Estimates a covariance matrix using Stein's isotonized covariance estimator, or a related estimator suggested by Haff.

This package provides functions for computing test subscores using different methods in both classical test theory (CTT) and item response theory (IRT). This package enables three types of subscoring methods within the framework of CTT and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) <doi:10.4324/9781410604729>, (2) Haberman's subscoring methods (Haberman, 2008) <doi:10.3102/1076998607302636>, and (3) Yen's objective performance index (OPI; Yen, 1987) <https://www.ets.org/research/policy_research_reports/publications/paper/1987/hrap>. It also includes functions to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's methods which are used to examine whether test subscores are of added value. In addition, the package includes a function to assess the local independence assumption of IRT with Yen's Q3 statistic (Yen, 1984 <doi:10.1177/014662168400800201>; Yen, 1993 <doi:10.1111/j.1745-3984.1993.tb00423.x>).

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

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.

Implementation of the SAM prior and generation of its operating characteristics for dynamically borrowing information from historical data. For details, please refer to Yang et al. (2023) <doi:10.1111/biom.13927>.

This package provides wrappers for common activity patterns in simmer trajectories.

This package provides a simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. SeuratExplorer requires a processed Seurat object, which is saved as rds or qs2 file.

This package provides a simple method to display and characterise the multidimensional ecological niche of a species. The method also estimates the optimums and amplitudes along each niche dimension. Give also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022) <doi:10.1002/ece3.8830> for further details.

This package provides a collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <doi:10.1016/j.spasta.2018.07.005>: simulation of spatial ARCH-type processes (spARCH, log/exponential-spARCH, complex-spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.

This package implements SelectBoost'-style variable selection workflows for functional data analysis. The package provides FDA-native design and preprocessing objects for raw curves, spline-basis expansions, Functional principal component analysis scores, and scalar covariates; grouped stability-selection routines based on repeated subject-level subsampling; multiple selector backends including lasso, group lasso, and sparse-group lasso; FDA-aware grouping functions and calibration helpers for SelectBoost'; method-comparison utilities; a formula interface; simulation, benchmarking, and validation helpers with mapped ground truth; targeted sensitivity-study utilities and shipped benchmark summaries for mean F1 comparisons between FDA-aware and plain SelectBoost workflows; small example datasets; and an optional adapter to the native stability-selection interface from the FDboost package.

This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the ouch methodology of Butler and King (2004) <doi:10.1086/426002>. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.

Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.

This package provides functions to format and summarise already computed outputs from commonly used statistical and psychometric functions into compact, single-row tables and simple graphs, with utilities to export results to CSV, Word, and Excel formats. The package does not implement new statistical methods or estimation procedures; instead, it organises and presents results obtained from existing functions such as psych::describe(), psych::alpha(), stats::t.test(), and gtsummary::tbl_summary() to streamline reporting workflows in clinical and psychological research.

Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes: Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) <doi:10.1177/0022243720952410> "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.

This package implements self-organising maps combined with hierarchical cluster analysis (SOM-HCA) for clustering and visualization of high-dimensional data. The package includes functions to estimate the optimal map size based on various quality measures and to generate a model using the selected dimensions. It also performs hierarchical clustering on the map nodes to group similar units. Documentation about the SOM-HCA method is provided in Pastorelli et al. (2024) <doi:10.1002/xrs.3388>.

This package provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>. However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs.

Send syslog protocol messages to a remote syslog server specified by host name and TCP network port.

This package provides a reusable shiny module for viewing tabular data with a searchable reactable table and a variable summary sidebar built with bslib'.

This package provides tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix. This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools. The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone. The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.

This package performs repeated nested cross-validation for Cox Proportionate Hazards, Cox Lasso, Survival Random Forest, and their ensemble. Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" <doi:10.1007/978-3-031-08337-2_15>, where the method is described as Ensemble 1.

Manages and display stellar tracks and isochrones from Pisa low-mass database. Includes tools for isochrones construction and tracks interpolation.

Utility functions that help with common base-R problems relating to lists. Lists in base-R are very flexible. This package provides functions to quickly and easily characterize types of lists. That is, to identify if all elements in a list are null, data.frames, lists, or fully named lists. Other functionality is provided for the handling of lists, such as the easy splitting of lists into equally sized groups, and the unnesting of data.frames within fully named lists.

Universal and robust algorithm for solving the total alkalinity-pH equation presented in G. Munhoven (2013) <doi:10.5194/gmd-6-1367-2013> and G. Munhoven (2021) <doi:10.5194/gmd-2020-447>. The total alkalinity-pH equation relates total alkalinity and pH for a given set of acid-base concentrations in a given water sample, among which carbonic acid. This package is particularly useful in marine chemistry involving dissolved inorganic carbon. Original package in Fortran can be found at <doi:10.5281/zenodo.4328965>.

Create mocked bindings to Shiny update functions within test function calls to automatically update input values. The mocked bindings simulate the communication between the server and UI components of a Shiny module in testServer().