This package provides a collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). Supports at the moment Simple, Bipartite, Multipartite and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, Barbillon et al. (2020) <doi:10.1111/rssa.12193> and Bar-Hen et al. (2020) <doi:10.48550/arXiv.1807.10138>.

Web application using shiny for the SSD (Species Sensitivity Distribution) module of the MOSAIC (MOdeling and StAtistical tools for ecotoxICology) platform. It estimates the Hazardous Concentration for x% of the species (HCx) from toxicity values that can be censored and provides various plotting options for a better understanding of the results. See our companion paper Kon Kam King et al. (2014) <doi:10.48550/arXiv.1311.5772>.

This package provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.

Enhance the bookmarkable state feature of shiny with additional customization such as storage location and storage repositories leveraging the pins package.

Computes the sBIC for various singular model collections including: binomial mixtures, factor analysis models, Gaussian mixtures, latent forests, latent class analyses, and reduced rank regressions.

Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).

The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) <doi:10.2307/2332748>. When s<t, SPAS implements the methods described in Plante, Rivest, and Tremblay (1988) <doi:10.2307/2533994>. Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program of the same name.

In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.

Function library for processing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software.

This package provides tools to help tag and validate data according to user-specified rules. The safeframe class adds variable level attributes to data.frame columns. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines clearer, more robust, and more reliable.

This package provides functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.

This package implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) <doi:10.1080/01621459.2023.2259028>. The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function.

Import, process, summarize and visualize raw data from metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing.

This package provides a set of tools for writing and sharing interactive courses to be used with swirl.

This package provides a collection of simple parameter estimation and tests for the comparison of multivariate means and variation, to accompany Chapters 4 and 5 of the book Multivariate Statistical Methods. A Primer (5th edition), by Manly BFJ, Navarro Alberto JA & Gerow K (2024) <doi:10.1201/9781003453482>.

The Hypothesis tests for the means of independent or paired groups. This package investigates the normality assumption automatically. Then, it tests the hypothesis tests for two independent or paired group means by using parametric or non-parametric tests. It uses the Shapiro-Wilk test to test the normality assumption. For independent two groups, If data comes from the normal distribution, the package uses the Z or t-test according to whether variances are known. For paired groups, it uses paired t-test under normal data sets. If data does not come from the normal distribution, the package uses the Wilcoxon test for independent and paired cases.

Detection of item-wise Differential Item Functioning (DIF) in fitted mirt', multipleGroup or bfactor models using score-based structural change tests. Under the hood the sctest() function from the strucchange package is used.

This package provides functions to generate K-fold cross validation (CV) folds and CV test error estimates that take into account how a survey dataset's sampling design was constructed (SRS, clustering, stratification, and/or unequal sampling weights). You can input linear and logistic regression models, along with data and a type of survey design in order to get an output that can help you determine which model best fits the data using K-fold cross validation. Our paper on "K-Fold Cross-Validation for Complex Sample Surveys" by Wieczorek, Guerin, and McMahon (2022) <doi:10.1002/sta4.454> explains why differing how we take folds based on survey design is useful.

Based on the compound Poisson risk process that is perturbed by a Brownian motion, saddlepoint approximations to some measures of risk are provided. Various approximation methods for the probability of ruin are also included. Furthermore, exact values of both the risk measures as well as the probability of ruin are available if the individual claims follow a hypo-exponential distribution (i. e., if it can be represented as a sum of independent exponentially distributed random variables with different rate parameters). For more details see Gatto and Baumgartner (2014) <doi:10.1007/s11009-012-9316-5>.

Automatically calculate direct, indirect, and total effects for piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). Confidence intervals are provided via bootstrapping.

Processor for selected ion flow tube mass spectrometer (SIFT-MS) output file from breath analysis. It allows the filtering of the SIFT output file (i.e., variation over time of the target analyte concentration) and the following analysis for the determination of: maximum, average, and standard deviation value of target concentration measured at each exhalation, and the respiratory rate over the measurement. Additionally, it is possible to align the SIFT-MS data with other on-line techniques such as cardio pulmonary exercise test (CPET) for a comprehensive characterization of breath samples.

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

This package provides a simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.

This package implements statistical methods for detecting evolutionary shifts in both the optimal trait value (mean) and evolutionary diffusion variance. The method uses an L1-penalized optimization framework to identify branches where shifts occur, and the shift magnitudes. It also supports the inclusion of measurement error. For more details, see Zhang, Ho, and Kenney (2023) <doi:10.48550/arXiv.2312.17480>.