Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.

Computes treatment patterns within a given cohort using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). As described in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.

Helps the R users to get data from Tushare Pro'<https://tushare.pro>. Tushare Pro is a platform as well as a community with a lot of staffs working in financial area. We support financial data such as stock price, financial report statements and digital coins data.

This package provides a latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) <doi:10.1198/016214504000001538> or the regression coefficient estimates (Jones and Crowley, 1992) <doi:10.2307/2336782>. The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis.

Uses the Distorted Wave Born Approximation (DWBA) to compute the acoustic backward scattering, the geometry of the object is formed by a volumetric mesh, composed of tetrahedrons. This computation is done efficiently through an analytical 3D integration that allows for a solution which is expressed in terms of elementary functions for each tetrahedron. It is important to note that this method is only valid for objects whose acoustic properties, such as density and sound speed, do not vary significantly compared to the surrounding medium. (See Lavia, Cascallares and Gonzalez, J. D. (2023). TetraScatt model: Born approximation for the estimation of acoustic dispersion of fluid-like objects of arbitrary geometries. arXiv preprint <arXiv:2312.16721>).

Simple utilities to generate a Dockerfile from a directory or project, build the corresponding Docker image, push the image to DockerHub, and publicly share the project via Binder.

Theme ggplot2', lattice', and base graphics based on a few choices, including foreground color, background color, accent color, and font family. Fonts that aren't available on the system, but are available via download on Google Fonts', can be automatically downloaded, cached, and registered for use with the showtext and ragg packages.

This package provides a variety of tools for assessing dose response curves, with an emphasis on toxicity test data. The main feature of this package are modular functions which can be combined through the namesake pipeline, runtoxdrc', to automate the analysis for large and complex datasets. This includes optional data preprocessing steps, like outlier detection, solvent effects, blank correction, averaging technical replicates, and much more. Additionally, this pipeline is adaptable to any long form dataset, and does not require specific column or group naming to work.

Prebuilt shiny modules containing tools for the generation of rmarkdown reports, supporting reproducible research and analysis.

Collection of ancillary functions and utilities to be used in conjunction with the TraMineR package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences.

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance.

An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) <doi:10.5334/labphon.196> Lobanov, Boris (1971) <doi:10.1121/1.1912396> Nearey, Terrance M. (1978) <https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf> Syrdal, Ann K., and Gopal, H. S. (1986) <doi:10.1121/1.393381> Watt, Dominic, and Fabricius, Anne (2002) <https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.

Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.

This package implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

Simple trustworthy utility functions to use TauDEM (Terrain Analysis Using Digital Elevation Models <https://hydrology.usu.edu/taudem/taudem5/>) command-line interface. This package provides a guide to installation of TauDEM and its dependencies GDAL (Geopatial Data Abstraction Library) and MPI (Message Passing Interface) for different operating systems. Moreover, it checks that TauDEM and its dependencies are correctly installed and included to the PATH, and it provides wrapper commands for calling TauDEM methods from R.

This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).

This package implements harmonic analysis of tidal and sea-level data. Over 400 harmonic tidal constituents can be estimated, all with daily nodal corrections. Time-varying mean sea-levels can also be used.

Find similarities between texts using the Smith-Waterman algorithm. The algorithm performs local sequence alignment and determines similar regions between two strings. The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at <doi:10.1016/0022-2836(81)90087-5>. This package implements the same logic for sequences of words and letters instead of molecular sequences.

This package provides a problem solving environment (PSE) for fitting separable nonlinear models to measurements arising in physics and chemistry experiments, as described by Mullen & van Stokkum (2007) <doi:10.18637/jss.v018.i03> for its use in fitting time resolved spectroscopy data, and as described by Laptenok et al. (2007) <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence Lifetime Imaging Microscopy (FLIM) data, in the study of Förster Resonance Energy Transfer (FRET). `TIMP` also serves as the computation backend for the `GloTarAn` software, a graphical user interface for the package, as described in Snellenburg et al. (2012) <doi:10.18637/jss.v049.i03>.

Instead of nesting function calls, annotate and transform functions using "#." comments.

For writing tables with custom formats in a Excel file ready to be distributed.

Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong. Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed. Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).

Conditional logistic regression with longitudinal follow up and individual-level random coefficients: A stable and efficient two-step estimation method.

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.