Computes Bayesian assurance under various settings characterized by different assumptions and objectives, including precision-based conditions, credible intervals, and goal functions. All simulation-based functions included in this package rely on a two-stage Bayesian method that assigns two distinct priors to evaluate the probability of observing a positive outcome, which addresses subtle limitations that take place when using the standard single-prior approach. For more information, please refer to Pan and Banerjee (2021) <arXiv:2112.03509>.
Reads chromatograms from binary formats into R objects. Currently supports conversion of Agilent ChemStation', Agilent MassHunter', Shimadzu LabSolutions', ThermoRaw', and Varian Workstation files as well as various text-based formats. In addition to its internal parsers, chromConverter contains bindings to parsers in external libraries, such as Aston <https://github.com/bovee/aston>, Entab <https://github.com/bovee/entab>, rainbow <https://rainbow-api.readthedocs.io/>, and ThermoRawFileParser <https://github.com/compomics/ThermoRawFileParser>.
R shiny web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package epiworldR (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. epiworldRShiny provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.
The EUNIS habitat classification is a comprehensive pan-European system for habitat identification <https://www.eea.europa.eu/data-and-maps/data/eunis-habitat-classification-1>. This is an R data package providing the EUNIS classification system. The classification is hierarchical and covers all types of habitats from natural to artificial, from terrestrial to freshwater and marine. The habitat types are identified by specific codes, names and descriptions and come with schema crosswalks to other habitat typologies.
This package provides a permutation-based hypothesis test for statistical comparison of two networks based on the invariance measures of the R package NetworkComparisonTest by van Borkulo et al. (2022), <doi:10.1037/met0000476>: network structure invariance, global strength invariance, edge invariance, and various centrality measures. Edgelists from dependent or independent samples are used as input. These edgelists are generated from concept maps and summed into two comparable group networks. The networks can be directed or undirected.
Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.
Finds the posterior modes for the mean and standard deviation for a truncated normal distribution with one or two known truncation points. The method used extends Bayesian methods for parameter estimation for a singly truncated normal distribution under the Jeffreys prior (see Zhou X, Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated data with applications to operational risk measurement". <doi:10.1080/14697688.2012.752103>). This package additionally allows for a doubly truncated normal distribution.
The NanoporeRNASeq package contains long read RNA-Seq data generated using Oxford Nanopore Sequencing. The data consists of 6 samples from two human cell lines (K562 and MCF7) that were generated by the SG-NEx project. Each of these cell lines has three replicates, with 1 direct RNA sequencing data and 2 cDNA sequencing data. Reads are aligned to chromosome 22 (Grch38) and stored as bam files. The original data is from the SG-NEx project.
This package provides methods for examining posterior MCMC samples from a single chain using trace plots and density plots, and from multiple chains by comparing posterior medians and credible intervals from each chain. These plotting functions have a variety of options, such as figure sizes, legends, parameters to plot, and saving plots to file. Functions interface with the NIMBLE software package, see de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017) <doi:10.1080/10618600.2016.1172487>.
This software tool is designed to extract data from a randomized subset of individuals within a cohort and make it available for exploration in a shiny application environment. It retrieves date-stamped, event-level records from one or more data sources that represent patient data in the Observational Medical Outcomes Partnership (OMOP) data model format. This tool features a user-friendly interface that enables users to efficiently explore the extracted profiles, thereby facilitating applications, such as reviewing structured profiles.
Convenient functions for ensemble forecasts in R combining approaches from the forecast package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy.
Machine learning hierarchical risk clustering portfolio allocation strategies. The implemented methods are: Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059>. Hierarchical clustering-based asset allocation (Raffinot, 2017) <DOI: 10.3905/jpm.2018.44.2.089>. Hierarchical equal risk contribution portfolio (Raffinot, 2018) <DOI: 10.2139/ssrn.3237540>. A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019) <https://www.ekon.sun.ac.za/wpapers/2019/wp142019/wp142019.pdf>.
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. InfiniumPurify provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
The function SurvRegCens() of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) <arXiv:1402.0432>.
Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema.
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)".
Offers a diverse collection of datasets focused on cardiovascular and heart disease research, including heart failure, myocardial infarction, aortic dissection, transplant outcomes, cardiovascular risk factors, drug efficacy, and mortality trends. Designed for researchers, clinicians, epidemiologists, and data scientists, the package features clinical, epidemiological, and simulated datasets covering a wide range of conditions and treatments such as statins, anticoagulants, and beta blockers. It supports analyses related to disease progression, treatment effects, rehospitalization, and public health outcomes across various cardiovascular patient populations.
Implementations of most of the existing proximity-based methods of link prediction in graphs. Among the 20 implemented methods are e.g.: Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>, Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>, Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.
This package provides functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. There are also functions that fit the distribution to data. There are functions for the mean, variance, skewness, kurtosis and mode of a given distribution and to calculate moments of any order about any centre. To assess goodness of fit, there are functions to generate a Q-Q plot, a P-P plot and a tail plot.
This package provides a hierarchy of classes and methods for manipulating matrices formed implicitly from the sums of the inverses of other matrices, a situation commonly encountered in spatial statistics and related fields. Enables easy use of the Woodbury matrix identity and the matrix determinant lemma to allow computation (e.g., solving linear systems) without having to form the actual matrix. More information on the underlying linear algebra can be found in Harville, D. A. (1997) <doi:10.1007/b98818>.
Allow users to obtain clean and tidy football (soccer) game, team and player data. Data is collected from a number of popular sites, including FBref', transfer and valuations data from Transfermarkt'<https://www.transfermarkt.com/> and shooting location and other match stats data from Understat'<https://understat.com/> and fotmob'<https://www.fotmob.com/>. It gives users the ability to access data more efficiently, rather than having to export data tables to files before being able to complete their analysis.
The package includes DNA methylation data for the primary Chronic Lymphocytic Leukemia samples included in the Primary Blood Cancer Encyclopedia (PACE) project. Raw data from the 450k DNA methylation arrays is stored in the European Genome-Phenome Archive (EGA) under accession number EGAS0000100174. For more information concerning the project please refer to the paper "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oles M, Lu J et al., J. Clin. Invest. (2018) and R/Bioconductor package BloodCancerMultiOmics2017.
Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
This package provides a system for submitting multiple IP information queries to IP2Location.io'รข s IP Geolocation API and storing the resulting data in a dataframe. You provide a vector of IP addresses and your IP2Location.io API key. The package returns a dataframe with one row per IP address and a column for each available data field (data fields not included in your API plan will contain NAs). This is the second submission of the package to CRAN.