Live-R: Healthcare & Life Sciences
Scalable Packages for Pharmaceuticals, Bio-tech & Medical Imaging
Live-R for Life Sciences is an intelligent mix of state-of-the-art R packages that meet the most diverse needs of life sciences organizations worldwide.
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Built on the community-developed R platform, Live-R for Life Sciences provides a flexible platform to increase the efficiency of lab & business processes to drive discovery, testing, profitability & growth.
Healthcare & Life Sciences Featured Content (a small sampling)
Bioconductor provides the packages for the analysis and comprehension of high-throughput genomic data.
|R Package||Package Description|
|annotate||annotate. – Using R enviroments for annotation. Read More|
|annafy||Annotation tools for Affymetrix biological metadata. – Using R enviroments for annotation. Read More|
|Biobase||Base functions for Bioconductor – Biobase contains standardized data structures to represent genomic data. Read More|
|BioStrings||String objects representing biological sequences, and matching algorithms. – Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. Read More|
|convert||Convert Microarray Data Objects – Define coerce methods for microarray data objects. Read More|
|genefitter||genefilter: methods for filtering genes from microarray experiments – Some basic functions for filtering genes. Read More|
|limma||Linear Models for Microarray Data – Data analysis, linear models and differential expression for microarray data. Read More|
|lumi||BeadArray Specific Methods for Illumina Methylation and Expression Microarrays – The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. Read More|
|marray||Exploratory analysis for two-color spotted microarray data- Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking.Read More|
|microRNA||Data and functions for dealing with microRNAs- Data and functions for dealing with microRNAs.Read More|
Chemometrics and computational physics are concerned with the analysis of data arising in chemistry and physics experiments, as well as the simulation of physico-chemico systems.
|R Package||Package Description|
|ALS||multivariate curve resolution alternating least squares- TAlternating least squares is often used to resolve components contributing to data with a bilinear structure; the basic technique may be extended to alternating constrained least squares. Commonly applied constraints include unimodality, non-negativity, and normalization of components. Several data matrices may be decomposed simultaneously by assuming that one of the two matrices in the bilinear decomposition is shared between datasets. Read More|
|chemCal||Calibration functions for analytical chemistry- chemCal provides 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 (LOQ) and limit of quantification (LOD). The functions work on model objects from – optionally weighted – linear regression (lm) or robust linear regression (rlm). Read More|
|deSolve||General solvers for initial value problems of ordinary differential equations (ODE), partial differential equations (PDE), differential algebraic equations (DAE), and delay differential equations (DDE)- 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. Read More|
|kohonen||Supervised and unsupervised self-organising maps- Supervised and unsupervised self-organising maps. Read More|
|nnls||The Lawson-Hanson algorithm for non-negative least squares (NNLS)-An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints. Read More|
|pls||Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR)-Multivariate regression by partial least squares regression (PLSR) and principal component regression (PCR). Read More|
|plsRgm||Partial least squares Regression for generalized linear models-This packages provides Partial least squares Regression for generalized linear models and kfold crossvalidation of such models using various criteria. It allows for missing data in the eXplanatory variables. Bootstrap confidence intervals constructions are also available.. Read More|
|PTAk||Principal Tensor Analysis on k modes-A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions. Read More|
Clinical trials involve the collection of data from interventional and observational health studies. This domain consists of packages for design, monitoring and analysis of data from clinical trials.
|R Package||Package Description|
|bifactorial||Inferences for bi- and trifactorial trial designs – This package makes global and multiple inferences for given bi- and trifactorial clinical trial designs using bootstrap methods and a classical approach. Read More|
|blockrand||Randomization for block random clinical trials- Create randomizations for block random clinical trials. Can also produce a pdf file of randomization cards. Read More|
|clinfun||Clinical Trial Design and Data Analysis Functions- Utilities to make your clinical collaborations easier if not fun.
|clinicalRobustPriors||Robust Bayesian Priors in Clinical Trials: An R Package for Practitioners- This package is useful to compute the distributions (prior, likelihood and posterior) and moments of the robust models: Cauchy/Binomial, Cauchy/Normal and Berger/Normal. Both, Binomial and Normal Likelihoods can be handled by the software. Furthermore, the assessment of the hyperparameters and the posterior analysis can be processed. Read More|
|experiment||R package for designing and analyzing randomized experiments-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. Read More|
|GroupSeq||Performing computations related to group sequential designs-The computations are done via the alpha spending approach i.e. interim analyses need not to be equally spaced, and their number need not to be specified in advance.
|gsDesign||Group Sequence Design- gsDesign is a package that derives group sequential designs and describes their properties.. Read More|
|hmisc||Harrell Miscellaneous- The Hmisc library contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of S objects to LaTeX code, and recoding variables. Read More|
|ldbounds||Lan-DeMets Method for Group Sequential Boundaries- Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Read More|
|MChtest||Monte Carlo hypothesis tests with Sequential Stopping- CThe 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. Read More|
|PWrGSD||Power in a Group Sequential Design- Tools the evaluation of interim analysis plans for sequentially monitored trials on a survival endpoint; tools to construct efficacy and futility boundaries, for deriving power of a sequential design at a specified alternative, template for evaluating the performance of candidate plans at a set of time varying alternatives. Read More|
|speff2trial||Semiparametric efficient estimation for a two-sample treatment effect- The package performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random. Read More|
Clustering is a method of unsupervised learning, a common technique for statistical data analysis used in many domains, including Biology & Medicine.
|R Package||Package Description|
|cluster||ClusterAnalysis Exteneded- A collection of functions that supports different forms of clustering, such as hierarchial clustering, partition based clustering, model-based clustering.Read More|
|flexclust||Flexible Cluster Algorithms – 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. Read More|
|flexmix||Flexible Mixture Modeling- 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. Read More|
|mclust||Model-Based Clustering / Normal Mixture Modeling-Model-based clustering and normal mixture modeling including Bayesian regularization.Read More|
Medical Imaging deals with techniques to produce diagnostic images of the human body to achieve better understanding, diagnoses & medical treatments.
|R Package||Package Description|
|adimpro||Adaptive Smoothing of Digital Images- This package implements tools for manipulationg digital images and the Propagation Separation approach by Polzehl and Spokoiny (2006) for smoothing digital images. Read More|
|AnalyzeFMRI||Functions for analysis of fMRI datasets stored in the ANALYZE or NIFTI format – Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or NIFTI format.. Read More|
|dcemriS4||A Package for Medical Image Analysis- A collection of routines and documentation that allows one to perform voxel-wise quantitative analysis of dynamic contrast-enhanced or diffusion-weighted MRI data. Read More|
|dti||Analysis of diffusion weighted imaging (DWI) data – 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), structural adaptive smoothing in in case of (DTI) (K. Tabelow, J. Polzehl, V. Spokoiny, and H.U. Voss, Diffusion Tensor Imaging: Structural Adaptive Smoothing, Neuroimage 39(4), 1763-1773 (2008)), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models and a stremaline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.Read More|
|fmri||Analysis of fMRI experiments- 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) and J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, NeuroImage, 52:515-523 (2010).
|mritc||MRI tissue classification- Various methods for MRI tissue classification.Read More|
|oro.dicom||Rigorous – DICOM Input / Output- Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle. Read More|
|PET||Simulation and Reconstruction of PET Images-This package implements different analytic/direct and iterative reconstruction methods of Peter Toft. It also offer the possibility to simulate PET data. Read More|
|Rniftilib||R interface to nifticlib (nifticlib-1.1.0) (read/write ANALYZE(TM)7.5/NIfTI-1 volume images)- Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle. Read More|
|RNiftyReg||Medical image registration using the NiftyReg library- This package provides an R interface to the NiftyReg image registration tools . Read More|
|tractor.base||A package for reading, manipulating and visualising magnetic resonance images- Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle.
Pharmacokinetics (PK) is a branch of pharmacology dedicated to the determination of the fate of substances administered to a living organism. The primary goal of PK data analysis is to determine the relationship between the dosing regimen and the body’s exposure to the drug as measured by the nonlinear concentration time curve or related summaries.
Phylogenetics is the study of relationships between various organism, including various statistical methods for analyzing historical patterns along phylogenetic trees.
|R Package||Package Description|
|ape||Analyses of Phylogenetics and Evolution – ape provides functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, analyses of diversification and macroevolution, computing distances from allelic and nucleotide data, reading nucleotide sequences, and several tools such as Mantel’s test, computation of minimum spanning tree, generalized skyline plots, estimation of absolute evolutionary rates and clock-like trees using mean path lengths, non-parametric rate smoothing and penalized likelihood. Phylogeny estimation can be done with the NJ, BIONJ, and ME methods. Read More|
Probability Distributions form a fundamental theory in statistics, which links the outcome of a statistical experiment with its probability of occurrence. The probability distribution describes the range of possible values that a random variable can attain and the probability that the value is within any subset of that range. The practical uses of probability distributions include calculation of confidence intervals for parameters, calculation of critical regions in hypothesis testing, curve fitting, and simulation studies using random generators. Depending upon nature of a random variable, different types of distributions are employed, of which Continuous Distribution, Discrete Distribution, and Copula (multivariate distribution) are discussed below.
|R Package||Package Description|
|actuar||Actuarial functions – Additional actuarial science functionality, mostly in the fields of loss distributions, risk theory (including ruin theory), simulation of compound hierarchical models and credibility theory, for the moment. Read More|
|copula||Multivariate dependence with copulas- Classes (S4) of commonly used copulas including elliptical (normal and t), Archimedean (Clayton, Gumbel, Frank, and Ali-Mikhail-Haq), extreme value (Gumbel, Husler-Reiss, Galambos, Tawn, and t-EV), and other families (Plackett and Farlie-Gumbel-Morgenstern). Methods for density, distribution, random number generation, bivariate dependence measures, perspective and contour plots. Functions for fitting copula models with variance estimate. Independence tests among random variables and random vectors. Serial independence tests for univariate and multivariate continuous time series. Goodness-of-fit tests for copulas based on multipliers and on the parametric bootstrap. Tests of extreme-value dependence. Read More|
|distr||Object oriented implementation of distributions – Object oriented implementation of distributions. Read More|
|fCopulae||Dependence Structures with Copulas – A collection of functions for bivariate copulae, bivariate elliptical copulae, bivariate
empirical copulae, and extreme value copulae. Also provides description of functions to compute multivariate densities and probabilities from skew normal and skew Student t-distribution functions. Read More
|gamlss.dist||Distributions to be used for GAMLSS modelling – Additional actuarial science functionality, mostly in the fields of loss distributions, risk theory (including ruin theory), simulation of compound hierarchical models and credibility theory, for the moment. Read More|
|lmomco||L-moments, Trimmed L-moments, L-comoments, Censored L-moments, and Many Distributions – The package implements the statistical theory of L-moments including L-moment estimation, probability-weighted moment estimation, parameter estimation for numerous familiar and not-so-familiar distributions, and L-moment estimation for the same distributions from the parameters. L-moments are derived from the expectations of order statistics and are linear with respect to the probability-weighted moments; choice of either can be made by mathematical convenience. L-moments are directly analogous to the well-known product moments; however, L-moments have many advantages including unbiasedness, robustness, and consistency with respect to the product moments. The method of L-moments can out perform the method of maximum likelihood. The lmomco package historically is oriented around canonical FORTRAN algorithms of J.R.M. Hosking, and the nomenclature for many of the functions parallels that of the Hosking library, which later became available in the lmom package. However, vast arrays of various extensions and curiosities are made by the author to aid and expand of the breadth of L-moment application. Such extensions venerable statistics as Sen weighted mean, Gini mean difference, plotting positions, and conditional probability adjustment. Much extension of L-moment theory has occurred in recent years, including extension of L-moments into right-tail censoring by known censoring threshold or by an indicator variable. Extension to left-tail censoring is readily made by variable flipping, treatment as right-tail censored, and finally back-flipping (re-transformation). E.A.H. Elamir and A.H. Seheult have developed the trimmed L-moments, which are implemented in this package. Further, Robert Serfling and Peng Xiao have extended L-moments into multivariate space; the so-called sample L-comoments are implemented here and might have considerable application in copula theory because they measure asymmetric correlation and higher co-moments. The supported distributions with moment type shown as L (L-moments) or TL (trimmed L-moments) and additional support for right-tail censoring ([RC]) include: Cauchy (TL), Exponential (L), Gamma (L), Generalized Extreme Value (L), Generalized Lambda (L & TL), Generalized Logistic (L), Generalized Normal (L), Generalized Pareto (L[RC] & TL), Gumbel (L), Kappa (L), Kumaraswamy (L), Normal (L), 3-parameter log-Normal (L), Pearson Type III (L), Rayleigh (L), Reverse Gumbel (L[RC]), Rice/Rician (L), Wakeby (L), and Weibull (L). Read More|
|mnormt||The multivariate normal and t distributions- This package provides functions for computing the density and the distribution function of multivariate normal and multivariate “t” variates, 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, where d denotes the number of dimensions. Read More|
|mvtnorm||Multivariate Normal and t Distributions- Computes multivariate normal and t probabilities, quantiles, random deviates and densities. Read More|
|SuppDists||Supplementary Distribution- Ten distributions supplementing those built into R. Inverse Gauss, Kruskal-Wallis, Kendall’s Tau, Friedman’s chi squared, Spearman’s rho, maximum F ratio, the Pearson product moment correlation coefficiant, Johnson distributions, normal scores and generalized hypergeometric distributions. In addition two random number generators of George Marsaglia are included. Read More|
|VGAM||Vector Generalized Linear and Additive Models- Vector generalized linear and additive models, and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Reduced-Rank VGAMs). This package fits many models and distribution by maximum likelihood estimation (MLE) or penalized MLE. Also fits constrained ordination models in ecology. Read More|
Statistical genetic is the analysis of genetic data. The study of genetics can be classified as Mendelian Genetics or quantitative genetics, where the former deals with the large influence of genes on phenotype, making a gene easily deducible, unlike the later which deals with several genes with complex traits that are difficult to deduce. Thus, statistical methods are used as the basis for inference of a complex trait, while considering the uncertainty from environmental factors.
|R Package||Package Description|
|gap||Gentic Analysis Package – 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, classic twin models, probability of familial disease aggregation, kinship calculation, some statistics in linkage analysis, and association analysis involving one or more genetic markers including haplotype analysis with or without environmental covariates. Read More|
|genetics||Population Genetics – 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. Read More|
|haplo.stats||Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous- 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. Read More|