Computes the trimmed-k mean by removing the k smallest and k largest values from a numeric vector. Created for STAT 5400 at the University of Iowa.

Computes sparse vector autoregressive coefficients and sparse precision matrices for time series chain graphical models. Methods are described in Abegaz and Wit (2013) <doi:10.1093/biostatistics/kxt005>.

In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points. The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The samon package allows the user to perform a (parameterized) sensitivity analysis of this assumption. In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function).

Implementation of analytical models for estimating streamflow depletion due to groundwater pumping, and other related tools. Functions are broadly split into two groups: (1) analytical streamflow depletion models, which estimate streamflow depletion for a single stream reach resulting from groundwater pumping; and (2) depletion apportionment equations, which distribute estimated streamflow depletion among multiple stream reaches within a stream network. See Zipper et al. (2018) <doi:10.1029/2018WR022707> for more information on depletion apportionment equations and Zipper et al. (2019) <doi:10.1029/2018WR024403> for more information on analytical depletion functions, which combine analytical models and depletion apportionment equations.

This package provides a high-level, object-oriented interface for Secure File Transfer Protocol (SFTP) operations built upon the curl package. The package implements an R6 class to manage persistent connections and provides tidyverse'-style functions for common file system tasks. Key features include recursive directory creation with idempotency support, "smart" local path resolution that distinguishes between files and directories, and the ability to download remote resources directly into memory as raw vectors for seamless integration into data processing pipelines. It is designed to handle common SFTP edge cases gracefully, providing informative error messages and robust path sanitization to ensure compatibility across different server configurations.

Perform common dendrometry operations such as inventory preparing, and inventory data analysis.

This package provides tools for statistical matching based on latent class models. The package implements statistical matching procedures based on latent class models. It allows researchers to perform data integration when no unique identifiers are available by modeling the joint distribution of variables through latent categorical structures. The package supports estimation of latent class models, probabilistic matching between donor and recipient data sets, and generation of synthetic linked data under uncertainty. It is particularly useful in survey research and data fusion applications where combining information from multiple sources is required while preserving statistical properties and accounting for measurement error and missing data mechanisms.

This package provides a novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. The method is highly scalable to large-scale image datasets without extracting neighborhood information for every single cell. The package supports spatially resolved cell-level data analysis, topic inference, visualization, and downstream biological interpretation of tissue microenvironments.

Do multi-gene descent probabilities (Thompson, 1983, <doi:10.1098/rspb.1983.0072>) and special cases thereof (Thompson, 1986, <doi:10.1002/zoo.1430050210>) including inbreeding and kinship coefficients. But does much more: probabilities of any set of genes descending from any other set of genes.

Streamlines the creation of descriptive frequency tables ('Table 1'), diagnostic test accuracy evaluations (sensitivity, specificity, predictive values), and multi-outcome regression summaries. Features automatic tables, prevalence and odds ratio calculations, and seamless integration with flextable for exporting results to Microsoft Word and PowerPoint'.

This package implements the Segment Profile Extraction via Pattern Analysis method for row-mean-centered multivariate data. Core capabilities include SVD-based row-isometric biplot construction, bias-corrected and accelerated, and percentile bootstrap confidence intervals for domain coordinates and per-person direction cosines, Procrustes alignment of bootstrap replicates across planes, parallel analysis for dimensionality selection, and segment profile reconstruction in planes defined by pairs of singular dimensions. A synthetic Woodcock-Johnson IV look-alike dataset is provided for examples and testing. The method is described in Kim and Grochowalski (2019) <doi:10.1007/s00357-018-9277-7>.

Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.

Conducting Bayesian Optimal Interval (BOIN) design for phase I dose-finding trials. simFastBOIN provides functions for pre-computing decision tables, conducting trial simulations, and evaluating operating characteristics. The package uses vectorized operations and the Iso::pava() function for isotonic regression to achieve efficient performance while maintaining full compatibility with BOIN methodology. Version 1.3.2 adds p_saf and p_tox parameters for customizable safety and toxicity thresholds. Version 1.3.1 fixes Date field. Version 1.2.1 adds comprehensive roxygen2 documentation and enhanced print formatting with flexible table output options. Version 1.2.0 integrated C-based PAVA for isotonic regression. Version 1.1.0 introduced conservative MTD selection (boundMTD) and flexible early stopping rules (n_earlystop_rule). Methods are described in Liu and Yuan (2015) <doi:10.1111/rssc.12089>.

This package provides functions to parse and analyze logs generated by ShinyProxy containers. It extracts metadata from log file names, reads log contents, and computes summary statistics (such as the total number of lines and lines containing error messages), facilitating efficient monitoring and debugging of ShinyProxy deployments.

Singular spectrum analysis (SSA) decomposes a time series into interpretable components like trends, oscillations, and noise without strict distributional and structural assumptions. For method details see Golyandina N, Zhigljavsky A (2013). <doi:10.1007/978-3-642-34913-3>.

Uses logistic regression to model the probability of detection as a function of covariates. This model is then used with observational survey data to estimate population size, while accounting for uncertain detection. See Steinhorst and Samuel (1989).

Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The spatPomp package extends pomp to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.

Characterize daily stream discharge and water quality data and subsample water quality data. Provide dates, discharge, and water quality measurements and streamsampler can find gaps, get summary statistics, and subsample according to common stream sampling protocols. Stream sampling protocols are described in Lee et al. (2016) <doi:10.1016/j.jhydrol.2016.08.059> and Lee et al. (2019) <doi:10.3133/sir20195084>.

For surface energy models and estimation of solar positions and components with varying topography, time and locations. The functions calculate solar top-of-atmosphere, open, diffuse and direct components, atmospheric transmittance and diffuse factors, day length, sunrise and sunset, solar azimuth, zenith, altitude, incidence, and hour angles, earth declination angle, equation of time, and solar constant. Details about the methods and equations are explained in Seyednasrollah, Bijan, Mukesh Kumar, and Timothy E. Link. On the role of vegetation density on net snow cover radiation at the forest floor. Journal of Geophysical Research: Atmospheres 118.15 (2013): 8359-8374, <doi:10.1002/jgrd.50575>.

Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" <doi:10.48550/arXiv.2008.05693>.

This package provides tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.

Algorithms of nonparametric sequential test and online change-point detection for streams of univariate (sub-)Gaussian, binary, and bounded random variables, introduced in following publications - Shin et al. (2024) <doi:10.48550/arXiv.2203.03532>, Shin et al. (2021) <doi:10.48550/arXiv.2010.08082>.

This package implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included.

Wrapper for the non-validating SQL parser Python module sqlparse <https://github.com/andialbrecht/sqlparse>. It allows parsing, splitting, and formatting SQL statements.