Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.

This package provides a port of the Scarabee toolkit originally written as a Matlab-based application. scaRabee provides a framework for simulation and optimization of pharmacokinetic-pharmacodynamic models at the individual and population level. It is built on top of the neldermead package, which provides the direct search algorithm proposed by Nelder and Mead for model optimization.

Takes one or more fitted Cox proportional hazards models and writes a shiny application to a directory specified by the user. The shiny application displays predicted survival curves based on user input, and contains none of the original data used to create the Cox model or models. The goal is towards visualization and presentation of predicted survival curves.

Develop spatial interaction models (SIMs). SIMs predict the amount of interaction, for example number of trips per day, between geographic entities representing trip origins and destinations. Contains functions for creating origin-destination datasets from geographic input datasets and calculating movement between origin-destination pairs with constrained, production-constrained, and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.

This package provides functions for validating the structure and properties of data frames. Answers essential questions about a data set after initial import or modification. What are the unique or missing values? What columns form a primary key? What are the properties of the numeric or categorical columns? What kind of overlap or mapping exists between 2 columns?

Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using Generalized Additive Models following Wood (2017) <doi:10.1201/9781315370279> and Error Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) <doi:10.1002/sim.8362>. Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and visualization of results.

DoRothEA is a gene regulatory network containing signed transcription factor (TF) - target gene interactions. DoRothEA regulons, the collection of a TF and its transcriptional targets, were curated and collected from different types of evidence for both human and mouse. A confidence level was assigned to each TF-target interaction based on the number of supporting evidence.

Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format.

GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA.

Has various functions designed to implement the Hermite-Gaussian Radial Velocity (HGRV) estimation approach of Holzer et al. (2020) <arXiv:2005.14083>, which is a particular application of the radial velocity method for detecting exoplanets. The overall approach consists of four sequential steps, each of which has a function in this package: (1) estimate the template spectrum with the function estimate_template(), (2) find absorption features in the estimated template with the function findabsorptionfeatures(), (3) fit Gaussians to the absorption features with the function Gaussfit(), (4) apply the HGRV with simple linear regression by calling the function hgrv(). This package is meant to be open source. But please cite the paper Holzer et al. (2020) <arXiv:2005.14083> when publishing results that use this package.

Automatic coding of open-ended responses to the Cognitive Reflection Test (CRT), a widely used class of tests in cognitive science and psychology that assess the tendency to override an initial intuitive (but incorrect) answer and engage in reflection to reach a correct solution. The package standardizes CRT response coding across datasets in cognitive psychology, decision-making, and related fields. Automated coding reduces manual effort and improves reproducibility by limiting variability from subjective interpretation of open-ended responses. The package supports automatic coding and machine scoring for the original English-language CRT (Frederick, 2005) <doi:10.1257/089533005775196732>, CRT4 and CRT7 (Toplak et al., 2014) <doi:10.1080/13546783.2013.844729>, CRT-long (Primi et al., 2016) <doi:10.1002/bdm.1883>, and CRT-2 (Thomson & Oppenheimer, 2016) <doi:10.1017/s1930297500007622>.

Automated Characterization of Health Information at Large-Scale Longitudinal Evidence Systems. Creates a descriptive statistics summary for an Observational Medical Outcomes Partnership Common Data Model standardized data source. This package includes functions for executing summary queries on the specified data source and exporting reporting content for use across a variety of Observational Health Data Sciences and Informatics community applications.

Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software CRExplorer (see <http://crexplorer.net>). Further extension to more plot variants is planned.

The congeneric normal-ogive model is a popular model for psychometric data (McDonald, R. P. (1997) <doi:10.1007/978-1-4757-2691-6_15>). This model estimates the model, calculates theoretical and concrete reliability coefficients, and predicts the latent variable of the model. This is the companion package to Moss (2020) <doi:10.31234/osf.io/nvg5d>.

This package provides an extension to the purrr family of mapping functions to apply a function to each combination of elements in a list of inputs. Also includes functions for automatically detecting output type in mapping functions, finding every combination of elements of lists or rows of data frames, and applying multiple models to multiple subsets of a dataset.

Quantify and visualise various measures of chemical diversity and dissimilarity, for phytochemical compounds and other sets of chemical composition data. Importantly, these measures can incorporate biosynthetic and/or structural properties of the chemical compounds, resulting in a more comprehensive quantification of diversity and dissimilarity. For details, see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.

Computes density function, cumulative distribution function, quantile function and random numbers for a multisection composite distribution specified by the user. Also fits the user specified distribution to a given data set. More details of the package can be found in the following paper submitted to the R journal Wiegand M and Nadarajah S (2017) CompDist: Multisection composite distributions.

This package contains the discrete nonparametric survivor function estimation algorithm of De Gruttola and Lagakos for doubly interval-censored failure time data and the discrete nonparametric survivor function estimation algorithm of Sun for doubly interval-censored left-truncated failure time data [Victor De Gruttola & Stephen W. Lagakos (1989) <doi:10.2307/2532030>] [Jianguo Sun (1995) <doi:10.2307/2533008>].

Data cleaning scripts typically contain a lot of if this change that type of statements. Such statements are typically condensed expert knowledge. With this package, such data modifying rules are taken out of the code and become in stead parameters to the work flow. This allows one to maintain, document, and reason about data modification rules as separate entities.

This package provides functions and classes designed to handle and visualise epidemiological flows between locations. Also contains a statistical method for predicting disease spread from flow data initially described in Dorigatti et al. (2017) <doi:10.2807/1560-7917.ES.2017.22.28.30572>. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Implementation of fused Markov graphical model (FMGM; Park and Won, 2022). The functions include building mixed graphical model (MGM) objects from data, inference of networks using FMGM, stable edge-specific penalty selection (StEPS) for the determination of penalization parameters, and the visualization. For details, please refer to Park and Won (2022) <doi:10.48550/arXiv.2208.14959>.

This package provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

This package implements Mander & Thompson's (2010) <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs optimal under the alternative hypothesis for phase II [cancer] trials. Also provides an implementation of Simon's (1989) <doi:10.1016/0197-2456(89)90015-9> original methodology and allows exploration of the operating characteristics of sub-optimal designs.