S4 class wrappers for the ODBC and Pool DBI connection, also provides some utilities to paste small datasets to clipboard, rename columns. It is used by the package stacomiR for connections to the database. Development versions of stacomiR are available in R-forge.

Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>. Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.

This package implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) <doi:10.2139/ssrn.3751162>, building on the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) <doi:10.1016/j.jfineco.2015.10.001>, to estimate the cumulative treatment effect of an event on treated firmsâ stock returns.

This package provides a small set of functions wrapping up the call stack and command line inspection needed to determine a running script's filename from within the script itself.

Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

Function for the computation of fractal dimension based on mass of soil particle size distribution by Tyler & Wheatcraft (1992) <doi:10.2136/sssaj1992.03615995005600020005x>. It also provides functions for calculation of mean weight and geometric mean diameter of particle size distribution by Perfect et al. (1992) <doi:10.2136/sssaj1992.03615995005600050012x>.

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

This package contains existing and novel methods for nonparametric analysis and Cox regression analysis with doubly truncated survival data, as described in Vazquez and Xie (2025) <doi:10.1007/s10985-025-09650-5>. Includes survival curves and hazard estimates through nonparametric maximum likelihood estimation, various tests for detecting group differences or non-ignorable sampling bias, and inverse probability weighted Cox regression with several options of nonparametric weights. Also implements diagnostics for key modeling assumptions such as quasi-independent truncation and the positivity assumption. Closed-form standard errors are available for all estimates, i.e. bootstrapping is not required.

Estimate necessary sample sizes for comparing the location of data from two groups or categories when the distribution of the data is skewed. The package offers a non-parametric method for a Wilcoxon Mann-Whitney test of location shift as well as methods for several generalized linear models, for instance, Gamma regression.

Fits linear difference-in-differences models in scenarios where intervention roll-outs are staggered over time. The package implements a version of an approach proposed by Sun and Abraham (2021) <doi:10.1016/j.jeconom.2020.09.006> to estimate cohort- and time-since-treatment specific difference-in-differences parameters, and it provides convenience functions both for specifying the model and for flexibly aggregating coefficients to answer a variety of research questions.

Spectral and Average Autocorrelation Zero Distance Density ('sazed') is a method for estimating the season length of a seasonal time series. sazed is aimed at practitioners, as it employs only domain-agnostic preprocessing and does not depend on parameter tuning or empirical constants. The computation of sazed relies on the efficient autocorrelation computation methods suggested by Thibauld Nion (2012, URL: <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by Bob Carpenter (2012, URL: <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).

This package provides tools for estimating, interpreting, and visualizing Spatial-X (SLX) regression models. Provides a formula-based interface with first-class support for variable-specific weights matrices, higher-order spatial lags, temporally-lagged spatial variables (TSLS), and tidy effects decomposition (direct, indirect, total). Designed to lower the barrier to SLX modeling for applied researchers who already work with sf and lm'-style formulas. Methods follow Wimpy, Whitten, and Williams (2021) <doi:10.1086/710089>.

This package provides tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package secr'.

Stock-and-flow models are a computational method from the field of system dynamics. They represent how systems change over time and are mathematically equivalent to ordinary differential equations. sdbuildR (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. sdbuildR simulates models in R and Julia', and supports computationally intensive ensemble simulations. Additionally, sdbuildR can import models created in Insight Maker (<https://insightmaker.com/>).

This is a graph database in SQLite'. It is inspired by Denis Papathanasiou's Python simple-graph project on GitHub'.

Handle POST requests on a custom path (e.g., /ingress) inside the same shiny HTTP server using user interface functions and HTTP responses. Expose latest payload as a reactive and provide helpers for query parameters.

This package provides a minimalist implementation of model stacking by Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness.

This package provides functions for obtaining p-values (for hypothesis tests), confidence intervals, and multivariate confidence sets. In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", <doi:10.48550/arXiv.2303.05328>.

We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. See Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23, <doi:10.1080/01621459.2013.870904>.

Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.

This package implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) <doi:10.3390/rs13122297>, and a hierarchical patch delineation algorithm PatchMorph (Girvetz & Greco, 2007) <doi:10.1007/s10980-007-9104-8>. Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) <doi:10.1016/j.isprsjprs.2015.08.007> with cylinder fitting methods from de Conto et al. (2017) <doi:10.1016/j.compag.2017.07.019>. PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on lidR (Roussel et al., 2020) <doi:10.1016/j.rse.2020.112061>.

Simple implementation of Semantic Versioning 2.0.0 ('SemVer') on the vctrs package. This package provides a simple way to create, compare, and manipulate semantic versions in R. It is designed to be lightweight and easy to use.

Reconstructs forest stand basal area and stem density at user-specified reference years using tree-level inventory data. The method estimates missing tree ages, back-calculates diameters using species-specific growth rates, and incorporates decay-class-based decomposition to infer mortality timing for dead trees. Results are returned in a tidy long format suitable for analysis and visualization.

Evaluating probabilistic forecasts via proper scoring rules. scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families. Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score. For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.