Add a searchbar widget to your Shiny application. The widget quickly integrates with any existing element containing text to highlight matches. Highlighting is done with the JavaScript library mark.js'. The widget includes buttons to cycle through multiple instances of the match and automatically scroll to the matches in an overflow element (or window). The widget also displays the total number of matches and which match is currently being cycled through. The widget is structured as a Bootstrap 3 input group.

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) <doi:10.48550/arXiv.1402.0432>.

This package provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). <https://cepa.stanford.edu/sites/default/files/workshops/GH_JSM%20Proceedings%202010.pdf> Huber, M. (2014). <doi:10.1002/jae.2341>). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples.

Elasticsearch is an open-source, distributed, document-based datastore (<https://www.elastic.co/products/elasticsearch>). It provides an HTTP API for querying the database and extracting datasets, but that API was not designed for common data science workflows like pulling large batches of records and normalizing those documents into a data frame that can be used as a training dataset for statistical models. uptasticsearch provides an interface for Elasticsearch that is explicitly designed to make these data science workflows easy and fun.

This package is designed for typesetting the programmable elements in digital hardware, i.e., registers. Such registers typically have many fields and can be quite wide; they are thus a challenge to typeset in a consistent manner. Register is similar in some aspects to the bytefield and bitpattern packages. Anyone doing hardware documentation using LaTeX should examine those packages. An example Perl module and script are provided, to convert the register specifications into structures suitable for, say, a pre-silicon test environment.

This package provides tools to study sorting patterns in matching markets and to estimate the affinity matrix of both the bipartite one-to-one matching model without frictions and with Transferable Utility by Dupuy and Galichon (2014) <doi:10.1086/677191> and its unipartite variant by Ciscato', Galichon and Gousse (2020) <doi:10.1086/704611>. It also contains all the necessary tools to implement the saliency analysis, to run rank tests of the affinity matrix and to build tables and plots summarizing the findings.

Various statistical methods for survival analysis in comparing survival curves between two groups, including overall hypothesis tests described in Li et al. (2015) <doi:10.1371/journal.pone.0116774> and Huang et al. (2020) <doi:10.1080/03610918.2020.1753075>, fixed-point tests in Klein et al. (2007) <doi:10.1002/sim.2864>, short-term tests, and long-term tests in Logan et al. (2008) <doi:10.1111/j.1541-0420.2007.00975.x>. Some commonly used descriptive statistics and plots are also included.

Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026> and Laborde et al. (2023) <doi: 10.1186/s12859-023-05210-6>. Such methods can inform whether clustering algorithms are appropriate for a data set.

Implementations of classical and machine learning models for survival analysis, including deep neural networks via keras and tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from Python via reticulate <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using Rcpp <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the Python package pycox <https://github.com/havakv/pycox>.

Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.

The package curatedPCaData offers a selection of annotated prostate cancer datasets featuring multiple omics, manually curated metadata, and derived downstream variables. The studies are offered as MultiAssayExperiment (MAE) objects via ExperimentHub, and comprise of clinical characteristics tied to gene expression, copy number alteration and somatic mutation data. Further, downstream features computed from these multi-omics data are offered. Multiple vignettes help grasp characteristics of the various studies and provide example exploratory and meta-analysis of leveraging the multiple studies provided here-in.

This library is a collection of pseudo random number generators.

While Common Lisp does provide a RANDOM function, it does not allow the user to pass an explicit SEED, nor to portably exchange the random state between implementations. This can be a headache in cases like games, where a controlled seeding process can be very useful.

For both curiosity and convenience, this library offers multiple algorithms to generate random numbers, as well as a bunch of generally useful methods to produce desired ranges.

TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class FastImputationPatterns'. FastImputation() function uses this FastImputationPatterns object to impute (make a good guess at) missing data in a single line or a whole data frame of data. This approximates the process used by Amelia <https://gking.harvard.edu/amelia> but is much faster when filling in values for a single line of data.

Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes: Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) <doi:10.1177/0022243720952410> "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.

This package provides an algebra over probability distributions enabling composition, sampling, and automatic simplification to closed forms. Supports normal, exponential, gamma, Weibull, chi-squared, uniform, beta, log-normal, Poisson, multivariate normal, empirical, and mixture distributions with algebraic operators (addition, subtraction, multiplication, division, power, exp, log, min, max) that automatically simplify when mathematical identities apply. Includes closed-form MVN conditioning (Schur complement), affine transformations, mixture marginals/conditionals (Bayes rule), and limiting distribution builders (CLT, LLN, delta method). Uses S3 classes for distributions and R6 for support objects.

Maintenance has been discontinued for this package. It has been superseded by GeneralizedHyperbolic'. GeneralizedHyperbolic includes all the functionality of HyperbolicDist and more and is based on a more rational design. HyperbolicDist 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.

The package provides access to the copy of the Synaptic proteome database. It was designed as an accompaniment for Synaptome.DB package. Database provides information for specific synaptic genes and allows building the protein-protein interaction graph for gene sets, synaptic compartments, and brain regions. In the current update we added 6 more synaptic proteome studies, which resulted in total of 64 studies. We introduced Synaptic Vesicle as a separate compartment. We also added coding mutations for Autistic Spectral disorder and Epilepsy collected from publicly available databases.

This package provides a set of tools for processing and analyzing in vitro toxicokinetic measurements in a standardized and reproducible pipeline. The package was developed to perform frequentist and Bayesian estimation on a variety of in vitro toxicokinetic measurements including -- but not limited to -- chemical fraction unbound in the presence of plasma (f_up), intrinsic hepatic clearance (Clint, uL/min/million hepatocytes), and membrane permeability for oral absorption (Caco2). The methods provided by the package were described in Wambaugh et al. (2019) <doi:10.1093/toxsci/kfz205>.

Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.

Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template R Markdown reports contained in the package.

Design parameters of the optimal two-period multiarm platform design (controlling for either family-wise error rate or pair-wise error rate) can be calculated using this package, allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) <doi:10.2307/2281208>.

TaskJuggler (tj3) is a project management tool for project planning and tracking using a domain specific language; projects are plain text files written using your favourite text editor. It includes reporting in HTML, CSV or iCalendar format and an email based status tracking system to send and receive time sheets from collaborators.

It covers the complete spectrum of project management tasks from the first idea to the completion of the project. It assists you during project scoping, resource assignment, cost and revenue planning, risk and communication management, status tracking and reporting.

The AFfunction() is a function which returns an estimate of the Attributable Fraction (AF) and a plot of the AF as a function of heritability, disease prevalence, size of target group and intervention effect. Since the AF is a function of several factors, a shiny app is used to better illustrate how the relationship between the AF and heritability depends on several other factors. The app is ran by the function runShinyApp(). For more information see Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.

Evaluates predictive performance under feature-level missingness in repeated-measures continuous glucose monitoring-like data. The benchmark injects missing values at user-specified rates, imputes incomplete feature matrices using an iterative chained-equations approach inspired by multivariate imputation by chained equations (MICE; Azur et al. (2011) <doi:10.1002/mpr.329>), fits Random Forest regression models (Breiman (2001) <doi:10.1023/A:1010933404324>) and k-nearest-neighbor regression models (Zhang (2016) <doi:10.21037/atm.2016.03.37>), and reports mean absolute percentage error and R-squared across missingness rates.