An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.

An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources.

An R interface to the Chemistry Development Kit, a Java library for chemoinformatics. Given the size of the library itself, this package is not expected to change very frequently. To make use of the CDK within R, it is suggested that you use the rcdk package. Note that it is possible to directly interact with the CDK using rJava'. However rcdk exposes functionality in a more idiomatic way. The CDK library itself is released as LGPL and the sources can be obtained from <https://github.com/cdk/cdk>.

Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.

An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.

Offers functions for fetching JSON data from the US EPA Air Quality System (AQS) API with options to comply with the API rate limits. See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details of the AQS API.

The TRUST4 or MiXCR is used to identify the clonotypes. The goal of rTCRBCRr is to process the results from these clonotyping tools, and analyze the clonotype repertoire metrics based on chain names and IGH isotypes. The manuscript is still under preparation for publication for now. The references describing the methods in this package will be added later.

Diagnostics and data preparation for random effects within estimator, random effects within-idiosyncratic estimator, between-within-idiosyncratic model, and cross-classified between model. Mundlak, Yair (1978) <doi:10.2307/1913646>. Hausman, Jeffrey (1978) <doi:10.2307/1913827>. Allison, Paul (2009) <doi:10.4135/9781412993869>. Neuhaus, J.M., and J. D. Kalbfleisch (1998) <doi:10.2307/3109770>.

R access to hundreds of millions data series from DBnomics API (<https://db.nomics.world/>).

This package provides methods for model building and model evaluation of mixed effects models using Monolix <https://monolix.lixoft.com>. Monolix is a software tool for nonlinear mixed effects modeling that must have been installed in order to use Rsmlx'. Among other tasks, Rsmlx provides a powerful tool for automatic PK model building, performs statistical tests for model assessment, bootstrap simulation and likelihood profiling for computing confidence intervals. Rsmlx also proposes several automatic covariate search methods for mixed effects models.

Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the validate package. The core algorithms have recently been moved to the lintools package, refer to lintools for a more basic interface and access to a version of the algorithm that works with sparse matrices.

This package provides an interface to the SurvStat web service from the Robert Koch Institute (<https://tools.rki.de/SurvStat/SurvStatWebService.svc>) allowing downloads of disease time series stratified by pathogen type and subtype, age, and geography from notifiable disease reports in Germany.

This package provides a collection of ROI optimization problems based on the NETLIB-LP collection. Netlib is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make this problems easily accessible from R as ROI optimization problems.

Implementation of JQuery <https://jquery.com> and CSS styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to Shiny applications or dashboards and Rmarkdown documents.Sharing capability on social media platforms including Facebook <https://www.facebook.com>, Linkedin <https://www.linkedin.com>, X/Twitter <https://x.com>, Tumblr <https://www.tumblr.com>, Pinterest <https://www.pinterest.com>, Whatsapp <https://www.whatsapp.com>, Reddit <https://www.reddit.com>, Baidu <https://www.baidu.com>, Blogger <https://www.blogger.com>, Weibo <https://www.weibo.com>, Instagram <https://www.instagram.com>, Telegram <https://www.telegram.me>, Youtube <https://www.youtube.com>.

This package provides classes and functions for modelling health care interventions using decision trees and semi-Markov models. Mechanisms are provided for associating an uncertainty distribution with each source variable and for ensuring transparency of the mathematical relationships between variables. The package terminology follows Briggs "Decision Modelling for Health Economic Evaluation" (2006, ISBN:978-0-19-852662-9).

Enhances the R Optimization Infrastructure ('ROI') package with the quadratic solver qpOASES'. More information about qpOASES can be found at <https://github.com/coin-or/qpOASES>.

Floating Percentile Model with additional functions for optimizing inputs and evaluating outputs and assumptions.

Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.

Apply sensitivity analysis for offline policy evaluation, as implemented in Jung et al. (2017) <arXiv:1702.04690> based on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.

The tools and utilities to estimate the model described in "Gremlin's in the Data: Identifying the Information Content of Research Subjects" (Howell et al. (2021) <doi:10.1177/0022243720965930>) using conjoint analysis data such as that collected in Sawtooth Software's Lighthouse or Discover products. Additional utilities are included for formatting the input data.

This package provides a set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data.

Non-linear inversion for hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation. Error ellipsoids and station influence are estimated via jackknife analysis. References include Iversen, E. S., and J. M. Lees (1996)<doi:10.1785/BSSA0860061853>.

R infrastructure for optimally robust estimation in general smoothly parameterized models using S4 classes and methods as described Kohl, M., Ruckdeschel, P., and Rieder, H. (2010), <doi:10.1007/s10260-010-0133-0>, and in Rieder, H., Kohl, M., and Ruckdeschel, P. (2008), <doi:10.1007/s10260-007-0047-7>.

This is a wrapper function for image(), which makes reasonable raster plots with nice axis and other useful features.