Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book.

An assortment of helper functions for doing structural equation modeling, mainly by lavaan for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible.

Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.

This package provides a collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.

Analyze public-use micro data from the Survey of Consumer Finances. Provides tools to download prepared data files, construct replicate-weighted multiply imputed survey designs, compute descriptive statistics and model estimates, and produce plots and tables. Methods follow design-based inference for complex surveys and pooling across multiple imputations. See the package website and the code book for background.

Create and format tables and APA statistics for scientific publication. This includes making a Table 1 to summarize demographics across groups, correlation tables with significance indicated by stars, and extracting formatted statistical summarizes from simple tests for in-text notation. The package also includes functions for Winsorizing data based on a Z-statistic cutoff.

Smart Adaptive Recommendations (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of SAR': a standalone implementation, and an interface to a web service in Microsoft's Azure cloud: <https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>. The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use.

Implement different Item Response Theory (IRT) based procedures for the development of static short test forms (STFs) from a test. Two main procedures are considered, specifically the typical IRT-based procedure for the development of STF, and a recently introduced procedure (Epifania, Anselmi & Robusto, 2022 <doi:10.1007/978-3-031-27781-8_7>). The procedures differ in how the most informative items are selected for the inclusion in the STF, either by considering their item information functions without considering any specific level of the latent trait (typical procedure) or by considering their informativeness with respect to specific levels of the latent trait, denoted as theta targets (the newly introduced procedure). Regarding the latter procedure, three methods are implemented for the definition of the theta targets: (i) theta targets are defined by segmenting the latent trait in equal intervals and considering the midpoint of each interval (equal interval procedure, eip), (ii) by clustering the latent trait to obtain unequal intervals and considering the centroids of the clusters as the theta targets (unequal intervals procedure, uip), and (iii) by letting the user set the specific theta targets of interest (user-defined procedure, udp). For further details on the procedure, please refer to Epifania, Anselmi & Robusto (2022) <doi:10.1007/978-3-031-27781-8_7>.

This package provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) <doi:10.1093/biomet/86.1.59> and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published.

This gadget allows you to use the recipes package belonging to tidymodels to carry out the data preprocessing tasks in an interactive way. Build your recipe by dragging the variables, visually analyze your data to decide which steps to use, add those steps and preprocess your data.

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

Visualization and analysis of spatially resolved transcriptomics data. The spatialGE R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models.

Makes the React library Chakra UI usable in Shiny apps. Chakra UI components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.

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

This package creates ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models) fitted with mgcv and spline effects from GLM (Generalized Linear Models). Supports interaction terms and provides hazard ratio plots with histograms for survival analysis. Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models.

Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with Mplus software, specifically when implementing the complex survey option. In such cases, the model estimator in Mplus defaults to ML with robust standard errors.

Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. sectorgap features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results. For details on the methodology and an illustrative example, see Streicher (2024) <https://www.research-collection.ethz.ch/handle/20.500.11850/653682>.

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

This package provides a system enables cross study Analysis by extracting and filtering study data for control animals from CDISC SEND Study Repository. These data types are supported: Body Weights, Laboratory test results and Microscopic findings. These database types are supported: SQLite and Oracle'.

Utility functions that help with common base-R problems relating to lists. Lists in base-R are very flexible. This package provides functions to quickly and easily characterize types of lists. That is, to identify if all elements in a list are null, data.frames, lists, or fully named lists. Other functionality is provided for the handling of lists, such as the easy splitting of lists into equally sized groups, and the unnesting of data.frames within fully named lists.

Programmatic interface to the SNOTEL snow data (<https://www.nrcs.usda.gov/programs-initiatives/sswsf-snow-survey-and-water-supply-forecasting-program>). Provides easy downloads of snow data into your R work space or a local directory. Additional post-processing routines to extract snow season indexes are provided.

This package performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.

Calculates the sample size needed for evaluating a diagnostic test based on sensitivity, specificity, prevalence, and desired precision. Based on Buderer (1996) <doi:10.1111/j.1553-2712.1996.tb03538.x>.

Send syslog protocol messages to a remote syslog server specified by host name and TCP network port.