This package provides a set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.

This package provides fundamental function support for SigBridgeR and its single-cell phenotypic screening algorithm, including optional functions.

S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) <doi:10.1103/PhysRevE.88.062819>. The high speed and the low memory usage make it suitable for large graphs.

R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

This package provides functions for automated noncompartmental pharmacokinetic (NCA) analysis using concentration-time data. The package estimates pharmacokinetic parameters including area under the concentration-time curve (AUC), area under the first moment curve (AUMC), maximum concentration (Cmax), time to maximum concentration (Tmax), terminal elimination rate constant (Kel), elimination half-life, clearance, volume of distribution, and mean residence time (MRT). It supports automatic terminal phase selection, bootstrap confidence intervals, and publication-ready concentration-time profiles. Methods are based on Gibaldi and Perrier (1982, ISBN:9780824710422).

Miscellaneous functions for working with stars objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.

Sequential triangular test for the arithmetic mean in one- and two- samples, proportions in one- and two-samples, and the Pearson's correlation coefficient.

The development of post-processing functionality for simulated snow profiles by the snow and avalanche community is often done in python'. This package aims to make some of these tools accessible to R users. Currently integrated modules contain functions to calculate dry snow layer instabilities in support of avalache hazard assessments following the publications of Richter, Schweizer, Rotach, and Van Herwijnen (2019) <doi:10.5194/tc-13-3353-2019>, and Mayer, Van Herwijnen, Techel, and Schweizer (2022) <doi:10.5194/tc-2022-34>.

Uses simulations from generalized linear mixed-effects models to incorporate random effects across multiple sources and levels of variation, and a dispersion parameter to account for overdispersion and capture unexplained variability. Covers design scenarios for both short-term and long-term trials evaluating the impact of single or combined vector control interventions. Methods build on Kipingu et al. (2025) <doi:10.1186/s12936-025-05454-y> and Johnson et al. (2015) <doi:10.1111/2041-210X.12306>.

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.

Simulates correlated multinomial responses conditional on a marginal model specification.

Simulates sea-level rise and fall from raster-based elevation models through functions for relative vertical datum transformation of Digital Terrain Models (DTMs) and integration with bathymetric data to produce continuous terrestrialâ marine Digital Elevation Models (DEMs). Supports coastal exposure modeling, paleogeographic reconstruction, submerged landscape analysis, and climate change impact assessment. Optional gap-filling and threshold-based terrain filtering facilitate reconstruction of incomplete elevation surfaces and scenario-based landscape simulation. Applications span coastal engineering, geomorphology, archaeology, environmental modeling, and geospatial analysis.

This package provides tools for small area estimation using a projection estimator with a linear multilevel working model. The main function fits a multilevel model to a smaller survey containing the response variable and auxiliary predictors. The fitted model is used to predict outcomes in a larger projection survey, and domain-level estimates are computed by combining synthetic predictions with a design-based residual correction. For methodological references, see Kim and Rao (2012) <doi:10.1093/biomet/asr063>, Food and Agriculture Organization of the United Nations (2021) <doi:10.4060/cb3253en>, and Moura and Holt (1999) <https://www150.statcan.gc.ca/n1/pub/12-001-x/1999001/article/4714-eng.pdf>.

Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a fixed-point algorithm and an interface to the CLUTO vcluster program.

This package provides a Shiny application designed to support the learning of basic concepts in statistics and probability. The tool provides an interactive interface that allows students to explore and visualize different statistical concepts intuitively, including descriptive statistics for continuous and qualitative variables, and probability distributions.

This package provides tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) <doi:10.5198/jtlu.2016.862>, but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) <doi:10.1016/j.jtrangeo.2017.08.012> and routing with locally hosted routing engines such as OSRM (Lowans et al. 2023) <doi:10.1016/j.enconman.2023.117337>. The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the od package); calculating routes on the transport network locally and via interfaces to routing services such as <https://cyclestreets.net/> (Desjardins et al. 2021) <doi:10.1007/s11116-021-10197-1>; and calculating route segment attributes such as bearing. The package implements the travel flow aggregration method described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779> and the OD jittering method described in Lovelace et al. (2022) <doi:10.32866/001c.33873>. Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>, and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) <doi:10.1007/s10109-020-00342-2>.

An updated and extended version of spm package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/study-design-power-calculation/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.

In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.

There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals.

This package provides a complete analysis pipeline for the WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) as described in Riley et al. (2016) <doi:10.2105/AJPH.2015.302962>. Imports raw survey data ('CSV', Excel', Stata', SPSS'), applies WHO-standard cleaning and recoding, sets up complex survey designs, computes all standard NCD indicators (tobacco, alcohol, diet, physical activity, anthropometry, blood pressure, biochemical), and generates publication-ready tables, visualisations, and Word'/'HTML reports (fact sheet, data book, country report).

This package provides a set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.

This package provides a tidy toolkit for distribution dynamics: analysing how a cross-sectional distribution of values evolves over time and where it settles in the long run. Provides discrete-time, spatial, rank and local indicator of spatial association ('LISA') Markov transition estimation, ergodic analysis (steady-state, mean first passage and sojourn times), rank-mobility measures (Kendall's tau and the Theta statistic) and Markov mobility indices. Methods use long-format id'/'time'/'value data rather than transition matrices and build on the distribution-dynamics literature (Quah (1993); Rey (2001) <doi:10.1111/j.1538-4632.2001.tb00444.x>). Results are validated for numerical parity against the reference giddy library.

An advanced version of package s2dverification'. Intended for seasonal to decadal (s2d) climate forecast verification, but also applicable to other types of forecasts or general climate analysis. This package is specifically designed for comparing experimental and observational datasets. It provides functionality for data retrieval, post-processing, skill score computation against observations, and visualization. Compared to s2dverification', s2dv is more compatible with the package startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The Climate Data Operators (CDO) version used in development is 1.9.8. Implements methods described in Wilks (2011) <doi:10.1016/B978-0-12-385022-5.00008-7>, DelSole and Tippett (2016) <doi:10.1175/MWR-D-15-0218.1>, Kharin et al. (2012) <doi:10.1029/2012GL052647>, Doblas-Reyes et al. (2003) <doi:10.1007/s00382-003-0350-4>.