Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper <doi:10.18637/jss.v097.i03>. Note that this package is modelled on the survsim package available in the Stata software (see Crowther and Lambert (2012) <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).

Interface to interact with the modelling framework SIMPLACE and to parse the results of simulations.

Contract helpers built with S7 for expressing runtime protocols around ordinary S7 dispatch. Structural interfaces describe small sets of required S7 generics, while explicit traits record registered implementations with optional default methods and associated metadata. Optional runtime checks can validate argument and return specifications in contract-scoped evaluation. Generative laws combine generators, deterministic shrinking, and one-result tinytest expectations.

This package provides the data sets used to exemplify specmine'. These data sets were formerly distributed with specmine', but they exceed current CRAN policy for package size.

Automates common plotting tasks to ease data exploration. Makes density plots (potentially overlaid on histograms), scatter plots with prediction lines, or bar or line plots with error bars. For each type, y, or x and y variables can be plotted at levels of other variables, all with minimal specification.

The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.

Can be used to model the fate of soil organic carbon and soil organic nitrogen and to calculate N mineralisation rates. Provides a framework that numerically solves differential equations of soil organic carbon models based on first-order kinetics and extends these models to include the nitrogen component. The name sorcering is an acronym for Soil ORganic Carbon & CN Ratio drIven Nitrogen modellinG framework'.

Identifies principal components whose eigenvalues exceed those expected under noise. Implements analytical thresholds derived from the Marchenko-Pastur distribution (Marchenko and Pastur, 1967) <doi:10.1070/SM1967v001n04ABEH001994> and empirical permutation tests, and provides functions for visualizing observed and null eigenvalue spectra.

This package provides an interface to shiny inputs used for filtering vectors, data.frames, and other objects. S7'-based implementation allows for seamless extensibility.

Visualize long timelines, extended sequences and temporally chained survey responses and experience sampling data using intuitive serpentine (snake) plots. Supports distribution bars, tick-mark plots, inter-item correlation arcs, faceted multi-construct panels, and daily time-of-day positioning for ecological momentary assessment data.

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.

Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).

This package provides a graphical user interface for cross-sectional network modeling with the statnet software suite <https://github.com/statnet>.

An input controller for R Shiny: a matrix with radio buttons, where only one option per row can be selected.

Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refer to Mark E. Glickman (1999) <https://www.glicko.net/research/glicko.pdf>; Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; Ruby C. Weng, Chih-Jen Lin (2011) <https://www.jmlr.org/papers/volume12/weng11a/weng11a.pdf>; W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.

The aim of this package is to offer new methodology for unit-level small area models under transformations and limited population auxiliary information. In addition to this new methodology, the widely used nested error regression model without transformations (see "An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data" by Battese, Harter and Fuller (1988) <doi:10.1080/01621459.1988.10478561>) and its well-known uncertainty estimate (see "The estimation of the mean squared error of small-area estimators" by Prasad and Rao (1990) <doi:10.1080/01621459.1995.10476570>) are provided. In this package, the log transformation and the data-driven log-shift transformation are provided. If a transformation is selected, an appropriate method is chosen depending on the respective input of the population data: Individual population data (see "Empirical best prediction under a nested error model with log transformation" by Molina and Martà n (2018) <doi:10.1214/17-aos1608>) but also aggregated population data (see "Estimating regional income indicators under transformations and access to limited population auxiliary information" by Würz, Schmid and Tzavidis <unpublished>) can be entered. Especially under limited data access, new methodologies are provided in saeTrafo. Several options are available to assess the used model and to judge, present and export its results. For a detailed description of the package and the methods used see the corresponding vignette.

This package provides methods for sensory discrimination methods; duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes. Methods for profile likelihood confidence intervals and plotting are included. Most methods are described in Brockhoff, P.B. and Christensen, R.H.B. (2010) <doi:10.1016/j.foodqual.2009.04.003>.

Computes the trimmed-k mean by removing the k smallest and k largest values from a numeric vector. Created for STAT 5400 at the University of Iowa.

Stock-and-flow models are a computational method from the field of system dynamics. They represent how systems change over time and are mathematically equivalent to ordinary differential equations. sdbuildR (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. sdbuildR simulates models in R and Julia', and supports computationally intensive ensemble simulations. Additionally, sdbuildR can import models created in Insight Maker (<https://insightmaker.com/>).

This package provides a systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the cMap (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.

Allows users to list data structures using path-based navigation. Provides intuitive methods for storing, accessing, and manipulating nested data through simple path strings. Key features include strict mode validation, path existence checking, recursive operations, and automatic parent-level creation. Designed for use cases requiring organized storage of complex nested data while maintaining simple access patterns. Particularly useful for configuration management, nested settings, and any application where data naturally forms a tree-like structure.

This package provides a time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. SPORTSCausal (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.

Population genetics package for designing diagnostic panels. Candidate markers, marker combinations, and different panel sizes are assessed for how well they can predict the source population of known samples. Requires a genotype file of candidate markers in STRUCTURE format. Methods for population cross-validation are described in Jombart (2008) <doi:10.1093/bioinformatics/btn129>.

The saemix package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.