically, and you can still build up a plot step by step
from multiple data sources. It also implements a sophisticated
multidimensional conditioning system and a consistent interface to map
data to aesthetic attributes. See http://ggplot2.org for more information,
documentation and examples.
A dataset about movies. This was previously contained in ggplot2,
but has been moved its own package to reduce the download size of ggplot2.
Graphic elements for exploring Random Forests using the
randomForestSRC package for survival, regression and classification forests and
ggplot2 package plotting.
Adds north symbols (18 options) and scale bars in kilometers to maps in geographic or metric coordinates created with 'ggplot2' or 'ggmap'.
ggsubplot makes it easy to embed customized
subplots within larger graphics. Subplots may be used
as a geom to explore interaction effects, spatial data,
and hierarchical data. Subplots can also be used to
explore big data without overplotting.
Offers various swiss maps as ggplot2 objects and gives the
possibility to add layers of data on the maps. Data are publicly available
from the swiss federal statistical office.
Extends the functionality of 'ggplot2', providing the capability to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally, 'ggtern' has implemented several NEW geometries which are unavailable to the standard 'ggplot2' release. For further examples and documentation, please proceed to the 'ggtern' website.
Some extra themes, geoms, and scales for `ggplot2`.
Provides `ggplot2` themes and scales that replicate the look of plots
by Edward Tufte, Stephen Few, `Fivethirtyeight`, `The Economist`, `Stata`,
`Excel`, and `The Wall Street Journal`, among others.
Provides `geoms` for Tufte's box plot and range frame.
An implementation of an interactive grammar of graphics, taking the
best parts of 'ggplot2', combining them with the reactive framework from
'shiny' and web graphics from 'vega'.
The GhcnDaily package provides the core functions required
to download and format the GHCN Daily Data (TMax and Tmin) into
Monthly datasets. It is currently restricted to operating with
temperature data from the daily files.
The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.
This package provides detailed functionality for working
with the univariate and multivariate Generalized Hyperbolic
distribution and its special cases (Hyperbolic (hyp), Normal
Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
and Gaussian distribution). Especially, it contains fitting
procedures, an AIC-based model selection routine, and functions
for the computation of density, quantile, probability, random
variates, expected shortfall and some portfolio optimization
and plotting routines as well as the likelihood ratio test. In
addition, it contains the Generalized Inverse Gaussian
distribution.
Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets.
Gibbs sampler for one-way linear mixed-effects models
(ANOVA, ANCOVA) with homoscedasticity of errors and uniform
priors.
This package provides two generic functions for performing
Markov chain sampling in a naive way for a user-defined target
distribution, which involves only continuous variables. The
function "gibbs_met" performs Gibbs sampling with each
1-dimensional distribution sampled with Metropolis update using
Gaussian proposal distribution centered at the previous state.
The function "met_gaussian" updates the whole state with
Metropolis method using independent Gaussian proposal
distribution centered at the previous state. The sampling is
carried out without considering any special tricks for
improving efficiency. This package is aimed at only routine
applications of MCMC in moderate-dimensional problems.
Generator and density function for the
Generalized Inverse Gaussian (GIG) distribution.
GillespieSSA provides a simple to use, intuitive, and
extensible interface to several stochastic simulation
algorithms for generating simulated trajectories of finite
population continuous-time model. Currently it implements
Gillespie's exact stochastic simulation algorithm (Direct
method) and several approximate methods (Explicit tau-leap,
Binomial tau-leap, and Optimized tau-leap). The package also
contains a library of template models that can be run as demo
models and can easily be customized and extended. Currently the
following models are included, decaying-dimerization reaction
set, linear chain system, logistic growth model, Lotka
predator-prey model, Rosenzweig-MacArthur predator-prey model,
Kermack-McKendrick SIR model, and a metapopulation SIRS model.
Automated identification and estimation of group- and
individual-level relations in time series data from within a structural
equation modeling framework.
We provide a set of functions to retrieve information about GIMMS
NDVI3g files currently available online; download and re-arrange the bi-monthly
datasets according to creation time; import downloaded files from native binary
(ENVI) format directly into R based on the widely applied 'raster' package;
calculate monthly value composites (e.g. maximum value composites, MVC) from the
bi-monthly input data; and derive long-term monotonous trends from the
Mann-Kendall trend test, optionally featuring pre-whitening to
account for lag-1 autocorrelation.
Maximum likelihood estimation under relational models, with or without the overall effect.
Supply classes and methods to represent and manipulate
graphs
Some mapping and spatial data manipulation tools - in particular
drawing choropleth maps with nice looking legends,  and aggregation of point
data to polygons.
Work with 'GitHub' 'gists' from 'R' (e.g., http://en.wikipedia.org/
wiki/GitHub#Gist, https://help.github.com/articles/about-gists/). A 'gist'
is simply one or more files with code/text/images/etc. This package allows
the user to create new 'gists', update 'gists' with new files, rename files,
delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists',
open a 'gist' in your default browser, get embed code for a 'gist', list
'gist' 'commits', and get rate limit information when 'authenticated'. Some
requests require authentication and some do not. 'Gists' website: https://
gist.github.com/.
Interface to the 'libgit2' library, which is a pure C
implementation of the 'Git' core methods. Provides access to 'Git'
repositories to extract data and running some basic 'Git'
commands.
Provides R functions to access the API of the project
and repository management web application gitlab.
For many common tasks (repository file access,
issue assignment and status, commenting) convenience wrappers
are provided, and in addition the full API can be used by specifying request locations.
Gitlab is open-source software and can be self-hosted or used on gitlab.com.
The goal of this package is to allow users to robustly
and quickly grid and quantify sizes of pinned colonies in plate images.
gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately.
It includes several image pre-processing techniques, such
as autorotation of plates, noise removal, contrast adjustment and image
resizing.
`Giza' offers a simple way of creating multiple pyramid
plots in one graphics window, exploiting the power of the
lattice package. It is a handy way of visualizing longitudinal
grouped (i.e.: age- and education-structured) data.
Imports the 'gkmSVM' v2.0 functionalities into R (www.beerlab.org/gkmsvm). It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
Efficient design matrix free procedure for Lasso regularized estimation in large scale 3-dimensional generalized linear array models. The Gaussian model with identity link, the Binomial model with logit link, the Poisson model with log link and the Gamma model with log link is currently implemented.
Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Enables calculation of image textures derived from grey-level
co-occurrence matrices (GLCMs). Supports processing images that cannot
fit in memory.
The  generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form.
This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different parameterisations of the distribution.
It provides the density function, distribution function, and Quantile-Quantile plots.
It implements a variety of estimation methods for the distribution, including diagnostic plots.
Estimation methods include the starship (all 4 parameterisations) and a number of methods for only the FKML parameterisation.
These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, L-Moments, Trimmed L-Moments and Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.
The generalized lambda distribution (GLD) is a versatile
distribution that can accommodate a wide range of shapes,
including fat-tailed and asymmetric distributions. This package
implements a more intuitive parameterization of the GLD that
expresses the location and scale parameters directly as the
median and inter-quartile range of the distribution. The
remaining two shape parameters characterize the asymmetry and
steepness of the distribution respectively. The fitting of the
GLD to empirical data can be reduced to a two-parameter
estimation problem where the location and scale parameters are
estimated by their robust sample estimators. Moreover, the
parameterization can be used to compare data sets in a
convenient asymmetry and steepness shape plot. The underline C
routines are written such that compilers that support
vectorized mathematical operations can automatically vectorize
the most time consuming loops (tested with icc 12.1.0). The
package includes the usual distribution functions, fitting
routines and a shape plot function.
Owing to the rich shapes of GLDs, GLD standard/quantile regression is a competitive flexible model compared to standard/quantile regression.  The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. The goodness of fit of the proposed model can be assessed via QQ plots and the Kolmogorov-Smirnov test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2014).
Routines for log-linear models of incomplete contingency tables,
including some latent class models, via EM and Fisher scoring
approaches.  Allows bootstrapping.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm
Fits generalized linear models where the parameters are
subject to linear constraints. The model is specified by giving
a symbolic description of the linear predictor, a description
of the error distribution, and a matrix of constraints on the
parameters.
Distributed training and prediction of generalized linear models using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
This package contains functions to perform generalized
linear mixed Dirichlet models using posterior simulation.
We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.
Efficient algorithms for fitting regularization paths for
linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Write bugs model files for hierarchical and spatial models, arranges unbalanced data in ragged arrays, and creates starting values.
Solve Generalized Linear Mixed Models using Gauss-Seidel
algorithm
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.
Binomial and Poisson regression for clustered data, fixed
and random effects with bootstrapping.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
This packages includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets
where the number of covariates exceeds the sample size or when there is collinearity among the covariates. This package uses the glmnet package
fitting algorithm.
A path-following algorithm for L1 regularized generalized
linear models and Cox proportional hazards model
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
Automated model selection and model-averaging. Provides a
wrapper for glm and other functions, automatically generating
all possible models (under constraints set by the user) with
the specified response and explanatory variables, and finding
the best models in terms of some Information Criterion (AIC,
AICc or BIC). Can handle very large numbers of candidate
models. Features a Genetic Algorithm to find the best models
when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
including parametric links and heteroskedastic latent variables.
'globalboosttest' implements a permutation-based testing
procedure to globally test the (additional) predictive value of
a large set of predictors given that a small set of predictors
is already available. Currently, 'globalboosttest' supports
binary outcomes (via logistic regression) and survival outcomes
(via Cox regression). It is based on boosting regression as
implemented in the package 'mboost'.
permutation based global test with deviance as test statistic
Initial metabolic networks often inaccurately predict in-silico growth or non-growth if compared to in-vivo data. This package refines metabolic network models by making networks changes (i.e., removing, adding, changing reversibility of reactions; adding and removing biomass metabolites) and simultaneously matching sets of experimental growth and non-growth data (e.g., KO-mutants, mutants grown under different media conditions,...)
Implementation of three different Gene set analysis (GSA) algorithms for combining the individual pvalues of a set of genetic variats (SNPs) in a gene level pvalue. The implementation includes the selection of the best inheritance model for each SNP.
It provides more controls on the option values such as validation
and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown") objects in R expressions by code
inspection using various strategies, e.g. conservative or liberal. The objective
of this package is to make it as simple as possible to identify global objects
for the purpose of exporting them in distributed compute environments.
Tools for the generalized logistic distribution (Type I,
also known as skew-logistic distribution), encompassing
basic distribution functions (p, q, d, r, score), maximum
likelihood estimation, and structural change methods.
R Interface to C API of GLPK, needs GLPK Version >= 4.42
Functions to conduct four generalized logrank tests and a score test under a proportional hazards model
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
Useful utilities in N-glycan data analysis. This package tries
to fill the gap in N-glycan data analysis by providing easy
to use functions for glycomics analysis
(see https://en.wikipedia.org/wiki/Glycomics for more
details on Glycomics). At the moment glycanr is mostly oriented
to data obtained by UPLC and LCMS analysis of Plasma and IgG glycome.
An interface to the Gmail RESTful API.  Allows access to your
Gmail messages, threads, drafts and labels.
Get distance and travel time between two points from Google Maps.  Four possible modes of transportation (bicycling, walking, driving and public transportation).
A general framework for utilizing R to harness the power of NVIDIA GPU's. The "gmatrix" and "gvector" classes allow for easy management of the separate device and host memory spaces. Numerous numerical operations are implemented for these objects on the GPU. These operations include matrix multiplication, addition, subtraction, the kronecker product, the outer product, comparison operators, logical operators, trigonometric functions, indexing, sorting, random number generation and many more.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
Copula Models.
Functions and a graphical user interface for graphical described multiple test procedures.
GMD is a package for non-parametric distance measurement between
two discrete frequency distributions.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
Tools for making the descriptive "Table 1" used in medical articles,
a transition plot for showing changes between categories,
a method for variable selection based on the SVD,
Bézier lines with arrows complementing the ones in the 'grid' package,
and more.
It is a complete suite to estimate models based on moment
conditions. It includes the two step Generalized method of
moments (GMM) of Hansen(1982), the iterated GMM and continuous
updated estimator (CUE) of Hansen-Eaton-Yaron(1996) and several
methods that belong to the Generalized Empirical Likelihood
(GEL) family of estimators, as presented by Smith(1997),
Kitamura(1997), Newey-Smith(2004) and Anatolyev(2005).
Likelihood-based Boosting for Generalized mixed models
An implementation of maximum simulated likelihood method for the
estimation of multinomial logit models with random coefficients.
Specifically, it allows estimating models with continuous heterogeneity
such as the mixed multinomial logit and the generalized multinomial logit.
It also allows estimating models with discrete heterogeneity such as the
latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Multiple Precision Arithmetic (big integers and rationals,
prime number tests, matrix computation), "arithmetic without limitations"
using the C library GMP (GNU Multiple Precision Arithmetic).
Interface between the GMT map-making software and R,
enabling the user to manipulate geographic data within R and call GMT commands
to draw and annotate maps in postscript format. The 'gmt' package is about
interactive data analysis, rapidly visualizing subsets and summaries of
geographic data, while performing statistical analysis in the R console.
Direct R interface to Support Vector Machine libraries ('LIBSVM' and 'SVMLight') and efficient C++ implementations of Growing Neural Gas and models developed by 'GMUM' group (Cross Entropy Clustering and 2eSVM).
Generalized Method of Wavelet Moments (GMWM) is an estimation technique for the parameters of time series models. It uses the wavelet variance in a moment matching approach that makes it particularly suitable for the estimation of certain state-space models. Furthermore, there exists a robust implementation of GMWM, which allows the robust estimation of some state-space models and ARIMA models. Lastly, the package provides the ability to quickly generate time series data, perform different wavelet decompositions, and visualizations.
Generalized Mann-Whitney type tests based on probabilistic
indices and new diagnostic plots.
Provide functions to compute standard and generalized Nash Equilibria. Optimization methods are available nonsmooth reformulation, fixed-point formulation, minimization problem and constrained-equation reformulation.
Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others.  Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.
This package performs generalized non-negative matrix factorization based on Renyi divergence.
Read data files readable by gnumeric into R. Can read
whole sheet or a range, from several file formats, including
the native format of gnumeric. Reading is done by using
ssconvert (a file converter utility included in the gnumeric
distribution http://projects.gnome.org/gnumeric/) to convert
the requested part to CSV. From gnumeric files (but not other
formats) can list sheet names and sheet sizes or read all
sheets.
A collection of functions to solve weighted and lexicographical
goal programming problems as specified by Lee (1972) and Ignizio (1976).
Implementation of model-checking techniques for generalized linear
models and linear structural equation models based on cumulative residuals
Several GoF tests for Copulae are provided. A new hybrid test is implemented which supports all of the individual tests. Estimation methods for the margins are provided. All the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau.
Tests of goodness-of-fit based on a kernel smoothing of the data.
Tests of fit for Gamma, inverse Gaussian, normal, multivariate normal and generalized Pareto distributions. Parameter estimators for the Gamma, inverse Gaussian and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
for continuous univariate distributions, using
efficient algorithms.
Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.
Implementation of the GO-GARCH model class
Create R functions that interact with OAuth2 Google APIs easily,
with auto-refresh and Shiny compatibility.
Provides a collection of functions designed for working with 'Google Public Data Explorer'. Automatically builds up the corresponding DSPL (XML) metadata files and CSV files; compressing all the files and leaving them ready to be published on the 'Public Data Explorer'.
R interface to Google Charts API, allowing users
to create interactive charts based on data frames. Charts
are displayed locally via the R HTTP help server. A modern
browser with Internet connection is required and for some
charts a Flash player. The data remains local and is not
uploaded to Google.
Implementation of multilayered visualizations for enhanced graphical
representation of functional analysis data. It combines and integrates omics
data derived from expression and functional annotation enrichment analyses.
Its plotting functions have been developed with an hierarchical structure in
mind: starting from a general overview to identify the most enriched
categories (modified bar plot, bubble plot) to a more detailed one displaying
different types of relevant information for the molecules in a given set of
categories (circle plot, chord plot, cluster plot, Venn diagram).
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate regression models
An interface to the Python package Goslate (Version 1.5.0).
Goslate provides an API to Google's free online language translation service
by querying the Google translation website.
See <https://bitbucket.org/zhuoqiang/goslate/> for more information about the Python
package.
This package purposes to deal with public survey data of
Japanese government via their Application Programming Interface
(http://statdb.nstac.go.jp/)
Produces a generalized pairs (gpairs) plot.
Gaussian process regression models, a.k.a. kriging models, are
applied to global multiobjective optimization of black-box functions.
Multiobjective Expected Improvement and Stepwise Uncertainty Reduction
sequential infill criteria are available. A quantification of uncertainty
on Pareto fronts is provided using conditional simulations.
Gradient Projection Algorithm Rotation for Factor Analysis. See ?GPArotation.Intro for more details.
A generalized polynomial chaos expansion of a model taking as input independent random variables is achieved. A statistical and a global sensitivity analysis of the model are also carried out.
This package implements guided principal components analysis for the detection of batch effects in high-throughput data.
General polygon clipping routines for R based on Alan
Murta's C library
This package implements an extension of principal component analysis (PCA) tailored to handle multiple data tables. It can handle Big Data in the sense that the variation in massive data can be described by intervals [a, b] and multiple tables.
Functions implement collocation-inference for
stochastic process driven by distributed delay differential equations.
They also provide tools for selecting the lags for distributed delay
using shrinkage methods, estimating time-varying coefficients,
and tools for inference and prediction.
This package computes the bootstrap goodness-of-fit test
for the generalized Pareto distribution by Villasenor-Alva and
Gonzalez-Estrada (2009). The null hypothesis includes heavy and
non-heavy tailed gPd's. A function for fitting the gPd to data
using the parameter estimation methods proposed in the same
article is also provided.
Use functional regression as the mean structure and Gaussian Process as the covariance structure.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Functions for estimating generalized partial linear models
Various R programming tools for plotting data, including:
- calculating and plotting locally smoothed summary functionals
('bandplot', 'wapply'),
- enhanced versions of standard plots ('barplot2', 'boxplot2',
'heatmap.2', 'smartlegend'),
- manipulating colors ('col2hex', 'colorpanel', 'redgreen',
'greenred', 'bluered', 'redblue', 'rich.colors'),
- calculating and plotting two-dimensional data summaries ('ci2d',
'hist2d'),
- enhanced regression diagnostic plots ('lmplot2', 'residplot'),
- formula-enabled interface to stats::lowess function ('lowess'),
- displaying textual data in plots ('textplot', 'sinkplot'),
- plotting a matrix where each cell contains a dot whose size
reflects the relative magnitude of the elements ('balloonplot'),
- plotting Venn diagrams ('venn'),
- displaying Open-Office style plots ('ooplot'),
- plotting multiple data sets on same region, with separate axes
('overplot'),
- plotting means and confidence intervals ('plotCI', 'plotmeans'),
- spacing points in an x-y plot so they don't overlap ('space').
Combining a generalized linear model with an additional tree part
on the same scale. A four-step procedure is proposed to fit the model and test
the joint effect of the selected tree part while adjusting on confounding factors.
We also proposed an ensemble procedure based on the bagging to improve prediction
accuracy and computed several scores of importance for variable selection.
This package contains tools for studying genotype-phenotype (GP) maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al. (2003) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Front. Genet. 4:216. doi: 10.3389/fgene.2013.00216 [\href{http://www.frontiersin.org/Journal/10.3389/fgene.2013.00216/full}{link}]
This package provides a minimalistic functionality necessary to apply Gaussian Process in R. They provide a selection of functionalities of GPML Matlab library.
Functional enrichment analysis, gene identifier conversion
and mapping homologous genes across related organisms via the
'g:Profiler' toolkit (http://biit.cs.ut.ee/gprofiler/).
Some functions for modeling sequence read counts as a
generalized poisson model and to use this model for detecting
differentially expressed genes in different conditions and
differentially spliced exons.
The gptk package implements a general-purpose toolkit for Gaussian
process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc).
Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.
Provides GPU enabled functions for R objects in a simple and
approachable manner.  New gpu* and vcl* classes have been provided to
wrap typical R objects (e.g. vector, matrix), in both host and device
spaces, to mirror typical R syntax without the need to know OpenCL.
Provides R interfaces to a handful of common
functions implemented using the Nvidia CUDA toolkit. Some of the
functions require at least GPU Compute Capability 1.3.
Thanks to Craig Stark at UC Irvine for donating time on his lab's Mac.
Maximum likelihood estimators are obtained via an EM algorithm.
Nucleic acid sequences that are rich in guanine are capable of forming highly stable four-stranded structures known as G quadruplex motifs. This package provide functions for predicting G quadruplex motif(s) in nucleotide sequences.
Use the graph-constrained estimation (Grace) procedure to estimate graph-guided linear regression coefficients and use the Grace and GraceR tests to perform graph-guided hypothesis test on the association between the response and the predictor.
Provides functions for matching student-answers to teacher answers for a variety of data types.
Functions to fit, visualise and compare Gaussian random field species distribution models.
Probability propagation in graphical independence networks, also known as Bayesian networks or probabilistic expert systems.
A native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.
Represents metagenomic samples on the Euclidean space to examine similarity amongst samples by studying clusters in the model. Given the matrix of metagenomic counts for samples, this package (1) quantifies dissimilarity between samples using Kendall's tau-distance, (2) constructs multidimensional models of different dimension, and (3) plots the models for visualization and comparison.
gRapfa is for modelling discrete longitudinal data using acyclic probabilistic finite automata (APFA). The package contains functions for constructing APFA models from a given data using penalized likelihood methods. For graphical display of APFA models, gRapfa depends on 'igraph package'. gRapfa also contains an interface function to Beagle software that implements an efficient model selection algorithm.
gRapHD is designed for efficient selection of high-dimensional undirected
graphical models. The package provides tools for selecting trees, forests
and decomposable models minimizing information criteria such as AIC or BIC,
and for displaying the independence graphs of the models. It has also some
useful tools for analysing graphical structures. It supports the use of
discrete, continuous, or both types of variables.
A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
Estimates within and between time point interactions in experience sampling data, using the Graphical VAR model in combination with LASSO and EBIC.
The package provides functions to generate
graphics files, compare them with ``model'' files,
and report the results.
This package implements a histogram principal components analysis. It provides numerical and graphical tools to deal with big data.
Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.
Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U
Create graticule lines and labels for maps. Control the creation of
lines by setting their placement (at particular meridians and parallels) and extent
(along parallels and meridians). Labels are created independently of lines.
The gRbase package provides general features
which are used by other graphical modelling packages, in particular
by the packages gRain, gRim and gRc.

gRbase contains several datasets relevant for use in connection with
graphical models. Almost all datasets used in the book Graphical
Models with R (2012) are contained in gRbase.

gRbase implements several graph algorithms (based mainly on
representing graphs as adjacency matrices - either in the form
of a standard matrix or a sparse matrix). Some graph
algorithms are:
(i) maximum cardinality search (for marked and unmarked graphs).
(ii) moralize.
(iii) triangulate.
(iv) junctionTree.

gRbase facilitates array operations,

gRbase implements functions for testing for conditional independence.

gRbase illustrates how hierarchical log-linear models (hllm) may be
implemented and describes concept of gmData (graphical meta
data). These features, however, are not maintained anymore and
remains in gRbase only because there exists a paper describing
these facilities: A Common Platform for Graphical Models in R:
The gRbase Package, Journal of Statistical Software, Vol 14,
No 17, 2005.

NOTICE Proper functionality of gRbase requires that the packages graph,
Rgraphviz and RBGL are installed from bioconductor; for
installation instructions please refer to the webpage given below.
Estimation, model selection and other aspects of
statistical inference in Graphical Gaussian models with edge
and vertex symmetries (Graphical Gaussian models with colours)
Contains many functions useful for
monitoring and reporting the results of clinical trials and other
experiments in which treatments are compared.  LaTeX is
used to typeset the resulting reports, recommended to be in the
context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
by 'greport' for high-level graphics.
Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.
Converts a spatial object from class SpatialGridDataFrame to
SpatialPolygonsDataFrame.
Functions for drawing scene trees representing
scenes that have been drawn using grid graphics.
Provides a number of user-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.
Functions to convert a page of plots drawn with the
graphics package into identical output drawn with the grid package.
The result looks like the original graphics-based plot, but consists
of grid grobs and viewports that can then be manipulated with
grid functions (e.g., edit grobs and revisit viewports).
Functions for drawing node-and-edge graphs that have been
laid out by graphviz.  This provides an alternative
rendering to that provided by the 'Rgraphviz' package, with
two main advantages:  the rendering provided by 'gridGraphviz'
should be more similar to what 'graphviz' itself would draw;
and rendering with 'grid' allows for post-hoc customisations
using the named viewports and grobs that 'gridGraphviz'
produces.
Functions to export graphics drawn with package grid to SVG
format.  Additional functions provide access to SVG features that
are not available in standard R graphics, such as hyperlinks,
animation, filters, masks, clipping paths, and gradient and pattern fills.
The function GriegSmith accepts either quadrat count data,
a point process object(ppp) or a matrix of x and y coordinates.
The function calculates a nested analysis of variance and
simulation envelopes.
gRaphical Interaction Models:

Models for for contingency tables (i.e. log-linear models)

Graphical Gaussian models for multivariate normal data (i.e. covariance selection
models)

Mixed interaction models
Functions for converting, importing, and drawing PostScript
pictures in R plots.
The program GRNN implements the algorithm proposed by
Specht (1991).
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models.
The package was developed to fit fit many growth curves
obtained under different conditions in order to derive a
conclusive dose-response curve, for instance for a compound
that potentially affects growth. grofit fits data to different
parametric models (function gcFitModel) and in addition
provides a model free spline fit (function gcFitSpline) to
circumvent systematic errors that might occur within
application of parametric methods.
Computing Gromov-Hausdorff type l^p distances for labeled metric spaces. These distances were introduced in V.Liebscher, Gromov meets Phylogenetics - new Animals for the Zoo of Metrics on Tree Space. preprint  arXiv:1504.05795,  for phylogenetic trees but may apply to much more situations.
Regression models for grouped and coarse data, under the
Coarsened At Random assumption.
An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.
A graphical user interface to compute group sequential designs
based on normally distributed test statistics, particularly critical
boundaries, power, drift, and confidence intervals of such designs. All
computations are based on the alpha spending approach by Lan-DeMets with
various alpha spending functions being available to choose among.
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
Employs a non-parametric formulation for by-subject random effect
parameters to borrow strength over a constrained number of repeated
measurement waves in a fashion that permits multiple effects per subject.
One class of models employs a Dirichlet process (DP) prior for the subject
random effects and includes an additional set of random effects that
utilize a different grouping factor and are mapped back to clients through
a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
A second class of models employs a dependent DP (DDP) prior for the subject
random effects that directly incorporates the multiple membership pattern.
Estimates a collection of time-indexed functions under
either of Gaussian process (GP) or intrinsic Gaussian Markov
random field (iGMRF) prior formulations where a Dirichlet process
mixture allows sub-groupings of the functions to share the same
covariance or precision parameters.  The GP and iGMRF formulations
both support any number of additive covariance or precision terms,
respectively, expressing either or both of multiple trend and
seasonality.
This is a simple package that fits the logistic equation to
microbial growth curve data (e.g., repeated absorbance measurements
taken from a plate reader over time). From this fit, a variety of
metrics are provided, including the maximum growth rate,
the doubling time, the carrying capacity, the area under the logistic
curve, and the time to the inflection point.
A compilation of nonlinear growth models used in many areas
A nonparametric empirical Bayes method for recovering
gradients (or growth velocities) from observations of smooth
functions (e.g., growth curves) at isolated time points.
Fits user specified (GLM-) models with Group Lasso penalty
The package implements the concave 1-norm and 2-norm group penalty in linear and logistic regression.
The concave 1-norm group penalty includes 1-norm group SCAD and 1-norm group MCP.
The concave 1-norm group penalty has bi-level selection features. That is it selects variables at group and individual levels with proper tuning parameters.
The concave 1-norm group penalty is robust to mis-specified group information.
The concave 2-norm group penalty includes 2-norm group SCAD and 2-norm group MCP. The concave 2-norm group penalty select variable at group level only.
The package can also fit group Lasso, which is a special case of concave 2-norm group penalty when the regularization parameter kappa equals zero.
The highly efficient (block) coordinate descent algorithm (CDA) is used to compute the solutions for both penalties in linear models.
The highly stable and efficient (block) CDA and minimization-majorization approach are used to compute the solution for both penalties in logistic models.
In the computation of solution surface, the solution path along kappa is implemented.
This provides a better solution path compared to the solution path along lambda.
The package also provides a tuning parameter selection method based on cross-validation for both linear and logistic models.
Efficient algorithms for fitting the regularization path of
linear or logistic regression models with grouped penalties.  This
includes group selection methods such as group lasso, group MCP, and
group SCAD as well as bi-level selection methods such as the group
exponential lasso, the composite MCP, and the group bridge.
Fit the regularization path of linear, logistic or poisson models with
overlapping grouped covariates based on the latent group lasso approach. Latent
group MCP/SCAD as well as bi-level selection methods, namely the group exponential
lasso and the composite MCP are also available. This package serves as an
extension of R package 'grpreg' (by Dr. Patrick Breheny <patrick-breheny@uiowa.edu>)
for grouped variable selection involving overlaps between groups.
Contains the tools to screen grouped variables, and select screened grouped variables afterwards. The main function grpss() can perform the grouped variables screening as well as selection for ultra-high dimensional data with group structure. The screening step is primarily used to reduce the dimensions of data so that the selection procedure can easily handle the moderate or low dimensions instead of ultra-high dimensions.
Functions to generate and analyze data for psychology
experiments based on the General Recognition Theory.
Offers functions for the comparison of Gutenberg-Richter
b-values. Several functions in GRTo are helpful for the assessment of the
quality of seismicity catalogs.
GSAgm is an R package that completes a self-contained gene set analysis (GSA) for RNA-seq and SNP data using the Gamma Method.
This package contains utility functions used by the Genome Analysis Toolkit (GATK) to load tables and plot data. The GATK is a toolkit for variant discovery in high-throughput sequencing data.
Write SARIMA models in (finite) AR representation and simulate
generalized multiplicative seasonal autoregressive moving average (time) series
with Normal / Gaussian, Poisson or negative binomial distribution.
Group Sequential Operating Characteristics for Clinical,
Bayesian two-arm Trials with known Sigma and Normal Endpoints.
gsDesign is a package that derives group sequential designs and describes their properties.
Robust Estimation of Multivariate Location and Scatter in the
Presence of Cellwise and Casewise Contamination and Missing Data.
Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.
Conducts a semi-gSEM statistical analysis (semi-supervised generalized structural equation modeling) on a data frame of coincident observations of multiple continuous variables, via two functions sgSEMp1() and  sgSEMp2(), representing fittings based on two statistical principles. Principle 1 determines the univariate relationships in the spirit of the Markovian process. The relationship between each pair of system elements, including predictors and the system level response, is determined with the Markovian property that assumes the value of the current predictor is sufficient in relating to the next level variable, i.e., the relationship is independent of the specific value of the preceding-level variable to the current predictor, given the current value. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the first-level predictors to the system level variable, such as, Time and unit level variables, acted on the system level variable collectively by an additive model. This collective additive model can be found with a generalized stepwise variable selection (using the step() function in R, which performs variable selection on the basis of AIC) and this proceeds iteratively.
calculate equivalence and futility boundaries based on the exact bivariate $t$ test statistics for group sequential designs in studies with equivalence hypotheses.
gsg (gam selection gradients) provides a unified approach to the regression analysis of selection from longitudinal data collected from natural populations.
Simple package to download Google Sheets using just the sharing link. Spreadsheets can be downloaded as a data frame, or as plain text to parse manually. Google Sheets is the new name for Google Docs Spreadsheets.
Global Soil Information Facilities - tools (standards and functions) and sample datasets for global soil mapping.
Family based association test via GEE Kernel Machine score test
An R wrapper for the special functions and quasi random number
generators of the Gnu Scientific Library
(http://www.gnu.org/software/gsl/).  See gsl-package.Rd for details of
overall package organization, and Misc.Rd for some functions that are
widely used in the package, and some tips on installation.
Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.
Tools rewritten in C for various smoothing tasks
A comprehensive package for structural multivariate
function estimation using smoothing splines.
A collection of routines for the estimation of copula density
and 2-D hazard function using smoothing splines.
We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions.
gsubfn is like gsub but can take a replacement function
or certain other objects instead of the replacement string.
Matches and back references are input to the replacement function and
replaced by the function output.   gsubfn can be used to split strings
based on content rather than delimiters and for quasi-perl-style string
interpolation. The package also has facilities for translating formulas
to functions and allowing such formulas in function calls instead of
functions.  This can be used with R functions such as apply, sapply,
lapply, optim, integrate, xyplot, Filter and any other function that
expects another function as an input argument or functions like cat
or sql calls that may involve strings where substitution is desirable.
Provides an interface to the Gibbs SeaWater (TEOS-10) C library, which derives from Matlab and other code written by WG127 (Working Group 127) of SCOR/IAPSO (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans).
Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.
Tools to make it easier to work with "tables" of grobs.
This package provides functions to calculate the
efficiencies (expected tests per unit) of hierarchical and
matrix group testing procedures.  Efficiencies can be
calculated in the presense of correlated responses under
multiple arrangements of clustesrs.  Efficiencies can also be
evaluated in the presense of test error.
Generalized Turnbull's estimator proposed by Dehghan and Duchesne
(2011).
Functions to assist in R programming, including:
- assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion',
'getDependencies', 'keywords', 'scat'),
- calculate the logit and inverse logit transformations ('logit', 'inv.logit'),
- test if a value is missing, empty or contains only NA and NULL values ('invalid'),
- manipulate R's .Last function ('addLast'),
- define macros ('defmacro'),
- detect odd and even integers ('odd', 'even'),
- convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'),
- perform a binary search ('binsearch'),
- sort strings containing both numeric and character components ('mixedsort'),
- create a factor variable from the quantiles of a continuous variable ('quantcut'),
- enumerate permutations and combinations ('combinations', 'permutation'),
- calculate and convert between fold-change and log-ratio ('foldchange',
'logratio2foldchange', 'foldchange2logratio'),
- calculate probabilities and generate random numbers from Dirichlet distributions
('rdirichlet', 'ddirichlet'),
- apply a function over adjacent subsets of a vector ('running'),
- modify the TCP\_NODELAY ('de-Nagle') flag for socket objects,
- efficient 'rbind' of data frames, even if the column names don't match ('smartbind'),
- generate significance stars from p-values ('stars.pval'),
- convert characters to/from ASCII codes.
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. This package provides with an implementation of the Game-Theoretically OPtimal (GTOP) reconciliation method proposed in van Erven and Cugliari (2015), which is guaranteed to only improve any given set of forecasts. This opens up new possibilities  for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level.
An interface for retrieving and displaying the information returned
online by Google Trends is provided. Trends (number of hits) over the time as
well as geographic representation of the results can be displayed.
Assorted tools for genetic association analyses.  The
current focus is on implementing (either exactly or
approximately) regression analyses using summary statistics
instead of using subject-specific data.  So far, functions
exist to support multi-SNP risk score analyses, multi-SNP
conditional regression analyses, and multi-phenotype analyses,
using summary statistics.  There are helper functions for
reading and manipulating subject-specific genotype data, which
provide a platform for calculating the summary statistics, or
for using R to conduct other analyses not supported by specific
GWAS analysis tools.
Provides an interface to the Open Platform's Content API of the Guardian Media Group. It retrieves content from news outlets The Observer, The Guardian, and guardian.co.uk from 1999 to current day
This package comprises maps of France in 1830, multivariate data from A.-M. Guerry and others, and statistical and
graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
development of statistical and graphic methods for multivariate data in a geo-spatial context of historical interest.
The GUILDS package combines a range of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and  the guilds sampling formula conditioned on guild size (Janzen et al. 2015).
Show graphically the results of profiling R functions by tracking their execution time.
Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).
Generalized UniFrac distance for comparing microbial
communities. Permutational multivariate analysis of variance
using multiple distance matrices.
Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood.
Several tools for Global Value Chain ('GVC') analysis are
implemented.
Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.
Functions for genome-wide association/interaction analysis and rare variant analysis on a continuous/dichotomous trait using family data, and for making genome-wide p-value plot and QQ plot.
This package contains a function to do exact
Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
as typically obtained in a Genome Wide Association Study
(GWAS).
This package provides functions to calculate the
significance threshold for controlling the type I error rate in
mixed-model association mapping analyses.
Based on calculations of 758 women this package calculates
positive predictive values (PPV) and negative predictive values
(NPV) for inadequate and excessive gestational weight gain
(GWG) for different prevalences for different BMI categories.
gWidgets provides a toolkit-independent API for building interactive GUIs. At least one of the 'gWidgetsXXX packages', such as gWidgetstcltk, needs to be installed. Some icons are on loan from the scigraphica project http://scigraphica.sourceforge.net.
Re-implementation of gWidgets API. The API is defined in this
package. A second, toolkit-specific package is required to use it. There
are three in development: 'gWidgets2RGtk2', 'gWidgets2Qt', and 'gWidgets2tcltk'.
Port of the gWidgets2 API for the tcltk
package.
Port of the gWidgets API to the tcltk package. Requires Tk 8.5 or greater.
Fit a geographically weighted logistic elastic net regression.
In GWmodel, we introduce techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
Statistical functions to fit, validate and describe a Generalized
Waring Regression Model (GWRM).
Fits geographically weighted regression (GWR) models and
has tools to diagnose and remediate collinearity in the GWR
models. Also fits geographically weighted ridge regression
(GWRR) and geographically weighted lasso (GWL) models.
Estimate genome-wide significance using extrapolation approach
Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
SNP-set association testing for censored phenotypes in the presence of intrafamilial correlation.
R scripting functionality for H2O, the open source
math engine for big data that computes parallel distributed
machine learning algorithms such as generalized linear models,
gradient boosting machines, random forests, and neural networks
(deep learning) within various cluster environments.
S4 Interface to the 'HDF5' library supporting fast storage and
retrieval of R-objects like vectors, matrices and arrays to binary files in
a language independent format. The 'HDF5' format can therefore be used as
an alternative to R's save/load mechanism. Since h5 is able to access only
subsets of stored data it can also handle data sets which do not fit into
memory.
A Haar-Fisz algorithm for Poisson intensity estimation
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Provides a framework for writing map/reduce scripts for
use in Hadoop Streaming. Also facilitates operating on data in
a streaming fashion, without Hadoop.
Read data from the City of Portland's 'HYDRA' (http://or.water.usgs.gov/precip/) rainfall datasets within R.
Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.
An S4 implementation of Eq. (3) and Eq. (7)
by David J. Hand and Robert J. Till (2001). A Simple Generalisation of
the Area Under the ROC Curve for Multiple Class Classification Problems.
Machine Learning, 45 (2), p. 171--186.  DOI:10.1023/A:1010920819831.
Provides an R routine for a Cramer-von Mises type two sample test which is based on empirical Hankel transforms of the non-negative sample variables. The test is non-parametric and not distribution free. The exact value of the test statistic for univariate data as well as the p-value and the critical value are computed.
The following R functions are used for inference of trait
associations with haplotypes and other covariates in
generalized linear models.  The functions are developed
primarily for data collected in cohort or cross-sectional
studies. They can accommodate uncertain haplotype phase and
handle missing genotypes at some SNPs.
The multi-locus stepwise regression (MSR) combines the advantages of stepwise regression and haplotype-based analysis. The MSR can be used to identify informative combinations of single nucleotide polymorphisms (SNPs) from unlinked SNPs (allele combinations) or SNPs within a chromosomal region (haplotypes).
The package provides a set of functions whose aim is to
propose 4 methods of QTL detection. HAPimLD is an
interval-mapping method designed for unrelated individuals with
no family information that makes use of linkage disequilibrium.
HAPimLDL is an interval-mapping method for design of half-sib
families. It combines linkage analysis and linkage
disequilibrium. HaploMax is based on an analysis of variance
with a dose haplotype effect. HaploMaxHS is based on an
analysis of variance with a sire effect and a dose haplotype
effect in half-sib family design.  Fundings for the package
development were provided to the LDLmapQTL project by the ANR
GENANIMAL program and APIS-GENE.
Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. Haplin estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. Haplin also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. Haplin allows special models, like X-inactivation, to be fitted on the X-chromosome.
'haplo.ccs' estimates haplotype and covariate relative
risks in case-control data by weighted logistic regression.
Diplotype probabilities, which are estimated by EM computation
with progressive insertion of loci, are utilized as weights.
Simulate haplotypes through meioses. Allows specification
of population parameters.
A suite of R routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em, haplo.glm, haplo.score, haplo.power, and seqhap; all of which have detailed examples in doc/manualHaploStats.pdf.
Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel coding methods (only simple indel coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and inferring haplotypes from DNA sequences or user provided absolute character difference matrix.  This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony.
Functions to perform ROR for sequence-based association
analysis
Contains tools for exploring Hardy-Weinberg equilibrium for
diallelic genetic marker data. All classical tests (chi-square, exact,
likelihood-ratio and permutation tests) for Hardy-Weinberg equilibrium
are included in the package, as well as functions for power computation and
for the simulation of marker data under equilibrium and disequilibrium.
Functions for testing equilibrium in the presence of missing data by
using multiple imputation are also provided. Implements several graphics
for exploring the equilibrium status of a large set of diallelic markers:
ternary plots with acceptance regions, log-ratio plots and Q-Q plots.
Fits the first harmonics in a Fourier expansion to one or more time series. Trend elimination can be performed.  Computed values include estimates of amplitudes and phases, as well as confidence intervals and p-values for the null hypothesis of Gaussian noise.
Functions for easy simulation and reproducability.
Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information.
This package implements a data structure similar to hashes
in Perl and dictionaries in Python but with a purposefully R
flavor.  For objects of appreciable size, access using hashes
outperforms native named lists and vectors.
This package provides common non-cryptographic hash
functions for R. For example, SpookyHash, Murmur3Hash, Google
CityHash.
An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.
Apply the SuperFastHash algorithm to any R object. Hash whole R objects or,
for vectors or lists, hash R objects to obtain a set of hash values that is stored
in a structure equivalent to the input.
Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set.
Import foreign statistical formats into R via the embedded
ReadStat C library (https://github.com/WizardMac/ReadStat). Package includes
preliminary support for writing Stata and SPSS formats.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Damage Functions (DFs), also known as
Vulnerability Functions, associate the physical damage
to a building or a structure (and also its contents and
inventory) from natural disasters to financial damage.
The Federal Emergency Management Agency (FEMA) in USA
developed several thousand DFs and these serve as a
benchmark in natural catastrophe modeling, both in
academia and industry. However, these DFs and their
documentation are buried within the HAZUS software are
not easily accessible for analysis and visualization.
This package provides more than 1300 raw DFs used by FEMA's
HAZUS software and also functionality to extract and
visualize DFs specific to the flood hazard. The vignette
included with this package demonstrates its use.
Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
A package for building hierarchical block matrices from association matrices and for performing multi-scale analysis. It specifically targets chromatin contact maps, generated from high-throughput chromosome conformation capture data, such as 5C and Hi-C, and provides methods for detecting movements and for computing chain hierarchy and region communicability across scales.
Contains functions for fitting hierarchical versions of
EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
our gamma signal detection model to recognition memory
confidence-ratings data.
Functions to compute small area estimates based on a basic
area or unit-level model. The model is fit using restricted
maximum likelihood, or in a hierarchical Bayesian way. In the
latter case numerical integration is used to average over the
posterior density for the between-area variance.  The output
includes the model fit, small area estimates and corresponding
MSEs, as well as some model selection measures. Additional
functions provide means to compute aggregate estimates and
MSEs, to minimally adjust the small area estimates to
benchmarks at a higher aggregation level, and to graphically
compare different sets of small area estimates.
This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
A new diagnostic check for model adequacy in regression
and generalized linear models is implemented.
This package calculates the interval estimates for the parameters of
linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
the percentile bootstrap and percentile bootstrap double. It is possible to calculate
consistent estimates of the covariance matrix of the parameters of linear regression models
with heteroskedasticity of unknown form. The package also provides function to calculate
consistently the covariance matrix of the parameters of linear models with heteroskedasticity
of unknown form.
Estimation of parameters in 3-segment (i.e. 2 change-point)
regression models with heteroscedastic variances is provided based on both
likelihood and hybrid Bayesian approaches, with and without continuity
constraints at the change points.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.
Cross-validated linear discriminant calculations determine
the optimum number of features. Test and training scores from
successive cross-validation steps determine, via a principal
components calculation, a low-dimensional global space onto which test
scores are projected, in order to plot them. Further functions are
included that serve didactic purposes.
Facilitates discovery and handling of hydrological data,
non-programmatic access to catalogues and databases.
A flexible and hierarchical framework for comparing categorical map composition and configuration (spatial pattern) along spatial, thematic, or external grouping variables.  Comparisons are based on measures of mutual information between thematic classes (colours) and location (spatial partitioning).  Results are returned in textual, tabular, and graphical forms.
Test the significance of coefficients in high dimensional generalized linear models.
Implementation of multiple approaches to perform inference in
high-dimensional models
Mimics the lm function found in the package stats to fit
high dimensional regression models with point estimates,
standard errors, and p-values. Methods for printing and
summarizing the results are given.
Build nomograms for high-dimensional Cox models, with support for model validation and calibration.
Algorithms for lasso and fused-lasso
problems: implementation of the lars algorithm [1], for the
lasso and fusion penalization and EM-based algorithms for (logistic) lasso
and fused-lasso.
Provides a complete interface to the United Nations
Development Programme Human Development Report API (<http://hdr.undp.org>). The API
includes a large amount of human development data, including all the series used
to compute the Human Development Index (HDI), as well as the HDI itself.
Computation of highest density regions in one and two dimensions,
kernel estimation of univariate density functions conditional on one covariate,
and multimodal regression.
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
Timeseries analysis is conducted using Korean mortality and environmental variables
The heatex package calculates heat storage in the body and
the components of heat exchange (conductive, convective,
radiative, and evaporative) between the body and the
environment during physical activity based on the principles of
partitional calorimetry. The program enables heat exchange
calculations for a range of environmental conditions when
wearing various clothing ensembles.
An improved heatmap package. Completely
compatible with the original R function 'heatmap',
and provides more powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
binary dependent variables, and calculates the related heatmap fit
statistic (Esarey and Pierce, 2012).
Allows heatmap matrix to have non-identical X- and
Y-dimensions.  Allows multiple tracks of annotation for
RowSideColors and ColSideColors.
This package contains functions to perform robust estimation considering
heavy-tailed distributions. Currently, the package includes linear regression, linear
mixed-effect models, multivariate location and scatter estimation, penalized splines
and random variate generation.
Health Economic Evaluation MODelling (HEEMOD). Markov models (homogenous and non-homogenous)
for health economic evaluation. Model comparison, sensitivity and probabilistic analysis.
Base R's default setting for 'stringsAsFactors' within
'data.frame()' and 'as.data.frame()' is supposedly the most often complained
about piece of code in the R infrastructure. The 'hellno' package provides
an explicit solution without changing R itself or having to mess around with
options. It tries to solve this problem by providing alternative
'data.frame()' and 'as.data.frame()' functions that are in fact simple
wrappers around base R's 'data.frame()' and 'as.data.frame()' with
'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE )
by default.
A dummy package to demonstrate how to interface to a jar
file that resides inside an R package.
Contains many functions useful for managing 'NetCDF' files (see http://en.wikipedia.org/wiki/NetCDF), get tide levels on any point of the globe, get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Tools for accessing various open data sources in the Helsinki
region in Finland. Current data sources include
the Real Estate Department (district maps),
the Environmental Services Authority,
Service Map API,
Linked Events API and
Helsinki Region Infoshare statistics API.
Provides HE plot functions for visualizing hypothesis tests in multivariate linear models. They represents sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions).
Estimate and simulate hierarchical exponential-family random graph models with local dependence.
Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
Probability functions and other utilities for the generalized Hermite distribution.
Functions for the fitting and summarizing of heteroscedastic t-regression.
An implementation of White's Test for Heteroskedasticity
as outlined in Doornik (1996).
Binning and plotting functions for hexagonal bins. Now
uses and relies on grid graphics and formal (S4) classes and
methods.
Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.
A data only package containing commercial domestic flights that
departed Houston (IAH and HOU) in 2011.
High-dimensional additive models as introduced by Meier,
van der Geer and Buehlmann (2009).
Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
Procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model.
This data-only package was created for distributing data used in the examples of the hglm package.
The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization
constants of unnormalized probability distributions by utilizing holonomic
systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method
to find maximal likelihood estimates by utilizing the HGM.
Contains functions for
identifying and correcting HGNC gene symbols which have been converted
to date format by Excel, for reversibly converting between HGNC
symbols and valid R names, identifying invalid HGNC symbols and
correcting synonyms and outdated symbols which can be mapped to an
official symbol.
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, anticipated 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Heller-Heller-Gorfine (HHG) tests are a set of powerful statistical tests of multivariate k-sample homogeneity and independence. For the univariate case, the package also offers implementations of the MinP DDP and MinP ADP tests, which are consistent against all continuous alternatives but are distribution-free, and are thus much faster to apply.
This package builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum. To see the version history and download the bleeding-edge version (at your own risk!), see the project website at code.google.com below.  See the other links for PDF files describing numerical and exact analytical methods for determining instantaneous frequency, some examples of signals processed with this package, and some examples of the ensemble empirical mode decomposition method.
Simulation from distributions supported by nested
hyperplanes, using the algorithm described in Petris &
Tardella, "A geometric approach to transdimensional Markov
chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4,
(2003).  Also random direction multivariate Adaptive Rejection
Metropolis Sampling.
We propose a multiple logistic regression model to assess the influences of genomic features such as DNA-binding proteins and functional elements on topological domain borders.
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical
clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
geographic masking (by continents, regions, or countries), data filtering by mean and/or variance
thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
with preliminary big data support, different clustering methods, hybrid hierarchical clustering,
multi-variate clustering (MVC), cluster validation, and visualization of region maps.
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.
Computes the F-test and Bonferroni-adjusted p-value for a test of interaction in two-factor studies.  Reports corresponding interaction plot and analysis of variance table and p-values from several other tests of non-additivity.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Performs linear discriminant analysis in high dimensional
problems based on reliable covariance estimators for problems
with (many) more variables than observations. Includes routines
for classifier training, prediction, cross-validation and
variable selection.
Inference of high dimensional max-stable
distributions, from the paper "Likelihood based inference for
high-dimensional extreme value distributions", by A. Bienvenüe
and C. Robert, arXiv:1403.0065 [stat.AP].
Functions for performing hierarchical analysis of distance
sampling data, with ability to use an areal spatial ICAR model on
top of user supplied covariates to get at variation in abundance
intensity.  The detection model can be specified as a function of
observer and individual covariates, where a parametric model is
supposed for the population level distribution of covariate values.
The model uses data augmentation and a reversible jump MCMC
algorithm to sample animals that were never observed.  Also
included is the ability to include point independence (increasing
correlation multiple observer's observations as a function of
distance, with independence assumed for distance=0 or first
distance bin), as well as the ability to model species
misclassification rates using a multinomial logit formulation on data
from double observers.  New in version 2.1 is the ability to
include zero inflation, but this is only recommended for cases where
sample sizes and spatial coverage of the survey are high.
Implementation of the convex banding procedure (using a
hierarchical group lasso penalty) for covariance estimation that is
introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
Matrix. Accepted for publication in JASA.
Hierarchical group-wise partitioning of phenotypic diversity into
within-group (alpha), among-group (beta), and pooled-total (gamma)
components using Hill numbers.
Turnover and overlap are also calculated as standardized alternatives to
beta diversity. Hierarchical bootstrapping is used to approximate
uncertainty around each diversity component.
Allows the estimation of hierarchical F-statistics from haploid or diploid genetic data
with any numbers  of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956;
<DOI:10.2307/2411227>. Functions are also given to test via randomisations the significance of each F and variance components,
using the likelihood-ratio statistics G.
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
Variance partition of a multivariate data set
Implementation of the convex hierarchical testing (CHT) procedure
introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing
of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.
Uses likelihood to estimate ancestry and heterozygosity.
Evaluates simple hybrid classifications (parentals, F1, F2,
backcrosses). Estimates genomic clines.
Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.
Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].
Provide functionality to manage, clean and match highfrequency
trades and quotes data, calculate various liquidity measures, estimate and
forecast volatility, and investigate microstructure noise and intraday
periodicity.
Syntax highlighter for R code based
on the results of the R parser. Rendering in HTML and latex
markup. Custom Sweave driver performing syntax highlighting
of R code chunks.
Provides various tests for comparing high-dimensional mean vectors in two groups.
Provides syntax highlighting for R source code. Currently it
supports LaTeX and HTML output. Source code of other languages is supported
via Andre Simon's highlight package (http://www.andre-simon.de).
Functions for determining and evaluating high-risk zones and
simulating and thinning point process data, as described in 'Determining
high risk zones using point process methodology - Realization by building
an R package' (Seibold, 2012) and 'Determining high-risk zones for
unexploded World War II bombs by using point process methodology' (Mahling
et al., 2013).
Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
Generate occupancy patterns based on arrival and departure timestamps
Estimation of heritability with confidence intervals in linear mixed models.
H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories.
Provides multi-core or GPU support (or both if the system
has GPU and multi-core CPU) for the recommended R package,
Matrix.
Based on hierarchical modeling, this package provides a
few practical functions to find and present the optimal designs
for a pooled NGS design.
This package implements methods for hierarchical semiparametric regression models for test statistics. Specifically, test statistics given the null/alternative hypotheses are modeled parametrically, whereas the unobservable status of null/alternative hypotheses are modeled using nonparametric additive logistic regression over covariates.
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification.
The HistData package provides a collection of small data sets
that are interesting and important in the history of statistics and data visualization.
The goal of the package is to make these available, both for instructional use
and for historical research.  Some of these present interesting challenges for graphics
or analysis in R.
In the framework of Symbolic Data Analysis, a relatively new approach
to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e.,
data described by univariate histograms. The  methods and the basic statistics for
histogram-valued data are mainly based
on the L2 Wasserstein metric between distributions, i.e., a Euclidean metric
between quantile functions. The package contains unsupervised classification techniques,
least square regression and tools for histogram-valued data and for histogram time series.
Using the MDL principle, it is possible to estimate
parameters for a histogram-like model. The package contains
the implementation of such an estimation method.
Automatic construction of regular and irregular histograms
as described in Rozenholc/Mildenberger/Gather (2009).
Provides a number of utility functions useful for manipulating large histograms.  This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning.  It also provides a protocol buffer representations of the default R histogram class to allow histograms over large data sets to be computed and manipulated in a MapReduce environment.
These sample data sets are intended for historians
learning R. They include population, institutional, religious,
military, and prosopographical data suitable for mapping,
quantitative analysis, and network analysis.
Hierarchical inference testing (HIT) for (generalized) linear
models with correlated covariates applicable to high-dimensional settings.
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
Hadoop InteractiVE facilitates distributed
computing via the MapReduce paradigm through R and Hadoop. An easy to use
interface to Hadoop, the Hadoop Distributed File System (HDFS),
and Hadoop Streaming is provided.
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
The functions in this package produce a complete set of mortality rates as a function of a combination of HIV prevalence and either life expectancy at birth (e0), child mortality (5q0), or child mortality with adult mortality (45q15)
Conversion and Reverse Conversion between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems. Algorithms follow Survey and Mapping Office Lands Department, Hong Kong Government (1995)
A suite of diagnostic tools for hierarchical
(multilevel) linear models. The tools include
not only leverage and traditional deletion diagnostics (Cook's
distance, covratio, covtrace, and MDFFITS) but also
convenience functions and graphics for residual analysis. Models
can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package,
but only two-level models fit using lme are currently supported.
Hierarchical latent space network model for fitting ensemble of networks.
Utilities for reading data from the Human Mortality Database (<www.mortality.org>), Human Fertility Database (<www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<http://www.fertilitydata.org/>), The Japanese Mortality Database (<http://www.ipss.go.jp/p-toukei/JMD>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
Scalar performance metrics, including the H-measure, based
on classification scores for several classifiers applied to the
same dataset.
Contains many functions useful for data
analysis, high-level graphics, utility operations, functions for
computing sample size and power, importing and annotating datasets,
imputing missing values, advanced table making, variable clustering,
character string manipulation, conversion of R objects to LaTeX code,
and recoding variables.
Easy to use library to setup, apply and make inference
with discrete time and discrete space Hidden Markov Models
The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
Fits hidden Markov models with discrete non-parametric
observation distributions to data sets.  Simulates data
from such models.  Finds most probable underlying hidden
states, the most probable sequences of such states, and the
log likelihood of a collection of observations given the
parameters of the model.
Functions for specifying and fitting marginal models for contingency tables proposed
by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al.
(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables
is described by a marginal model.
Inequality constraints on the parameters are allowed and can be tested.
Analysing time-series accelerometer data to quantify length and intensity of physical activity.
This package provides several functions to perform formal
hypothesis testing, and power and sample size calculations for
human microbiome experiments. Dirichlet-Multinomial
distribution is proposed to model species abundance and ranked
abundance data.
In this package, we introduce Object Oriented Data Analysis (OODA) methods to analyze Human Microbiome taxonomic trees directly. We provide tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions  of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).
Information retrieval from National Center for Biotechnology Information (NCBI) databases, with main focus on identifying genes in unannotated organisms via Blast similarity search in annotated organisms.
Simulates hands and tournaments of Texas Holdem.
Contains trading holiday and half-day data that is automatically registered when loaded.
Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
A collection of functions useful for the analysis of
building physics experiments.
Homomorphic computations in R for privacy-preserving applications. Currently only
the Paillier Scheme is implemented.
A theoretical model to simulate  radical polymerization. Material, energy and population balances are integrated for batch, semi batch and continuous process in a ideally mixed reactor. Limitations: single monomer (i.e.homo polymer), one phase (organic, aqueous). Datasets with chemical-physical data for the most common monomers is included. Some background in polymer science is suggested for its use. Graphical interface for a quick and friendly use is available.
A collection of homogeneity tests described in: Viglione
A., Laio F., Claps P. (2007) ``A comparison of homogeneity
tests for regional frequency analysis'', Water Resources
Research, 43, W03428, doi:10.1029/2006WR005095.  More on
Regional Frequency Analysis can be found in package nsRFA.
A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component.
Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
Hot deck imputation methods to resolve missing data.
A set of R functions and data sets which implements Hotelling's T^2 test, and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations
Contains data for software hotspot analysis, along with a function performing the analysis itself.
Calculates a hot spot cutoff and associated analyses for
statistical populations
When testing multiple hypotheses simultaneously, this
package provides functionality to calculate a lower bound for
the number of correct rejections (as a function of the number
of rejected hypotheses), which holds simultaneously -with high
probability- for all possible number of rejections.  As a
special case, a lower bound for the total number of false null
hypotheses can be inferred.  Dependent test statistics can be
handled for multiple tests of associations. For independent
test statistics, it is sufficient to provide a list of
p-values.
This package provides all the functions necessary to
estimate the 8 parameters of the Heligman Pollard mortality
model using a Bayesian Melding procedure with IMIS as well as
to convert those parameters into age-specifc probabilities of
death and a corresponding life table
Probabilistic models describing the behavior
of workload and queue on a High Performance Cluster and computing GRID
under FIFO service discipline basing on modified Kiefer-Wolfowitz
recursion. Also sample data for inter-arrival times, service times,
number of cores per task and waiting times of HPC of Karelian
Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
Functions provided to import/export workload traces in Standard Workload Format (swf).
Stability condition of the model may be verified either exactly, or approximately.
Collection of functions for manipulating sets of HPO terms and
plotting them with a various options.
This package contains some miscellaneous convenience functions,
to create a matrix of dummy columns from a factor,
to determine whether x lies in range [a,b],
to add a rectangular bracket to an existing plot,
and to convert frequencies between Hz, semitones, mel and Bark.
This package also contains an example data set of a stratified sample
of 80 talkers of Dutch.
Efficient algorithms for fitting entire regularization paths for Huber loss regression and quantile regression penalized by lasso or elastic-net.
Tests for main and simple treatment effects, time effects, as well as treatment by time interactions in possibly high-dimensional multi-group repeated measures designs.
Print beautiful horizontal rules in your R scripts.
A library of the Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm.
Functions, data sets, analyses and examples from the book
`A Handbook of Statistical Analyses Using R' (Brian S. Everitt and Torsten
Hothorn, Chapman & Hall/CRC, 2006). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the
second edition of the book
`A Handbook of Statistical Analyses Using R' (Brian S. Everitt and Torsten
Hothorn, Chapman & Hall/CRC, 2008). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available. In addition,
the package contains Sweave code for producing slides for selected
chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the
third edition of the book
`A Handbook of Statistical Analyses Using R' (Torsten Hothorn and Brian S.
Everitt, Chapman & Hall/CRC, 2014). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available. In addition,
Sweave source code for slides of selected chapters is included in
this package (see HSAUR3/inst/slides).
Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.
hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Canonical correlation analysis that extracts nonlinear
correlation through the use of Hilbert Schmidt Independence
Criterion and Centered Kernel Target Alignment.
A package for computation of hidden semi markov models
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data
Implements a model for joint meta-analysis of sensitivity and specificity of the diagnostic test under evaluation, while taking into account the possibly imperfect sensitivity and specificity of the reference test. This hierarchical model accounts for both within and between study variability. Estimation is carried out using a Bayesian approach, implemented via a Gibbs sampler. The model can be applied in situations where more than one reference test is used in the selected studies.
A data mining tool for discovering subgroups of patients and genes that simultaneously display unusual levels of variability compared to other genes and patients. Based on sparse singular value decomposition (SSVD), the method can detect both mean and variance biclusters in the presence of heterogeneous residual variance.
HTML tables are a valuable data source but extracting and recasting
these data into a useful format can be tedious. htmltab is a package for
extracting structured information from HTML tables. It is similar to
readHTMLTable() of the XML package but provides two major advantages. First,
the function automatically expands row and column spans in the header and
body cells. Second, users are given more control over the identification of
header and body rows which will end up in the R table. Additionally, the
function preprocesses table code, removes unneeded parts and so helps to
alleviate the need for tedious post-processing.
Tables with state-of-the-art layout elements such as
row spanners, column spanners, table spanners, zebra striping,
and more. While allowing advanced layout, the underlying CSS structure is
simple in order to maximize compatibility with word processors such as
MS Word or LibreOffice.
The package also contains a few text formatting functions that help outputting
text compatible with HTML/LaTeX.
Facilitates automated HTML report creation, in particular
framed HTML pages and dynamically sortable tables.
A framework for creating HTML widgets that render in various
contexts including the R console, 'R Markdown' documents, and 'Shiny'
web applications.
Methods for analysing and forecasting hierarchical and grouped time series.
This package implements a Poisson mixture model to cluster observations (e.g., genes) in high throughput sequencing data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).
Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") using data obtained from relatively high throughput, in vitro studies. Both physiologically-based ("PBTK") and empirical (e.g., one compartment) "TK" models can be parameterized for several hundred chemicals and multiple species. These models are solved efficiently, often using compiled (C-based) code. A Monte Carlo sampler is included for simulating biological variability and measurement limitations. Functions are also provided for exporting "PBTK" models to "SBML" and "JARNAC" for use with other simulation software. These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVIVE") of high throughput screening data (e.g., ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK").
Find and explain the meaning of 'HTTP' status codes.
Functions included for searching for codes by full or partial number,
by message, and get appropriate dog and cat images for many
status codes.
A suite of functions to ping 'URLs' and to time
'HTTP' 'requests'. Designed to work with 'httr'.
HTTP Request protocols. Implements the GET, POST and multipart POST request.
Provides low-level socket and protocol support for handling
HTTP and WebSocket requests directly from within R. It is primarily
intended as a building block for other packages, rather than making it
particularly easy to create complete web applications using httpuv alone.
httpuv is built on top of the libuv and http-parser C libraries, both of
which were developed by Joyent, Inc. (See LICENSE file for libuv and
http-parser license information.)
Useful tools for working with HTTP organised by HTTP verbs
(GET(), POST(), etc). Configuration functions make it easy to control
additional request components (authenticate(), add_headers() and so on).
Provides a general framework for
high-dimensional undirected graph estimation. It integrates
data preprocessing, neighborhood screening, graph estimation,
and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is
applied to help relax the normality assumption. In the graph
estimation stage, the graph structure is estimated by
Meinshausen-Buhlmann graph estimation or the graphical lasso,
and both methods can be further accelerated by the lossy
screening rule preselecting the neighborhood of each variable
by correlation thresholding. We target on high-dimensional data
analysis usually d >> n, and the computation is
memory-optimized using the sparse matrix output. We also
provide a computationally efficient approach, correlation
thresholding graph estimation. Three
regularization/thresholding parameter selection methods are
included in this package: (1)stability approach for
regularization selection (2) rotation information criterion (3)
extended Bayesian information criterion which is only available
for the graphical lasso.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
to discriminate the class labels, visualizing the ROC curve for two or three class labels.
Format quantities of time or bytes into human-friendly strings.
Human names are complicated and nonstandard things. Humaniformat, which is based on Anthony Ettinger's 'humanparser'
project (https://github.com/chovy/humanparser) provides functions for parsing human names,
making a best-guess attempt to distinguish sub-components such as prefixes, suffixes, middle names and salutations.
Fits models for genotypic disequilibria, as described in
Huttley and Wilson (2000), Weir (1996) and Weir and Wilson (1986).
Contrast terms are available that account for first order interactions
between loci.  Also implements, for a single locus in a single
population, a conditional exact test for Hardy-Weinberg equilibrium.
Estimation and testing of HWE using Bayesian methods.
Three models are currently considered: HWE, a model
parameterized in terms of the allele frequencies and a single
inbreeding coefficient f, and the saturated model. Testing is
based on Bayes factors.
General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.
HW.pval calculates plain and fully conditional
root-mean-square, chi-square, and log likelihood-ratio P-values
for the user-provided genotypic counts to be consistent with
the Hardy-Weinberg equilibrium model.  For further information
on the Hardy-Weinberg equilibrium model and the pseudocode,
refer to the paper "Testing Hardy-Weinberg equilibrium with a
simple root-mean-square statistic" by Rachel Ward.
Easy-to-use and versatile functions to output R objects in
HTML format
This package extends the package hwriter providing
facilities such as the inclusion of output from R, the
results of an R session, the display of mathematical
expressions using LaTeX notation and the incorporation of
SVG graphical objects.  It uses MathJax to display
mathematical expressions, in contrast to R2HTML which
uses ASCIIMathML.
Provides methods to test whether time series is consistent
with white noise.
Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. Functions to cross-validate the hybrid ensemble and plot and summarize the results are also provided. There is also a function to assess the importance of the predictors.
Hybrid hierarchical clustering via mutual clusters.  A mutual cluster is a set of points closer to each other than to all other points.  Mutual clusters are used to enrich top-down hierarchical clustering.
This package is an R implementation of the Hybrid Monte
Carlo and Multipoint Hybrid Monte Carlo sampling techniques
described in Liu (2001): "Monte Carlo Strategies in Computing".
Facilities for easy implementation of hybrid Bayesian networks
using R. Bayesian networks are directed acyclic graphs representing joint
probability distributions, where each node represents a random variable and
each edge represents conditionality. The full joint distribution is
therefore factorized as a product of conditional densities, where each node
is assumed to be independent of its non-descendents given information on its
parent nodes. Since exact, closed-form algorithms are computationally
burdensome for inference within hybrid networks that contain a combination
of continuous and discrete nodes, particle-based approximation techniques
like Markov Chain Monte Carlo are popular.  We provide a user-friendly
interface to constructing these networks and running inference using the 'rjags' package.
Econometric analyses (maximum expected utility under competing policies,
value of information) involving decision and utility nodes are also
supported.
Package providing tools for hydrological applications and models developed for regional analysis in Northwestern Italy.
Contains one function for drawing Piper diagrams (also
called Piper-Hill diagrams) of water analyses for major ions.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
This package is version 2 of HydroMe v.1 package. It
estimates the parameters in infiltration and water retention
models by curve-fitting method. The models considered are those
that are commonly used in soil science. It has new models for
water retention characteristic curve and debugging of errors in
HydroMe v.1
This package implements a state-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable). hydroPSO can be used as a replacement of the 'optim' R function for (global) optimization of non-smooth and non-linear functions. However, the main focus of hydroPSO is the calibration of environmental and other real-world models that need to be executed from the system console. hydroPSO is model-independent, allowing the user to easily interface any computer simulation model with the calibration engine (PSO). hydroPSO  communicates with the model through the model's own input and output files, without requiring access to the model's source code. Several PSO variants and controlling options are included to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. hydroPSO is parallel-capable, to alleviate the computational burden of complex models with "long" execution time. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian).
Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Focuses on data processing and visualization in hydrology and
climate forecasting. Main function includes data extraction, data downscaling, data
resampling, gap filler of precipitation, bias correction of forecasting data, flexible
time series plot, and spatial map generation. It is a good pre-processing and
post-processing tool for hydrological and hydraulic modellers.
This package provides functions for the hyperbolic and
related distributions. Density, distribution and quantile
functions and random number generation are provided for the
hyperbolic distribution, the generalized hyperbolic
distribution, the generalized inverse Gaussian distribution and
the skew-Laplace distribution. Additional functionality is
provided for the hyperbolic distribution, including fitting of
the hyperbolic to data.
A suite of routines for the hyperdirichlet distribution
Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelised C code
The Gaussian hypergeometric function for complex numbers.
Comfortable ways to work with hyperspectral data sets.
I.e. spatially or time-resolved spectra, or spectra with any other kind
of information associated with each of the spectra. The spectra can be data
as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
etc. More generally, any data that is recorded over a discretized variable,
e.g. absorbance = f (wavelength), stored as a vector of absorbance values
for discrete wavelengths is suitable.
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation. Builds n-dimensional convex hulls (polytopes). Can measure the n-dimensional ecological hypervolume and perform species distribution modeling.
A Bay Area high level phylogenetic analysis package mostly
using the birth-death process. Analysis of species tree
branching times and simulation of species trees under a number
of different time variable birth-death processes.  Analysis of
gene tree species tree reconciliations and simulations of gene
trees in species trees.
Compute the confidence interval of the population mean
with one sample or of the difference of population means of two
samples from normal distributions or t-distributions.Compute
the confidence interval of population variance with one sample
or of the difference of population variances of two samples by
chi-square tests.Test for population mean or the differences of
two normal samples under normality with the given null
hypothesis H0,which depends on the user,so that he can know if
he can reject H0 or not at the significance level alpha.Do the
chi-square tests with one or two samples which have multinomial
distributions by using an approximate chi-square distribution
when n is large enough.
Fit, summarize and plot sinusoidal hysteretic processes using:
two-step simple harmonic least squares, ellipse-specific non-linear least
squares, the direct method, geometric least squares or linear least squares.
A collection of tools for modeling the shape of 1 dimensional clines.
Synthesizes information across collaborating research. Created specifically for Integrative Analysis of Longitudinal Studies of Aging (IALSA).
Test the robustness of a user's QCA solutions to randomness, using iraQCA. iaQCA is packaged with the irrQCA function, which provides recommendations for improving QCA solutions to reach typical significance levels.
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.
Contains a function to implement the standard D-Scoring algorithm
(Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT)
data, as well as various other functions to produce plots of IAT data.
Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015).
Bayesian integrative models of gene expression and comparative genomic hybridization data. The package provides inference on copy number variations and their association with gene expression.
This package contains several utility functions related to incomplete block designs. The package contains function to generate efficient incomplete block designs with given numbers of treatments, blocks and block size. The package also contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Package also allows performing analysis of variance of data and computing least square means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.
Functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
A method to test genetic linkage with covariates by
regression methods with response IBD sharing for relative
pairs.  Account for correlations of IBD statistics and
covariates for relative pairs within the same pedigree.
R package for simulation of IBD sharing among family members. Using sex specific recombination rates from the Decode map (2010) of the human genome, phased chromosomes are simulated for all pedigree members, either by unconditional 'gene dropping' or conditional on a specified IBD pattern. Regions compatible with the query IBD pattern (possibly different from the conditional pattern) are subsequently detected and summarized.
A data-driven and adaptive hierarchical-scale decomposition method for irregular-lattice field (represented by polygons).
Some basic functions to implement belief functions including:
transformation between belief functions using the method introduced by
Philippe Smets (arXiv:1304.1122 [cs.AI]), evidence combination, evidence
discounting, decision-making, and constructing masses. Currently, thirteen
combination rules and five decision rules are supported. It can also be
used to generate different types of random masses when working on belief
combination and conflict management.
Implementation of an incremental model construction method called IBHM which
stands for Incrementally Built Heterogeneous Model. The method is designed for solving
real number approximation problems in a highly automated fashion.
Functionality required to efficiently use R with
IBM DB2(C) for Linux, Unix and Windows as well as IBM dashDB(C).
Many basic and complex R operations are pushed down into the database, which removes
the main memory boundary of R and allows to make full use of parallel processing in
the underlying database.
The package implements a general algorithm to obtain
iterated bootstrap tests and confidence sets for a
p-dimensional parameter based on the unstudentized version of
the Rao statistic.
Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
Provides native R access to Interactive Brokers Trader Workstation API.
This package has provided an interface to WinBUGS/OpenBUGS/JAGS
via R2WinBUGS and R2jags. Some options will be intelligently guessed, e.g.
the path to WinBUGS/OpenBUGS/JAGS and the data/parameter list. Users can
specify all the options in a GUI.
Implementation of the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.
Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.
Lorenz and concentration curves; Atkinson, Generalized
entropy and SGini indices (with decomposition)
Calculation of IC50 values, automatic drawing of
dose-response curves and validation of compound screens on 96-
and 384-well plates.
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
Imperialist Competitive Algorithm (ICA)
<http://en.wikipedia.org/wiki/Imperialist_competitive_algorithm>
is a computational method that is used to solve optimization
problems of different types and it is the mathematical model
and the computer simulation of human social evolution.
The package provides a minimum value for the cost function
and the best value for the optimization variables by
Imperialist Competitive Algorithm.
Users can easily define their own objective function
depending on the problem at hand.
This version has been successfully applied to solve
optimization problems, for continuous functions.
The icamix package provides R functions as well as a C++ library which facilitate the estimation of ICA mixture models. We have developed and implemented the NSMM-ICA algorithm that currently integrates npEM and Fast-ICA for non-parametric estimation of ICA mixture models. We will continue to build in algorithms that interweave the EM step with other types of ICA techniques.
Removes eye-movement and other types of known (i.e., recorded) or unknown (i.e., not recorded) artifacts using the fastICA package. The correction method proposed in this package is largely based on the method described in on Flexer, Bauer, Pripfl, and Dorffner (2005). The process of correcting electro- and magneto-encephalographic data (EEG/MEG) begins by running function ``icac'', which first performs independent components analysis (ICA) to decompose the data frame into independent components (ICs) using function ``fastICA'' from the package of the same name. It then calculates for each trial the correlation between each IC and each one of the noise signals -- there can be one or more, e.g., vertical and horizontal electro-oculograms (VEOG and HEOG), electro-myograms (EMG), electro-cardiograms (ECG), galvanic skin responses (GSR), and other noise signals. Subsequently, portions of an IC corresponding to trials at which the correlation between it and a noise signal was at or above threshold (set to 0.4 by default; Flexer et al., 2005, p. 1001) are zeroed-out in the source matrix, ``S''. The user can then identify which ICs correlate with the noise signals the most by looking at the summary of the ``icac'' object (using function ``summary.icac''), the scalp topography of the ICs (using function ``topo_ic''), the time courses of the ICs (using functions ``plot_tric'' and ``plot_nic''), and other diagnostic plots. Once these ICs have been identified, they can be completely zeroed-out using function ``update.icac'' and the resulting correction checked using functions ``plot_avgba'' and ``plot_trba''. Some worked-out examples with R code are provided in the package vignette.
Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources
Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds model, and probit model) to interval-censored time-to-event data.
Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.
A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (in press). iccbeta quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.
Provides functions to calculate the requisite sample size for studies where ICC is
the primary outcome. Can also be used for calculation of power. In both cases it
allows the user to test the impact of changing input variables by calculating the outcome
for several different values of input variables. Based off the work of Zou.
Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with
precision and assurance. Statistics in medicine, 31(29), 3972-3981.
Calculate comorbidities, Charlson scores, perform fast and accurate
validation, conversion, manipulation, filtering and comparison of ICD-9-CM
(clinical modification) codes. ICD-9 codes appear numeric but leading and
trailing zeroes, and both decimal and non-decimal "short" format codes
exist. The package enables a work flow from raw lists of ICD-9 codes from
hospital billing databases to comorbidities. ICD-9 to comorbidity mappings
from Quan (Deyo and Elixhauser versions), Elixhauser and AHRQ included. Any
other mapping of codes, such as ICD-10, to comorbidities can be used.
Kernel Estimators for Interval-Censored Data
This package implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
Given two unbiased samples of patient level data on cost and effectiveness
for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
with economic preferences from an ICE map with specified values of lambda, beta and gamma
parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation
in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves
from an ICE map with specified values of lambda, beta and gamma or eta parameters.
Regression models for interval censored data. Currently supports Cox-PH and proportional odds models. Allows for both semi and fully parametric models. Includes functions for easy visual diagnostics of model fits and imputed cross-validation. Includes functions for fitting both the univariate and bivariate NPMLE.
We consider studies in which information from error-prone
diagnostic tests or self-reports are gathered sequentially to determine the
occurrence of a silent event. Using a likelihood-based approach
incorporating the proportional hazards assumption, we provide functions to
estimate the survival distribution and covariate effects. We also provide
functions for power and sample size calculations for this setting.
ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.
This package implements parameter estimation in normal (linear) models under linear equality and inequality constraints and implements normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.
A GUI designed to support the analysis of financial-economic time
series data.
Integrative clustering of multiple genomic data types
using a joint latent variable model.
Implementation of Tyler et al.'s and Oja et al.'s method of two different scatter
matrices to obtain an invariant coordinate system or independent
components, depending on the underlying assumptions.
Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.
Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.
Provides the necessary tools to estimate average treatment effects with an instrumental variable by re-weighting observations using a model of compliance.
Interactive dendrogram that enables the user to select and
color clusters, to zoom and pan the dendrogram, and to visualize
the clustered data not only in a built-in heat map, but also in
'GGobi' interactive plots and user-supplied plots.
This is a backport of Qt-based 'idendro'
(https://github.com/tsieger/idendro) to base R graphics and Tcl/Tk GUI.
A fast way to clean unique identifiers used by the Los Angeles County Probation Department.
Calculate identity coefficients, based on Mark Abney's C
code.
Principal Component Analysis and Multiple Correspondence Analysis using incremental eigenvalue decomposition methods.
The IDPmisc package contains different high-level graphics
functions for displaying large datasets, displaying circular
data in a very flexible way, finding local maxima, brewing
color ramps, drawing nice arrows, zooming 2D-plots, creating
figures with differently colored margin and plot region.  In
addition, the package contains auxiliary functions for data
manipulation like omitting observations with irregular values
or selecting data by logical vectors, which include NAs. Other
functions are especially useful in spectroscopy and analyses of
environmental data: robust baseline fitting, finding peaks in
spectra.
This package contains functions to perform robust
nonparametric survival analysis with right censored
data using a prior near-ignorant Dirichlet Process.
This is a package for estimating the copula mixture model and plotting correspondence curves in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel
It is a method to identify individually turtles using the plastral biometries. It is also useful to detect errors on the identification of turtles as is explained in Valdeon & Longares 2015.
iDynoMiCS is a computer program, developed by an international team of researchers, whose purpose is to model and simulate microbial communities in an individual-based way. It is described in detail in the paper "iDynoMiCS: next-generation individual-based modelling of biofilms" by Lardon et al, published in Environmental Microbiology in 2011. The simulation produces results in XML file format, describing the state of each species in each timestep (agent_State), a summary of the species statistics for a timepoint (agent_Sum), the state of each solute grid in each timestep (env_State) and a summary of the solutes for a timestep (env_Sum). This R package provides a means of reading this XML data into R such that the simulation response can be statistically analysed. iDynoMiCS is available from the website iDynoMiCS.org, where a full tutorial on using both the simulation and this R package is provided.
A pair of functions for getting and setting the IEEE
rounding mode for floating point computations.
The package performes Independent Factor Analysis
This package implements a Bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.
Tools, tutorials, and demos of Item Factor Analysis using OpenMx.
Provides utility functions to deal with Italian fiscal
code ('codice fiscale').
A suite for identifying causal models using relative concordances and identifying causal polymorphisms in case-control genetic association data, especially with large controls re-sequenced data.
A collection of statistical tests and utilities for genetic association studies
Provides function to read data from the 'Igor Pro' data analysis
program by Wavemetrics. The data formats supported are 'Igor' packed
experiment format (pxp) and 'Igor' binary wave (ibw). See:
http://www.wavemetrics.com/ for details. Also includes functions to load
special pxp files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp'
packages for recording and analysing neuronal data. See
http://www.neuromatic.thinkrandom.com/ for details.
Routines for simple graphs and network analysis. It can
handle large graphs very well and provides functions for generating random
and regular graphs, graph visualization, centrality methods and much more.
A small collection of various network data sets,
to use with the 'igraph' package: the Enron email network, various food webs,
interactions in the immunoglobulin protein, the karate club network,
Koenigsberg's bridges, visuotactile brain areas of the macaque monkey,
UK faculty friendship network, domestic US flights network, etc.
This program facilitates exporting igraph graphs to the
SoNIA file format
A dataset of the top colours of photos from Instagram
taken in 2014 in the city of Vancouver, British Columbia, Canada.
It consists of: top colour and counts data. This data was
obtained using the Instagram API. Instagram is a web photo
sharing service. It can be found at: <https://instagram.com>.
The Instagram API is documented at: <https://instagram.com/developer/>.
Density, distribution function, quantile function and random generation for the inverse hyperbolic sine distribution. This package also provides a function that can fit data to the inverse hyperbolic sine distribution using maximum likelihood estimation.
The package offers an S4 infrastructure to store climatological
station data of various temporal aggregation scales. In-built quality
control and homogeneity tests follow the methodology from the European
Climate Assessment & Dataset project. Wrappers for climate indices
defined by the Expert Team on Climate Change Detection and Indices
(ETCCDI), a quick summary of important climate statistics and climate
diagram plots provide a fast overview of climatological
characteristics of the station.
Improved Laplace approximation for integrals of unimodal functions. The method requires user-supplied R functions for: the integrand, its gradient and its Hessian matrix. The computations are run in parallel.
Fitting a class of Lee-Carter mortality models using iterative fitting algorithm.
Compute moments of images and perform reconstruction from
moments.
Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R.
Zoom in and out of maps or any supplied lines or points,
with control for color, poly fill, and aspect.
A package to share plots using the image hosting service Imgur.com
IMIS algorithm draws samples from the posterior
distribution. The user has to define the following R functions
in advance: prior(x) calculates prior density of x,
likelihood(x) calculates the likelihood of x, and
sample.prior(n) draws n samples from the prior distribution.
Implements some item response models for multiple
ratings, including the hierarchical rater model,
conditional maximum likelihood estimation of linear
logistic partial credit model and a wrapper function
to the commercial FACETS program.
Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).
In this package, you can find two functions proposed in
Ding, Geng and Zhou (2011) to estimate direct and indirect
causal effects with randomization and multiple-component
intervention using instrumental variable method.
A collection of tools for conducting an imprecise inference is provided.   Imprecise prior is used for this inference, and imprecise probability theory introduced by Peter Walley (1991) is its underlying theoretical foundation.   The package is developed based on the PhD thesis work of Lee (2014).  Poisson and zero-truncated Poisson sampling models are mainly studied with two types of prior distributions.
This is an alternative mechanism for importing
objects from packages. The syntax allows for importing multiple objects
from a package with a single command in an expressive way. The import
package bridges some of the gap between using library (or require) and
direct (single-object) imports. Furthermore the imported objects are not
placed in the current environment. It is also possible to import
objects from stand-alone .R files. For more information, refer to
the package vignette.
Import and export data from the most common statistical formats by using
R functions that guarantee the least loss of the data information, giving special
attention to the date variables and the labelled ones.
A minimum distance estimator is calculated for an
imprecise probability model. The imprecise probability model
consists of upper coherent previsions whose credal sets are
given by a finite number of constraints on the expectations.
The parameter set is finite. The estimator chooses that
parameter such that the empirical measure lies next to the
corresponding credal set with respect to the total variation
norm.
The package contains a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
This package provides various approaches to handling
missing values for the MDR analysis to identify gene-gene
interactions using biallelic marker data in genetic association
studies
Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.
This package provides a general imputation framework based on
variable selection methods including regularisation methods,
tree-based models and dimension reduction methods.
Imputation (replacement) of missing values in univariate time
series.
Method brings less bias and more efficient estimates for AFT models.
Graphical User Interface (GUI) to some of the functions in the package extRemes version >= 2.0 are included.
Fits mixtures models for longitudinal data. Appropriate when the data are counts and when the correlation structure is assumed to be AR(1).
A framework for analysing inbreeding and heterozygosity-fitness
correlations (HFCs) based on microsatellite and SNP markers.
Functions for testing mutual independence between many
numerical random vectors or serial independence of a
multivariate stationary sequence. The proposed test works when
some or all of the marginal distributions are singular with
respect to Lebesgue measure.
Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites. Also includes functions to measure species niche breadth using resource categories.
Provides functions to download and parse information from INEGI (Official Mexican statistics agency).
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation of species diversity (Hill numbers) for individual-based (abundance) data or sampling-unit-based (incidence) data.
This package contains functions to perform calculations of
the infine-dimensional model (IDM) and to produce 95%
confidence intervals around the model elements through
bootstrapping.
Collection of functions to extract inferential values
(point estimates, confidence intervals, p-values, etc) of a
fitted model object into a matrix-like object that can be used
for table/report generation; transform point estimates via the
delta method.
The InferenceSMR package provides functions to make
inference about the standardized mortality ratio (SMR) when
evaluating the effect of a screening program. The package is
based on methods described in Sasieni (2003) and Talbot et al.
(2011).
Provides methods for estimating causal effects in the presence of interference. Currently it implements the IPW estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele in "On causal inference in the presence of interference" (Statistical Methods in Medical Research, 21(1) 55-75).
Implementation of methods Extremum Surface Estimator and
Extremum Distance Estimator for identifying the inflection
point of a convex/concave curve.
Provides a collection of tools for
detecting influential cases in generalized mixed effects
models. It analyses models that were estimated using lme4. The
basic rationale behind identifying influential data is that
when iteratively single units are omitted from the data, models
based on these data should not produce substantially different
estimates. To standardize the assessment of how influential a
(single group of) observation(s) is, several measures of
influence are common practice, such as DFBETAS and Cook's Distance.
In addition, we provide a measure of percentage change of the fixed point
estimates and a simple procedure to detect changing levels of significance.
Provides functionality to compute various node centrality measures on networks.
Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
SNAP library), implementations of Burt's constraint and effective
network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's
bridging metric. On Unix systems, the betweenness, Key Players, and
bridging implementations are parallelized with OpenMP, which may run
faster on systems which have OpenMP configured.
Influence measures in Structural Equation Models: a set of tools for evaluating several measures of case influence for Structural Equation Models following the approach of Pek and MacCallum (2011).
InfoDecompuTE is capable of generating the structure of
the analysis of variance (ANOVA) table of the two-phase
experiments. By inputting the design and the relationships of
the random and fixed factors using the Wilkinson-Rogers'
syntax, infoDecompuTE can generate the structure of the ANOVA
table with the coefficients of the variance components for the
expected mean squares. This package can also study the balanced
incomplete block design and provides the efficiency factors of
the fixed effects.
Performs exploratory data analysis and variable screening for binary classification models using weight-of-evidence (WOE) and information value (IV). In order to make the package as efficient as possible, aggregations are done in data.table and creation of WOE vectors can be distributed across multiple cores. The package also supports exploration for uplift models (NWOE and NIV).
Provides companion function for analysing the performance of classification models. Also, provides function to optimise probability cut-off score based on used specified objectives, Plot 'ROC' Curve in 'ggplot2', 'AUROC', 'IV', 'WOE' Calculation, 'KS Statistic' etc to aid accuracy improvement in binary classification models.
Aids in creating sequence statistics for Butts's 'relevent' software.
This package implements various measures of information theory based on several entropy estimators.
Takes a data frame containing latitude and longitude coordinates and downloads images from map servers to determine their file size as a proxy of infrastructure
Replace parameters in strings and/or text files with specified values.
Calculation of information utility (i.e., Lindley
information) quantities for item response models.
R dependency injection framework. Dependency injection allows
a program design to follow the dependency inversion principle. The user
delegates to external code (the injector) the responsibility of providing its
dependencies. This separates the responsibilities of use and construction.
Spatial Econometrics models using Bayesian Model Averaging
on models fitted with INLA. The INLA package can be obtained from
http://www.r-inla.org . We recommend the testing version, which can be
downloaded by running:
source("http://www.math.ntnu.no/inla/givemeINLA-testing.R")
Functionality to dynamically define R functions and S4 methods
with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.
Generates Rd files from R source code with comments.
The main features of the default syntax are that
(1) docs are defined in comments near the relevant code,
(2) function argument names are not repeated in comments, and
(3) examples are defined in R code, not comments.
It is also easy to define a new syntax.
Non-parametric resampling-based inference tests for ExPosition.
insideRODE package includes buildin functions from
deSolve, compiled functions from compiler, and C/FORTRAN code
interfaces to nlme. It includes nlmLSODA, nlmODE,
nlmVODE,nlmLSODE for general purpose; cfLSODA,cfLSODE, cfODE,
cfVODE call C/FORTRAN compiled dll functions.ver2.0 add
sink()function into example it helps to directly combine
c/fortran source code in R files. Finally, with new compiler
package, we generated compiled functions: nlmODEcp, nlmVODEcp,
nlmLSODEcp,nlmLSODAcp and cpODE, cpLSODA, cpLSODE, cpVODE. They
will help to increase speed.
Computes individual causes of death and population cause-specific mortality fractions using the InSilicoVA algorithm. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used InterVA4 method. This package provides general model fitting and customization for InSilicoVA algorithm and basic graphical visualization of the output.
Functions to compute insolation on complex terrain.
Checks the local R library(ies) to see if the required
package(s) is/are installed or not. If the package(s) is/are
not installed, then the package(s) will be installed along with
the required dependency(ies). This function pulls source or
binary packages from the Revolution Analytics CRAN mirror and/or
the USGS GRAN Repository. Lastly, the chosen package(s)
is/are loaded.
R is great for installing software.  Through the 'installr'
package you can automate the updating of R (on Windows, using updateR())
and install new software. Software installation is initiated through a
GUI (just run installr()), or through functions such as: install.Rtools(),
install.pandoc(), install.git(), and many more. The updateR() command
performs the following: finding the latest R version, downloading it,
running the installer, deleting the installation file, copy and updating
old packages to the new R installation.
Provides an interface to the Instagram API <https://instagram.com/developer/>, which allows R users to download public pictures filtered by hashtag, popularity, user or location, and to access public users' profile data.
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
A package that provides classes and methods for automated
spatial interpolation.
A package that provides  additional functionality for spatial interpolation in the intamap package.
Implementation of ICM-Algorithm by Wei Pan, J. Comp. & Gr.
Stat. 78: 109-120, 1999 Algorithm for the Cox proportional
hazard model for interval censored data
Offers modelling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
This package searches for marginal interactions in a
binary response model. Interact uses permutation methods to
estimate false discovery rates for these marginal interactions
and has some, limited visualization capabilities
Implements the procedures suggested in Esarey and Sumner (2015) for controlling the false discovery rate or familywise error rate when constructing marginal effects plots for models with interaction terms.
An extension of the package 'igraph'. This package create
possibly to work with 'igraph' objects interactively.
A shiny application for designing adaptive clinical trials. For
more details, see: http://arxiv.org/abs/1404.0734
Chi-square tests are computed with corrections.
Computes large sample confidence intervals of Liu and Hudgens (2014), exact confidence intervals of Tchetgen Tchetgen and VanderWeele (2012), and exact confidence intervals of Rigdon and Hudgens (2014) for treatment effects on a binary outcome in two-stage randomized experiments with interference.
Functions implemented in this package allow to coerce (i.e.
convert) network data between classes provided by other R packages.
Currently supported classes are those defined in packages: network and
igraph.
Search the Internet Archive, retrieve metadata, and download
files.
Plots the conditional coefficients ("marginal effects") of
variables included in multiplicative interaction terms.
A package for numerical encoding as well as for linear and
non-linear interpolation of amino acid sequences.
Hourly interpolation of daily minimum and maximum temperature
series. Carries out interpolation on multiple series ad once. Requires some
hourly series for calibration (alternatively can use default calibration
table).
Compute permutation- based performance measures and create partial
dependence plots for (cross-validated) 'randomForest' and 'ada' models.
Generates three inter-related genomic datasets : methylation, gene expression and protein expression.
Provides an R version of the InterVA4 software for
coding cause of death from verbal autopsies. It also provides simple
graphical representation of individual and population level statistics.
Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests.
Tools for working with and comparing sets of points and intervals.
This package allows a dynamic Bayesian network to be inferred from microarray timecourse data with interventions (inhibitors).
This package calculates the integrated likelihood numerically. Given the Likelihood function and the prior function, this package integrates out the nuisance parameters by Metropolis-Hastings (MCMC) Algorithm.
Solves linear programming problems by the interior point
method, and plots the graphical solution of a linear
programming problem of two dimensions.
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.
Estimating interval regression models.  Supports both common and observation-specific boundaries.
Performs Goodness of Fit for regression models using
introgress provides functions for analyzing introgression
of genotypes between divergent, hybridizing lineages, including
estimating genomic clines from multi-locus genotype data and
testing for deviations from neutral expectations. Functions are
also provided for maximum likelihood estimation of molecular
hybrid index and graphical analysis.
Provides tools for importing, merging, and analysing data from
international assessment studies (TIMSS, PIRLS, PISA. ICILS, and PIAAC).
Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
This package describes the associated cost games to inventory situations.
Functions to facilitate inverse estimation/calibration in linear,
generalized linear, nonlinear, and (linear) mixed models. A generic function
is also provided for plotting fitted regression models with or without
confidence/prediction bands that may be of use to the general user.
invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
Provides two functions for the numerical inversion of Laplace-Transformed functions, returning the value of the standard (time) domain function at a specified value.  The first algorithm is the first optimum contour algorithm described by Evans and Chung (2000)[1].
The second algorithm uses the Bromwich contour as per the definition of the inverse Laplace Transform.  The latter is unstable for numerical inversion and mainly included for comparison or interest.  There are also some additional functions provided for utility, including plotting and some simple Laplace Transform examples, for which there are known analytical solutions.  Polar-cartesian conversion functions are included in this package and are used by the inversion functions.
[1] Evans & Chung, 2000: Laplace transform inversions using optimal contours in the complex plane; International Journal of Computer Mathematics v73 pp531-543.
One function to read files. One function to write files. One
function to direct plots to screen or file. Automatic file format inference
and directory structure creation.
Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls.
Two methods for calculation of the number of required flows for semiconductor sequencing: 1. Using a simulation, the number of flows can be calculated for a concrete list of amplicons. 2. An exact combinatorial model is evaluated to calculate the number of flows for a random ensemble of sequences.
Density, spectral density, and regression estimation using infinite order kernels.
This package provides functions to estimate an IPD linear
mixed effects model for a continuous outcome and any
categorical covariate from study summary statistics. There are
also functions for estimating the power of a
treatment-covariate interaction test in an individual patient
data meta-analysis from aggregate data.
Functions are provided to interpolate georeferenced point data via Inverse Path Distance Weighting. Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances.
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen by cross-validation.
A fast (C) implementation of the iterative proportional fitting
procedure. Based on corresponding code from the networkTomography package.
IPMpack takes demographic vital rates and (optionally) environmental data to build integral projection models.  A number of functional forms for growth and survival can be incorporated, as well as a range of reproductive strategies.  The package also includes a suite of diagnostic routines, provides classic matrix model output (e.g., lambda, elasticities, sensitivities), and produces post-hoc metrics (e.g., passage time and life expectancy).
Improved predictive models by indirect classification and
bagging for classification, regression and survival problems
as well as resampling based estimators of prediction error.
This package provides functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states.
This package contains the Sweave source code used to
generate IPSUR, an introductory probability and statistics
textbook, alongside other supplementary materials such as the
parsed R code for the book and data for the examples and
exercises.  The book is released under the GNU Free
Documentation License.
A toolkit for manipulating,
validating and testing IP addresses and ranges, along with
datasets relating to IP addresses. While it primarily has
support for the IPv4 address space, more extensive IPv6 support
is intended.
Functions to estimate the probability to receive the observed treatment, based on
individual characteristics. The inverse of these probabilities can be used as weights when
estimating causal effects from observational data via marginal structural models. Both point
treatment situations and longitudinal studies can be analysed. The same functions can be used to
correct for informative censoring.
Builds statistical control charts with exact limits for
univariate and multivariate cases.
Estimate an optimal dynamic treatment regime using Interactive Q-learning.
Iterated racing for automatic algorithm configuration.
"iRefR" allows the user to load any version of the consolidated protein interaction database "iRefIndex" and perform tasks such as: selecting databases, pmids, experimental methods, searching for specific proteins, separate binary interactions from complexes and polymers, generate complexes according to an algorithm that looks after possible binary-represented complexes, make general database statistics and create network graphs, among others.
This package contains some important regression methods
for interval-valued variables. For each method, it is available
the fitted values, residuals and some goodness-of-fit measures.
This package calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements. ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.
Classes and functions for metrics calculation as part of the
'IRIS DMC MUSTANG' project. The functionality in this package
builds upon the base classes of the 'IrisSeismic' package.
Metrics include basic statistics as well as higher level
'health' metrics that can help identify problematic seismometers.
Provides classes and methods for seismic data analysis. The
base classes and methods are inspired by the python code found in
the 'ObsPy' python toolbox <http://github.com/obsypy/obspy>. Additional classes and
methods support data returned by web services provided by the 'IRIS
DMC' <http://service.iris.edu/>.
Fast and memory efficient methods for truncated singular and eigenvalue decompositions and principal component analysis of large sparse or dense matrices.
Coefficients of Interrater Reliability and Agreement for
quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
Robinson'A, Kendall's W, Cohen's Kappa, ...
Provides a simple common interface to the estimation of item
parameters in IRT models for binary responses with three different programs
(ICL, BILOG-MG, and ltm, and a variety of functions useful with IRT models.
Multidimensional Person Item Response Theory probability distributions
Helper functions and example data sets accompanying De
Boeck, P. and Partchev, I. (2012) IRTrees: Tree-Based Item
Response Models of the GLMM Family, Journal of Statistical
Software - Code Snippets, 48(1), 1-28.
Interactive shiny application for running Item Response Theory
analysis.
The ISA is a biclustering algorithm that finds modules
in an input matrix. A module or bicluster is a block of the
reordered input matrix.
Selection of features for sparse regression estimation (like the LASSO). Selection of blocks of features when the regression parameter is sparse and constant by blocks (like the Fused-LASSO). Application to cgh arrays.
Provides datasets for Introduction to Statistical Data Analysis for the Life Sciences
describes a set of operations for symbolic data type based
on interval-valued. The operations are processing of punctuals
variables to interval variables, construction of a 3D graphic
interval, linear regression interval and interval descriptive
statistics such as mean, median, variance, standard deviation
and mode.
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
Classical Ising Model is a land mark system in statistical physics. Model explains physics of spin glasses and magnetic materials, but used in many different context for example phase transitions and neural systems. This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions.
Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.
The collection of datasets used in the book "An
Introduction to Statistical Learning with Applications in R"
Functions to support the computations carried out in
`An Introduction to Statistical Modeling of Extreme Values' by
Stuart Coles. The functions may be divided into the following
groups; maxima/minima, order statistics, peaks over thresholds
and point processes.
Linear order and unimodal order (univariate)
isotonic regression; bivariate isotonic regression
with linear order on both variables.
Some functions for confidence intervals for current status data based on transformations and bootstrap.
This is a package to draw isotonic inference for circular data.
ISO language, territory, currency, script and character codes.
Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217
currency codes, ISO 15924 script codes, and the ISO 8859 character codes
as well as the UN M.49 area codes.
Offers framework for testing for monotonic relationship between gene expression and doses in a microarray experiment. Several testing procedures including the global likelihood-ratio test (Bartholomew, 1961), Williams (1971, 1972), Marcus (1976), M (Hu et al. 2005) and the modified M (Lin et al. 2007) are used to test for the monotonic trend in gene expression with respect to doses. BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2004) FDR controlling procedures are applied to adjust the raw p-values obtained from the permutations.
Isopam clustering algorithm and utilities.
Isopam optimizes clusters and optionally cluster numbers in
a brute force style and aims at an optimum separation
by all or some descriptors (typically species).
The function calculates the isotopic pattern (fine
structures) for a given chemical formula.
Nonparametric estimation of an isotonic covariate effect for proprotional hazards model.
Deconvolution of mixed tumour profiles into normal and cancer for each patient, using
the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires
mixed tumour profiles and a set of unmatched "basis" normal profiles.
Contains two main functions: one for
solving general isotone regression problems using the
pool-adjacent-violators algorithm (PAVA); another one provides
a framework for active set methods for isotone optimization
problems with arbitrary order restrictions. Various types of
loss functions are prespecified.
Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Estimates diet contributions from isotopic sources using JAGS.
Includes estimation of concentration dependence and measurement error.
This is an substitute for the %V and %u formats which are
not implemented on Windows. In addition, the package offers
functions to convert from standard calender format yyyy-mm-dd
to and from ISO 8601 week format yyyy-Www-d.
Independent Surrogate Variable Analysis is an algorithm
for feature selection in the presence of potential confounding
factors.
Data sets and scripts for text examples and exercises in
P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales).
Iterates through a phylogenetic tree to identify regions
of rate variation using the parametric rate comparison test.
Support for iterators, which allow a programmer to traverse
through all the elements of a vector, list, or other collection
of data.
The iterLap (iterated Laplace approximation) algorithm
approximates a general (possibly non-normalized) probability
density on R^p, by repeated Laplace approximations to the
difference between current approximation and true density (on
log scale). The final approximation is a mixture of
multivariate normal distributions and might be used for example
as a proposal distribution for importance sampling (eg in
Bayesian applications).  The algorithm can be seen as a
computational generalization of the Laplace approximation
suitable for skew or multimodal densities.
It helps in 1) generating permutations and combinations with or without replacement; 2) with distinct items or non-distinct items (multiset); 3) calculating number of combinations or permutations in various situations. All generated sequences are in lexicographical order (AKA, dictionary order). The algorithms to generate permutations and combinations are memory efficient. These iterative algorithms enable users to process all sequences without putting all results in the memory at the same time.  The algorithms are written in C/C++ for faster performances.
Various tools for creating iterators, many patterned after
functions in the Python itertools module, and others patterned
after functions in the 'snow' package.
A port of Python's excellent itertools module to R for efficient
looping.
This package is based on the code of the rpart package.
It extends rpart by adding additional splitting methods
emphasizing interpretable/parsimonious trees.  Unless indicated
otherwise, it is safe to assume that all functions herein are
extensions of or copied directly from similar or nearly
identical rpart methods.  As such, the authors of rpart are
authors of this package as well.  However, please direct any
error reports or other questions about itree to the maintainer
of this package; they are welcome and appreciated.
The its package contains an S4 class for handling
irregular time series
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package mgcv (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
This package provides a subset of the functionality found in the Windows-based program ITSM. The intended audience is students using the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis.
This package includes functions to incorporate
uncertainties in estimated propensity scores and provide
adjusted standard errors for making valid causal inference.
This package allows one incorporate instrument and covariate uncertainty into instrumental variable regression.
Fit an Instrumental least square dummy variable model
GMM estimation of triangular systems using heteroscedasticity based instrumental variables as in Lewbel (2012)
Contains functions for carrying out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics.
This package contains functions for carrying out instrumental variable estimation of causal effects and power analyses for instrumental variable studies.
Fit the instrumental panel data models: the fixed effects, random
effects and between models.
ivprobit fit an Instrumental variables probit model using the
generalized least squares estimator
Improved methods based on inverse probability weighting
and outcome regression for causal inference and missing data
problems
An estimation method that can use computer simulations to
approximate maximum-likelihood estimates even when the likelihood function can not
be evaluated directly. It can be applied whenever it is feasible to conduct many
simulations, but works best when the data is approximately Poisson distributed.
It was originally designed for demographic inference in evolutionary
biology. It has optional support for conducting coalescent simulation using
the 'coala' package.
Computing the original and modified jackknife estimates of Kaplan-Meier estimators.
Significance test for association between variables
and their estimated latent variables.
Latent variables may be estimated by principal component analysis (PCA),
logistic factor analysis (LFA), and other techniques.
Implements the classical Jacobi (1846) algorithm for the
eigenvalues and eigenvectors of a real symmetric matrix, both in
pure R and in C++ using Rcpp. Mainly as a programming example
for teaching purposes.
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given.
A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags').  A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.
Implements a novel score test that measures 1) the overall shift in the gene expression due to genotype (additive genetic effect), and 2) group-specific changes in gene expression due to genotype (interaction effect) in a mixed-effects model framework.
Analyze and visualize results of studies performed with the
analysis tools in 'JAMES', a modern object-oriented Java
framework for discrete optimization using local search
metaheuristics (see http://www.jamesframework.org).
R modules for JASPAR data processing and visualization
Graphics device routing all graphics commands to a Java
program. The actual functionality of the JavaGD depends on the
Java-side implementation. Simple AWT and Swing implementations
are included.
Collection of several tools and helper functions used across the other packages of J. Buttlar ('ncdf.tools' and 'spectral.methods').
On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.
Fits two different joint generalized estimating equation models to multivariate longitudinal data.
The Joint Graphical Lasso is a generalized method for
estimating Gaussian graphical models/ sparse inverse covariance
matrices/ biological networks on multiple classes of data.  We
solve JGL under two penalty functions: The Fused Graphical
Lasso (FGL), which employs a fused penalty to encourage inverse
covariance matrices to be similar across classes, and the Group
Graphical Lasso (GGL), which encourages similar network
structure between classes.  FGL is recommended over GGL for
most applications.
Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS. This can be downloaded from JGR website: http://rforge.net/JGR/
Chinese text segmentation, keyword extraction and speech tagging
For R.
jiebaR is a package for Chinese text segmentation, keyword extraction
and speech tagging. This package provides the data files required by jiebaR.
Shared parameter models for the joint modeling of longitudinal and time-to-event data.
Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC.
Fit joint mean-covariance models for longitudinal data. The models
and their components are represented using S4 classes and methods. The core
computational algorithms are implemented using the 'Armadillo' C++ library
for numerical linear algebra and 'RcppArmadillo' glue.
Performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown.
The main purpose of this package is to make it easy for userR's to interact with 'jMetrik' an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by 'jMetrik'. It also allows useR's to read *.jmetrik files (e.g. output from an analysis) for follow-up analysis in R. The *.jmetrik format is a flat file that includes a multiline header and the data as comma separated values. The header includes metadata about the file and one row per variable with the following information in each row: variable name, data type, item scoring, special data codes, and variable label.
Implements z-normalization, SAX, HOT-SAX, VSM, and SAX-VSM algorithms
facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly),
and characteristic pattern discovery along with interpretable time series classification.
RE.Johnson performs the Johnson Transformation to increase the normality.
Johnson curve distributions.  Implementation of AS100 and
AS99.
Analysis of repeated measurements and time-to-event data
via random effects joint models. Some plotting functions and
the variogram are also included.
Perform regression analyses under the joint Cox proportional hazards model between tumour progression and death for meta-analysis.
The method is applicable for meta-analysis combining several studies or for cluster survival data.
Different algorithms to perform approximate joint
diagonalization of a finite set of square matrices. Depending
on the algorithm, orthogonal or non-orthogonal diagonalizer is
found. These algorithms are particularly useful in the context
of blind source separation.
A bivariate integration method to estimate risk caused by two extreme and dependent forcing variables.
A joint regression model for mixed correlated binary and
continuous responses is presented. In this model binary
response can be dependent on the continuous response. With this
model, the dependence between responses can be taken into
account by the correlation between errors in the models for
binary and continuous responses.
Similarly to Schafer's package pan, jomo is a package for multilevel joint modelling multiple imputation.
Novel aspects of jomo are the possibility of handling binary and categorical data through latent normal variables and the option to use cluster-specific covariance matrices.
JOP is a tool for simultaneous optimization of multiple
responses and visualization of the results. The visualization
is done by the joint optimization plot introduced by Kuhnt and
Erdbruegge (2004).
Implementation of some unit level EBLUP and GREG estimators as well as the estimate of their variances to further document the publication of Breidenbach and Astrup (2011). The vignette further explains the use of the implemented functions.
This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.
A Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.
Functions, methods, and datasets for cancer survival
analysis, including the proportional hazard relative survival
model, the join point relative survival model.
Simultaneous estimation of multiple related networks.
Implements the basic financial analysis
functions similar to (but not identical to) what
is available in most spreadsheet software. This
includes finding the IRR and NPV of regularly
spaced cash flows and annuities. Bond pricing and
YTM calculations are included. In addition, Black
Scholes option pricing and Greeks are also
provided.
A set of utility functions for working with JavaScript in R.
Currently includes functions to compile, validate, reformat, optimize
and analyze JavaScript code.
This package enables users to access MongoDB by running queries
and returning their results in R data frames. Usually, data in MongoDB is
only available in the form of a JSON document. jSonarR uses data
processing and conversion capabilities in the jSonar Analytics Platform
and the JSON Studio Gateway (http://www.jsonstudio.com), to convert it to
a tabular format which is easy to use with existing R packages.
A fast JSON parser and generator optimized for statistical data
and the web. Started out as a fork of 'RJSONIO', but has been completely
rewritten in recent versions. The package offers flexible, robust, high
performance tools for working with JSON in R and is particularly powerful
for building pipelines and interacting with a web API. The implementation is
based on the mapping described in the vignette (Ooms, 2014). In addition to
converting JSON data from/to R objects, 'jsonlite' contains functions to
stream, validate, and prettify JSON data. The unit tests included with the
package verify that all edge cases are encoded and decoded consistently for
use with dynamic data in systems and applications.
Transforming univariate non-normal data to normality using Johnson
families of distributions. Johnson family is a comprehensive distribution
family that accommodates many kinds of non-normal distributions. A bunch of
distributions with various parameters will be fit and the corresponding
p-values under a user-specified normality test will be given. The final
transformation will be the one with the largest p-value under the given
normality test.
A set of convenience functions to build simple APIs.
Generates image data for fractals (Julia and Mandelbrot
sets) on the complex plane in the given region and resolution.
Many method to compute, predict and back-test VaR. For more detail, see the report: Value at Risk <researchgate.net>.
Identify and sort genes overexpressed and associated to transmembrane protein in Affymetrix expression set or any other results of microarray experiment.
Offers a complete and interactive GUI to work out Cohen's and Fleiss' Kappa.
S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
This package contains basic tools for the purpose of
sample size estimation in studies of interobserver/interrater
agreement (reliability).  This package contains sample size
estimation functions for both the power-based and confidence
interval-based methods, with binary or multinomial outcomes and
two through six raters.
this package allows to quantify the congruence between two patchy
mosaics or landscapes. This "vectorial Kappa" approach extends the
principle of Cohen's Kappa index by calculating areas of intersected
patches between two mosaics rather than agreement between pixels. It
provides an exact alternative for patchy mosaics when a Kappa index is
needed.
This package provides some routines to conduct the K-adaptive parititioning (kaps) algorithm for survival data. A function kaps is an implementation version of our algorithm.
Package for kinetic analysis of longitudinal voxel-wise Dynamic Contrast Enhanced MRI data. Includes tools for visualization and exploration of voxel-wise parametric maps.
Create, Simulate, Fit, Solve k-Cube Thurstonian IRT Models
Provides fast implementations of kernel smoothing techniques for
bivariate copula densities, in particular density estimation and resampling.
A non-parametric method for identifying potential
outlying observations in a collection of phylogenetic trees based
on the methods of Owen and Provan (2011). Such discordant trees
may indicate problems with sequence annotation or tree
reconstruction, or they may represent interesting biological
phenomena, such as horizontal gene transfers.
Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data.
Provides arrays with flexible control over dimension dropping when subscripting.
Uses Bessel functions to calculate the
fundamental and complementary analytic solutions to the
Kelvin differential equation.
Computes the Kendall rank correlation and Mann-Kendall
trend test. See documentation for use of block bootstrap when
there is autocorrelation.
Implements the kernel method of test equating using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Nonparametric kernel distribution function estimation is
performed. Three automatic bandwidth selection methods for
nonparametric kernel distribution function estimation are
implemented: the plug-in of Altman and Leger, the plug-in of
Polansky and Baker, and the modified cross-validation of
Bowman, Hall and Prvan. The exceedance function, the mean
return period and the return level are also computed.
An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.
Gaussian Process models with customised covariance kernels.
Contains functions for estimation and model selection of kernel
deep stacking networks. The model selection includes direct optimization or
model based alternatives with arbitrary loss functions.
A novel implementation that solves the DWD and the kernel DWD which can be hundred times faster than the existing implementation of DWD based on the second-order-cone programming (SOCP). The package also handles some other general loss functions including large-margin unified machines (LUM).
Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.
In self-reported or anonymized data the user often encounters
heaped data, i.e. data which are rounded (to a possibly different degree
of coarseness). While this is mostly a minor problem in parametric density
estimation the bias can be very large for non-parametric methods such as kernel
density estimation. This package implements a partly Bayesian algorithm treating
the true unknown values as additional parameters and estimates the rounding
parameters to give a corrected kernel density estimate. It supports various
standard bandwidth selection methods. Varying rounding probabilities (depending
on the true value) and asymmetric rounding is estimable as well. Additionally,
bivariate non-parametric density estimation for rounded data is supported.
Kernel-based machine learning methods for classification,
regression, clustering, novelty detection, quantile regression
and dimensionality reduction.  Among other methods kernlab
includes Support Vector Machines, Spectral Clustering, Kernel
PCA, Gaussian Processes and a QP solver.
Functions for kernel smoothing (and density estimation)
corresponding to the book:
Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
This package fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools.
Provides group centrality measures and identifies the most central group of players in a network.
Wait for a single key press at the 'R' prompt.
This works in terminals, but does not currently work
in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'),
in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in
'R Studio'. In these cases 'keypress' stops with an
error message.
Functions for Kalman filtering, smoothing,
forecasting and simulation of multivariate exponential family state space
models with exact diffuse initialization and sequential processing.
A streamlined cross-referencing system for R Markdown documents
generated with 'knitr'. R Markdown is  an authoring format for generating
dynamic content from R. 'kfigr' provides a hook for anchoring code
chunks and a function to cross-reference document elements generated from
said chunks, e.g. figures and tables.
Naive implementation of the Kalman filter, smoother and disturbance
smoother for state space models.
A collection of useful functions not found anywhere else, mainly
for programming: Generalized lagged differences, checking containment in an
interval, creating a factor where the levels maintain the order of
appearance, sampling rows from a data frame, converting seconds from
midnight to and from H:M:S format, choosing the first non-NA value,
transposing lists of lists, returning the name of the file currently
sourced, smart named lists and vectors, and an alternative interface to
assign().
Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific
cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are
retrospectively built from relatives of probands for whom the genotype is known. Currently
the method of moments and marginal maximum likelihood are implemented. Confidence intervals
are calculated from bootstrap samples.
Most of the code is a translation from previous 'MATLAB' code by N. Chatterjee.
Data cleaning, processing, visualisation and analysis for manual (Goldmann) and automated (Octopus 900) kinetic visual field data.
The FOCUS Kinetics Report first published in 2006 describes mathematical
models and recommends statistical methods for the evaluation of
chemical degradation data. This package implements fitting the kinetic
models suitable for observations of the decline of a single chemical
compound (no metabolite formation/decline or multi-compartment kinetics).
Please note that no warranty is implied for correctness of results or
fitness for a particular purpose.  'kinfit' is maintained, but not
actively developed at the moment. Please check the 'mkin' package for an
actively developed package for kinetic evaluations of degradation data.
Routines to handle family data with a pedigree object. The initial purpose was to create correlation structures that describe
family relationships such as kinship and identity-by-descent, which
can be used to model family data in mixed effects models, such as in the
coxme function.  Also includes a tool for pedigree drawing which is
focused on producing compact layouts without intervention.  Recent additions
include utilities to trim the pedigree object with various criteria, and
kinship for the X chromosome.
This package allows one to compose kintone REST API requests and convenient functions to convert kintone-JSON/R-Objects in R scriptings.
Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, stochastic migration model. Providing accessibility for considering species migration in niche-based species distribution models.
Provides tools to calculate the theoretical hydrodynamic response
of an aquifer undergoing harmonic straining or pressurization. There are
two classes of models here: (1) for sealed wells, based on the model of
Kitagawa et al (2011), and (2) for open wells, based on the models of
Cooper et al (1965), Hsieh et al (1987), Rojstaczer (1988), and Liu et al
(1989). These models treat strain (or aquifer head) as an input to the
physical system, and fluid-pressure (or water height) as the output. The
applicable frequency band of these models is characteristic of seismic
waves, atmospheric pressure fluctuations, and solid earth tides.
Weighted k-Nearest Neighbors for Classification, Regression and Clustering.
Miscellaneous functions for classification and visualization
developed at the Fakultaet Statistik, Technische Universitaet Dortmund
A set of functions designed to quickly generate results of a
multiple choice test. Generates detailed global results, lists for
anonymous feedback and personalised result feedback (in LaTeX and/or PDF
format), as well as item statistics like Cronbach's alpha or disciminatory
power. klausuR also includes a plugin for the R GUI and IDE RKWard,
providing dialogs for its basic features. To use them, install RKWard from
http://rkward.sf.net (plugins are detected automatically). Due to some
restrictions on CRAN, the full package sources are only available from the
project homepage.
The package implements efficient ways to evaluate and
solve equations of the form Ax=b, where A is a kronecker
product of matrices.  Functions to solve least squares problems
of this type are also included.
Linearly constrained Kaplan-Meier estimator for right censored data. This version does empirical likelihood ratio test with right censored data with linear type constraint and hypothesis testing for coefficients in accelerated failure time model.
Computes various confidence intervals for the Kaplan-Meier
estimator, namely: Petos CI, Rothman CI, CI's based on
Greenwoods variance, Thomas and Grunkemeier CI and the
simultaneous confidence bands by Nair and Hall and Wellner.
Computes and plots an exact nonparametric band for any user-specified level of confidence from a single-sample survfit object
Compute p-values of metabolite differential expression analysis using the kernel-based approach.
Distributed k-means clustering algorithm written using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
The package performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions.
This package contains a collection of functions for common data
extraction and reshaping operations, string manipulation, and
functions for table and plot generation for R Markdown documents.
An implementation of k-means specifically design
to cluster longitudinal data. It provides facilities to deal with missing
value, compute several quality criterion (Calinski and Harabatz,
Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical
interface for choosing the 'best' number of clusters.
An implementation of k-means specifically design
to cluster joint trajectories (longitudinal data on
several variable-trajectories).
Like 'kml', it provides facilities to deal with missing
value, compute several quality criterion (Calinski and Harabatz,
Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
interface for choosing the 'best' number of clusters. In addition, the 3D graph
representing the mean joint-trajectories of each cluster can be exported through
LaTeX in a 3D dynamic rotating PDF graph.
'kmlcov' Cluster longitudinal data using the likelihood as a
metric of distance. The generalised linear model allow the user to
introduce covariates with different level effects (2 levels).
An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, "Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.
Data sets and functions for Klein and Moeschberger (1997),
"Survival Analysis, Techniques for Censored and Truncated
Data", Springer.
Provides the ability to create dynamic citations
in which the bibliographic information is pulled from the web rather
than having to be entered into a local database such as 'bibtex' ahead of
time. The package is primarily aimed at authoring in the R 'markdown'
format, and can provide outputs for web-based authoring such as linked
text for inline citations.  Cite using a 'DOI', URL, or
'bibtex' file key.  See the package URL for details.
Provides several helper functions for working with 'knitr' and 'LaTeX'.
It includes 'xTab' for creating traditional 'LaTeX' tables, 'lTab' for generating
'longtable' environments, and 'sTab' for generating a 'supertabular' environment.
Additionally, this package contains a knitr_setup() function which fixes a
well-known bug in 'knitr', which distorts the 'results="asis"' command when used
in conjunction with user-defined commands; and a com command (<<com=TRUE>>=)
which renders the output from 'knitr' as a 'LaTeX' command.
Provides a general-purpose tool for dynamic report generation in R
using Literate Programming techniques.
A framework to create Bootstrap 3 HTML reports from knitr
Rmarkdown.
Scale categorical variables in such a way as
to make NN classification as accurate as possible. The code also
handles continuous variables and prior probabilities, and does
intelligent variable selection and estimation of both error rates
and the right number of NN's.
Multi-distance based k-Nearest Neighbors Classification
with K Threshold Value Check and Same K_i Problem Dealing,
Missing Observations Filling
This package provides the implementation of an exact formula of the
ith nearest neighbour distance distribution and implementations of tests of
independence based on that formula. Furthermore the package provides a
general framework to benchmark tests of independence.
The knockoff filter is a procedure for controlling the false
discovery rate (FDR) when performing variable selection. For more information,
see the website below and the accompanying paper.
The tuna Regional Fisheries Management Organisations (tRFMOs) use a common framework for providing scientific advice, i.e. the Kobe II Framework. This is based on maintaining fishing mortality below FMSY and stock biomass above  BMSY. This package provides methods for summarising results from stock assessments and Management Strategy Evaluations in the Kobe format.
KODAMA (KnOwledge Discovery by Accuracy MAximization) is an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data.
Function kofnGA uses a genetic algorithm to choose a subset of a
fixed size k from the integers 1:n, such that a user-supplied objective function
is minimized at that subset.  The selection step is done by tournament selection
based on ranks, and elitism may be used to retain a portion of the best solutions
from one generation to the next.
Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Functions to train supervised and self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
Provides an alternative, more efficient evaluation of extreme
probabilities of Kolmogorov's goodness-of-fit measure, Dn, when compared to
the original implementation of Wang, Marsaglia, and Tsang. These
probabilities are used in Kolmogorov-Smirnov tests when comparing two
samples.
Korean language processing package. Morphological
analyzer, POS tagger, Keystroke converter, Hangul automata,
Concordance, Mutual Information..
A set of tools to analyze texts. Includes, amongst others,
functions for automatic language detection, hyphenation, several indices of
lexical diversity (e.g., type token ratio, HD-D/vocd-D, MTLD) and readability
(e.g., Flesch, SMOG, LIX, Dale-Chall). Basic import functions for language
corpora are also provided, to enable frequency analyses (supports Celex and
Leipzig Corpora Collection file formats) and measures like tf-idf. Note: For
full functionality a local installation of TreeTagger is recommended. 'koRpus'
also includes a plugin for the R GUI and IDE RKWard, providing graphical dialogs
for its basic features. The respective R package 'rkward' cannot be installed
directly from a repository, as it is a part of RKWard. To make full use of this
feature, please install RKWard from https://rkward.kde.org (plugins are detected
automatically). Due to some restrictions on CRAN, the full package sources are
only available from the project homepage.
Consider linear regression model and autoregressive model of order p where errors in the linear regression model and innovations in the autoregression model are independent and symmetrically distributed. Hira L. Koul proposed a nonparametric minimum distance estimation method by minimizing L2-type distance between certain weighted residual empirical distribution functions. He also proposed a simpler version of  the loss function by using symmetry of the integrating measure in the distance. This package contains two functions: KoulLrMde() and KoulArMde(). KoulLrMde() and KoulArMde() provide minimum distance estimators for linear regression model and autoregression model, respectively, where both are based on Koul's method. These two functions take much less time for the computation than those based on parametric minimum distance estimation methods.
The kpodclustr package implements the k-POD method for clustering
partially observed data.
Provides basic functions for Continuation-Passing Style development.
Simple and highly optimized ordinary kriging algorithm to plot geographical data
Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence.
Kernel smoothers for univariate and multivariate data.
Compares k samples using the Anderson-Darling test,
Kruskal-Wallis type tests with different rank score criteria,
and Steel's multiple comparison test.  It computes asymptotic,
simulated or (limited) exact P-values, all valid under
randomization, with or without ties, or conditionally under
random sampling from populations, given the observed tie
pattern.  Except for Steel's test it also combines these tests
across several blocks of samples.  Also analyzed are 2 x t
contingency tables and their blocked combinations using the
Kruskal-Wallis criterion.  Steel's test is inverted to provide
simultaneous confidence bounds for shift parameters.  A
plotting function shows that asymptotic approximations for
small sample sizes provide only very limited utility when
comparing tail probabilities with those obtained via simulation
or exact calculations.
Selection of k in k-means clustering based on Pham et al. paper
``Selection of k in k-means clustering''.
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Knowledge Space Theory is a set-theoretical framework, which
proposes mathematical formalisms to operationalize knowledge structures in a
particular domain. The kst-package provides basic functionalities to
generate, handle, and manipulate knowledge structures and knowledge spaces.
This function is designed for use with an arbitrary set of equations with
an arbitrary set of unknowns.
The user selects "fixed" values for enough unknowns to leave as many variables as
there are equations, which in most cases means the system is properly
defined and a unique solution exists. The function, the fixed values
and initial values for the remaining unknowns are fed to a nonlinear backsolver.
The original version of "TK!Solver" was the inspiration for this function.
These functions compute the k best pairs of genes used to classify samples based on the relative rank of the genes expression within each profile. A score based on the sensitivity and the specificity is calculated for every possible pair. The k pairs with the highest score will be selected with the restriction that a gene can appear in at most one pair. The value of k is either set as a parameter chosen by the user or computed through crossvalidation. Other functions related to the k-TSP are also available, for example the functions prediction, summary, plot, etc. can be found in the package.
Provides various functions and data sets from experiments at the Faculty of Life Sciences, University of Copenhagen. This package will be discontinued and archived, and the functions and datasets will be maintained and updated in the MESS package
Calculation groundwater mounding beneath an infiltration basin based on the Hantush (1967) equation (http://doi.org/10.1029/WR003i001p00227). The correct implementation is shown with a verification example based on a USGS report (page 25, http://pubs.usgs.gov/sir/2010/5102/support/sir2010-5102.pdf).
A miscellaneous set of functions for printing, plotting, kernels, etc. Additional contributors are acknowledged on individual function help pages.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
A colletion of functions to implement Kolmogorov-Zurbenko
Fourier transform based periodograms and smoothing methods
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
L1 estimation for linear regression and random number generation for
the multivariate Laplace distribution.
Efficient implementation of Friedman's boosting algorithm
with l2-loss function and coordinate direction (design matrix
columns) basis functions.
A variety of ordination and community analyses
useful in analysis of data sets in community ecology.
Includes many of the common ordination methods, with
graphical routines to facilitate their interpretation,
as well as several novel analyses.
Support labeled loop and escape from nested loop
Provides a range of axis labeling algorithms
An implementation of distance-based ranking algorithms to predict rankings of labels.  Two common algorithms are included: the naive Bayes and the nearest neighbor algorithms.
The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
The package offers a dendrogram representation of series
of labels, this is specially needed in Markov chain Monte Carlo
clustering. If you have a dendrogram in your mind you can
easily put series of meaningful labels in a matrix and heights
in a vector, then convert them to a dendrogram abject.
Insieme di funzioni di supporto al volume "Laboratorio di
Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006.
This package contains sets of functions defined in "Laboratorio
di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia,
2006. Function names and docs are in italian as well.
Estimation of indicators on social exclusion and poverty, as well
as Pareto tail modeling for empirical income distributions.
The package contains functions to compare and group means.
Methods for fast access to large ASCII files.  Currently the
following file formats are supported: comma separated format (csv) and fixed
width format. It is assumed that the files are too large to fit into memory,
although the package can also be used to efficiently access files that do
fit into memory. Methods are provided to access and process files blockwise.
Furthermore, an opened file can be accessed as one would an ordinary
data.frame. The LaF vignette gives an overview of the functionality
provided.
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is also provided, as are associated wrapper routines for blackbox optimization under constraints via an augmented Lagrangian scheme, and large scale computer model calibration.
Provides the tables from Sean Lahman's Baseball Database as a set of R data.frames.
It uses the data on pitching, hitting and fielding performance and other tables
from 1871 through 2014, as recorded in the 2015 version of the database.
A collection of tools for the calculation of freewater metabolism.
Lake morphometry metrics are used by limnologists to understand,
among other things, the ecological processes in a lake.  This
package provides the tools to calculate a typical suite of these
metrics from an input elevation model and lake polygon.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Currently the package includes 14 methods for calculating internal
consistency reliability but is still growing.  The package allows users
access to whichever reliability estimator is deemed most appropriate for
their situation.
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Provides tools that manipulate and transform data using methods
and techniques consistent with functional programming. The idea is that
through the use of these tools, a program can be reasoned about insomuch
that the implementation can be proven to be equivalent to the mathematical
model.
Lambert W x F distributions are a generalized framework to analyze
skewed, heavy-tailed data. They are based on an input/output system, where
the input random variable (RV) X ~ F, and the output Y is a non-linearly
transformed version of X with similar properties, but slightly skewed and/or
heavy-tailed. This transformed RV Y has a Lambert W x F distribution.
This package contains functions to model and analyze skewed, heavy-tailed
data the Lambert Way: simulate random samples, estimate parameters, compute
quantiles, and plot/print results nicely. Probably the most important
function is 'Gaussianize', which works similarly to 'scale', but actually
makes the data Gaussian.  A do-it-yourself toolkit allows users to define
their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis
right away.
Implements both real-valued branches of the Lambert-W function, also known as the product logarithm, without the need for installing the entire GSL.
Using Liquid Association for Network Dynamics Detection.
Provides functions to estimate survival and a treatment effect using a landmark estimation approach.
This package includes functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307.
Processing of Landsat or other multispectral satellite
imagery. Includes relative normalization, image-based
radiometric correction, and topographic correction options.
Functions for converted Landsat 8 multispectral satellite imagery
rescaled to the top of atmosphere (TOA) reflectance, radiance
and/or at satellite brightness temperature using radiometric
rescaling coefficients provided in the metadata file (MTL file).
Estimate drift and diffusion functions from time series and
generate synthetic time series from given drift and diffusion coefficients.
Data sets exemplifying statistical methods, and some
facilitatory utility functions used in "Analyzing Linguistic
Data: A practical introduction to statistics using R",
Cambridge University Press, 2008.
Solves the problem of Laplace deconvolution with noisy discrete
non-equally spaced observations on a finite time interval based on expansions
of the convolution kernel, the unknown function and the observed signal over
Laguerre functions basis. It implements the methodology proposed in the paper
"Laplace deconvolution on the basis of time domain data and its application to
Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y.
Rozenholc in ArXiv (http://arxiv.org/abs/1405.7107).
This package is intended for researchers studying historical labour relations (see http://www.historyoflabourrelations.org). The package allows for easy access of excel files in the standard defined by the Global Collaboratory on the History of Labour Relations. The package also allows for visualisation of labour relations according to the Collaboratory's format.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
Efficient procedures for fitting an entire lasso sequence
with the cost of a single least squares fit. Least angle
regression and infinitesimal forward stagewise regression are
related to the lasso, as described in the paper below.
laser implements maximum likelihood methods based on the
birth-death process to test whether diversification rates have
changed over time and whether rates vary among lineages.
Routines and documentation for solving regression problems
while imposing an L1 constraint on the estimates, based on
the algorithm of Osborne et al. (1998)
Use the lasso regression method to perform approximate inference
in high dimensions, by penalizing the effects of nuisance parameters.
L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting is fast. This
implementation can choose which coefficients to penalize. It
support coefficient-specific penalities and it can take X'X and
X'y instead of X and y.
This is a simple wrapper for the LASVM Solver (see http://leon.bottou.org/projects/lasvm). LASVM is basically an online variant of the SMO solver.
Writes a file of commands for the dot program to draw a graph
proposed by Rosenbaum and useful for checking some properties of various
sorts of latent scale
Fit and simulate latent position and cluster models for statistical networks.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Parses and converts LaTeX math formulas to R's plotmath
expressions, used to enter mathematical formulas and symbols to be rendered as
text, axis labels, etc. throughout R's plotting system.
A powerful and elegant high-level data visualization
system inspired by Trellis graphics, with an emphasis on
multivariate data. Lattice is sufficient for typical graphics needs,
and is also flexible enough to handle most nonstandard requirements.
See ?Lattice for an introduction.
This package contains functions that compute the
lattice-based density estimator of Barry and McIntyre, which
accounts for point processes in two-dimensional regions with
irregular boundaries and holes.  The package also implements
two-dimensional non-parametric regression for similar regions.
Extra graphical utilities based on lattice
Functions for the interpolation of large spatial
datasets. This package follows a "fixed rank Kriging" approach using
a large number of basis functions and provides spatial estimates
that are comparable to standard families of covariance functions.
Using a large number of basis functions allows for estimates that
can come close to interpolating the observations (a spatial model
with a small nugget variance.)  Moreover, the covariance model for this method
can approximate the Matern covariance family but also allows for a
multi-resolution model and supports efficient computation of the
profile likelihood for estimating covariance parameters. This is
accomplished through compactly supported basis functions and a
Markov random field model for the basis coefficients. These features
lead to sparse matrices for the computations. An extension of this
version over previous ones ( < 5.4 ) is the support for different
geometries besides a rectangular domain.
One benefit of the LatticeKrig model/approach
is the facility to do unconditional and conditional
simulation of the field for large numbers of arbitrary points. There
is also the flexibility for estimating non-stationary covariances. Included are
generic methods for prediction, standard errors for prediction,
plotting of the estimated surface and conditional and unconditional
simulation.
Estimation and simulation of latent variable models.
Fit a variety of latent variable models, including confirmatory
factor analysis, structural equation modeling and latent growth curve models.
Fit structural equation models (SEM) including factor analysis,
multivariate regression models with latent variables and many other latent
variable models while correcting estimates, standard errors, and
chi-square-derived fit measures for a complex sampling design.
Incorporate clustering, stratification, sampling weights, and
finite population corrections into a SEM analysis.
Wrapper around packages lavaan and survey.
lava plugin allowing combinations of left and right
censored and binary outcomes
Client for 'Turfjs' (http://turfjs.org/) for
'geospatial' analysis. The package revolves around using 'GeoJSON'
data. Functions are included for creating 'GeoJSON' data objects,
measuring aspects of 'GeoJSON', and combining, transforming,
and creating random 'GeoJSON' data objects.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness,
measures of relative variability etc, the package contains new robust tests of symmetry,
omnibus and directional tests of normality, and their graphical counterparts such as
Robust QQ plot; a robust trend tests for variances etc. All implemented tests and methods
are illustrated by simulations and real-life examples from legal statistics, economics,
and biostatistics.
By combining constant, linear, and quadratic local models,
lazy estimates the value of an unknown multivariate function on
the basis of a set of possibly noisy samples of the function
itself.  This implementation of lazy learning automatically
adjusts the bandwidth on a query-by-query basis through a
leave-one-out cross-validation.
Supplies a LazyData facility for packages which have data
sets but do not provide LazyData.  A single function is
is included, requireData, which is a drop-in replacement for
base::require, but carrying the additional
functionality. By default, it suppresses package
startup messages as well.  See argument 'reallyQuitely'.
A disciplined approach to non-standard evaluation.
Provides the functionality to write LaTeX code from within R
without having to learn LaTeX.  Functionality also exists to create HTML
and Markdown code.  While the functionality still exists to write
complete documents with lazyWeave, it is generally easier to do so with
with markdown and knitr.  lazyWeave's main strength now is the ability
to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way contingency table with an exploratory variable and a response variable. It is specially designed for compositional data.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0 (2011) limited
memory BFGS minimizer with bounds on parameters.
The package offers various length-bias corrections to
survival curve estimation.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
This package performs a Latent Class Analysis of
phenotypic measurements in pedigrees and a model selection
based on one of two methods: likelihood-based cross-validation
and Bayesian Information Criterion. It computes also individual
and triplet child-parents weights in a pedigree using an
upward-downward algorithm. It takes into account the familial
dependence defined by the pedigree structure by considering
that a class of a child depends on his parents classes via
triplet-transition probabilities of the classes. The package
handles the case where measurements are available on all
subjects and the case where measurements are available only on
symptomatic (i.e. affected) subjects. Distributions for
discrete (or ordinal) and continuous data are currently
implemented. The package can deal with missing data.
Functions for learning chain graphs (and as a special
case, Bayesian networks) via the decomposition approach.
Local Discrimination via Latent Class Models
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.
Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.
Collections of functions allowing random number generations and estimation of Liouville copulas.
The main purpose of lctools is to provide researchers and educators with easy-to-learn
user friendly tools for calculating key spatial statistics and to apply simple as well as
advanced methods of spatial analysis in real data. These include: Local Pearson and
Geographically Weighted Pearson Correlation Coefficients, Spatial Inequality Measures
(Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I),
several Geographically Weighted Regression techniques and other Spatial Analysis tools
(other geographically weighted statistics). This package also contains functions for
measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Implements latent Dirichlet allocation (LDA)
and related models.  This includes (but is not limited
to) sLDA, corrLDA, and the mixed-membership stochastic
blockmodel.  Inference for all of these models is
implemented via a fast collapsed Gibbs sampler written
in C.  Utility functions for reading/writing data
typically used in topic models, as well as tools for
examining posterior distributions are also included.
Performs group matching by backward elimination using linear discriminant analysis.
For this first version only metrics to estimate the best fitting
number of topics are implemented.
Tools to create an interactive web-based visualization of a
topic model that has been fit to a corpus of text data using
Latent Dirichlet Allocation (LDA). Given the estimated parameters of
the topic model, it computes various summary statistics as input to
an interactive visualization built with D3.js that is accessed via
a browser. The goal is to help users interpret the topics in their
LDA topic model.
Computations related to group sequential boundaries.
Includes calculation of bounds using the Lan-DeMets
alpha spending function approach.
The package provides a set of functions which aim is to
propose four measures of linkage disequilibrium: the usual r^2
measure, the r^2_S measure (r^2 corrected by the structure
sample), the r^2_V (r^2 corrected by the relatedness of
genotyped individuals), the r^2_VS measure (r^2 corrected by
both the relatedness of genotyped individuals and the structure
of the sample).
Produces a graphical display, as a heat map, of measures
of pairwise linkage disequilibria between SNPs. Users may
optionally include the physical locations or genetic map
distances of each SNP on the plot.
ldlasso requires data be of class gwaa.data from the the
package GenABEL
this package provides functions for Finding Locally
D-optimal designs for Logistic, Negative Binomial, Poisson,
Michaelis-Menten, Exponential, Log-Linear, Emax, Richards,
Weibull and Inverse Quadratic regression models and also
functions for auto-constructing Fisher information matrix and
Frechet derivative based on some input variables and without
user-interfere.
Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D. Tasche), algorithm proposed by M. van der Burgt, K. Pluto and D. Tasche's most prudent estimation methodology.
Functions, methods, and data sets for fitting likelihood-based dimension reduction in regression, using principal fitted components (pfc), likelihood acquired directions (lad), covariance reducing models (core).
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace.
Exact tests for Linkage Disequilibrium (LD) and Hardy-Weinberg Equilibrium (HWE). - 2-sided LD tests based on different measures of LD (Kulinskaya and Lewin 2008) - 1-sided Fisher's exact test for LD - 2-sided Haldane test for HWE (Wiggington 2005) - 1-sided test for inbreeding - conditional p-values proposed in Kulinskaya (2008) to overcome the problems of asymetric distributions (for both LD and HWE)
The leader clustering algorithm provides
a means for clustering a set of data points. Unlike many other clustering
algorithms it does not require the user to specify the number of clusters,
but instead requires the approximate radius of a cluster as its primary
tuning parameter. The package provides a fast implementation of this
algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
and infinity) as well as for spatial data using the Haversine
formula, which takes latitude/longitude pairs as inputs and clusters
based on great circle distances.
A leaf angle distribution is a special distribution that is defined between 0 and 90 degrees, and a number of distributions are used to characterize the leaf angle distribution in real plant canopies. This package includes methods to fit distributions to data, visualize the fit, and compare fits of nine different distributions.
An interface for the image processing program ImageJ, which allows a rapid digital image analysis for particle sizes. This package includes function to write an ImageJ macro which is optimized for a leaf area analysis by default.
Create and customize interactive maps using the 'Leaflet' JavaScript
library and the 'htmlwidgets' package. These maps can be used directly from
the R console, from 'RStudio', in Shiny apps and R Markdown documents.
Display your spatial data on interactive web-maps using the open-source JavaScript library Leaflet. 'leafletR' provides basic web-mapping functionality to combine vector data and online map tiles from different sources. See <http://leafletjs.com> for more information on Leaflet.
Contains LEAPFrOG Gradient Optimisation and Expectation Maximisation functions for inferring parental admixture proportions from an offspring with SNP genotypes.
These functions take a gene expression value matrix, a
primary covariate vector, an additional known covariates
matrix.  A two stage analysis is applied to counter the effects
of latent variables on the rankings of hypotheses.  The
estimation and adjustment of latent effects are proposed by
Sun, Zhang and Owen (2011).  "leapp" is developed in the
context of microarray experiments, but may be used as a general
tool for high throughput data sets where dependence may be
involved.
Regression subset selection including exhaustive search
LearnBayes contains a collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Crabs in the English channel, deer skulls, English
monarchs, half-caste Manga characters, Jamaican cities,
Shakespeare's The Tempest, drugged up cyclists and sexually
transmitted diseases.
Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
Allows students to use R as an interactive educational environment
for statistical concepts, ranging from p-values to confidence intervals
to stability in time series.
Utilizing phylogenetic and functional information for the analyses of ecological datasets. The analyses include methods for quantifying the phylogenetic and functional diversity of assemblages.
Estimate the slope and intercept of a bivariate
linear relationship by calculating a posterior density
that is invariant to interchange and scaling of the
coordinates.
This package provides functions and datasets for the
reader of the book "Le logiciel R: Maitriser le langage,
Effectuer des analyse statistiques". The documentation and help
pages are written in french.
Each function accomplishes the work of several or more standard R functions. For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate.  Other functions provide for descriptive statistics, a comprehensive regression analysis, analysis of variance and t-test, plotting, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function, variable labels, color themes, and a built-in help system. A confirmatory factor analysis of multiple indicator measurement models is also available as are pedagogical routines for data simulation such as for the Central Limit Theorem. Compatible with 'RStudio' and 'knitr' including generation of 'knitr' instructions for interpretative output.
This package contains some simple objects and functions to do
statistics using linear models and a Bayesian framework.
R functions for handling, processing, and analyzing geographic
data on species' distributions and environmental variables as well as species'
description and conservation information.
Functions to plot Lexis Diagrams for Demographic purposes.
Provides an extension to factors called 'lfactor' that are similar to factors but allow users to refer to 'lfactor' levels by either the level or the label.
Functions for performing and visualizing Local Fisher Discriminant Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised Local Fisher Discriminant Analysis(SELF).
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors with many levels as pure control variables.
Includes support for instrumental variables, conditional F statistics for weak instruments,
robust and multi-way clustered standard errors, as well as limited mobility bias correction.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
rules, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy
rule-based ensemble (FRBE).
The "Manual on Low-flow Estimation and Prediction", published by the World Meteorological Organisation (WMO), gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.
Tools for linear grouping analysis. Three user-level
functions: gap, rlga and lga.
Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.
Spatial and spatio-temporal modelling of point patterns using the
log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
multivariate and aggregated point processes using Markov chain Monte Carlo.
Functions for testing the genetic association/gene-environment interaction in longitudinal gene-environment-wide interaction studies. Generalized score type tests are used for set based analyses. Then GEE based score tests are applied to all single variants within the defined set.
Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.
A very simple implementation of a class for
longitudinal data.
This package provides a number of methods for creating and
augmenting Latin Hypercube Samples
R interface to the open-source, ANSI C library 'libamtrack' (http://libamtrack.dkfz.org). 'libamtrack' provides computational routines for the prediction of detector response and radiobiological efficiency in heavy charged particle beams. It is designed for research in proton and ion dosimetry and radiotherapy. 'libamtrack' also includes many auxiliary physics routines for proton and ion beams. Original package and C-to-R conversion routines developed by Felix A. Klein.
A wrapper around the LIBLINEAR C/C++ library for
machine learning (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
LIBLINEAR is a simple library for solving large-scale regularized linear
classification and regression. It currently supports L2-regularized
classification (such as logistic regression, L2-loss linear SVM and L1-loss
linear SVM) as well as L1-regularized classification (such as L2-loss linear
SVM and logistic regression) and L2-regularized support vector regression
(with L1- or L2-loss). The main features of LiblineaR include multi-class
classification (one-vs-the rest, and Crammer & Singer method), cross
validation for model selection, probability estimates (logistic regression
only) or weights for unbalanced data. The estimation of the models is
particularly fast as compared to other libraries.
Efficient procedures for fitting the regularization path
for linear, binomial, multinomial, Ising and Potts models with lasso,
group lasso or column lasso(only for multinomial) penalty.
The package uses Linearized Bregman Algorithm to solve the
regularization path through iterations. Bregman Inverse Scale Space Differential
Inclusion solver is also provided for linear model with lasso penalty.
Estimates predictive states from spatio-temporal data and
consequently can provide provably optimal forecasts.
Currently this implementation
supports an N-dimensional spatial grid observed over equally spaced time
intervals. E.g. a video is a 2D spatial systems observed over time. This
package implements mixed LICORS, has plotting tools (for (1+1)D and (2+1)D
systems), and methods for optimal forecasting.  Due to memory limitations
it is recommend to only analyze (1+1)D systems.
This package presents a general method for assessing the
local influence of minor perturbations of case weight for the
linear regression models.
Classes and methods that allow the user to manage life table, actuarial tables (also multiple decrement tables). Moreover, functions to easily perform demographic, financial and actuarial mathematics on life contingencies insurances calculations are contained therein.
Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.
Functions supplied in this package will implement
discriminant analysis to select an appropriate life table
family, select an appropriate alpha level based on a desired
life expectancy at birth, produce a model mortality pattern
based on family and level as well as plot the results.
Compute the top decile lift and plot the lift curve. Cumulative lift curves are also supported.
Dockerize R Markdown documents with support for Rabix.
The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
by itself cannot and will not create any visualizations.
A Poisson Subordinated Distribution to capture major
leptokurtic features in log-return time series of financial
data.
Tools for maximum likelihood estimation of parameters
of scientific models.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Tools to determine DNA profile Weight of Evidence.
For further information see the likeLTD guide provided,
or the paper under citation.
Functions to analyze and visualize likert type items.
Functions that read and solve linear inverse problems (food web problems, linear programming problems).
These problems find solutions to linear or quadratic functions:
min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
subject to equality constraints Ex=f and inequality constraints Gx>=h. Uses package limSolve.
Values below a specified limit of detection are stacked in
rows in order to reduce overplotting and create a clear
graphical representation of your data.
Functions that (1.) Find the minimum/maximum of a linear or quadratic function:
min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
subject to equality constraints Ex=f and/or inequality constraints Gx>=h.
(2.) Sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b.
(3.) Solve a linear system Ax=B for the unknown x. Includes banded and tridiagonal linear systems.
The package calls Fortran functions from LINPACK
Short for 'linear binning', the linbin package provides functions
for manipulating, binning, and plotting linearly referenced data. Although
developed for data collected on river networks, it can be used with any interval
or point data referenced to a 1-dimensional coordinate system. Flexible bin
generation and batch processing makes it easy to compute and visualize variables
at multiple scales, useful for identifying patterns within and between variables
and investigating the influence of scale of observation on data interpretation.
Estimate and confidence/credible intervals for an unknown
regressor x0 given an observed y0.
L-systems or Lindenmayer systems are parallel rewriting systems which can
be used to simulate biological forms and certain kinds of fractals.
Briefly, in an L-system a series of symbols in a string are replaced
iteratively according to rules to give a more complex string. Eventually,
the symbols are translated into turtle graphics for plotting. Wikipedia has
a very good introduction: en.wikipedia.org/wiki/L-system This package
provides basic functions for exploring L-systems.
Train and predict using fast prototype-based Linearized
Support-Vector Regression methods.
Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Tools for detecting and correcting sample mix-ups between two sets
of measurements, such as between gene expression data on two tissues.
Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities. linkcomm provides a set of tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type. The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities.
Matrices implemented as collections of matrix-like nodes, linked by
columns or rows.
A linkage information based method for imputing missing diploid genotypes
Creates kinematic and static force models of 3D levers and linkage mechanisms, with particular application to the fields of engineering and biomechanics.
This package implements the methodology of
Likelihood-based Imprecise Regression (LIR) for the case of
linear regression with interval data.
This package can be used to solve Linear Programming /
Linear Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions
and marginal effects for different kinds of regression models. In this version
linear regression models, generalized linear models, generalized additive
models and linear mixed-effects models are supported.
Major features are the interactive approach and the handling of the effects of categorical covariates:
if two or more factors are used as covariates every combination of the levels of each
factor is treated separately. The automatic calculation of
marginal effects and a number of possibilities to customize the graphical output
are useful features as well.
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators. In particular it solves the problem of variable selection by choosing the best predictor among  predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Tools for checking R code for style compliance.  Tools for
creating style compliance tests.  A suite of pre-built tests is
provided.
Checks adherence to a given style, syntax errors and possible
semantic issues.  Supports on the fly checking of R code edited with Emacs,
Vim and Sublime Text.
Package for Bayesian linear regression in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables), time evolution of slopes, normalization and scatters, Malmquist and Eddington bias, and break of linearity. The posterior distribution of the regression parameters  is sampled with a Gibbs method exploiting the JAGS library.
Fits lasso (total variation) penalised additive isotone models
Though SRFI-1 scopes both list-processing and higher-order
programming, we'll save some list-orthogonal functions for the
`functional' package; this is freely a mixture of
implementation and API.
This is an unofficial package aimed at automating the
import of LISREL output in R.  This package or its maintainer
is not in any way affiliated with the creators of LISREL and
SSI, Inc.
Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011), Blair and Imai (2012), Blair, Imai, and Lyall (2013), Imai, Park, and Greene (2014), and Aronow, Coppock, Crawford, and Green (2015). This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, and a method for combining list experiments with direct questions. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.
List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a=1, b=2); for (i in seq_along(x)) x[[i]] <- x[[i]]^2; y <- as.list(x)'.
Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.
Provides a function that, as an alternative to base::list, allows default values to be inherited from another list.
A scripting and command-line front-end
is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
the GNU R language and environment for statistical computing and graphics.
While R can be used in batch mode, the r binary adds full support for
both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
expression as the first line in scripts) as well as command-line use in
standard Unix pipelines. In other words, r provides the R language without
the environment.
Provides functionality to train and evaluate algorithm selection models for portfolios.
LLE is a non-linear algorithm for mapping high-dimensional
data into a lower dimensional (intrinsic) space. This package
provides the main functions to performs the LLE alogrithm
including some enhancements like subset selection, calculation
of the intrinsic dimension etc.
Applies local log-linear capture-recapture models (LLLMs) for
closed populations, as described in the doctoral thesis of Zachary Kurtz.
The method is relevant when there are 3-5 capture occasions, with auxiliary
covariates available for all capture occasions.  As part of estimating the
number of missing population units, the method estimates the "rate of
missingness" as it varies over the covariate space.  In addition,
user-friendly functions are provided to recreate (approximately) the method
of Zwane and van der Heijden (2004), which applied the VGAM package
in a way that is closely related to LLLMs.
Analyses experimental data from liquid-liquid phase diagrams and provide a simple way to obtain its parameters and a simplified report. Designed initially to analyse Aqueous Two-Phases Systems, the package will include (every other update) new functions in order to comprise useful tools in liquid-liquid analysis.
Adds standardized regression coefficients to objects created by lm. Also extends the S3 methods print, summary and coef with additional boolean argument standardized.
Exact significance tests for a changepoint in linear or multiple linear regression.
Confidence regions with exact coverage probabilities for the changepoint.
Fit linear and generalized linear mixed-effects models.
The models and their components are represented using S4 classes and
methods.  The core computational algorithms are implemented using the
'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
This package includes a function to fit a linear
mixed-effects model in the formulation described in Laird and
Ware (1982) but allowing for censored normal responses. In this
version, the with-in group errors are assumed independent and
identically distributed.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Different kinds of tests for linear mixed effects models as implemented
in 'lme4' package are provided. The tests comprise types I - III F tests
for fixed effects, LR tests for random effects.
The package also provides the calculation of population means for fixed factors
with confidence intervals  and corresponding plots. Finally the backward
elimination of non-significant effects is implemented.
Add smoothing spline modelling capability to nlme. Fit
smoothing spline terms in Gaussian linear and nonlinear
mixed-effects models
Fit certain versions of the Latent Markov model for longitudinal categorical data.
Test zero variance components in linear mixed models and
test additivity in nonparametric regression
using the restricted likelihood ratio test and the generalized F-test.
This R package provide methods for estimation and statistical
inference on directional and fluctuating selection in age-structured
populations.
Functions related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, and recovery of tree diameter distributions. Datasets on tree height-diameter relationship and light response of moss photosynthesis are included.
Some improved procedures for linear mixed models.
This package fits (gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using a Lasso-type approach
for the fixed-effects parameter.
Fit right censored Multiple Ordinal Tobit (MOT) model.
Linear Mixed effect Model Splines (lmms) implements linear mixed effect model splines for
modelling and differential expression for highly dimensional data sets:
investNoise for quality control and filterNoise for removing
non-informative trajectories; lmmSpline to model time course expression
profiles and lmmsDE performs differential expression analysis to identify
differential expression between groups, time and/or group x time
interaction.
Computes model II simple linear regression using ordinary
least squares (OLS), major axis (MA), standard major axis (SMA), and
ranged major axis (RMA).
Functions related to L-moments: computation of L-moments
and trimmed L-moments of distributions and data samples; parameter
estimation; L-moment ratio diagram; plot vs. quantiles of an
extreme-value distribution.
Extensive functions for the theory of L-moments (LMs) in addition to probability-weighted moments (PWMs), and parameter (p) estimation for numerous familiar and not-so-familiar distributions. Estimation of LMs for the same distributions is provided as are L-moment ratio diagrams. Estimation of LMs for right-tail and left-tail censoring by known or unknown threshold and also by indicator variable is available. Asymmetric trimmed LMs (TL-moments, TLMs) are supported as are numerical integrations to dynamically compute trajectories of select TLM ratios for TL-moment ratio diagram construction.  LMs of residual (resid.) and reversed (rev.) resid. life are implemented along with 13 operators on quantile functions that are useful in reliability, lifetime, and survival analysis.  Exact analytical bootstrap estimates of order statistics, LMs, and variances-covariances of LMs are provided. The Harri-Coble Tau34-squared Test for Normality is available. Distribution support with "L" (LMs), "TL" (TLMs) and added (+) support for right-tail censoring (RC) encompasses: Asymmetric (Asy.) Exponential (Exp.) Power [L], Asy. Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen.) Exp. Poisson [L], Gen. Extreme Value [L], Gen. Lambda [L,TL], Gen. Logistic [L), Gen. Normal [L], Gen. Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3-p log-Normal [L], Pearson Type III [L], Rayleigh [L], Rev. Gumbel [L+RC], Rice/Rician (L), Slash [TL], 3-p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. LMs have multivariate analogs; the sample L-comoments (LCMs) are implemented and might have considerable application with copulas because LCMs measure asymmetric association and higher comoments or comovements of variables.
The Lmoments package contains functions to estimate
L-moments and trimmed L-moments from the data. The package also
contains functions to estimate the parameters of the normal
polynomial quantile mixture and the Cauchy polynomial quantile
mixture from L-moments and trimmed L-moments.
Functions for regional frequency analysis using the methods
of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis:
an approach based on L-moments".
Accompanies Markus Brauer, Bas Rokers, and John Curtin's two-course graduate series in
General, Generalized, and Multi-level Linear Models (PSY 610/710).
A collection of tests, data sets, and examples
for diagnostic checking in linear regression models. Furthermore,
some generic tools for inference in parametric models are provided.
Predict modulators regulating the ability of effectors to regulate their targets and produce
modulator-effector-target triplets followed by goterm functional enrichment and survival analysis.This
is mainly applied to long non-coding RNAs (lncRNAs) as candidate modulators regulating the ability of
transcription factors (TFs) to regulate their corresponding targets.
This package, Lattice Options and Add-ins (or loa), contains various plots and
functions that make use of the lattice/trellis plotting framework. The plots (which include
loaPlot, GoogleMap and trianglePlot) use panelPal, a function that extends lattice and hexbin
package methods to automate plot subscripting and panel-to-panel and panel-to key
synchronization/management. See ?loa for further details.
Simplicial, Mahalanobis and Ellipsoid Local and Global Depth
Computational routines for estimating and visualizing local Gaussian parameters. Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48.
The package converts R data onto input and data for LocalSolver,
executes optimization and exposes optimization results as R data.
LocalSolver (http://www.localsolver.com/) is an optimization engine
developed by Innovation24 (http://www.innovation24.fr/). It is designed to
solve large-scale mixed-variable non-convex optimization problems.  The
localsolver package is developed and maintained by WLOG Solutions
(http://www.wlogsolutions.com/en/) in collaboration with Decision Support
and Analysis Division at Warsaw School of Economics
(http://www.sgh.waw.pl/en/).
Computation of local false discovery rates.
The main idea of the Local FDR algorithm is to estimate both proportion of null observations and the ratio of null and alternative densities. In the case that there are many null observations, this can be done reliably, through maximum likelihood or generalized linear models. This package implements this in the case that the null and alternative densities are Poisson.
Local regression, likelihood and density estimation.
Provides test of second-order stationarity for time
series. Provides localized autocovariance, with confidence
intervals, for locally stationary (nonstationary) time series.
Datasets for "Statistics:Unlocking the Power of Data" by
Lock, Lock, Lock, Lock and Lock
Data sets and other utilities for
'Statistics: Unlocking the Power of Data'
by Lock, Lock, Lock, Lock and Lock
(ISBN : 978-0-470-60187-7, http://lock5stat.com/).
Genome-wide association (GWAS) analyses
of a biomarker that account for the limit of detection.
logr4 provides an object-oriented logging system that uses an API
roughly equivalent to log4j and its related variants.
Methods for fitting log-link GLMs and GAMs to binomial data,
using EM-type algorithms with more stable convergence properties than standard methods.
Computes a log-concave (maximum likelihood) estimator for
i.i.d. data in any number of dimensions.
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach, and Schuhmacher (2011, preprint).
Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Computes maximum likelihood estimate of a log-concave density  with fixed and known location of the mode. Performs inference about the mode via a likelihood ratio test. Extension of the logcondens package.
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Computes a cox PH model with a log concave baseline distribution. If no covariates are provided, estimates the log concave NPMLE. Built specifically for interval censored data, where data is a n by 2 matrix with [i, 1] as the left side of the interval for subject i and [i,2] as right side. Uncensored data can be entered by setting [i,1] = [i,2]. Alternatively, this can also handle uncensored data. If all the data is uncensored, you may enter data as a length(n) vector.
logging is a pure R package that implements the ubiquitous
log4j package.
Two classification ensemble methods based on logic regression models.  LogForest uses a bagging approach to construct an ensemble of logic regression models.  LBoost uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome.
This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
Routines for fitting Logic Regression models.
Firth's bias reduced logistic regression approach with
penalized profile likelihood based confidence intervals for
parameter estimates.
Diagnostic tests and plots for GLMs (generalized linear models)
with binomial/ binary outcomes, particularly logistic regression.
Dimensionality reduction techniques for binary data including logistic
PCA.
Functions, data and code for Hilbe, J.M. 2015. Practical Guide to Logistic Regression, by Chapman and Hall/CRC.
Fits linear discrete logit choice models with l2 regularization. To handle reasonably sized datasets, we employ an accelerated version of generalized gradient descent.
LogitNet is developed for inferring network of binary
variables under the high-dimension-low-sample-size setting
Density, distribution, quantile and random generation
function for the logitnormal distribution. Estimation of the
mode and the first two moments. Estimation of distribution
parameters.
r,d,p,q functions for the double log normal distribution
Functions to fit log-multiplicative models using gnm, with
support for convenient printing, plots, and jackknife/bootstrap
standard errors. For complex survey data, models can be fitted from
design objects from the 'survey' package. Currently supported models
include UNIDIFF (Erikson & Goldthorpe), a.k.a. log-multiplicative
layer effect model (Xie), and several association models: Goodman's
row-column association models of the RC(M) and RC(M)-L families
with one or several dimensions; two skew-symmetric association
models proposed by Yamaguchi and by van der Heijden & Mooijaart.
LogrankA provides a logrank test across unlimited groups with the
possibility to input aggregated survival data.
Routines for the logspline density estimation. oldlogspline
uses the same algorithm as the logspline 1.0.x package - the Kooperberg
and Stone (1992) algorithm (with an improved interface). The recommended
routine logspline uses an algorithm from Stone et al (1997).
Kernel regression smoothing with adaptive local or global plug-in
bandwidth selection.
Computes the Lomb-Scargle Periodogram for unevenly sampled time series. Includes a randomization procedure to obtain reliable p-values.
Package for Plotting Categorical Longitudinal and Time-Series Data
Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure.
Contains general data structures and
functions for longitudinal data with multiple variables,
repeated measurements, and irregularly spaced time points.
Also implements a shrinkage estimator of dynamical correlation
and dynamical covariance.
Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).
Datasets and Functionality from the textbook Jan Beran
(1994). Statistics for Long-Memory Processes; Chapman & Hall.
The longpower package contains functions for computing
power and sample size for linear models of longitudinal data
based on the formula due to Liu and Liang (1997) and Diggle et
al (2002). Either formula is expressed in terms of marginal
model or Generalized Estimating Equations (GEE) parameters.
This package contains functions which translate pilot mixed
effect model parameters (e.g. random intercept and/or slope)
into marginal model parameters so that the formulas of Diggle
et al or Liu and Liang formula can be applied to produce sample
size calculations for two sample longitudinal designs assuming
known variance.
Interface to the 'LongURL' API to identify known URL shortener services
and expand vectors of short URLs with optional error checking and
URL validation. See <http://longurl.org/> for more information about
'LongURL'.
Efficient approximate leave-one-out cross-validation (LOO)
using Pareto smoothed importance sampling (PSIS), a new procedure for
regularizing importance weights. As a byproduct of the calculations, we also
obtain approximate standard errors for estimated predictive errors, and for
the comparison of predictive errors between models. We also compute the
widely applicable information criterion (WAIC).
Fits look-up tables by filling entries with the mean or median values of observations
fall in partitions of the feature space. Partitions can be determined by user of the
package using input argument feature.boundaries, and dimensions of the feature space
can be any combination of continuous and categorical features provided by the data set.
A Predict function directly fetches corresponding entry value, and a default value is
defined as the mean or median of all available observations.
The table and other components are represented using the S4 class lookupTable.
The program can perform loop analysis and plot network
structure (especially for food webs),including minimum spanning
tree, loop decomposition of weighted directed graphs, and other
network properties which may be related to food chain
properties in ecology.
Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means sign only (i.e. increases, decreases, non change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as graphviz (graphviz.org). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.
Remedies a common problem in piping: not having access to
intermediate outputs of the pipe. Within a "loop", a piping intermediate
is  stored in a stack archive, data is processed, and then both the
stored intermediate and the current output are reintegrated using an
"ending" function. Two special ending functions are provided: amend and
insert. However, any ending function can be specified, including merge
functions, join functions, setNames(), etc. This framework allows the
following work-flow: focus on a particular aspect or section of a
data set, conduct specific operations, and then reintegrate changes into
the whole.
Analysis of Differential Item Functioning (DIF) for
dichotomous and polytomous items using an iterative hybrid of
ordinal logistic regression and item response theory (IRT).
Estimate covariance matrices that contain low rank and sparse components
LOST includes functions for simulating missing morphometric
data randomly, with taxonomic bias and with anatomical bias. LOST also
includes functions for estimating linear and geometric morphometric data.
Running Lotka's Law following Pao (1985)(DOI: 10.1016/0306-4573(85)90055-X). The Law is based around the proof that the number of authors making n contributions is about 1/n^{a} of those making one contribution.
This package contains routines and documentation for
solving quadratic programming problems where the hessian is
represented as the product of two matrices.
Optimization of bipartite modularity using LP-BRIM (Label propagation
followed by Bipartite Recursively Induced Modularity).
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
Fitting multivariate data patterns with local principal curves; including simple tools for data compression (projection), bandwidth selection, and measuring goodness-of-fit.
Estimates the intensity function, or its derivative of a given order, of a multiplicative counting process using the local polynomial method
Apply Univariate Long Memory Models,
Multivariate Short Memory Models To Hydrological Dataset.
Two local polynomial estimators for solving the errors-in-variables problem: one is in Delaigle, Fan, and Carroll (2009), and one is in Huang and Zhou (2014+). The SIMEX bandwidth selection method is also provided for both estimators.
LPmerge creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.
lpmodeler is a set of user-friendly functions to simplify the modelling of linear programs (LP) and mixed integer programs (MIP). It provides a unified interface compatible with optimization packages: Rsymphony.
Local Polynomial Regression with Ridging.
An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.
Lp_solve is freely available (under LGPL 2) software for
solving linear, integer and mixed integer programs. In this
implementation we supply a "wrapper" function in C and some R
functions that solve general linear/integer problems,
assignment problems, and transportation problems. This version
calls lp_solve version 5.5.
The lpSolveAPI package provides an R interface for lp_solve,
a Mixed Integer Linear Programming (MILP) solver with support for pure
linear, (mixed) integer/binary, semi-continuous and special ordered sets
(SOS) models.
Learned Pattern Similarity (LPS) for time series.
Implements a novel approach to model the dependency structure
in time series that generalizes the concept of autoregression to local
auto-patterns. Generates a pattern-based representation of time series
along with a similarity measure called Learned Pattern Similarity (LPS).
Introduces a generalized autoregressive kernel.This package is based on the
'randomForest' package by Andy Liaw.
Specially designed rank transform based Legendre Polynomial-like (LP) orthonormal transformations are implemented for non-linear signal processing.
This package provides some basic infrastructure and tools
to fit Generalized Linear Models (GLMs) via penalized
likelihood inference. Estimating procedures already implemented
are the LQA algorithm (that is where its name come from),
P-IRLS, RidgeBoost, GBlockBoost and ForwardBoost.
This is a collection of functions to fit quantile regression models for independent and hierarchical data.
It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term.
This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution.
It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.
Provides functions for calculating test statistics, simulating quantiles
and simulating p-values of likelihood ratio contrast tests in regression models
with a lack of identifiability.
Implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution.
When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. To deal with multicollinearity several estimators are proposed in the literature. Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Ridge Regression Estimator (ORRE), Restricted Least Square Estimator (RLSE), Ordinary Mixed Regression Estimator (OMRE), Liu Estimator (LE), Restricted Liu Estimator (RLE), Stochastic Restricted Liu Estimator (SRLE), Type (1) Liu Estimator (Type-1 LTE), Type (2) Liu Estimator (Type-2 LTE), Type (3) Liu Estimator (Type-3 LTE), Type (1) Adjusted Liu Estimator (Type-1 ALTE), Type (2) Adjusted Liu Estimator (Type-2 ALTE), Type (3) Adjusted Liu Estimator (Type-3 ALTE), Almost Unbiased Ridge Estimator (AURE), Almost Unbiased Liu Estimator (AULE), Stochastic Restricted Ridge Estimator (SRRE) and Restricted Ridge Regression Estimator (RRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically.
Estimates two-dimensional local wavelet spectra
Wavelet-based methods for testing stationarity and quadtree segmenting of images
The basic idea of latent semantic analysis (LSA) is,
that text do have a higher order (=latent semantic) structure which,
however, is obscured by word usage (e.g. through the use of synonyms
or polysemy). By using conceptual indices that are derived statistically
via a truncated singular value decomposition (a two-mode factor analysis)
over a given document-term matrix, this variability problem can be overcome.
Provides functions that allow for convenient working
with Latent Semantic Analysis. For actually building an LSA space, use the
package 'lsa' or other specialized software.
Functions for performing least-squares bilinear clustering of three-way data. The
method uses the bilinear decomposition (or biadditive model) to model two-way matrix slices
while clustering over the third way. Up to four different types of clusters are included, one
for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously
on (a subset of) their overall means, row margins, column margins and row-column interactions.
The orthogonality of the bilinear model results in separability of the joint clustering problem
into four separate ones. Three of these subproblems are specific k-means problems, while a
special algorithm is implemented for the interactions. Plotting methods are provided, including
biplots for the low-rank approximations of the interactions.
Estimators and visualization for local statistical complexity of
(N+1)D fields. In particular for 0, 1 and 2 dimensional space this package
provides useful visualization.
Create lots of colorful plots in a plethora of variations (try the LSD demotour() )
Interfaces R with LSD. Reads object-oriented data in results files (.res) produced by LSD and creates appropriate multi-dimensional arrays in R. Also provides functions to extract basic information and statistics from those files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente (documentation and downloads available at <http://labsimdev.org>).
Fit a least square dummy variable regression
It contains functions that solve least squares linear
regression problems under linear equality/inequality
constraints. It is developed based on the 'Fortran' program of
Lawson and Hanson (1974, 1995), which is public domain and
available at http://www.netlib.org/lawson-hanson.
Linear multiple output using sparse group lasso. The algorithm finds the sparse group lasso penalized maximum likelihood estimator. This result in feature and parameter selection, and parameter estimation. Use of multiple processors for cross validation and subsampling is supported through OpenMP. Development version is on github.
Calculates the (localised) length of the shorth and
supplies corresponding diagnostic plots.
Conduct structural equation modeling via penalized likelihood.
Obtain least-squares means for many linear, generalized linear,
and mixed models. Compute contrasts or linear functions of least-squares
means, and comparisons of slopes. Plots and compact letter displays.
The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.
Implements the LS-PLS (least squares - partial least
squares) method described in for instance Jørgensen, K.,
Segtnan, V. H., Thyholt, K., Næs, T. (2004) A Comparison of
Methods for Analysing Regression Models with Both Spectral and
Designed Variables.  Journal of Chemometrics, 18(10), 451--464.
A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.
Due to lack of proper inference procedure and software,
the ordinary linear regression model is seldom used in practice
for the analysis of right censored data. This paper presents an
S-Plus/R program that implements a recently developed inference
procedure (Jin, Lin and Ying, 2006)\cite{Jin} for the
accelerated failure time model based on the least-squares
principle.
Has a set of functions that allows stationary analysis and locally stationary time series analysis.
Functions for simulating realizations from the posterior distribution of
a latent trait of an item response model. Distributions are conditional on one or
a subset of response patterns (e.g., sum scores). Functions for computing likelihoods,
Fisher and observed information, posterior modes, and profile likelihood confidence
intervals are also included. These functions are designed to be easily amenable to
user-specified models.
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Targeted Maximum Likelihood Estimation (TMLE) of
treatment/censoring specific mean outcome or marginal structural model for
point-treatment and longitudinal data.
Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.
A set of functions to execute the linear-transformation of
replicate (LTR) algorithm for preprocessing of microarray data
Methods of developing linear time series modelling.
Methods are given for loglikelihood computation, forecasting
and simulation.
This is a new tool to estimate Ridge and Liu estimators based on LTS method in multiple linear regression analysis.
Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively.
Functions to work with date-times and timespans: fast and user
friendly parsing of date-time data, extraction and updating of components of
a date-time (years, months, days, hours, minutes, and seconds), algebraic
manipulation on date-time and timespan objects. The 'lubridate' package has
a consistent and memorable syntax that makes working with dates easy and
fun.
Likelihood inference in case-control studies of a rare
disease under independence or simple dependence of genetic and
non-genetic covariates
Print vectors (and data frames) of floating point numbers
using a non-scientific format optimized for human readers.  Vectors
of numbers are rounded using significant digits, aligned at the
decimal point, and all zeros trailing the decimal point are dropped.
Reformat currency-based data as numeric values (or numeric values as currency-based data) and convert between currencies.
Classes and methods for spatially explicit land use change
modelling in R.
A simple client for the Lumen Database <https://lumendatabase.org/> (formerly Chilling Effects) API.
A collection of various R functions for the purpose of Luminescence
dating data analysis. This includes, amongst others, data import, export,
application of age models, curve deconvolution, sequence analysis and
plotting of equivalent dose distributions.
Provides functions to calculate lunar and other environmental
covariates.
Provides flexible but lightweight logging facilities for R scripts. Supports priority levels for logs and messages, flagging messages, capturing script output, switching logs, and logging to files or connections.
Latent variable models for network data using fast inferential
procedures.
A model for mixed ordinal and continuous responses is
presented where the heteroscedasticity of the variance of the
continuous response is also modeled. In this model ordinal
response can be dependent on the continuous response. The aim
is to use an approach similar to that of Heckman (1978) for the
joint modelling of the ordinal and continuous responses. With
this model, the dependence between responses can be taken into
account by the correlation between errors in the models for
continuous and ordinal responses
The manual approach of Litchfield and Wilcoxon (1949)
<http://jpet.aspetjournals.org/content/96/2/99.abstract>
for evaluating dose-effect experiments
is automated so that the computer can do the work.
Functions to quickly read LXB parameter data.
We provide a clonality score estimator that takes full advantage of the multi-biological-replicate structure of modern sequencing experiments; it specifically takes into account the reality that, typically, the clonal coverage is well below 0.1%.
This package contains functions to read in and manipulate
air quality model output from Models3-formatted files.  This
format is used by the Community Multiscale Air Quaility (CMAQ)
model.
Provides binomial tree models for European, American and Asian
Options as well as Interest Rates. Monte Carlo Simulation and Methods for
Solving Differential Equations are also included.
Performs binary and multiclass boosting in maximum-margin, sparse, smooth and normal settings
as described in "A Boosting Framework on Grounds of Online Learning" by T. Naghibi and B. Pfister, (2014).
For further information regarding the algorithms, please refer to http://arxiv.org/abs/1409.7202
This is an integrated meta-analysis package for conducting
a correlational research synthesis. One of the unique features
of this package is in its integration of user-friendly
functions to facilitate statistical analyses at each stage in a
meta-analysis with correlations. It uses recommended procedures
as described in The Handbook of Research Synthesis and
Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
'Maclinical' implements class prediction using both
microarray data and clinical parameters. It addresses the
question of the additional predictive value of microarray data.
Class prediction is performed using a two-step method combining
(pre-validated) PLS dimension reduction and random forests.
A collection of functions for conducting a meta-analysis with mean differences data.  It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter&Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
Contains the Maddison Project database, which provides estimates of
GDP per capita for all countries in the world between AD 1 and 2010. See
http://www.ggdc.net/maddison for more information.
An object that supports automatic differentiation
of matrix- and multidimensional-valued functions with
respect to multidimensional independent variables.
Automatic differentiation is via 'forward accumulation'.
Performs distance sampling analyses on a number of species
accounting for unidentified sightings, model uncertainty and covariate
uncertainty.
This function allows to generate two biological conditions
synthetic microarray dataset which has similar behavior to
those currently observed with common platforms. User provides a
subset of parameters.  Available default parameters settings
can be modified.
A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.
Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).
a collection of efficient, vectorized algorithms for the
creation and investigation of magic squares and hypercubes, including
a variety of functions for the manipulation and analysis of
arbitrarily dimensioned arrays.  The package includes methods for
creating normal magic squares of any order greater than 2.  The
ultimate intention is for the package to be a computerized embodiment
all magic square knowledge, including direct numerical verification
of properties of magic squares (such as recent results on the
determinant of odd-ordered semimagic squares).  Some antimagic
functionality is included.  The package also
serves as a rebuttal to the often-heard comment "I thought R
was just for statistics".
Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
Provides a mechanism for chaining commands with a
new forward-pipe operator, %>%. This operator will forward a
value, or the result of an expression, into the next function
call/expression. There is flexible support for the type
of right-hand side expressions. For more information, see
package vignette.
To quote Rene Magritte, "Ceci n'est pas un pipe."
Provides a Graphical User Interface for Microarray Data Analysis. Protein protein interactions can also be predicted from two normalized microarray data.
Easy to use package for sending email notifications with status information from R
Interface to Apache Commons Email to send emails
from R.
Implements methodologies for modelling interval data, considering appropriate parameterizations of the variance-covariance matrix that take into account the intrinsic nature of interval data, and lead to five different possible configuration structures.
This package creates an empty framework of files and
directories for the "Load, Clean, Func, Do" structure described
by Josh Reich.
A complete analysis pipeline for matrix-assisted laser
desorption/ionization-time-of-flight (MALDI-TOF) and other
two-dimensional mass spectrometry data. In addition to commonly
used plotting and processing methods it includes distinctive
features, namely baseline subtraction methods such as
morphological filters (TopHat) or the statistics-sensitive
non-linear iterative peak-clipping algorithm (SNIP), peak
alignment using warping functions, handling of replicated
measurements as well as allowing spectra with different
resolutions.
Functions for reading (tab, csv, Bruker fid, Ciphergen
XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and
writing (tab, csv, mMass MSD, mzML, imzML) different file
formats of mass spectrometry data into/from MALDIquant objects.
This package allows you to train topic models in mallet and load results directly into R.
Implementation of methods for microarray meta-analysis.
Designing multi-arm multi-stage studies with normal endpoints and known variance.
This package calculates the nonparametric adaptive MAMSE
weights for univariate, right-censored or multivariate data.
The MAMSE weights can be used in a weighted likelihood or to
define a mixture of empirical distribution functions.
This will allow easier management of a CRAN-style repository on
local networks (i.e. not on CRAN). This might be necessary where hosted
packages contain intellectual property owned by a corporation.
High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.
Datasets designed to be used in conjunction with Mango Solutions
training materials and the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).
Provides access to coded election programmes from
the Manifesto Corpus and to the Manifesto Project's Main Dataset and routines
to analyse this data. The Manifesto
Project (https://manifesto-project.wzb.eu) collects and analyses election
programmes across time and space to measure the political preferences
of parties. The Manifesto Corpus contains the collected
and annotated election programmes in the Corpus format of the package 'tm' to enable
easy use of text processing and text mining functionality. Specific functions
for scaling of coded political texts are included.
Interactive plotting functions for use within RStudio.
The manipulate function accepts a plotting expression and a set of
controls (e.g. slider, picker, checkbox, or button) which are used
to dynamically change values within the expression. When a value is
changed using its corresponding control the expression is
automatically re-executed and the plot is redrawn.
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models. It also provides capabilities for forward and backward model selection procedures.
Performs the multiple testing procedures of Cox (2011) and Wang and Cox (2007).
Allows the user to generate a list of features (gene, pseudo, RNA,
CDS, and/or UTR) directly from NCBI database for any species with a current
build available. Option to save downloaded and formatted files is available,
and the user can prioritize the feature list based on type and assembly builds
present in the current build used. The user can then use the list of features
generated or provide a list to map a set of markers (designed for SNP markers
with a single base pair position available) to the closest feature based on
the map build. This function does require map positions of the markers to be
provided and the positions should be based on the build being queried through
NCBI.
This package contains functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure.
Supplement to maps package, providing the larger and/or
higher-resolution databases.
Create static choropleth maps of Denmark using the ggplot2 package.
Estimation methods for phase-type
distribution (PH) and Markovian arrival process (MAP) from
empirical data (point and grouped data) and density function.
Contains functions for mapping odds ratios or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting for confounding variables, using methods described by Kelsall and Diggle (1998) and Webster at al. (2006).  Includes convenient functions for mapping, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
MAPLES is a general method for the estimation of age
profiles that uses standard micro-level demographic survey
data. The aim is to estimate smoothed age profiles and relative
risks for time-fixed and time-varying covariates.
A minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections.
Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.
Converts latitude/longitude into projected coordinates.
Display of maps.  Projection code and larger maps are in
separate packages ('mapproj' and 'mapdata').
Automated calculation and visualization of survey data statistics on a color-coded map.
Set of tools for manipulating and reading geographic data, in particular ESRI shapefiles; C code used from shapelib. It includes binary access to GSHHG shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as PBSmapping, spatstat, maps, RArcInfo, Stata tmap, WinBUGS, Mondrian, and others.
Posterior maximization for topic models (LDA) in text analysis,
as described in Taddy (2012) `on estimation and selection for topic models'.  Previous versions of this code were included as part of the textir package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Functions with example data for graphing, pruning, and
mapping models from hierarchical clustering, and classification
and regression trees.
Methods to view spatial objects interactively.
R functions for multivariate autoregressive analysis
The MAR1 package provides basic tools for preparing
ecological community time-series data for MAR modeling,
building MAR-1 models via model selection and bootstrapping,
and visualizing and exporting model results. It is intended to
make MAR analysis (sensu Ives et al. (2003) Analysis of
community stability and ecological interactions from
time-series data) a more accessible tool for anyone studying
community dynamics.  The user need not necessarily be familiar
with time-series modeling or command-based statistics programs
such as R.
Multiplicative AR(1) with Seasonal is a stochastic
process model built on top of AR(1). The package provides the
following procedures for MAR(1)S processes: fit, compose, decompose,
advanced simulate and predict.
Datasets, constants, conversion factors, utilities for MArine, Riverine,
Estuarine, LAcustrine and Coastal science.
The package contains among others: (1) chemical and
physical constants and datasets, e.g. atomic weights, gas
constants, the earths bathymetry; (2) conversion factors
(e.g. gram to mol to liter, barometric units,
temperature, salinity); (3) physical functions, e.g. to
estimate concentrations of conservative substances, gas
transfer and diffusion coefficients, the Coriolis force
and gravity; (4) thermophysical properties of the
seawater, as from the UNESCO polynomial or from the more
recent derivation based on a Gibbs function.
Local recombination rates are graphically estimated across a genome using marey maps.
Likelihood inference based on higher order approximations
for linear nonnormal regression models
Provides R bindings to the 'Sundown' 'Markdown' rendering library
(https://github.com/vmg/sundown). 'Markdown' is a plain-text formatting
syntax that can be converted to 'XHTML' or other formats. See
http://en.wikipedia.org/wiki/Markdown for more information about 'Markdown'.
Functions for fitting various models to capture-recapture data
including fixed and mixed-effects Cormack-Jolly-Seber(CJS) for survival
estimation and POPAN structured Jolly-Seber models for abundance
estimation. Includes a CJS models that concurrently estimates and corrects
for tag loss. Hidden Markov model (HMM) implementations of CJS and
multistate models with and without state uncertainty.
maRketSim is a market simulator for R.  It was initially designed
around the bond market, with plans to expand to stocks.  maRketSim is
built around the idea of portfolios of fundamental objects.  Therefore
it is slow in its current incarnation, but allows you the flexibility of
seeing exactly what is in your final results, since the objects are retained.
Automatic marking of R assignments for students and teachers based
on testthat test suites.
Allows for fitting of maximum likelihood models using Markov chains
on phylogenetic trees for analysis of discrete character data. Examples of such
discrete character data include restriction sites, gene family presence/absence,
intron presence/absence, and gene family size data. Hypothesis-driven user-
specified substitution rate matrices can be estimated. Allows for biologically
realistic models combining constrained substitution rate matrices, site rate
variation, site partitioning, branch-specific rates, allowing for non-stationary
prior root probabilities, correcting for sampling bias, etc.
Functions and S4 methods to create and manage discrete time Markov chains (DTMC) more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of DTMC proprieties) analysis are provided.
a collection of tools for analyzing significance of
Markowitz portfolios.
Functions provided allow data simulation; construction of weighted relative likelihood functions; clustering and principal component analysis based on weighted relative likelihood functions.
Import xyz data from the NOAA, GEBCO and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
This algorithm provides a numerical solution to the
problem of minimizing a function. This is more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum. A new convergence test is
implemented (RDM) in addition to the usual stopping criterion :
stopping rule is when the gradients are small enough in the
parameters metric (GH-1G).
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data.  Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided.  MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.  Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks.  The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.  Type RShowDoc("UserGuide", package="MARSS") at the R command line to open the MARSS user guide.  Online workshops (lectures and computer labs) at http://faculty.washington.edu/eeholmes/workshops.shtml  See the NEWS file for update information.
An S4 implementation of the unbiased extension of the model-assisted synthetic-regression estimator proposed by Mandallaz (2013), Mandallaz et al. (2013) and Mandallaz (2014).
It yields smaller variances than the standard bias correction, the generalised regression estimator.
Functions and datasets to support Venables and Ripley,
"Modern Applied Statistics with S" (4th edition, 2002).
Estimation equations are from a variety of sources but are, in general, based on regressions between skeletal measurements (e.g., femoral circumference) and body mass in living taxa.
The primary aim of MasterBayes is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data.  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled C++ for efficiency.
Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence
intervals, which are constructed using single-model estimators and model
weights.
Provides functions for multivariate and propensity score matching
and for finding optimal balance based on a genetic search algorithm.
A variety of univariate and multivariate metrics to
determine if balance has been obtained are also provided.
Returns the subset of the data with the minimum imbalance for
every possible subset size (N - 1, N - 2, ...), down to the data set with the
minimum possible imbalance. Also includes tools for the estimation
of causal effects for each subset size, functions for visualization
and data export, and functions for calculating
model dependence as proposed by Athey and Imbens.
Implements structural estimators to correct for
the sample selection bias from observed outcomes in matching
markets. This includes one-sided matching of agents into
groups as well as two-sided matching of students to schools.
The package also contains R code for three matching algorithms:
the deferred-acceptance (or Gale-Shapley) algorithm for
stable marriage and college admissions problems, the
top-trading-cycles algorithm for house allocation and a
partitioning linear programme for the roommates problem.
Computes matching algorithms quickly using Rcpp.
Implements the Gale-Shapley Algorithm to compute the stable
matching for two-sided markets, such as the stable marriage
problem and the college-admissions problem. Implements Irving's
Algorithm for the stable roommate problem. Implements the top
trading cycle algorithm for the indivisible goods trading problem.
MatchIt selects matched samples of the the original
treated and control groups with similar covariate distributions
-- can be used to match exactly on covariates, to match on
propensity scores, or perform a variety of other matching
procedures.
Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
Simple tools for constructing and manipulating objects of
class mathgraph from the book "S Poetry", available at
http://www.burns-stat.com/pages/spoetry.html
Uses a ratio of weighted distributions to estimate association between variables in a data set.
Provides users to call MATLAB from using the "system" command.
Allows users to submit lines of code or MATLAB m files.
This is in comparison to 'R.matlab', which creates a MATLAB server.
A collection of matrix functions for teaching and learning matrix
linear algebra as used in multivariate statistical methods. These functions are
mainly for tutorial purposes in learning matrix algebra ideas using R. In some
cases, functions are provided for concepts available elsewhere in R, but where
the function call or name is not obvious. In other cases, functions are provided
to show or demonstrate an algorithm.
A general framework for computing powers of matrices.  A
key feature is the capability for users to write callback functions,
called after each iteration, thus enabling customization for specific
applications.  Diverse types of matrix classes/matrix multiplication
are accommodated.  If the multiplication type computes in parallel,
then the package computation is also parallel.
An analysis platform for metagenomics combining
specialized tools and workflows, easy handling of the BIOM
format, and transparent access to MG-RAST resources.  matR integrates
easily with other R packages and non-R software.
Classes and methods for dense and sparse matrices and
operations on them using 'LAPACK' and 'SuiteSparse'.
A collection of functions to support matrix calculations
for probability, econometric and numerical analysis. There are
additional functions that are comparable to APL functions which
are useful for actuarial models such as pension mathematics.
This package is used for teaching and research purposes at the
Department of Finance and Risk Engineering, New York
University, Polytechnic Institute, Brooklyn, NY 11201.
Matrix eQTL is designed for fast eQTL analysis on large datasets.
Matrix eQTL can test for association between genotype and gene expression using linear regression
with either additive or ANOVA genotype effects.
The models can include covariates to account for factors
as population stratification, gender, and clinical variables.
It also supports models with heteroscedastic and/or correlated errors,
false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs.
Modelling with sparse and dense 'Matrix' matrices, using
modular prediction and response module classes.
Partial Least Squares Path Modeling
algorithm and related algorithms. The algorithm implementations aim for
computational efficiency using matrix algebra and covariance data. The
package is designed toward Monte Carlo simulations and includes functions
to perform simple Monte Carlo simulations.
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.  There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedians().
Implements cast, aggregate, and merge/join for Matrix and matrix-like objects.
This package in includes functions for receiver operating
characteristic (ROC) analyses as well as Monte Carlo
simulation.  It includes specific graphical functions for
interpreting the output of these techniques.
Interactive shiny application for running a meta-analysis,
provides support for both random effects and fixed effects models with the 'metafor' package.
Additional support is included for calculating effect sizes plus
support for single case designs, graphical output, and detecting publication bias.
This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance).
If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated.
If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too.
Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented.
The program computes the p-values and FDR (false discovery rate) q-values for all gene sets.
The p-values for individual genes in a significant gene set are also listed.
MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set.
A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.
Perform exact MAX3 or MAX2 test for one-locus genetic
association analysis and trend test for dominant, recessive and
additive models. It can also calculate approximated p-value
with the normal approximation method.
maxent is an R package with tools for low-memory
multinomial logistic regression, also known as maximum entropy.
The focus of this maximum entropy classifier is to minimize
memory consumption on very large datasets, particularly sparse
document-term matrices represented by the tm package. The
classifier is based on an efficient C++ implementation written
by Dr. Yoshimasa Tsuruoka.
Complex niche models show low performance in identifying
the most important range-limiting environmental variables and in
transferring habitat suitability to novel environmental conditions
(Warren and Seifert, 2011; Warren et al., 2014). This package helps to
identify the most important set of uncorrelated variables and to
fine-tune Maxent's regularization multiplier. In combination, this
allows to constrain complexity and increase performance of Maxent niche
models (assessed by information criteria, such as AICc (Akaike, 1974) ,
and by the area under the receiver operating characteristic (AUC)
(Fielding and Bell, 1997). Users of this package should be familiar with
Maxent niche modelling.
Functions for Maximum Likelihood (ML) estimation and non-linear
optimization, and related tools.  It includes a unified way to call
different optimizers, and classes and methods to handle the results from
the ML viewpoint.  It also includes a number of convenience tools for testing
and developing your own models.
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
Generate a maximum projection (MaxPro) design, a MaxPro Latin hypercube design or improve an initial design based on the MaxPro criterion. Details of the MaxPro criterion can be found in: Joseph, V. R., Gul, E., and Ba, S. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika.
Maximally selected rank statistics with
several p-value approximations.
A suite of conversion scripts to create internally standardized
spatial polygons dataframes. Utility scripts use these datasets to return
values such as country, state, timezone, watershed, etc. associated with a
set of longitude/latitude pairs. (They also make cool maps.)
Scattered data interpolation with Multilevel B-Splines
Distributions that are typically used for exposure rating in
general insurance, in particular to price reinsurance contracts.
The vignettes show code snippets to fit the distribution to
empirical data.
Cluster genes based on Poisson or Negative-Binomial model
for RNA-Seq or other digital gene expression (DGE) data
MBESS implements methods that are especially useful to
researchers working within the behavioral, educational, and
social sciences (both substantive researchers and
methodologists), Many of the methods contained within MBESS are
applicable to quantitative research in general,
Implements methods from the paper "Fast Moment-Based Estimation
for Hierarchical Models," by Perry (2015).
Over 20 multiple-site diversity indices can be calculated.
Later versions will include phylogenetic diversity
This package provides linear models based on Theil-Sen
single median and Siegel repeated medians. They are very robust
(29 or 50 percent breakdown point, respectively), and if no
outliers are present, the estimators are very similar to OLS.
The MBmca package provides data sets and lightweight utilities for
nucleic acid melting curve analysis and presentation on microbead surfaces
but also for reactions in solution (e.g., qPCR).
Model Based Multifactor Dimension Reduction proposed by
Calle et al. (2008) as a dimension reduction method for
exploring gene-gene interactions.
Functional gradient descent algorithm
(boosting) for optimizing general risk functions utilizing
component-wise (penalised) least squares estimates or regression
trees as base-learners for fitting generalized linear, additive
and interaction models to potentially high-dimensional data.
Access to the MBTA API for R. Creates an easy-to-use bundle of
functions to work with all the built-in calls to the MBTA API. Allows users
to download realtime tracking data in dataframe format that is manipulable
in standard R analytics functions.
Bayesian variable selection methods for data with continuous multivariate responses and multiple covariates.
Various distributions and utilities to ease the use of R to build and study Two-Dimensional Monte-Carlo simulations
Post processes MC2 output, especially for use by Path or ST-Sim.  MC2 (short for "MC1 version 2") is a dynamic global vegetation model (en.wikipedia.org/wiki/DGVM).  Path (essa.com/tools/path) and ST-Sim (www.apexrms.com) are state-and-transition model (STM) engines. MC2 has a user website at sites.google.com/site/mc1dgvmusers.  Since 2001, MC1 has been used to simulate changes in natural vegetation due to climate change at scales from regional to global.  In 2012, MC1 was reimplemented in C++ to make it faster and to reduce storage requirements.  This newer version is referred to as MC2, an abbreviation of "MC1 version 2".  Beginning in 2011, output from MC1 and MC2 has been used to inform regional state-and-transition model simulations by the U.S. Forest Service and the Washington State Department of Natural Resources.  Projects to date have involved study areas in central Oregon, the Olympic Peninsula, the Blue Mountains ecoregion, southwestern Oregon, and southeastern Oregon.  In the first of this series of projects, the netCDF output files from MC2 were manually post-processed, mostly in Excel, to produce input .csv files for the STM engines. Beginning with the second project, R scripts were used to automate the post-processing work.  These R scripts have been collected into the MC2toPath R-package.
Weather and air pollution data, risk estimates, and other
information from the Medicare Air Pollution Study (MCAPS) of
204 U.S. counties, 1999--2002
Computes the prime implicants or a minimal disjunctive normal form for a
logic expression presented by a truth table or a logic tree. Has been particularly
developed for logic expressions resulting from a logic regression analysis, i.e.
logic expressions typically consisting of up to 16 literals, where the prime implicants
are typically composed of a maximum of 4 or 5 literals.
A number of biomedical problems involve performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared to the doubled p-value, due to asymmetry in the null distribution. Exact (permutation) testing approaches are attractive, but can be computationally intensive and cumbersome. MCC is an approximation to exact association testing of two vectors that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled p-values.
Implements methods for processing a sample of (hard)
clusterings, e.g. the MCMC output of a Bayesian clustering
model. Among them are methods that find a single best
clustering to represent the sample, which are based on the
posterior similarity matrix or a relabelling algorithm.
Functions which can be useful to support the analyst in the Multicriteria Decision Aiding (MCDA) process involving multiple, conflicting criteria.
An R implementation of Four Multi-Criteria Decision Making (MCDM) Methods: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), "VIseKriterijumska Optimizacija I Kompromisno Resenje" (VIKOR), both Multi-Objective Optimization by Ratio Analysis and Full Multiplicative Form (Multi-MOORA) and Weighted Aggregated Sum Product ASsessment (WASPAS). In addition, this package provides a MetaRanking function which combines the output of the previous methods.
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
Machine coded genetic algorithm (MCGA) is a fast tool for
real-valued optimization problems. It uses the byte
representation of variables rather than real-values. It
performs the classical crossover operations (uniform) on these
byte representations. Mutation operator is also similar to
classical mutation operator, which is to say, it changes a
randomly selected byte value of a chromosome by +1 or -1 with
probability 1/2. In MCGAs there is no need for
encoding-decoding process and the classical operators are
directly applicable on real-values. It is fast and can handle a
wide range of a search space with high precision. Using a
256-unary alphabet is the main disadvantage of this algorithm
but a moderate size population is convenient for many problems.
Package also includes multi_mcga function for multi objective
optimization problems. This function sorts the chromosomes
using their ranks calculated from the non-dominated sorting
algorithm.
'mcgibbsit' provides an implementation of Warnes & Raftery's
MCGibbsit run-length diagnostic for a set of (not-necessarily
independent) MCMC samplers.  It combines the estimate error-bounding
approach of the Raftery and Lewis MCMC run length diagnostic with
the between verses within chain approach of the Gelman and
Rubin MCMC convergence diagnostic.
The package performs global optimization combining Monte Carlo and Quasi Monte Carlo simulation with a local search. \n       The local searches can be easily speeded-up by using a network of local workstations.
mcheatmaps serves to visualize multiple different symmetric matrices and matrix clusters in a single figure using a dendogram, two half matrices and various color labels.
The package performs Monte Carlo hypothesis tests. It
allows a couple of different sequential stopping boundaries (a
truncated sequential probability ratio test boundary and a
boundary proposed by Besag and Clifford, 1991). Gives valid
p-values and confidence intervals on p-values.
This package provides functions to estimate two popular IRT-models: The Nominal Response Model (Bock 1972) and the quite recently developed Nested Logit Model (Suh & Bolt 2010). These are two models to examine multiple-choice items and other multicategorial response formats.
Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.
Maximum likelihood estimation using a Monte Carlo local likelihood (MCLL) method
This packages provides a function to estimate parameters for
the conditional logit model (also with multinomial counts), and for the
mixed conditional logit model, or conditional logit with random effects
(random intercepts only, no random slopes yet).
The current implementation of random effects is limited to
the PQL technique, which requires large cluster sizes.
Normal Mixture Modelling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.
Simulates continuous distributions of random vectors using
Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
R function that evaluates the log unnormalized density.  Algorithms
are random walk Metropolis algorithm (function metrop), simulated
tempering (function temper), and morphometric random walk Metropolis
(Johnson and Geyer, Annals of Statistics, 2012, function morph.metrop),
which achieves geometric ergodicity by change of variable.
Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions.It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.
This package implements poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using ggplot2 functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt
This package contains functions to perform Bayesian
inference using posterior simulation for a number of
statistical models. Most simulation is done in compiled C++
written in the Scythe Statistical Library Version 1.0.3. All
models return coda mcmc objects that can then be summarized
using the coda package.  MCMCpack also contains some useful
utility functions, including some additional density functions
and pseudo-random number generators for statistical
distributions, a general purpose Metropolis sampling algorithm,
and tools for visualization.
Functions for convenient plotting and viewing of MCMC output.
Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: http://bit.ly/1Nwo4CB.
Provides tools for computing Monte Carlo standard
errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
computation for expectation and quantile estimators is
supported as well as multivariate estimations. The package also provides
functions for computing effective sample size and for plotting
Monte Carlo estimates versus sample size.
Functions for multiple criteria optimization using genetic
algorithms and related test problems
The 1001 time series from the M-competition (Makridakis et
al. 1982) and the 3003 time series from the IJF-M3 competition
(Makridakis and Hibon, 2000).
The package aids in creating a Conjoint Analysis design
with extra cards.  Unlike traditional "holdout" cards these
cards are used to create a set of "good" (balanced and low
correlation) designs. Each of these designs is analyzed and the
average calculated.
Multiple contrast tests and simultaneous confidence
intervals based on normal approximation. With implementations for
binomial proportions in a 2xk setting (risk difference and odds ratio),
poly-3-adjusted tumour rates, biodiversity indices (multinomial data)
and expected values under lognormal assumption. Approximative power
calculation for multiple contrast tests of binomial and Gaussian data.
Provides a function that wraps
mcparallel() and mccollect() from 'parallel' with temporary variables and a
task handler.  Wrapped in this way the results of an mcparallel() call
can be returned to the R session when the fork is complete
without explicitly issuing a specific mccollect() to retrieve the value.
Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with
a single call to mcparallelDoCheck().
A Monte Carlo permutation method for multiple test
correlation.
This package implements a methodology for the design and analysis of
dose-response studies that combines aspects of multiple comparison procedures
and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748).
The package provides tools for the analysis of dose finding trials as well as a variety
of tools necessary to plan a trial to be conducted with the MCPMod methodology.
***NOTE*** The MCPMod package will not be further developed, all future development of
the MCP-Mod methodology will be done in the DoseFinding R-package. ***NOTE***
Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.
This package provides regression methods to quantify the relation between two measurement methods. In particular it addresses regression problems with errors in both variables and without repeated measurements. The package provides implementations of Deming regression, weighted Deming regression, and Passing-Bablok regression following the CLSI EP09-A3 recommendations for analytical method comparison and bias estimation using patient samples.
Perform the model confidence set procedure of Hansen et al (2011).
mcsm contains a collection of functions that allows the
reenactment of the R programs used in the book EnteR Monte
Carlo Methods without further programming. Programs being
available as well, they can be modified by the user to conduct
one's own simulations.
Locally weighted regression, semiparametric and
conditionally parametric regression, fourier and cubic spline
functions, GMM and linearized spatial logit and probit,
k-density functions and counterfactuals, nonparametric quantile
regression and conditional density functions, Machado-Mata
decomposition for quantile regressions, spatial AR model,
repeat sales models, conditionally parametric logit and probit
Transition matrices (probabilities or counts) estimation for discrete Markov Chains of order n (1 <= n <= 5).
Mixture and flexible discriminant analysis, multivariate
adaptive regression splines (MARS), BRUTO, ...
Package implements projection based methods for preprocessing,
exploring and analysis of multivariate data used in chemometrics.
Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
Normalize data to minimize the difference between sample plates
(batch effects). For given data in a matrix and grouping variable (or
plate), the function 'normn_MA' normalizes the data on MA coordinates.
More details are in the citation. The primary method is 'Multi-MA'. Other
fitting functions on MA coordinates can also be employed e.g. loess.
The multinomial diversity model is a toolbox for relating diversity to complex predictors. It is based on (1) Shannon diversity; (2) the multinomial logit model, and (3) the link between Shannon diversity and the log-likelihood of the MLM.
The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.
Performs multifactor dimensionality reduction (MDR) to
detect potential gene-gene interactions in case-control
studies.
A set of functions to obtain modified score test for generalized linear models.
Tools associated with General
Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 4x4 and more
general confusion matrices, associated plotting and model comparison tools,
and tests of marginal response invariance and report independence.
A graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.
Identification of ring borders on scanned image sections from dendrochronological samples. Processesing of image reflectances to produce gray matrices and a time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders, or control of automatic detection. Routines to visually include/exclude ring borders on the R graphical device, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to positive/negative extreme values in the smoothed time-series of gray values.
This package performs maximum entropy density based
dependent data bootstrap. An algorithm is provided to create a
population of time series (ensemble) without assuming
stationarity. The reference paper (Vinod, H.D., 2004) explains
how the algorithm satisfies the ergodic theorem and the central
limit theorem.
Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.
Run flexible mediation analyses using natural
effect models as described in Lange, Vansteelandt and Bekaert (2012),
Vansteelandt, Bekaert and Lange (2012) and Loeys, Moerkerke, De Smet,
Buysse, Steen and Vansteelandt (2013).
Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
Provides a general framework for clinical trial simulations based
on the Clinical Scenario Evaluation (CSE) approach. The package supports a
broad class of data models (including clinical trials with continuous, binary,
survival-type and count-type endpoints as well as multivariate outcomes that are
based on combinations of different endpoints), analysis strategies and commonly
used evaluation criteria.
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010), Imai, Keele and Tingley (2010), Imai, Tingley and Yamamoto (2013), Imai and Yamamoto (2013) and Yamamoto (2013). In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Generates risk estimates and comorbidity flags from ICD-9-CM
codes available in administrative medical datasets. The package supports
the Charlson Comorbidity Index, the Elixhauser Comorbidity
classification, the Revised Cardiac Risk Index, and the Risk Stratification
Index.  Methods are table-based, fast, and use the plyr package, so
parallelization is possible for large jobs. Also includes a sample of
real ICD-9 data for 100 patients from a publicly available dataset.
This package contains the functions used to perform some
confidence statistics based in population median.
This is a C++ implementation of Sparse Topical Coding
(STC), a model of discrete data which is fully described in Zhu
et al. (2011) (http://www.cs.cmu.edu/~junzhu/stc/stc.pdf). It
can be used for multi-class classification and describing
documents with underlying sparse topics.
MEET (Motif Elements Estimation Toolkit) is a R-package
that integrates a set of computational algorithms for the
detection of Transcription Factor Binding Sites (TFBS).
A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
An S4 update of the 'mefa' package
using sparse matrices for enhanced efficiency.
Sparse array-like objects are supported via
lists of sparse matrices.
Given a set of taxa and marker, the goal is build a complete and correct sequence alignment for phylogenetic analysis.
Exploratory model analysis.  Fit and graphical
explore ensembles of linear models.
Modelization of influenza epidemics in order to monitor future activity.
Memgene can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation.  Memgene has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.
One of the aims of this package is to make life easier for
useRs who deal with survey data sets. It provides an
infrastructure for the management of survey data including
value labels, definable missing values, recoding of variables,
production of code books, and import of (subsets of) SPSS and
Stata files. Further, it provides functionality to produce
tables and data frames of arbitrary descriptive statistics and
(almost) publication-ready tables of regression model
estimates. Also some convenience tools for graphics,
programming, and simulation are provided.
Cache the results of a function so that when you call it
again with the same arguments it returns the pre-computed value.
Data sets and sample analyses from Pinheiro and Bates,
"Mixed-effects Models in S and S-PLUS" (Springer, 2000).
How much ram do you need to store a 100,000 by 100,000 matrix?
How much ram is your current R session using? How much ram do you even have?
Learn the scintillating answer to these and many more such questions with
the 'memuse' package.
Set of configurable menus built with GTK+ to graphically interface new functions.
A GUI for merging datasets in R using gWidgets.
N>=3 methods are used to measure each of n items.
The data are used to estimate simultaneously systematic error (bias)
and random error (imprecision). Observed measurements for each method
or device are assumed to be linear functions of the unknown true values
and the errors are assumed normally distributed. Maximum likelihood
estimation is used for the imprecision standard deviation estimates.
Pairwise calibration curves and plots can be easily generated.
Provides methods for extracting results from merMod objects in the lme4 package. Allows construction of prediction intervals efficiently from large scale LMM and GLMM models.
A mixed collection of useful and semi-useful functions, some of
which may even be referenced in The R Primer book.
User-friendly general package providing standard methods for meta-analysis:
- fixed effect and random effects meta-analysis;
- several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
- statistical tests and trim-and-fill method to evaluate bias in meta-analysis;
- import data from RevMan 5;
- prediction interval, Hartung-Knapp and Paule-Mandel method for random effects model;
- cumulative meta-analysis and leave-one-out meta-analysis;
- meta-regression (if R package 'metafor' is installed).
Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <http://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos="http://www.math.ntnu.no/inla/R/testing").
A package for meta-analysis of genome-wide association
scans between quantitative or binary traits and SNPs
Fits probabilistic principal components analysis,
probabilistic principal components and covariates analysis and
mixtures of probabilistic principal components models to
metabolomic spectral data.
A collection of functions to aid in the statistical analysis of metabolomic data
Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002. The package also includes functions to visualize ecological networks, and to calculate modularity as a replacement to boundary clumping.
Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP)
meta-analytical approaches with correlation coefficients as
effect sizes.
Provides two functions-meta2d and meta3d for detecting
rhythmic signals from time-series datasets. For analyzing
time-series datasets without individual information, 'meta2d' is
suggested, which could incorporates multiple methods from ARSER,
JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For
analyzing time-series datasets with individual information, 'meta3d' is
suggested, which takes use of any one of these three methods to analyze
time-series data individual by individual and gives out integrated values
based on analysis result of each individual.
MetaDE package implements 12 major meta-analysis methods
for differential expression analysis.
The metafolio R package is a tool to simulate salmon
metapopulations and apply financial portfolio optimization concepts. The
package accompanies the paper 'Portfolio conservation of metapopulations
under climate change'. See citation("metafolio").
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.
For each covariate, cluster its coefficient effects across different data sets during
data integration. Supports Gaussian, binomial and Poisson regression models.
Functionalities for facilitating systematic reviews, data extractions, and meta-analyses.  It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter-reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software.  Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031.
Provides methods for making inference in the random effects meta
regression model such as point estimates and confidence intervals for the
heterogeneity parameter and the regression coefficients vector.  Inference
methods are based on different approaches to statistical inference.
Methods from three different schools are included: methods based on the
method of moments approach, methods based on likelihood, and methods based
on generalised inference.  The package also includes tools to run extensive
simulation studies in parallel on high performance clusters in a modular
way.  This allows extensive testing of custom inferential methods with all
implemented state-of-the-art methods in a standardised way.  Tools for
evaluating the performance of both point and interval estimates are
provided.  Also, a large collection of different pre-defined plotting
functions is implemented in a ready-to-use fashion.
Automation of preprocessing often requires computationally costly
preprocessing combinations. This package helps to find near-best combinations
faster. Sub heuristics supported are random and grid restarts, taboo list,
decreasing probability for accepting inferior solutions and location of
previously best solution candidate is compared to. The package is intended
to be used with package 'preprocomb' and takes its 'GridClass' object as input.
Tools to generate random landscape graphs, evaluate species
occurrence in dynamic landscapes, simulate future landscape occupation and
evaluate range expansion when new empty patches are available (e.g. as a
result of climate change).
First- and higher-order likelihood inference in
meta-analysis and meta-regression models.
Combines either p-values or modified effect sizes from different
studies to find differentially expressed genes
This package provides functions for diagnostic and
prognostic meta-analyses. It estimates univariate, bivariate
and multivariate models, and allows the aggregation of
previously published prediction models with new data.
Resolves complex metagenomic mixtures by analysing
deep sequencing data, using a mixture model based approach.
The use of parallel Monte Carlo Markov chains for the exploration
of the species space enables the identification of the set
of species more likely to contribute to the mixture.
A novel meta-analytic method that allows an unbiased inclusion of studies with Non Statistically-Significant Unreported Effects (NSUEs). Briefly, the method first calculates the interval where the unreported effects (e.g. t-values) should be according to the threshold of statistical significance used in each study. Afterwards, maximizing likelihood techniques are used to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance and statistical significance bounds. Finally, a restricted-maximum likelihood random-effects meta-analysis is separately conducted for each set of imputations, and estimations from these meta-analyses are pooled. Please read the reference in 'meta.nsue' for details of the procedure.
Meta-analysis of significance values. The package
provides routines for a number of methods for doing this, a
number of datasets to replicate published results, and a
routine for graphical display
Perform the Meta-analysis for Pathway Enrichment (MAPE)
methods introduced by Shen and Tseng (2010). It includes functions to
automatically perform MAPE_G (integrating multiple studies at
gene level), MAPE_P (integrating multiple studies at pathway
level) and MAPE_I (a hybrid method integrating MAEP_G and
MAPE_P methods). In the simulation and real data analyses in
the paper, MAPE_G and MAPE_P have complementary advantages and
detection power depending on the data structure. In general,
the integrative form of MAPE_I is recommended to use. In the
case that MAPE_G (or MAPE_P) detects almost none pathway, the
integrative MAPE_I does not improve performance and MAPE_P (or
MAPE_G) should be used. Reference: Shen, Kui, and George C
Tseng. Meta-analysis for pathway enrichment analysis when
combining multiple microarray studies.Bioinformatics (Oxford,
England) 26, no. 10 (April 2010): 1316-1323.
doi:10.1093/bioinformatics/btq148.
http://www.ncbi.nlm.nih.gov/pubmed/20410053.
MetaPCA implements simultaneous dimension reduction using
PCA when multiple studies are combined. We propose two basic
ideas to find a common PC subspace by eigenvalue maximization
approach and angle minimization approach, and we extend the
concept to incorporate Robust PCA and Sparse PCA in the
meta-analysis realm.
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 doi:10.1002/sim.2897 or Baker and Jackson, 2008 doi:10.1007/s10729-007-9041-8) or mixtures of normals (Beath, 2014 doi:10.1002/jrsm.1114).
MetaQC implements our proposed quantitative quality
control measures: (1) internal homogeneity of co-expression
structure among studies (internal quality control; IQC); (2)
external consistency of co-expression structure correlating
with pathway database (external quality control; EQC); (3)
accuracy of differentially expressed gene detection (accuracy
quality control; AQCg) or pathway identification (AQCp); (4)
consistency of differential expression ranking in genes
(consistency quality control; CQCg) or pathways (CQCp). (See
the reference for detailed explanation.) For each quality
control index, the p-values from statistical hypothesis testing
are minus log transformed and PCA biplots were applied to
assist visualization and decision. Results generate systematic
suggestions to exclude problematic studies in microarray
meta-analysis and potentially can be extended to GWAS or other
types of genomic meta-analysis. The identified problematic
studies can be scrutinized to identify technical and biological
causes (e.g. sample size, platform, tissue collection,
preprocessing etc) of their bad quality or irreproducibility
for final inclusion/exclusion decision.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
A collection of functions for conducting meta-analysis using a
structural equation modeling (SEM) approach via the 'OpenMx' package.
It also implements the two-stage SEM approach to conduct meta-analytic
structural equation modeling on correlation and covariance matrices.
The following methods to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis are implemented:
- Copas selection model;
- limit meta-analysis;
- upper bound for outcome reporting bias.
Functions for Meta-analysis Burden test, SKAT and SKAT-O. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.
This package fits meta regression models and generates a
number of statistics: next to t- and z-tests, the likelihood
ratio, bartlett corrected likelihood ratio and permutation
tests are performed on the model coefficients.
This package allows doing meta-analysis for primary studies with classification outcomes in order to evaluate systematically the accuracies of classifiers, namely, the diagnostic tests. It provides functions to fit the bivariate model of Reitsma et al.(2005). Moreover, if the reference employed in the classification process isn't a gold standard, its deficit can be detected and its influence to the underestimation of the diagnostic test's accuracy can be corrected, as described in Botella et al.(2013).
Spatio-temporal geostatistical mapping of meteorological data. Global spatio-temporal models calculated using publicly available data are stored in package.
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS, MeteoGalicia, NAM, and RAP.
meteogram is a collection of programs for plotting
meteograms for meteorological data such as atmospheric cross
section, temperatures plots.
Fit and plot macroecological patterns predicted by the Maximum
Entropy Theory of Ecology (METE).
Functions for selection of visual meteor data, calculations of Zenithal Hourly Rate (ZHR) and population index, graphics of ZHR and magnitude distribution
Meth27QC is a tool for analyzing Illumina Infinium
HumanMethylation27 BeadChip Data and generating QC reports.
This package allows users quickly assess data quality of the
Assay. Users can evaluate the data quality in the way that
Illumina GenomeStudio/BeadStudio recommended based on the
control probes. The package reads files exported from
GenomeStudio/BeadStudio software, generating intensity and
standard deviation plots grouped by the types of the control
probes. Meth27 carries 40 control probes for staining,
hybridization, target removal, extension, bisulfite conversion,
specificity, negative and non-polymorphic controls. Details of
those control probes can be found in the Infinium Assay for
Methylation Protocol Guide from Illumina.We also used the other
non-control probes to plot intensity of detected genes, signal
average for green and red. Outliers can be identified.
Methods (standard and advanced) for analysis of agreement
between measurement methods.
It plots the output from Methpup (https://github.com/XinYang6699/Methpup)
Provides a univariate and several high dimensional multivariate test statistics for detecting differentially methylated regions based on MethylCap-seq data.
Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package contains a collection of R functions which can be used to remove unwanted variation and obtain normalized metabolomics data.
Metrics is a set of evaluation metrics that is commonly
used in supervised machine learning.
Provides an 'htmlwidgets' interface to the
'MetricsGraphics.js' ('D3'-based) charting library which is geared towards
displaying time-series data. Chart types include line charts, scatterplots,
histograms and rudimentary bar charts. Support for laying out multiple charts
into a grid layout is also provided. All charts are interactive and many
have an option for line, label and region annotations.
metRology provides classes and calculation and plotting functions
for metrology applications, including measurement uncertainty estimation
and inter-laboratory metrology comparison studies.
Implementation of various statistical models for multivariate
event history data. Including multivariate cumulative incidence models, and
bivariate random effects probit models (Liability models). Also contains
two-stage binomial modelling that can do pairwise odds-ratio dependence
modelling based marginal logistic regression models. This is an alternative
to the alternating logistic regression approach (ALR).
An easy to use Graphical User Interface for estimating sample sizes required for metabolomic experiments even when experimental pilot data is not available.
A diverse collection of metabolomics related statistical tools.
Exact simulation from max-stable processes and multivariate extreme value distributions for various parametric models.
Computes the average of a sequence of random vectors
in a moving expanding window using a fixed amount of storage
MExPosition is for descriptive (i.e., fixed-effects)
multi-table multivariate analysis the singular value
decomposition.
Calculate MF (mitigated fraction) with clustering and bootstrap options. See http://goo.gl/pcXYVr for definition of MF.
Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
Multivariate functional halfspace depth and median for two-dimensional functional data.
The package implements several time series filters useful
for smoothing and extracting trend and cyclical components of a
time series. The routines are commonly used in economics and
finance, however they should also be interest to other areas.
Currently, Christiano-Fitzgerald, Baxter-King,
Hodrick-Prescott, Butterworth, and trigonometric regression
filters are included in the package.
Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Convenience functions for multivariate MCMC using univariate samplers including:
slice sampler with stepout and shrinkage (Neal, 2003),
adaptive rejection sampler (Gilks and Wild, 1992),
adaptive rejection Metropolis (Gilks et al 1995), and
univariate Metropolis with Gaussian proposal.
Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output.
GAMs, GAMMs and other generalized ridge regression with
multiple smoothing parameter estimation by GCV, REML or UBRE/AIC.
Includes a gam() function, a wide variety of smoothers, JAGS
support and distributions beyond the exponential family.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
Functions to estimate and sample from a mixed Graphical model.
Extends distribution and density functions to parametric
multivariate generalized Pareto distributions (MGPD of Type
II), and provides fitting functions which calculate maximum
likelihood estimates for bivariate and trivariate models. (Help
is under progress)
Each function in the package performs three main functions
i) it reads spatial data and produces basic graphs including
pie chart, bar chart, box plots, histogram, scatter plots, and
lines ii)it reads non-spatial data such as "csv", "txt", "dat"
data and produces basic graphs and iii) it plots map(s) of the
input attribute(s) of spatial data by setting "type" parameter
to "map"
Convenience Functions for R Language Access to the v.1 API of the MG-RAST Metagenome Annotation Server, part of the US Department of Energy (DOE) Systems Biology Knowledge Base (KBase).
Implements Multi-Group Sparse Discriminant Analysis proposal of 'I.Gaynanova, J.Booth and M.Wells (2015), Simultaneous sparse estimation of canonical vectors in the p>>N setting'.
Performs gene set analysis based on GSZ scoring function and asymptotic p-value. It is different from GSZ in that it implements asymptotic p-values instead of empirical p-values. Asymptotic p-values are calculated by fitting suitable distribution model to the null distribution. Unlike empirical p-values, resolution of asymptotic p-values are independent of the number of permutations and hence requires considerably fewer permutations. In addition, this package allows gene set analysis with seven other popular gene set analysis methods.
Performs general Metropolis-Hastings Markov Chain Monte
Carlo sampling of a user defined function which returns the
un-normalized value (likelihood times prior) of a Bayesian
model.  The proposal variance-covariance structure is updated
adaptively for efficient mixing when the structure of the
target distribution is unknown.  The package also provides some
functions for Bayesian inference including Bayesian Credible
Intervals (BCI) and Deviance Information Criterion (DIC)
calculation.
Implementation of a goodness-of-fit test for normality using the Minimum Hellinger Distance.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
Estimation of models with zero left-censored variables.
Null values may be caused by a selection process, insufficient
resources or infrequency of purchase.
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. Each variable has its own imputation
model. Built-in imputation models are provided for continuous data
(predictive mean matching, normal), binary data (logistic regression),
unordered categorical data (polytomous logistic regression) and ordered
categorical data (proportional odds). MICE can also impute continuous
two-level data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between variables. Various
diagnostic plots are available to inspect the quality of the imputations.
Contains some auxiliary functions for multiple imputation which
complements existing functionality in R.
In addition to some utility functions, main features include
plausible value imputation, multilevel imputation functions,
imputation using partial least squares (PLS) for high dimensional
predictors, nested multiple imputation, and two-way imputation.
Tools for microeconomic analysis and microeconomic modelling
Demand analysis with the Almost Ideal Demand System (AIDS)
suggested by Deaton and Muellbauer (1980)
Tools for economic analysis and economic modelling
with a Constant Elasticity of Substitution (CES) function
Production analysis with the Symmetric Normalized Quadratic (SNQ) profit function
Provides infrastructure to accurately measure and compare
the execution time of R expressions.
The MicroDatosEs package provides utilities for reading and processing microdata from Spanish official statistics with R.
This group of functions simplifies the creation of linked micromap
plots.
This package provides the user with the ability to quickly create Linked
Micromap plots of the 50 U.S. states and District of Columbia (51 areas).
Linked Micromap plots are visualizations of geo-referenced data that link statistical
graphics to an organized series of small maps.
The Help description contains examples of how to use the micromapST function.
This package contains the US and State boundary files and U. S. FIPS code to State conversion tables
used by the micromapST function and several data sets used in the micromapST examples.
Copyrighted 2013, 2014 and 2015 by Carr, Pearson and Pickle.
A collection of functions for computations and visualizations of microbial pan-genomes.
Deploys your R Analytic to MicroStrategy
This entry-level toolkit allows performing generic continuous-time microsimulation for a wide range of demograhic applications.
Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.
Apply tests of multiple comparisons based
on studentized midrange and range distributions. The tests are: Tukey Midrange test,
Student-Newman-Keuls Midrange test, Skott-Knott Midrange test and Skott-Knott Range test.
Functions for implementing species dispersal into projections
of species distribution models (e.g. under climate change scenarios).
Indirect methods for estimating bilateral migration flows in the presence of partial or missing data. Methods might be relevant to other categorical data situations on non-migration data, where for example, marginal totals are known and only auxiliary bilateral data is available.
This package provides various indices, like Crude Migration Rate,
different Gini indices or the Coefficient of Variation among others, to
show the (un)equality of migration.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
Implements multiple imputation for proportional hazards regression
with interval censored data or proportional sub-distribution hazards
regression for interval censored competing risks data. The main functions
allow to estimate survival function, cumulative incidence function, Cox
and Fine & Gray regression coefficients and associated variance-covariance
matrix. 'MIICD' functions call 'Surv', 'survfit' and 'coxph' from the
'survival' package, 'crprep' from the 'mstate' package, and 'mvrnorm' from
the 'MASS' package.
Functions for estimating structural equation models using model-
implied instrumental variables.
The MILC package is designed to predict individual trajectories using the continuous time microsimulation model MILC, that describes the natural history of lung cancer.
Guesses the MIME type from a filename extension using the data
derived from /etc/mime.types in UNIX-type systems.
Tools to combine results for multiply-imputed data using
mixture approximations
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
R wrapper for cmine implementation of Maximal
Information-based Nonparametric Exploration statistics (MIC and
MINE family)
This package implements the core idea of games known as
'Minesweeper' on Microsoft Windows or 'KMines' for KDE on
Unix-like operating systems.
Makes it possible to create an internally consistent
repository consisting of selected packages from CRAN-like repositories.
The user specifies a set of desired packages, and miniCRAN recursively
reads the dependency tree for these packages, then downloads only this
subset. The user can then install packages from this repository directly,
rather than from CRAN.  This is useful in production settings, e.g. server
behind a firewall, or remote locations with slow broadband access.
quick and simple tktcl miniGUI to call functions.
The minimax family of distributions is a two-parameter
family like the beta family, but computationally a lot more
tractible.
A binding to the minimist JavaScript library. This module implements
the guts of optimist's argument parser without all the fanciful decoration.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Package minPtest is designed for estimating a gene region-level summary for SNP data from case-control studies using a permutation-based resampling method, called min P test, allowing execution on a compute cluster or multicore computer.
Derivative-free optimization by quadratic approximation
based on an interface to Fortran implementations by M. J. D.
Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
Learns direct microbe-microbe interaction networks using a Poisson
multivariate-normal hierarchical model with an L1 penalized precision
matrix. Optimization is carried out using an iterative conditional modes
algorithm.
This package implements entropy optimization distribution
under specified constraints. It also offers an R interface to
the MinxEnt and MaxEnt distributions.
An implementation of the iterative proportional fitting (IPFP),
maximum likelihood, minimum chi-square and weighted least squares procedures
for updating a N-dimensional array with respect to given target marginal
distributions (which, in turn can be multi-dimensional). The package also
provides an application of the IPFP to simulate multivariate Bernoulli
distributions.
This package contains functions to carry out processing of
high throughput data analysis and detection of putative
hits/mutants.  Contents include a function for post-hoc quality
control for removal of outlier sample sets, a median-based
normalization method for use in datasets where there are no
explicit controls and where most of the responses are of the
wildtype/no response class (see accompanying paper).  The
package also includes a way to prioritize individuals of
interest using am empirical cumulative distribution function.
Methods for generating synthetic data as well as data from the
Chloroplast 2010 project are included.
This package collects algorithms/functions developed for
microRNA profiling data analyses.  Analytical platforms include
traditional hybridization microarray, CGH, beads-based
microarray, and qRT-PCR array.
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of dichotomous and polytomous response data using
unidimensional and multidimensional latent trait models under the Item
Response Theory paradigm. Exploratory and confirmatory models can be
estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
bi-factor and two-tier analyses are available for modeling item testlets.
Multiple group analysis and mixed effects designs also are available for
detecting differential item functioning and modelling item and person
covariates.
Provides tools to generate an HTML interface for creating adaptive
and non-adaptive educational and psychological tests using the shiny
package. Suitable for applying unidimensional and multidimensional
computerized adaptive tests using item response theory methodology and for
creating simple questionnaires forms to collect response data directly in R.
A collection of miscellaneous 3d plots, including
isosurfaces.
Various functions for random number generation, density
estimation, classification, curve fitting, and spatial data
analysis.
LaTeX tables, Kalman filter, web scraping, development tools,
relative risk, odds ratio
A set of miscellaneous R tools to simplify the work with several
data types and formats.
Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence.
The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation. It can be run in parallel to
save computation time.
Provides numeric and graphical
summaries for the missing values from both categorical
and quantitative variables. A variety of imputation
methods are applied, including the univariate imputations
like fixed or random values, multivariate imputations
like the nearest neighbors and multiple imputations,
and imputations conditioned on a categorical variable.
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA.
To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR).
For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)," Journal of Statistical Software,  56(6), 1-31. URL http://www.jstatsoft.org/v56/i06/.
Test for association between a set of SNPS/genes and
continuous or binary outcomes by including variant
characteristic information and using (weighted) score
statistics.
This R package is providing all the data sets and statistical analysis applications used in "Modern Industrial Statistics: with applications in R, MINITAB and JMP" by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2013, which is a second revised and expanded revision of "Modern Industrial Statistics: Design and Control of Quality and Reliability", R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
Various reliability analysis methods for: 1) computing
failure probability (probability that the output of a numerical
model exceeds a threshold); 2) computing the size of a sample
to estimate a quantile with a confidence level.
Flexible multivariate function approximation using adapted
Mixture of Student t Distributions. Mixture of t distribution
is obtained using Importance Sampling weighted Expectation
Maximization algorithm.
Provides tools for multiple imputation of missing data in multilevel
modeling. Includes a user-friendly interface to the 'pan' package, and
several functions for visualization, data management and the analysis
of multiply imputed data sets.
Tools to perform analyses and combine results from
multiple-imputation datasets.
Estimation/multiple imputation programs for mixed categorical
and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for estimating parametric mixture models with missing data.
Mixed effects cumulative and baseline logit link models
for the analysis of ordinal or nominal responses, with
non-parametric distribution for the random effects
This package contains functions for fitting finite mixture
distribution models to grouped data and conditional data by the
method of maximum likelihood using a combination of a
Newton-type algorithm and the EM algorithm.
Fits mixed membership models with discrete multivariate data (with
or without repeated measures) following the general framework of Erosheva et al
(2004). This package uses a Variational EM approach by approximating the
posterior distribution of latent memberships and selecting hyperparameters
through a pseudo-MLE procedure. Currently supported data types are
Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al     (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.
The estimation of the parameters in Mixed Poisson models.
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Routines for the analysis (unsupervised clustering) of
networks using MIXtures of Erdos-Renyi random graphs
Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution.The first model 'MGHD' is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD', mixture of  multiple scaled generalized hyperbolic distributions. The 'cMSGHD' is a 'MSGHD' with convex contour plots. The 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm and glm. A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm, glm and associated summaries from the package stats.
A collection of functions to implement the MixMAP algorithm, which performs gene
level tests of association using data from a previous GWAS or data from a
meta-analysis of several GWAS.  Conceptually, genes are detected as
significant if the collection of p-values within a gene are determined to
be collectively smaller than would be observed by chance.
We provide statistical integrative techniques and
variants to analyse highly dimensional data sets: regularized
Canonical Correlation Analysis ('rCCA') and sparse Partial Least Squares variants ('sPLS')
to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) where the p and
q variables are measured on the same samples or individuals n. These data may come from high throughput
technologies, such as 'omics' data (e.g. transcriptomics, metabolomics or proteomics
data) that require an integrative or joint analysis. However, 'mixOmics' can also be applied to any other
large data sets where p + q >> n. 'rCCA' is a regularized version of Canonical Correlation Analysis to deal with
the large number of variables. 'sPLS' allows variable selection in a one step procedure and two frameworks
are proposed: regression and canonical analysis. Numerous graphical outputs are provided to help interpreting
the results. Recent methodological developments include: sparse PLS-Discriminant Analysis ('sPLS-DA'), Independent
Principal Component Analysis ('IPCA'), multilevel analysis using variance decomposition of the data and integration
of multiple data sets with regularized Generalised Canonical Correlation Analysis ('rGCCA') and variants (sparse 'GCCA'). More details  can be found
on our website.
Provides the function 'mixord' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
A collection of tools implemented to facilitate the analysis of the components of a finite mixture distributions. The package has some functions to generate random samples coming from a finite mixture. The package provides a C++ implementation for the construction of a hierarchy over the components of a given finite mixture.
Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Estimates Rasch models and mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model.
Fits mixtures of (possibly multivariate) regressions
(which has been described as doing ANCOVA when you don't
know the levels).
Separates DNA mixtures using a statistical model within a
greedy algorithm with a useful tcl/tk GUI.
The utility of this package is in simulating mixtures of Gaussian
distributions with different levels of overlap between mixture
components.  Pairwise overlap, defined as a sum of two
misclassification probabilities, measures the degree of
interaction between components and can be readily employed to
control the clustering complexity of datasets simulated from
mixtures. These datasets can then be used for systematic
performance investigation of clustering and finite mixture
modeling algorithms. Among other capabilities of 'MixSim', there
are computing the exact overlap for Gaussian mixtures,
simulating Gaussian and non-Gaussian data, simulating outliers
and noise variables, calculating various measures of agreement
between two partitionings, and constructing parallel
distribution plots for the graphical display of finite mixture
models.
Functions to fit finite mixture of scale mixture of
skew-normal (FM-SMSN) distributions.
Differential expression analysis of RNA-Seq data when replicates under two conditions are available is performed. First, mixtures of Negative Binomial distributions are fitted on the data in order to estimate the dispersions, then the Wald test is computed.
A collection of R functions for analyzing finite mixture models.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
Curve Fitting for monotonic(sigmoidal) & non-monotonic(J-shaped)
concentration-response data. Toxicity prediction of chemical mixtures using reference
models such as 'concentration addition' and 'independent action' (Backhaus, T., Faust,
M., Environmental Science and Technology. 2012, 46, 2564-2573. doi:10.1021/es2034125).
An implementation of all 14 Gaussian parsimonious
clustering models (GPCMs) for model-based clustering and
model-based classification.
Likelihood-based methods for inference for finite mixture
models
A set of classes and methods to set up and run multispecies, trait
based and community size spectrum ecological models, focussed on the marine
environment.
Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.
Calculation routines based on the FOCUS Kinetics Report (2006).
Includes a function for conveniently defining differential equation models,
model solution based on eigenvalues if possible or using numerical solvers
and a choice of the optimisation methods made available by the 'FME' package.
If a C compiler (on windows: 'Rtools') is installed, differential
equation models are solved using compiled C functions.
Please note that no warranty is implied for correctness of results or fitness
for a particular purpose.
Package for fast computation of the maximum kernel
likelihood estimator (mkle)
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals, and generation of similarity matrices.
mkssd is a package that generates efficient balanced
non-aliased multi-level k-circulant supersaturated designs by
interchanging the elements of the generator vector. The package
tries to generate a supersaturated design that has chisquare
efficiency more than user specified efficiency level (mef). The
package also displays the progress of generation of an
efficient multi-level k-circulant design through a progress
bar. The progress of 100% means that one full round of
interchange is completed. More than one full round (typically
4-5 rounds) of interchange may be required for larger designs.
A collection of artificial and real-world machine learning
benchmark problems, including, e.g., several data sets from the
UCI repository.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multi-dimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact.
Functions necessary to perform the machine learning-based
differential network analysis of transcriptome data.
Exploratory data analysis and manipulation functions for multi-
label data sets along with an interactive Shiny application to ease their use.
Large collection of multilabel datasets along with the functions
needed to export them to several formats, to make partitions, and to obtain
bibliographic information.
Difference scaling is a method for scaling perceived
supra-threshold differences.  The package contains functions that
allow the user to design and run a difference scaling experiment,
to fit the resulting data by maximum likelihood and test the
internal validity of the estimated scale.
This package provides a unified interface to various
machine learning algorithms. Confusion matrices are provided
too.
This package contains functions to compute the
nonparametric maximum likelihood estimator (MLE) for the
bivariate distribution of (X,Y), when realizations of (X,Y)
cannot be observed directly.  To be more precise, we consider
the situation where we observe a set of rectangles that are
known to contain the unobservable realizations of (X,Y). We
compute the MLE based on such a set of rectangles.  The methods
can also be used for univariate censored data (see data set
'cosmesis'), and for censored data with competing risks (see
data set 'menopause').  We also provide functions to visualize
the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
univariate and multi-dimensional outputs with diagnostic plots.
Contact the maintainer for a package version that implements
sensitivity analysis functionality.
Provides functions for unit root testing using MLE method
Processing and analysis of high throughput (Roche 454)
sequences generated from multiple loci and multiple biological
samples. Sequences are assigned to their locus and sample of
origin, aligned and trimmed. Where possible, genotypes are
called and variants mapped to known alleles.
An R code implementation of the maximum likelihood (fixed
point) algorithm of Hyvaerinen, Karhuna, and Oja for
independent component analysis.
Used for mediation analysis with generalized multilevel models.
A collection of evaluation metrics, including loss, score and utility functions,
that measure regression and classification performance.
Computational strategies for multivariate linear
mixed-effects models with missing values, Schafer and Yucel
(2002), Journal of Computational and Graphical Statistics, 11,
421-442.
Data and examples from a multilevel modelling software review
as well as other well-known data sets from the multilevel modelling
literature.
Estimation of the multinomial logit model
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
Phase haplotypes from genotypes based on a list of known
haplotypes. Suited to highly diverse loci such as HLA.
Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.
This package provides Multilinear Regression Model Population
Analysis to build a pool of models between quantitative activity and chemical
descriptors. Also some useful model validation functions. Contains all
molecular descriptors of 101 organic compounds and acticity dataset.
Generate a stream of pseudo-random numbers generated using the MLS
Junk Generator algorithm. Functions exist to generate single pseudo-random
numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Compute estimates of the multivariate vector autoregression model as
used to (but not limited to) analyze experience sampling method data in
clinical psychology. The model can be extended through treatment effects,
covariates and pre- and post-assessment effects.
Simulation and visualization of complex
models for longitudinal data. The models are encoded using the model coding
language 'Mlxtran', automatically converted into C++ codes, compiled on the
fly and linked to R using the 'Rcpp' package. That allows one to implement
very easily complex ODE-based models and complex statistical models,
including mixed effects models, for continuous, count, categorical, and
time-to-event data.
Description: Various utilities for the Multiplicative
Multinomial distribution
Description: A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human MB patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA plots and heatmaps.
Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594').
Used for general multiple mediation analysis.
The analysis method is described in Yu et al. 2014, "General Multiple Mediation Analysis With an Application to Explore Racial Disparity in Breast Cancer Survival", published on Journal of Biometrics & Biostatistics, 5(2):189.
Provides tools for performing mathematical morphology operations, such as erosion and dilation, on data of arbitrary dimensionality. Can also be used for resampling, filtering, smoothing and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile
Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
This package provides an implementation of modified
maximum contrast methods and the maximum contrast method. This
version supports functions mmcm.mvt, mcm.mvt that gives P-value
by using randomized quasi-Monte Carlo method from pmvt function
of package mvtnorm, and mmcm.resamp that gives P-value by using
the permutation method.
This library implements mixture model distance sampling
methods. See Miller and Thomas (in prep.).
mme fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one
random effect in each category of the response variable; Model 2, introducing
independent time effect; Model 3, introducing correlated time effect.
mme calculates analytical and parametric bootstrap MSE estimators.
Fit multivariate mixture of normal distribution using
covariance structure.
This package implements a novel multivariate meta-analysis.
fits multivariate marginal models for multivariate longitudinal data for both continuous and discrete responses
Fits multivariate marginal models with shared regression parameters for discrete and continuous responses
The package implements a multi-marker molecular signature (MMMS) approach
for treatment-specific subgroup identification.
Provides functions for measuring
population divergence from genotypic data.
Compute similarities and distances between marked point processes.
Fixed effects Selection in Linear Mixed Models
Fits a family of mixtures of multivariate t-distributions under a continuous t-distributed latent variable structure for the purpose of clustering or classification. The alternating expectation-conditional maximization algorithm is used for parameter estimation.
The first day of any MMWR week is Sunday.
MMWR week numbering is sequential beginning with 1
and incrementing with each week to a maximum of 52
or 53. MMWR week #1 of an MMWR year is the first week
of the year that has at least four days in the calendar
year. This package provides functionality to convert
Dates to MMWR day, week, and year and the reverse.
Time and memory efficient estimation of multinomial logit models using maximum likelihood method. Numerical optimization performed by Newton-Raphson method using an optimized, parallel C++ library to achieve fast computation of Hessian matrices. Motivated by large scale multiclass classification problems in econometrics and machine learning.
The package provides multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components.
Computes integral of f(x)*x_i^k on a product of intervals,
where f is the density of a gaussian law.
This a is small alteration of the mnormt code from A. Genz and A. Azzalini.
Functions are provided for computing the density and the
distribution function of multivariate normal and "t" random variables,
and for generating random vectors sampled from these distributions.
Probabilities are computed via a non-Monte Carlo method. Different routines
are used for the case d=1, d=2, d>2, if d denotes the number of dimensions.
MNP is a publicly available R package that fits the
Bayesian multinomial probit model via Markov chain Monte Carlo.
The multinomial probit model is often used to analyze the
discrete choices made by individuals recorded in survey data.
Examples where the multinomial probit model may be useful
include the analysis of product choice by consumers in market
research and the analysis of candidate or party choice by
voters in electoral studies.  The MNP software can also fit the
model with different choice sets for each individual, and
complete or partial individual choice orderings of the
available alternatives from the choice set. The estimation is
based on the efficient marginal data augmentation algorithm
that is developed by Imai and van Dyk (2005). ``A Bayesian
Analysis of the Multinomial Probit Model Using the Data
Augmentation,'' Journal of Econometrics, Vol. 124, No. 2
(February), pp. 311-334. Detailed examples are given in Imai
and van Dyk (2005). ``MNP: R Package for Fitting the
Multinomial Probit Model.'' Journal of Statistical Software,
Vol. 14, No. 3 (May), pp. 1-32.
An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
Some canned plots and functions designed for the mobilize project.
Designed to be called remotely.
This package provides methods to analyze cluster
alternatives based on multi-objective optimization of cluster
validation indices.
Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
This package provides estimators of the mode of univariate
unimodal data or univariate unimodal distributions
Given independent and identically distributed observations X(1), ..., X(n) from a density f,
provides five methods to perform a multiscale analysis about f as well as the necessary critical
values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
correction term Gamma in either method offers another two approaches which are more powerful on small scales and
less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
equivalent to the first and was introduced in Rufibach and Walther (2010).
Local linear estimation of psychometric functions.
Provides functions for nonparametric estimation of a
psychometric function and for estimation of a derived threshold
and slope, and their standard deviations and confidence
intervals
Bootstrap cross-validation for ROC, AUC and Brier score to assess
and compare predictions of binary status responses.
This package will create sophisticated models of training data and validate the models with an independent test set, cross validation, or in the case of Random Forest Models, with Out Of Bag (OOB) predictions on the training data. It will create graphs and tables of the model validation results. It will apply these models to GIS .img files of predictors to create detailed prediction surfaces. It will handle large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.
A collection of tools to deal with statistical models.
The functionality is experimental and the user interface is likely to
change in the future. The documentation is rather terse, but packages `coin'
and `party' have some working examples. However, if you find the
implemented ideas interesting we would be very interested in a discussion
of this proposal. Contributions are more than welcome!
Package for processing downloaded MODIS Cloud Product HDF
files and derived files. Specifically, MOD35_L2 cloud product
files, and the associated MOD03 geolocation files (for
MODIS-TERRA); and MYD35_L2 cloud product files, and the
associated MYD03 geolocation files (for MODIS-AQUA).  The
package will be most effective if the user installs MRTSwath
(MODIS Reprojection Tool for swath products;
https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath),
and adds the directory with the MRTSwath executable to the
default R PATH by editing ~/.Rprofile.
Provides several functions for downloading, storing and processing
subsets of MODIS Land Processes data as a batch process.
The algorithms implemented here are used to detect the community structure of a network.
These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Contains basic tools for performing
multiple-output quantile regression and computing
regression quantile contours by means of directional
regression quantiles. In the location case, one can thus
obtain halfspace depth contours in two to six dimensions.
Fits a Constrained Segmented Distributed Lag regression model
to epidemiological time series of mortality, temperature, and other confounders.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
Provides modules as an organizational unit for source code. Modules
enforce to be more rigorous when defining dependencies and have
a local search path. They can be used as a sub unit within packages
or in scripts.
Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for datasets with large number of variables.
A package for analysis between rare variants and
quantitative traits by CMC (the combined multivariate and
collapsing method).
mokken contains functions for performing Mokken
scale analysis on test and questionnaire data. It includes an automated
item selection algorithm, and various checks of model assumptions.
Surface topography calculations of Dirichlet's normal energy,
relief index, and orientation patch count for teeth using scans of enamel caps.
Importantly, for the relief index and orientation patch count calculations to
work, the scanned tooth files must be oriented with the occlusal plane parallel
to the x and y axes, and perpendicular to the z axis. The files should also be
simplified, and smoothed in some other software prior to uploading into R.
Model selection and parameter estimation based on non-local priors. Routines are provided to compute Bayes factors, marginal densities and to perform variable selection in regression setups. Routines to evaluate prior densities, distribution functions, quantiles and modes are included.
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Functions to calculate: moments, Pearson's kurtosis,
Geary's kurtosis and skewness; tests related to them
(Anscombe-Glynn, D'Agostino, Bonett-Seier).
Momocs is intended to ease and popularize shape analysis
of outlines (especially using elliptical Fourier analysis). It
mostly hinges on the functions developed in Morphometrics with
R (Claude, 2008). From outline extraction of images and
elliptical Fourier calculation to multivariate analysis and the
visualization of transformations within the morphological
space, Momocs provides a complete and convenient toolkit to
specialists within every field that are, or may be, interested
in morphological comparisons of outlines.
'MetaOMineR' suite is a set of R packages that offers many functions and modules needed for the analyses
of quantitative metagenomics data. 'momr' is the core package and contains routines for biomarker identification and exploration.
Developed since the beginning of field, 'momr' has evolved and is structured around the different modules
such as preprocessing, analysis, vizualisation, etc. See package help for more information.
Keep track of dates in terms of months.
Model dates as at close of business.
Perform date arithmetic in units of "months" and "years" (multiples of months).
Allow "infinite" dates to model "ultimate" time spans.
Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.
High-level, high-performance MongoDB client based on libmongoc and
jsonlite. Includes support for aggregation, indexing, map-reduce, streaming,
SSL encryption and SASL authentication. The vignette gives a brief overview
of the available methods in the package.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
Train and make predictions from a multi-layer perceptron neural
network with partial monotonicity constraints.
Geometric morphometric and evolutionary biology analyses of anchor shape from four-anchored monogeneans.
Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology charts and distributional and diversity maps are also available.
Estimation of multivariate normal and student-t data of
arbitrary dimension where the pattern of missing data is monotone.
Through the use of parsimonious/shrinkage regressions
(plsr, pcr, lasso, ridge,  etc.), where standard regressions fail,
the package can handle a nearly arbitrary amount of missing data.
The current version supports maximum likelihood inference and
a full Bayesian approach employing scale-mixtures for Gibbs sampling.
Monotone data augmentation extends this
Bayesian approach to arbitrary missingness patterns.
A fully functional standalone interface to the Bayesian lasso
(from Park & Casella), Normal-Gamma (from Griffin & Brown),
Horseshoe (from Carvalho, Polson, & Scott), and ridge regression
with model selection via Reversible Jump, and student-t errors
(from Geweke) is also provided
Requires rooted resolved phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways.
Functions for fitting monotone polynomials to data.
Estimates monotone regression and variance functions in a
nonparametric model.
Several analysis-related functions for the book entitled "R
statistics and graph for medical articles" (written in Korean), version 1,
by Keon-Woong Moon with Korean demographic data with several plot
functions.
A collection of basic astronomical routines for R based on
"Practical astronomy with your calculator" by Peter
Duffet-Smith.
A multi-objective optimization solver based on particle
swarm optimization with crowding distance.
MOrder provide functions to check time homogeneity and order
of markov chain by using chi-squared test, AIC value and BIC value.
This package is a port of the MATLAB colourmap functions
accompanying the paper M. Geissbuehler and T. Lasser, "How to display data
by color schemes compatible with red-green color perception deficiencies,"
Opt. Express 21, 9862-9874 (2013) to R.
A toolset for Geometric Morphometrics and mesh processing. This
includes (among other stuff) mesh deformations based on reference points,
permutation tests, detection of outliers, processing of sliding
semi-landmarks and semi-automated surface landmark placement.
Tools for ecotoxicologists and regulators dedicated to the
mathematical and statistical modelling of bioassay data. They use advanced and
innovative methods for a valuable quantitative environmental risk assessment.
MorseGen is a program for generating raw data based on
user-specified summary (descriptive) statistics. Samples based
on the supplied statistics are drawn from a normal distribution
(or, in some cases, an exponential distribution) and scaled to
match the desired descriptive statistics. Intended uses include
creating raw data that fits desired characteristics or to
replicate the results in a published study.
Smoothing one- and two-dimensional Poisson counts with
P-splines specifically tailored to mortality data.
Extra-Poisson variation can be accounted as well as forecasting.
Collection of mortality data and a specific function for
selecting those data by country, sex, age and years.
Data sets and utilities from Project MOSAIC (mosaic-web.org) used
to teach mathematics, statistics, computation and modeling.  Funded by the
NSF, Project MOSAIC is a community of educators working to tie together
aspects of quantitative work that students in science, technology,
engineering and mathematics will need in their professional lives, but
which are usually taught in isolation, if at all.
Data sets from Project MOSAIC (mosaic-web.org) used
to teach mathematics, statistics, computation and modeling.  Funded by the
NSF, Project MOSAIC is a community of educators working to tie together
aspects of quantitative work that students in science, technology,
engineering and mathematics will need in their professional lives, but
which are usually taught in isolation, if at all.
Learning, manipulation and  evaluation of mixtures of  truncated basis  functions
(MoTBFs),  which include mixtures of  polynomials (MOPs) and  mixtures of truncated
exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
networks. The  package provides  functionality for learning  univariate, multivariate and
conditional  densities, with the  possibility of incorporating prior  knowledge. Structural
learning of hybrid Bayesian  networks is also provided. A set of useful tools is provided,
including  plotting, printing  and likelihood  evaluation. This  package  makes use of  S3
objects, with two new classes called 'motbf' and 'jointmotbf'.
Statistics to quantify tracks of moving things (x-y-z-t data),
such as cells, bacteria or animals. Available measures include mean square
displacement, confinement ratio, autocorrelation, straightness, turning angle,
and fractal dimension.
Functions to estimate start and duration of moult from moult
data (of types 1 to 5), based on models developed in Underhill
and Zucchini (1988, 1990). Allows modelling parameters as
functions of covariates.
Lattice functions for drawing folded empirical cumulative
distribution plots, or mountain plots. A mountain plot is similar
to an empirical CDF plot, except that the curve increases from
0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
the right side.  See: Monti (1995), Folded empirical distribution
function curves-mountain plots.  The American Statistician, 49, 342-345.
Extract from two-dimensional x-y coordinates of an arm-reaching trajectory, several dependent measures such as area under the curve, latency to start the movement, x-flips, etc.; which characterize the action-dynamics of the response. Mainly developed to analyze data coming from mouse-tracking experiments.
Contains functions to access movement data stored in movebank.org as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.
Provides tools for animal movement modelling using hidden Markov models.
These include processing of tracking data, fitting HMMs to movement data, visualization of
data and fitted model, decoding of the state process...
Fit and simulate mixtures of von Mises-Fisher distributions.
Multidimensional projection techniques are used to create two dimensional representations of multidimensional data sets.
Preprocess and analysis of genomic data. MPAgenomics
provides wrappers from commonly used packages to streamline their repeated
manipulation, offering an easy-to-use pipeline. The segmentation of
successive multiple profiles is performed with an automatic choice of
parameters involved in the wrapped packages. Considering multiple profiles
in the same time, MPAgenomics wraps efficient penalized regression methods
to select relevant markers associated with a given outcome.
Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty.
It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
Multivariate process capability analysis using the multivariate process capability vector. Allows to analyze a multivariate process with both normally and non-normally distributed and also with dependent and independent quality characteristics.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
A fast approach to compute approximate persistent homology using a multiscale approach
(1) Our system provides a network-based strategies for metabolite pathway identification.(2) The MPINet can support the identification of pathways using Hypergeometric test based on metabolite set. (3)MPINet can support pathways from multiple databases.
Computing modified profile likelihood estimates for Weibull Shape and Regression Parameters. Modified likelihood estimates are provided.
Model stability and variable inclusion plots [Mueller and Welsh
(2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
(2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
(2008, <doi:10.1214/07-AOS517>); Jiang et al.
(2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
The MplusAutomation package leverages the flexibility of the R
language to automate latent variable model estimation and interpretation
using Mplus, a powerful latent variable modeling program developed by
Muthen and Muthen (www.statmodel.com). Specifically, MplusAutomation
provides routines for creating related groups of models, running batches of
models, and extracting and tabulating model parameters and fit statistics.
Exploratory graphical analysis of multivariate data,
specifically gene expression data with different projection
methods: principal component analysis, correspondence analysis,
spectral map analysis.
Tools for constructing linkage maps, reconstructing
haplotypes, estimating linkage disequilibrium and QTL mapping
in multi-parent RIL designs (e.g. MAGIC)
Functions to compute and plot multivariate (partial)
Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
for comparisons between all types of variables including continuous vs
continuous, continuous vs discrete and discrete vs discrete. Uses a
nonparametric bias correction giving Bias Corrected Mutual Information
(BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
large genomic datasets.
Symbolic computing with multivariate polynomials in R.
Mposterior package provides a general framework for estimating a
median of subset posterior measures (M-posterior). Each subset posterior measure
is represented as a matrix of posterior samples; rows represent the atoms and
columns index the dimensions. All subset measures are represented as list of
matrices; length of the list equals the number of subsets (or machines). The
distance between subset posterior measures and M-posterior is measured using the RBF kernel.
M-posterior is represented as a weighted combination of empirical measures based on
subsets measures. These weights are estimated using Weiszfeld algorithm implemented
in this package.
A procedure to test for dependence between point processes on the real line, e.g. causal dependence, correlation, inhibition or anti-correlation. The package also provides a number of utilities for plotting simultaneous point processes, and combining p-values.
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
Fitting and testing multinomial processing tree (MPT) models, a
class of statistical models for categorical data.  The parameters are the
link probabilities of a tree-like graph and represent the latent cognitive
processing steps executed to arrive at observable response categories
(Batchelder & Riefer, 1999; Erdfelder et al., 2009; Riefer &
Batchelder, 1988).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
Most of this package consists of data sets from the
textbook Introduction
to Linear Regression Analysis (3rd ed), by Montgomery et al.
Some additional data sets and functions useful in an
undergraduate regression course are included.
mQTL provides a complete QTL analysis pipeline for metabolomic data.
Distinctive features include normalisation using PQN approach, peak alignment
using RSPA approach, dimensionality reduction using SRV approach and finally
QTL mapping using R/qtl package.
Analysis of mark-recapture data using covariates. Models: CJS open population; Huggin's closed population. Link functions: logit, sine, hazard.  Model selection and model averaging routines. Plot methods. Simulation routine. CJS methods produce estimates of population size using the Horvitz-Thompson estimator.
With this package, it is possible to perform
(simultaneous) inferences for ratios of linear combinations of
coefficients in the general linear model. In particular, tests
and confidence interval estimations for ratios of treatment
means in the normal one-way layout and confidence interval
estimations like in (multiple) slope ratio and parallel line
assays can be carried out. Moreover, it is possible to
calculate the sample sizes required in comparisons with a
control based on relative margins. For the simple two-sample
problem, functions for a t-test for ratio-formatted hypotheses
and the corresponding Fieller-type confidence interval are
provided assuming homogeneous or heterogeneous group variances.
Multivariate regression with covariance estimation
The MRCV package provides functions for analyzing the association between
one single response categorical variable (SRCV) and one multiple response
categorical variable (MRCV), or between two or three MRCVs.  A modified Pearson
chi-square statistic can be used to test for marginal independence for the one or
two MRCV case, or a more general loglinear modeling approach can be used to examine
various other structures of association for the two or three MRCV case.  Bootstrap-
and asymptotic-based standardized residuals and model-predicted odds ratios are
available, in addition to other descriptive information.
Animal abundance estimation via conventional, multiple covariate
and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
fitting is performed via maximum likelihood. Also included are diagnostics
and plotting for fitted detection functions. Abundance estimation is via a
Horvitz-Thompson-like estimator.
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics
Used on survival data to jointly estimate the hazard rate and the effects of covariates on failure times. Can accommodate covariates under the proportional and non-proportional hazards setting, and is ideal for analysis of survival data with long-term follow-up.
Provides three Modified Rand Indices and three Modified Adjusted Rand Indices for comparing two partitions, which are usually obtained on two different sets of units, where one is a subset of another set of units. Splitting and merging of clusters have a different affects on the value of the indices.
Provide a compact storage for medical imaging data with access and display possibilities. Basic tools for processing brain imaging data are proposed like extraction of brain voxels, morphological image segmentation, and filtering / normalization of contrast parameters. Specific tools are also provided for blood perfusion imaging to calculate hypoperfusion and reperfusion volumes.
Various methods for MRI tissue classification.
Conditional maximum likelihood estimation via the EM
algorithm and information-criterion-based model selection in
binary mixed Rasch models.
This package contains a set of function to compute mutual information matrices from continuous, categorical and survival variables. It also contains function to perform feature selection with mRMR and a new ensemble mRMR technique.
We can calculate directly used this package the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.
An implementation of the MRS algorithm for comparison across distributions.
The model is based on a nonparametric process taking the form of a Markov model
that transitions between a "null" and a "alternative" state
on a multi-resolution partition tree of the sample space.
MRS effectively detects and characterizes a variety of underlying differences.
These differences can be visualized using several plotting functions.
Fits regularized multinomial response models using penalized loglikelihood methods with structured penalty terms.
This package contains functions, data and examples to compute a multiplicative-regression model for relative survival.
The Bayesian procedure starts with one warplet in the model and uses the posterior distributions as priors for a more extended model with one more warplet. The model is built with adding one warplet at a time and allows for amplitude variations.
Statistical Analyses of Methylation-sensitive Amplification Polymorphism (MSAP) assays.
The output of an affinity-purification mass spectrometry
experiment is typically a list of proteins that were observed
in the experiment, identified by UniProt identifiers
(http://www.uniprot.org/).  This package takes as input a list of
UniProt identifiers, and the associated Mascot scores from the
experiment (which indicate the likelihood that the protein has
been correctly identified), clusters them by gene ontology
category (http://geneontology.org/), then draws diagrams
showing the results in hierarchical clusters by category, with lines
for individual proteins representing the associated Mascot score.
The results are in general not publication-ready, but will rather
require editing via a graphics editor that can interpret SVG (scalable
vector graphics) format.  As an alternative representation, the
package will also generate tag clouds based on the Mascot scores.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2014).
Provides methods for estimating frequentist and
Bayesian Vector Autoregression (VAR) models and Markov-switching
Bayesian VAR (MSBVAR).  Functions for reduced
form and structural VAR models are also available. Includes
methods for the generating posterior inferences for these models,
forecasts, impulse responses (using likelihood-based error bands),
and forecast error decompositions.  Also includes utility functions
for plotting forecasts and impulse responses, and generating draws
from Wishart and singular multivariate normal densities.  Current
version includes functionality to build and evaluate models with
Markov switching.
Find common entities detected in both positive and negative
ionization mode, delete this entity in the less sensible mode and combine both
matrices.
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.
This package provides functions for metabolite set
enrichment analysis (MSEA) and principal component analysis
(PCA), and converting metabolite set list from your own csv
files or KEGG's tar.gz files to XML documents.  This package is
suitable for computation of MSEA for factor loading in PCA.
Package for the detection of molecules in complex mixtures
of compounds. It creates an initial_DATA matrix from several
GC-MS analyses by collecting and assembling the information
from chromatograms and mass spectra (MS.DataCreation), It can
read several format (ASCII, CDF, mzML, mzXML or mzData).It
tests for the best unsupervised clustering method to group
similar mass spectra into molecules (MS.test.clust).It runs the
optimal unsupervised clustering method on the initial_DATA
matrix, identifies the optimal number of clusters, produces
different files for facilitating the quality control and
identification of putative molecules, and returns
fingerprinting or profiling matrices (MS.clust).It converts
output files from MS.clust for NIST mass spectral library
search and ARISTO webtool search
A Tcl/Tk GUI for some basic functions in the MSeasy
package
A companion to the Chinese book ``Modern Statistical
Graphics'' by Yihui Xie.
Multinomial logistic regression with sparse group lasso penalty. Suitable for high dimensional multiclass classification with many classes. The algorithm finds the sparse group lasso penalized maximum likelihood estimator. This result in feature and parameter selection, and parameter estimation. Use of multiple processors for cross validation and subsampling is supported through OpenMP. Development version is on github.
This is the library that can serialize or unserialize MessagePack format data.
This package computes the degrees of freedom of the lasso,
elastic net, generalized elastic net and adaptive lasso based
on the generalized path seeking algorithm.  The optimal model
can be selected by model selection criteria including Mallows'
Cp, bias-corrected AIC (AICc), generalized cross validation
(GCV) and BIC.
An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
A decision tree classifier for detecting microsatellite instability (MSI) in somatic mutation data from whole exome sequencing. MSI is detected based on different mutation rates in all sites as well as in simple sequence repeats. This mechanism can also be applied to sequence data of targeted gene panels with shorter sequence length.
Functions for fitting continuous-time Markov and hidden
Markov multi-state models to longitudinal data.  Designed for
processes observed at arbitrary times in continuous time (panel data)
but some other observation schemes are supported. Both Markov
transition rates and the hidden Markov output process can be modelled
in terms of covariates, which may be constant or piecewise-constant
in time.
There are several functions to implement the method for analysis in a multiblock multivariable data. If the input is only a matrix, then the principal components analysis (PCA) is implemented. If the input is a list of matrices, then the multiblock PCA is implemented. If the input is two matrices for exploratory and objective variables, then the partial least squares (PLS) analysis is implemented. If the input is two list of matrices for exploratory and objective variables, then the multiblock PLS analysis is implemented. Moreover, if the extra outcome variable is specified, then the supervised version for the methods above is implemented. For each methods, the sparse modeling is also incorporated. Functions to select the number of components and the regularized parameters are also provided.
This package provides functions and datasets from the book "Methods of Statistical Model Estimation".
Contains necessary Multivariate Analysis methods and Datasets used in STAT 571 at the University of Illinois at Urbana-Champaign
This package is a toolkit for multivariate process monitoring. It contains the main alternatives of multivariate control chart such as: Hotelling, Chi squared, MEWMA, MCUSUM and Generelized Variance control chart. Also, it includes some tools for assessing multivariate normality like: Mardia, Royston and Henze Zirkler test and the univariate D'Agostino test. Moreover, it possess ten didactic datasets.
Discrete Morse-Smale complex approximation based on kNN graph. The Morse-Smale complex provides a decomposition of the domain. This package provides methods to compute a hierarchical sequence of Morse-Smale complicies and tools that exploit this domain decomposition for regression and visualization of scalar functions.
Calculates ARMSS (age related multiple sclerosis severity), and both local and global MSSS (multiple sclerosis severity score).
Nonparametric estimation for right censored, left truncated time to event data in multistate models.
Constructs trees for multivariate survival data using marginal and frailty models.
The mstate packages contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models.
Univariate Autoregressive Markov Switching Models for Linear and Generalized Models
MTK (Mexico ToolKit) is a generic platform for the sensitivity and uncertainty analysis of complex models. It provides functions and facilities for experimental design, model simulation, sensitivity and uncertainty analysis, methods integration and data reporting, etc.
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index, transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
This is an EM algorithm based method for imputation of
missing values in multivariate normal time series. The
imputation algorithm accounts for both spatial and temporal
correlation structures. Temporal patterns can be modelled using
an ARIMA(p,d,q), optionally with seasonal components, a
non-parametric cubic spline or generalised additive models with
exogenous covariates. This algorithm is specially tailored for
climate data with missing measurements from several monitors
along a given region.
Provides programmatic access to the Amazon Mechanical Turk (MTurk) Requester API.
A graphical user interface (GUI) for the MTurkR package.
Functions for downloading flight data from
http://www.munich-airport.de and for analyzing flight patterns.
This package builds multi-fidelity cokriging models from
responses with different levels of fidelity.  Important
functions : MuFicokm, predict.MuFicokm, summary.MuFicokm.
A package for producing a smooth estimate of the hazard
function for censored data.
A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.
Integer Programming Formulations Applied to Univariate and Multivariate Allocation Problems.
Simultaneous tests and confidence intervals
for general linear hypotheses in parametric models, including
linear, generalized linear, linear mixed effects, and survival models.
The package includes demos reproducing analyzes presented
in the book "Multiple Comparisons Using R" (Bretz, Hothorn,
Westfall, 2010, CRC Press).
Convert a logical vector or a vector of
p-values or a correlation, difference, or distance
matrix into a display identifying the pairs for
which the differences were not significantly
different.  Designed for use in conjunction with
the output of functions like TukeyHSD, dist{stats},
simint, simtest, csimint, csimtest{multcomp},
friedmanmc, kruskalmc{pgirmess}.
Equivalence tests and related confidence intervals for the
comparison of two treatments, simultaneously for one or many
normally distributed, primary response variables (endpoints).
The step-up procedure of Quan et al. (2001) is both applied for
differences and extended to ratios of means. A related
single-step procedure is also available.
GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
Efficient finite difference method for valuing European and American multi-asset options.
Algorithms for performing joint parameter estimation in
astronomical survey data acquired in multiple bands.
A GUI with which users can construct and interact
with Multibiplot Analysis and provides inferential results by using Bootstrap Methods.
Calculate the polygenic and major gene models for quantitative trait
linkage analysis using variance components approach. The 0.2.2
release includes bug fixes that allow 'multic' to run properly
on 64-bit systems. The 0.3.0 release includes a fully implemented
sw2mloci() function. As of 0.3.8, Splus version is no longer
supported. Additional documentation and examples can be found
in the inst directory.
This package provides a tool for analysis of multiple CNV.
Includes functions designed to examine relationships among multivariate constructs (e.g., personality). This includes functions for profile (within-person) analysis, dealing with large numbers of analyses, lens model analyses, and structural summary methods for data with circumplex structure. The package also includes functions for graphically comparing and displaying group means.
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from http://home.comcast.net/~tamivox/dave/multinomial/index.html and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here http://jeromekelleher.net/partitions.php. The C++ code and Python code are distributed without conditions.
multiDimBio is a package designed to support a systems biology research program from inception through publication.  It focuses on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.
Read a table of fixed width formatted data of different types into
a data.frame for each type.
Uses a transformed, rotated and optionally adapted n-dimensional grid of quadrature
points to calculate the numerical integral of n multivariate normal
distributed parameters.
Several functions are presented in order to study data in a group structure,
where the same set of variables are measured on different groups of
individuals.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
The functions in this package are designed to be used in
the analysis of multilevel data by applied psychologists.  The
package includes functions for estimating common within-group
agreement and reliability indices. The package also contains
basic data manipulation functions that facilitate the analysis
of multilevel and longitudinal data.
Functions to estimate and visualize propensity
score analysis for multilevel, or clustered, data.
Capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive ``marks'', such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using capture-recapture data consisting of a single conventional mark or multiple non-invasive marks.
Allows running a meta-analysis of multivariate Genome Wide
Association Studies (GWAS) and easily visualizing results through custom
plotting functions. The multivariate setting implies that results for each
single nucleotide polymorphism (SNP) include several effect sizes (also
known as "beta coefficients", one for each trait), as well as related
variance values, but also covariance between the betas. The main goal of
the package is to provide combined beta coefficients across different
cohorts, together with the combined variance/covariance matrix. The method
is inverse-variance based, thus each beta is weighted by the inverse of its
variance-covariance matrix, before taking the average across all betas. The
default options of the main function \code{multi_meta} will work with files
obtained from GEMMA multivariate option for GWAS (Zhou & Stephens, 2014).
It will work with any other output, as soon as columns are formatted to
have the according names. The package also provides several plotting
functions for QQ-plots, Manhattan Plots and custom summary plots.
This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
An implementation of a method for building simultaneous
confidence intervals for the probabilities of a multinomial
distribution given a set of observations, proposed by Sison and
Glaz in their paper Sison, C.P and J. Glaz. Simultaneous
confidence intervals and sample size determination for
multinomial proportions. Journal of the American Statistical
Association, 90:366-369 (1995).  The method is an R translation
of the SAS code implemented by May and Johnson in their paper:
May, W.L. and W.D. Johnson. Constructing two-sided simultaneous
confidence intervals for multinomial proportions for small
counts in a large number of cells. Journal of Statistical
Software 5(6) (2000). Paper and code available at
http://www.jstatsoft.org/v05/i06
MNL and overdispersed multinomial regression using robust
(LQD and tanh) estimation
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006).
Performs genetic association tests between SNPs
(one-at-a-time) and multiple phenotypes (separately or in joint
model).
Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.
Fit Cox proportional hazard models with a weighted
partial likelihood. It handles one or multiple endpoints, additional matching
and makes it possible to reuse controls for other endpoints.
Algebraic procedures for the analysis of multiple social networks are delivered with
this package. Among other things, it is possible to create and manipulate multivariate network
data with different formats, and there are effective ways available to treat multiple networks
with routines that combine algebraic systems like the partially ordered semigroup or the
semiring structure together with the relational bundles occurring in different types of
multivariate network data sets. As well an algebraic approach for two-mode networks is made
through Galois derivations between families of the pair of subsets.
Various utilities to manipulate multivariate polynomials
Functions to calculate Unique Trait Combinations (UTC) and scaled
Unique Trait Combinations (sUTC) as measures of multivariate richness. The
package can also calculate beta-diversity for trait richness and can
partition this into nestedness-related and turnover components. The code
will also calculate several measures of overlap.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Functions to perform sensitivity analysis on a model with multivariate output.
Implements two version of Multi-SOM algorithm namely stochastic Multi-SOM and batch Multi-SOM. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
MultiSV is an R package for identification of structural
variations in multiple populations based on whole genome resequencing. It
fits linear mixed model and identifies structural variations in multiple
populations using whole genome sequencing data. It could also be
manipulated to use on RNA-seq data for differential gene expression
(implementation in future releases). Main steps for analysis include
generating read depth in bins using ComputeBinCounts. conversion of bins to
MultiSV format using Bin2MultiSV. Finally, identification of structural
variations using CallMultiSV.
Data frames are integral to R.  They provide a standard format for passing data to model-fitting and plotting functions, and this standard makes it easier for experienced users to learn new functions that accept data as a single data frame.  Still, many data sets do not easily fit into a single data frame; data sets in ecology with a so-called fourth-corner problem provide important examples. Manipulating such inherently multiple-table data using several data frames can result in long and difficult-to-read work-flows.  This package provides new data storage objects called data.list objects, which extend the data.frame concept to explicitly multiple-table settings.  Like data frames, data lists are lists of variables stored as vectors; what is new is that these vectors have dimension attributes that make accessing and manipulating them easier.  As data.list objects can be coerced to data.frame objects, they can be used with all R functions that accept an object that can be coerced to a data.frame.
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.
A multivariate generalization of the emulator package
Computation of an estimation of the long-memory parameters and
the long-run covariance matrix using a multivariate model
(Lobato, 1999; Shimotsu 2007). Two semi-parametric methods are
implemented: a Fourier based approach (Shimotsu 2007) and a wavelet based
approach (Achard and Gannaz 2014).
Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality and non-negativity). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Exports two functions implementing
multi-way clustering using the method suggested by Cameron, Gelbach, &
Miller (2011) and cluster (or block)
bootstrapping for estimating variance-covariance matrices. Normal one and
two-way clustering matches the results of other common statistical
packages.  Missing values are handled transparently and rudimentary
parallelization support is provided.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods.   Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics.
Implementation of commonly used p-value-based and
parametric multiple testing procedures (computation of adjusted
p-values and simultaneous confidence intervals) and parallel
gatekeeping procedures based on the methodology presented in
the book "Multiple Testing Problems in Pharmaceutical
Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Preprocessing of high-throughput data (normalization and
scalings); Principal Component Analysis with help tool for
choosing best-separating principal components and automatic
testing for outliers; automatic univariate analysis for
parametric and non-parametric data, with generation of specific
reports (volcano and box plots); partial least square
discriminant analysis (PLS-DA); orthogonal partial least square
discriminant analysis (OPLS-DA); Statistical Total Correlation
Spectroscopy (STOCSY); Ratio Analysis Nuclear Magnetic
Resonance (NMR) Spectroscopy (RANSY).
Model selection and model averaging based on information criteria
(AICc and alike).
This package provides Schoeneman's algorithm for metric
multidimensional unfolding and Procrustes rotation of unfolding results.
Functions for exploring and using the Munsell
colour system
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, sRGB, CIE Lab or CIE Luv. Chromas can be odd and hue steps can be real numbers. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
This package is a collection of function for converting and modifying mono dimensional nuclear magnetic resonance spectra.
Performs Wilcox rank sum test, Kruskal rank sum test,
Friedman rank sum test and McNemar test.
The Mutoss package and accompanying mutossGUI package are
designed to ease the application and comparison of multiple
hypothesis testing procedures.
The mutossGUI package provides a graphical user interface for the MuToss Project.
Functions, data sets, analyses and examples from the book
`An Introduction to Applied Multivariate Analysis with R'
(Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
multivariate abundance data in community ecology. See
'mvabund-package.Rd' for details of overall package organization.
The package is implemented with the Gnu Scientific Library
(http://www.gnu.org/software/gsl/) and Rcpp
(http://dirk.eddelbuettel.com/code/rcpp.html) R / C++ classes.
Pacote para analise multivariada, que possui funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos. Tambem possui outras funcoes uteis para a analise multivariada.
Fit log-linear model for multivariate Bernoulli
distribution with mixed effect models and LASSO
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy
package prep, editing of packages while loaded, per-object
lazy-loading, easy documentation, macro functions, and
miscellaneous utilities. Needed by debug package.
An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
This package computes the continuous wavelet transform of
irregularly sampled time series.
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.
This package computes regression deletion diagnostics for
multivariate linear models and provides some associated
diagnostic plots.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees.
Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests.
This package computes the Nelson-Aalen estimator of the cumulative transition hazard for multistate models.
The package provides computationally efficient tools related to the multivariate normal distribution. The main functionalities are: simulating multivariate normal random vectors, evaluating multivariate normal densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API.
Package for  moving grid adjustment
in plant breeding field trials.
Finds the maximum likelihood estimate of the mean vector
and variance-covariance matrix for multivariate normal data
with missing values.
Generalization of shapiro-wilk test for multivariate
variables.
Given a vector of multivariate normal data, a matrix of
covariates and the data covariance matrix, generate new multivariate normal
samples that have the same covariance matrix based on permutations of
the transformed data residuals.
various methods for multivariate outlier detection.
Tools for estimating multivariate probit models,
calculating conditional and unconditional expectations,
and calculating marginal effects on conditional and unconditional
expectations.
Computes orthant probabilities multivariate normal distribution.
Provides methods to construct multivariate grids, which can be used
for multivariate quadrature. This grids can be based on different quadrature
rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
multidimensional grid the product-rule or the combination- technique can be
applied.
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data.
It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm).
Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom.
Key features include:
(i) Normalization and/or variance stabilization of the data,
(ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Mean, variance, and random variates for left/right truncated normal distributions.
Generalization of the Shapiro-Francia test for
multivariate variables.
This package implements the generalization of the Shapiro-Wilk test
for multivariate normality proposed by Villasenor-Alva and
Gonzalez-Estrada (2009).
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history.
Routines to perform estimation and inference under the multivariate t-distribution.
Currently, the following methodologies are implemented: multivariate mean and covariance
estimation, hypothesis testing about the mean, equicorrelation and homogeneity of variances,
the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate
generation. Some auxiliary functions are also provided.
Fits a multivariate model of decision trees for multiple,
continuous outcome variables. A model for each outcome variable is fit separately, selecting
predictors that explain that explain covariance in multiple outcomes.
Package is built on top of 'gbm'.
This package contains functions to run fixed effects or
random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles,
random deviates and densities.
A function for plotting multivariate time series data
Matched Wake Analysis (mwa) grants insights into causal relationships in spatiotemporal event data.
Computes the Wavelet deconvolution estimate of a common signal
present in multiple channels that have possible different levels of blur
and long memory additive error.
mxkssd is a package that generates efficient balanced
mixed-level k-circulant supersaturated designs by interchanging
the elements of the generator vector. The package tries to
generate a supersaturated design that has EfNOD efficiency more
than user specified efficiency level (mef). The package also
displays the progress of generation of an efficient mixed-level
k-circulant design through a progress bar. The progress of 100
per cent means that one full round of interchange is completed.
More than one full round (typically 4-5 rounds) of interchange
may be required for larger designs.
Feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. MXM stands for "Mens eX  Machina", meaning "Mind from the Machine" in Latin.
The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
Perform correlation and linear regression test
among the numeric columns in a data frame automatically
and make plots using pairs or lattice::parallelplot.
Useful functions for accessing MyEpisodes feeds and
episode information as well as other tv episode related actions
through www.myepisodes.com
Recommendation engine based on Myrrix. Myrrix is a complete,
real-time, scalable clustering and recommender system, evolved from Apache
Mahout. It uses Alternating Least Squares to build a recommendation engine.
External jars required for package Myrrix. Myrrix is a
recommendation engine
A statistical framework allowing users interested in the evolution of biological processes to capture evolutionary signals in transcriptomes.
Linear and logistic regression models for quantitative genetic analysis of data from monozygotic twins.
An R wrapper for 'libnabo', an exact or approximate k nearest
neighbour library which is optimised for low dimensional spaces (e.g. 3D).
'libnabo' has speed and space advantages over the 'ANN' library wrapped by
package 'RANN'. 'nabor' includes a knn function that is designed as a
drop-in replacement for 'RANN' function nn2. In addition, objects which
include the k-d tree search structure can be returned to speed up repeated
queries of the same set of target points.
Contains methods described by Dennis R. Helsel in
his book "Nondetects And Data Analysis: Statistics
for Censored Environmental Data"
Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (http://www.vsni.co.uk/software/asreml).
Designed for association studies in nested association mapping (NAM) panels, also handling biparental and random panels. It includes functions for genome-wide associations mapping of multiple populations, marker quality control, solving mixed models and finding variance components through REML and Gibbs sampling.
This package provides user-level functions to manage
namespaces not (yet) available in base R: 'registerNamespace',
'unregisterNamespace', 'makeNamespace', and
'getRegisteredNamespace'

('makeNamespaces' is extracted from the R 'base' package source code:
src/library/base/R/namespace.R)
This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to calculate the associated pair-distribution function and X-ray/neutron total-scattering signals. It also provides a target function that can be used for simultaneous fitting of small- and wide-angle total scattering data in real and reciprocal spaces. The target function can be generated either as a sum of weighted residuals for individual datasets or as a vector of residuals suitable for optimization using multi-criteria algorithms (e.g. Pareto methods).
A set of tools for normalizing, diagnostics and visualization of NanoString nCounter data.
Enables the processing and normalisation of the standard mRNA data output from the Nanostring nCounter software.
This package contains tidied data from the ASA 2006 data expo,
as well as a number of useful other related data sets.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
biological image data, especially traced neurons. Reads and writes 3D images
in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
format. Traced neurons can be imported from and written to SWC and 'Amira'
LineSet and SkeletonGraph formats. These data can then be visualised in 3D
via 'rgl', manipulated including applying calculated registrations, e.g.
using the 'CMTK' registration suite, and analysed. There is also a simple
representation for neurons that have been subjected to 3D skeletonisation
but not formally traced; this allows morphological comparison between
neurons including searches and clustering (via the 'nat.nblast' extension
package).
Extends package nat (NeuroAnatomy Toolbox) by providing a
collection of NBLAST-related functions.
Extends package 'nat' (NeuroAnatomy Toolbox) by providing objects
and functions for handling template brains.
The package provides functions related to human natural ordering.
It handles adjacent digits in a character sequence as a number
so that natural sort function arranges a character vector by their numbers, not digit characters.
It is typically seen when operating systems lists file names.
For example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered
because 1 < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms arrange
it into a-1.png, a-10.png, a-2.png owing to their third and fourth characters.
Utility functions that may be of general interest but are
specifically required by the 'NeuroAnatomy Toolbox' ('nat'). Includes
functions to provide a basic make style system to update files based on
timestamp information, file locking and 'touch' utility. Convenience
functions for working with file paths include 'abs2rel', 'split_path'
and 'common_path'. Finally there are utility functions for working with
'zip' and 'gzip' files including integrity tests.
Estimate the effective population size of a closed population using genetic data collected from two or more data points.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Calls the 'Jupyter'/'IPython' script 'nbconvert' to create vignettes from notebooks.
Those notebooks ('.ipynb' files) are files containing rich text, code, and its output.
Code cells can be edited and evaluated interactively.
See <http://ipython.org/notebook.html> for more information.
The Dirichlet (aka NBD-Dirichlet) model describes the
purchase incidence and brand choice of consumer products.  We
estimate the model and summarize various theoretical quantities
of interest to marketing researchers. Also provides functions
for making tables that compare observed and theoretical
statistics.
Perform non-bipartite matching and matched randomization. A
"bipartite" matching utilizes two separate groups, e.g. smokers being
matched to nonsmokers or cases being matched to controls. A "non-bipartite"
matching creates mates from one big group, e.g. 100 hospitals being
randomized for a two-arm cluster randomized trial or 5000 children who
have been exposed to various levels of secondhand smoke and are being
paired to form a greater exposure vs. lesser exposure comparison. At the
core of a non-bipartite matching is a N x N distance matrix for N potential
mates. The distance between two units expresses a measure of similarity or
quality as mates (the lower the better). The 'gendistance()' and
'distancematrix()' functions assist in creating this. The 'nonbimatch()'
function creates the matching that minimizes the total sum of distances
between mates; hence, it is referred to as an "optimal" matching. The
'assign.grp()' function aids in performing a matched randomization. Note
bipartite matching can be performed using the prevent option in
'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
regression inferences from RNA-Sequencing Data.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2015. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods)
NCA identifies necessary (but not sufficient) conditions in datasets. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
(Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output).
Performs nonlinear calibration and curve fitting for data from Luminex, RT-PCR, ELISA, RPPA etc. Its precursor is Ruminex.
A flexible tool is presented here that can perform
(i) traditional non-compartmental analysis (NCA) and
(ii) simulation-based posterior predictive checks for a population
pharmacokinetic (PK) model using NCA metrics.
making NCBI taxonomic data locally available and
searchable as an R object
This is deprecated and will be removed
from CRAN in early 2016: use 'RNetCDF' or 'ncdf4' instead.
The original description follows: it refers to 'netCDF' version 3 files.
This package provides a high-level R interface to
Unidata's 'netCDF' data files, which are portable across
platforms and include metadata information in addition to the
data sets. Using this package 'netCDF' files can be opened and
data sets read in easily. It is also easy to create new 'netCDF'
dimensions, variables, and files, or manipulate existing 'netCDF'
files. This interface provides considerably more functionality
than the old 'netCDF' package for R, and is not compatible with
the old 'netCDF' package for R. Release 1.2 (2005-01-24) adds
better support for character variables, and miscellaneous bug
fixes.  Release 1.3 (2005-03-27) is for miscellaneous bug
fixes, and improves the documentation.  Release 1.4
(2005-06-27) improves the efficiency, and adds small bug fixes.
Release 1.5 (2006-02-27) adds support for byte variables, plus
small bug fixes. Release 1.6 (2006-06-19) adds various bug
fixes, plus support for making dimensions WITHOUT dimvars
(coordinate variables), although I think this is a bad idea in
general.  ALSO, the default behavior for 'put.var.ncdf()' with
unlimited variables and NO specified start and count parameters
has changed!  Before, the default was to append to the end of
the existing variable.  Now, the default is to assume a start
of 1 along each dimension, and a count of the current length of
each dimension.  This really can be ambiguous when using an
unlimited dimension. I always specify both start and count when
writing to a variable with an unlimited dimension, and suggest
you do as well.  I may require this in a future release, as it
seems to cause people problems.