Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models.

This package provides a Rstudio addin to download, merge and upload Rstudio settings and keymaps, essentially syncing them at will. It uses Google Drive as a cloud storage to keep the settings and keymaps files.

This package implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.

Contains, as a main contribution, a function to fit a regression model with possibly right, left or interval censored observations and with the error distribution expressed as a mixture of G-splines. Core part of the computation is done in compiled C++ written using the Scythe Statistical Library Version 0.3.

The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species.

Univariate stratification of survey populations with a generalization of the Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account a discrepancy between the stratification variable and the survey variable. The determination of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure that some specific units are in the sample. The well known cumulative root frequency rule of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.

Conducts hierarchical partitioning to calculate individual contributions of spatial and predictors (groups) towards total R2 for spatial simultaneous autoregressive model.

Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The rjags package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).

Create mixed models with repeated measures using natural cubic splines applied to an observed continuous time variable, as described by Donohue et al. (2023) <doi:10.1002/pst.2285>. Iterate through multiple covariance structure types until one converges. Categorize observed time according to scheduled visits. Perform subgroup analyses.

This package provides functions common to members of the SISTM team.

This package provides a spectral framework to map quantitative trait loci (QTLs) affecting joint differential networks of gene co-Expression. Test the equivalence among multiple biological networks via spectral statistics. See reference Hu, J., Weber, J. N., Fuess, L. E., Steinel, N. C., Bolnick, D. I., & Wang, M. (2025) <doi:10.1371/journal.pcbi.1012953>.

This package provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) <doi:10.1080/01621459.2014.957289>.

This package performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.

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.

Computes spatial position models: the potential model as defined by Stewart (1941) <doi:10.1126/science.93.2404.89> and catchment areas as defined by Reilly (1931) or Huff (1964) <doi:10.2307/1249154>.

This package provides utilities for cleaning survey data, computing weights, and performing descriptive statistical analysis. Methods follow Lohr (2019, ISBN:978-0367272454) "Sampling: Design and Analysis" and Lumley (2010) <doi:10.1002/9780470580066>.

Use piping, verbs like group_by and summarize', and other dplyr inspired syntactic style when calculating summary statistics on survey data using functions from the survey package.

This package provides functions for generating Standardized Climate Indices (SCI). Functions for generating Standardized Climate Indices (SCI). SCI is a transformation of (smoothed) climate (or environmental) time series that removes seasonality and forces the data to take values of the standard normal distribution. SCI was originally developed for precipitation. In this case it is known as the Standardized Precipitation Index (SPI).

Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.

This package provides an XY pad input for the Shiny framework. An XY pad is like a bivariate slider. It allows to pick up a pair of numbers.

An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.

Exporting shiny applications with shinylive allows you to run them entirely in a web browser, without the need for a separate R server. The traditional way of deploying shiny applications involves in a separate server and client: the server runs R and shiny', and clients connect via the web browser. When an application is deployed with shinylive', R and shiny run in the web browser (via webR'): the browser is effectively both the client and server for the application. This allows for your shiny application exported by shinylive to be hosted by a static web server.

Enables the creation of Chain Event Graphs over spatial areas, with an optional Shiny user interface. Allows users to fully customise both the structure and underlying model of the Chain Event Graph, offering a high degree of flexibility for tailored analyses. For more details on Chain Event Graphs, see Freeman, G., & Smith, J. Q. (2011) <doi:10.1016/j.jmva.2011.03.008>, Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604) and Barclay, L. M., Hutton, J. L., & Smith, J. Q. (2014) <doi:10.1214/13-BA843>.

This package provides a complete suite of tools for interacting with the Survey Solutions GraphQL API <https://demo.mysurvey.solutions/graphql/>. This package encompasses all currently available queries and mutations, including the latest features for map uploads. It is built on the modern httr2 package, offering a streamlined and efficient interface without relying on external GraphQL client packages. In addition to core API functionalities, the package includes a range of helper functions designed to facilitate the use of available query filters.