This package performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.

An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) <arXiv:2004.07429> where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution.

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.

An interface to explore trends in Twitter data using the Storywrangler Application Programming Interface (API), which can be found here: <https://github.com/janeadams/storywrangler>.

Analyse species-habitat associations in R. Therefore, information about the location of the species (as a point pattern) is needed together with environmental conditions (as a categorical raster). To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.

This package provides functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics.

This package implements the distance-adjusted propensity score with interference (iDAPS) and recoverU+ methods for estimating the average treatment effect on the treated (ATT) from spatial observational data in the presence of both spatial confounding and spatial interference. iDAPS matches units on a data-driven composite of propensity-score distance, spatial proximity and neighbourhood-exposure distance. recoverU+ is a doubly robust estimator that augments the propensity-score and control-outcome models with a partially recovered spatial confounder and a neighbourhood-exposure term. The package also provides the naive propensity score, DAPS and recoverU comparators, and a simulator for the spatial confounding/interference data-generating process.

Extension to the spatstat family of packages, for analysing large datasets of spatial points on a network. The geometrically- corrected K function is computed using a memory-efficient tree-based algorithm described by Rakshit, Baddeley and Nair (2019).

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (GÃ E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear GÃ E interactions simultaneously (Ren et al. (2020) <doi:10.1002/sim.8434>). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

This package provides functions to visually and statistically analyze single system data.

This package provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; <doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979, <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>; Nestler, 2018, <doi:10.3102/1076998617741106>) and least squares estimation is supported (Bond & Malloy, 2018, <doi:10.1016/B978-0-12-811967-9.00014-X>).

Fast Multiplication and Marginalization of Sparse Tables <doi:10.18637/jss.v111.i02>.

This package provides a facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.

This package contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.

Calculates parameters of the seawater carbonate system and assists in design of ocean acidification perturbation experiments.

Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.

This package contains functions for math taught in school. A main focus is set to prime-calculation. The package also contains a dataset of all primes between 1 and 99,999,999.

Builds, evaluates and validates a nomogram with survey data and right-censored outcomes. As described in Capanu (2015) <doi:10.18637/jss.v064.c01>, the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots.

Sometimes it is handy to be able to view an image file on an R graphics device. This package just does that. Currently it supports PNG files.

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.

Penalized spatial median regression using LASSO. The iteratively reweighted least squares algorithm is used to solve the spatial median regression problem and weighted LASSO is employed.

This package provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.

This package provides a mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.