An interactive shiny application for Health Technology Assessment (HTA) parameter estimation. Converts between rates, probabilities, odds, and hazard ratios (HR); extrapolates survival curves (Exponential, Weibull, Log-Logistic); fits probabilistic sensitivity analysis (PSA) distributions (Beta, Gamma, LogNormal, Dirichlet) via the method of moments; calculates incremental cost-effectiveness ratios (ICERs), net monetary benefit (NMB), value-based pricing, and budget impact; adjusts costs for inflation, discounting, and purchasing power parity (PPP) across 30 countries; and adjusts background mortality using life-table methods. Designed for researchers building cost-effectiveness and budget-impact models who need auditable, formula-documented parameter transformations. Methods include Zhang and Yu (1998) <doi:10.1001/jama.280.19.1690> for odds ratio (OR) to relative risk (RR) conversion and Chinn (2000, Statistics in Medicine, 19, 3127-3131) for effect-size transformations.

Pedigree related functions.

This package provides a lightweight and intuitive framework for building interactive data analysis pipelines. You add R functions one by one, and pipeflow wires them into a pipeline that stays consistent as you go. Modify, remove, or insert steps at any stage, manage all parameters in one place, fast execution (C++-powered DAG) for interactive use and Shiny backends.

This package provides functions and datasets to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).

This package provides tools for exploratory process data analysis. Process data refers to the data describing participants problem-solving processes in computer-based assessments. It is often recorded in computer log files. This package provides functions to read, process, and write process data. It also implements two feature extraction methods to compress the information stored in process data into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are wrappers of functions in keras'.

An API wrapper around the ProPublica API <https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the RPublica package because it is for the ProPublica U.S. Congress data API, and the RPublica package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs.

Palettes inspired by Paris 2024 Olympic and Paralympic Games for data visualizations. Length of color palettes is configurable.

Calculate Predictive Moran's Eigenvector Maps (pMEM) for spatially-explicit prediction of environmental variables, as defined by Guénard and Legendre (2024) <doi:10.1111/2041-210X.14413>. pMEM extends classical MEM by enabling interpolation and prediction at unsampled locations using spatial weighting functions parameterized by range (and optionally shape). The package implements multiple pMEM types (e.g., exponential, Gaussian, linear) and features a modular architecture that allows programmers to define custom weighting functions. Designed for ecologists, geographers, and spatial analysts working with spatially-structured data.

Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042>. For full functionality, the software ncbi-blast+ is needed, see <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html> for more information.

Numerical derivatives through finite-difference approximations can be calculated using the pnd package with parallel capabilities and optimal step-size selection to improve accuracy. These functions facilitate efficient computation of derivatives, gradients, Jacobians, and Hessians, allowing for more evaluations to reduce the mathematical and machine errors. Designed for compatibility with the numDeriv package, which has not received updates in several years, it introduces advanced features such as computing derivatives of arbitrary order, improving the accuracy of Hessian approximations by avoiding repeated differencing, and parallelising slow functions on Windows, Mac, and Linux.

Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.

Plot both fixed and random effects of linear mixed models, multilevel models in a single spaghetti plot. The package allows to visualize the effect of a predictor on a criterion between different levels of a grouping variable. Additionally, confidence intervals can be displayed for fixed effects. Calculation of predicted values of random effects allows only models with one random intercept and/or one random slope to be plotted. Confidence intervals and predicted values of fixed effects are computed using the ggpredict function from the ggeffects package. Lüdecke, D. (2018) <doi:10.21105/joss.00638>.

The main attribute of PopVar is the prediction of genetic variance in bi-parental populations, from which the package derives its name. PopVar contains a set of functions that use phenotypic and genotypic data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>). A dataset think_barley.rda is included for reference and examples.

Installs an updated version of pomdp-solve and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.

Computes predicted probabilities and marginal effects for binary & ordinal logit and probit, (partial) generalized ordinal & multinomial logit models estimated with the glm(), clm() (in the ordinal package), and vglm() (in the VGAM package) functions.

This package provides a collection of process capability index functions, such as C_p(), C_pk(), C_pm(), and others, along with metadata about each, like LaTeX equations and R expressions. Its primary purpose is to form a foundation for other quality control packages to build on top of, by providing basic resources and functions. The indices belong to the field of statistical quality control, and quantify the degree to which a manufacturing process is able to create items that adhere to a certain standard of quality. For details see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8).

Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.

This package provides a testing workbench to evaluate tools that calculate precision-recall curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.

This package provides functions for data normalization and transformation in preprocessing stages. Implements scaling methods (min-max, Z-score, L2 normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox transformation is described in Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Yeo-Johnson transformation in Yeo and Johnson (2000) <doi:10.1093/biomet/87.4.954>.

This package provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) <doi:10.2307/2528247> for methodological details.

Make statistical inference on the probability of being in response, the duration of response, and the cumulative response rate up to a given time point. The method can be applied to analyze phase II randomized clinical trials with the endpoints being time to treatment response and time to progression or death.

Makes output files from select PreSens Fiber Optic Oxygen Transmitters easier to work with in R. See <http://www.presens.de> for more information about PreSens (Precision Sensing GmbH). Note: this package is neither created nor maintained by PreSens.

Perform simultaneous estimation and variable selection for correlated bivariate mixed outcomes (one continuous outcome and one binary outcome per cluster) using penalized generalized estimating equations. In addition, clustered Gaussian and binary outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported. Cross-validation can be performed to find the optimal regularization parameter(s).