Quick and easy access to datasets that let you replicate the empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and Applications" (ISBN: 9780521848053).The data are available as soon as you install and load the package (lazy-loading) as data frames. The documentation includes reference to chapter sections and page numbers where the datasets are used.

For ordinal rating data, estimate and test models within the family of CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions); Simulation routines, plotting facilities and fitting measures are also provided.

Extra family objects in "weird" scenarios, particularly logistic or log-linear model with unbounded or non-binary/non-integer outcomes. Provides binomial_extra() and poisson_extra() as generalizations of binomial() and poisson(). The use of canonical link with the corresponding working likelihood in glm() ensures convexity, making model fitting reliable and independent of starting value. Robert WM Wedderburn (1974) <doi:10.1093/biomet/61.3.439> and Peter McCullagh (1983) <doi:10.1214/aos/1176346056> justified this method to fit generalized linear (mean) models with quasi-/working likelihood.

This package provides functions to compute and plot Coverage Probability Excursion (CoPE) sets for real valued functions on a 2-dimensional domain. CoPE sets are obtained from repeated noisy observations of the function on the entire domain. They are designed to bound the excursion set of the target function at a given level from above and below with a predefined probability. The target function can be a parameter in spatially-indexed linear regression. Support by NIH grant R01 CA157528 is gratefully acknowledged.

Core functions for simulating quantities of interest from generalised linear models (GLM). This package will form the backbone of a series of other packages that improve the interpretation of GLM estimates.

Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026> and Laborde et al. (2023) <doi: 10.1186/s12859-023-05210-6>. Such methods can inform whether clustering algorithms are appropriate for a data set.

Evaluates stimuli using Large Language Models. Supports multiple LLM providers: OpenAI', Anthropic', Ollama', LM Studio', DeepSeek', Groq', Mistral', and OpenAI-compatible endpoints. Stimuli: plain text, local image/audio files, or image URLs. Audio is transcribed via OpenAI Whisper before rating. Supports numeric, text, and raw return types.

Generate a candidate code list for the Observational Medical Outcomes Partnership (OMOP) common data model based on string matching. For a given search strategy, a candidate code list will be returned.

This package implements the three-step workflow for robust analysis of change in two repeated measurements of continuous outcomes, described in Ning et al. (in press), "Robust estimation of the effect of an exposure on the change in a continuous outcome", BMC Medical Research Methodology.

This package provides functions for performing comparative cohort studies in an observational database in the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Can extract all necessary data from a database. This implements large-scale propensity scores (LSPS) as described in Tian et al. (2018) <doi:10.1093/ije/dyy120>, using a large set of covariates, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc. Large scale regularized regression is used to fit the propensity and outcome models as described in Suchard et al. (2013) <doi:10.1145/2414416.2414791>. Functions are included for trimming, stratifying, (variable and fixed ratio) matching and weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming. Supported outcome models are (conditional) logistic regression, (conditional) Poisson regression, and (stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for the stratification or matching.

This package provides functions for the input/output and visualization of medical imaging data in the form of CIFTI files <https://www.nitrc.org/projects/cifti/>.

Estimation of population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990).

Encrypts and decrypts using basic ciphers. None of these should be used in place of real encryption using state of the art tools. The ciphers included use methods described in the ciphers's Wikipedia and cryptography hobby websites.

Simple, fast, and automatic encodings for category data using a data.table backend. Most of the methods are an implementation of "Sufficient Representation for Categorical Variables" by Johannemann, Hadad, Athey, Wager (2019) <arXiv:1908.09874>, particularly their mean, sparse principal component analysis, low rank representation, and multinomial logit encodings.

This package provides functions to test and compare causal models using Confirmatory Path Analysis.

Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ) that make up the 2001 definition of the Greater Glasgow and Clyde health board.

Provide standard tables, listings, and graphs (TLGs) libraries used in clinical trials. This package implements a structure to reformat the data with dunlin', create reporting tables using rtables and tern with standardized input arguments to enable quick generation of standard outputs. In addition, it also provides comprehensive data checks and script generation functionality.

Assesses niche climate exposure by interpreting projected climate change relative to the climate conditions a species currently occupies. Using occurrence records, range data or thresholded SDM suitability maps, current environmental rasters and future projections, the package separates climate change amount, change in distance to the current niche centre, composition change and exceedance beyond an empirical niche boundary.

An algorithm of optimal subset selection, related to Covariance matrices, observation matrices and Response vectors (COR) to select the optimal subsets in distributed estimation. The philosophy of the package is described in Guo G. (2024) <doi:10.1007/s11222-024-10471-z>.

Estimates a lasso penalized precision matrix via blockwise coordinate descent (BCD). This package is a simple wrapper around the popular glasso package and extends and enhances its capabilities. These enhancements include built-in cross validation and visualizations. See Friedman et al. (2008) <doi:10.1093/biostatistics/kxm045> for details regarding the estimation method.

Calculation of sex- and age-standardized growth metrics based on the 2000 CDC growth charts. Provides functions to generate z-scores and percentiles for weight, height, and body mass index (BMI) using the LMS method (lambda-mu-sigma). Includes extended BMI z-scores for values above the 95th percentile to more accurately characterize the sex- and age-standardized BMI of children with very high BMIs.

Compute the standard suite of climate indices from daily weather observations. Provides the canonical ETCCDI 27 (Expert Team on Climate Change Detection and Indices), the ET-SCI heatwave and cold-wave families plus the Excess Heat Factor of Nairn and Fawcett (2013), and agroclimatic, drought, and human-comfort families. Drought indices ('SPI', SPEI') accept a choice of distribution (gamma or Pearson III for SPI; log-logistic or generalised extreme value for SPEI). Reference evapotranspiration is available via Hargreaves and the FAO-56 Penman-Monteith method (Allen et al. 1998). Percentile-based indices support the Zhang (2005) in-base bootstrap. Daily inputs are numeric vectors plus a Date vector; outputs are tidy data frames. Optional gridded support via terra applies any index over a SpatRaster and reads netCDF input. No external API calls; pairs with data packages such as readnoaa'. References: Alexander et al. (2006) <doi:10.1029/2005JD006290>; Zhang et al. (2011) <doi:10.1002/wcc.147>; Zhang et al. (2005) <doi:10.1175/JCLI3366.1>.

Generates continuous test norms in psychometrics and biometrics, and analyzing model fit. The package offers both distribution-free modeling using Taylor polynomials and parametric modeling using the beta-binomial and the Sinh-Arcsinh distribution. Originally developed for achievement tests, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package provides several advantages: It minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and as well supports conventional norming. It generates norm tables including confidence intervals. Methods for addressing representativeness issues are available through Iterative Proportional Fitting. Based on Lenhard et al. (2016) <doi:10.1177/1073191116656437>, Lenhard et al. (2019) <doi:10.1371/journal.pone.0222279>, Lenhard and Lenhard (2021) <doi:10.1177/0013164420928457> and Gary et al. (2023) <doi:10.1007/s00181-023-02456-0>.

This package implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.