Work with and download road traffic casualty data from Great Britain. Enables access to the UK's official road safety statistics, STATS19'. Enables users to specify a download directory for the data, which can be set permanently by adding `STATS19_DOWNLOAD_DIRECTORY=/path/to/a/dir` to your `.Renviron` file, which can be opened with `usethis::edit_r_environ()`. The data is provided as a series of `.csv` files. This package downloads, reads-in and formats the data, making it suitable for analysis. See the stats19 vignette for details. Data available from 1979 to 2024. See the official data series at <https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-accidents-safety-data>. The package is described in a paper in the Journal of Open Source Software (Lovelace et al. 2019) <doi:10.21105/joss.01181>. See Gilardi et al. (2022) <doi:10.1111/rssa.12823>, Vidal-Tortosa et al. (2021) <doi:10.1016/j.jth.2021.101291>, Tait et al. (2023) <doi:10.1016/j.aap.2022.106895>, and León et al. (2025) <doi:10.18637/jss.v114.i09> for examples of how the data can be used for methodological and empirical research.

Annotates single-cell and spatial-transcriptomic (ST) data using marker datasets. Supports unified markers list ('Markers_list') creation from built-in databases (e.g., Cellmarker2', PanglaoDB', scIBD', TCellSI', PCTIT', PCTAM'), Seurat objects, or user-supplied Excel files. SlimR can predict calculation parameters by machine learning algorithms (e.g., Random Forest', Gradient Boosting', Support Vector Machine', Ensemble Learning'), and based on Markers_list, calculate gene expression of different cell types and predict annotation information, and calculate corresponding AUC and annotate it, then verify it. At the same time, it can calculate gene expression corresponding to the cell type to generate a reference map for manual annotation (e.g., Heat Map', Feature Plots', Combined Plots'). For more details, see Kabacoff (2020, ISBN:9787115420572).

Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.

This package provides functions to simulate from joint survival and marker models. The user can specific all basis functions of time, random or deterministic covariates, random or deterministic left-truncation and right-censoring times, and model parameters.

This package creates 3D animated, interactive visualizations that can be viewed in a web browser.

Includes built-in methods for generating long SQL CASE statements, and other SQL statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to SQL'-like databases, such as when using the RODBC package. The current methods include casewhen() for building CASE statements, inlist() for building IN statements, and updatetable() for building UPDATE statements.

Data visualization tours animates linear projection of multivariate data as its basis (ie. orientation) changes. The spinifex packages generates paths for manual tours by manipulating the contribution of a single variable at a time Cook & Buja (1997) <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand (random walk) and guided (optimizing some objective function) are available in the tourr package Wickham et al. <doi:10.18637/jss.v040.i02>. spinifex builds on tourr and can render tours with gganimate and plotly graphics, and allows for exporting as an .html widget and as an .gif, respectively. This work is fully discussed in Spyrison & Cook (2020) <doi:10.32614/RJ-2020-027>.

This package provides functions for obtaining p-values (for hypothesis tests), confidence intervals, and multivariate confidence sets. In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", <doi:10.48550/arXiv.2303.05328>.

Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical or genetic data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman's third-order (<DOI:10.1007/BF02293811>) or Headrick's fifth-order (<DOI:10.1016/S0167-9473(02)00072-5>) polynomial transformation. Binary and ordinal variables are simulated using a modification of the ordsample() function from GenOrd'. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Correlation Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli's 2012 method, <DOI:10.1002/asmb.901>). In Correlation Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari's 2015 modification of GenOrd, <DOI:10.1002/asmb.2072>). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds.

Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.

Fit a spatial-temporal occupancy models using a probit formulation instead of a traditional logit model.

Tool for statistical simulations that have two components. One component generates the data and the other one analyzes the data. The main aims of the package are the reduction of the administrative source code (mainly loops and management code for the results) and a simple applicability of the package that allows the user to quickly learn how to work with it. Parallel computing is also supported. Finally, convenient functions are provided to summarize the simulation results.

This package provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo analyses. There are numerous options for model plots. The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models. There is also a function for partial and semipartial correlations and a function for conducting Cohen's set correlation analyses.

Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.

To determine sample size or power for case-control studies to be analyzed using logistic regression.

Computes synchrony as windowed cross-correlation based on two-dimensional time series in a text file you can upload. SUSY works as described in Tschacher & Meier (2020) <doi:10.1080/10503307.2019.1612114>.

Prototype your shiny apps quickly with these Lorem-Ipsum-like Helpers.

This package provides a stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.

Customise Shiny disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.

This package implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains.

Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) <doi:10.1111/rssb.12499>. SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model.

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>.

Set of functions that access information about deputies and votings in Polish diet from webpage <http://www.sejm.gov.pl>. The package was developed as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty of Mathematics and Information Science, Warsaw University of Technology.

This package provides a set of functions to build a scoring model from beginning to end, leading the user to follow an efficient and organized development process, reducing significantly the time spent on data exploration, variable selection, feature engineering, binning and model selection among other recurrent tasks. The package also incorporates monotonic and customized binning, scaling capabilities that transforms logistic coefficients into points for a better business understanding and calculates and visualizes classic performance metrics of a classification model.