This package provides statistical diagnostics to evaluate whether proxy indicators reliably represent an unobservable target construct. The main function senser() assesses proxies across multiple dimensions including monotonicity, information content, stability, distributional alignment, and potential bias risk. It prints a concise, interpretable summary suitable for analytical and policy-oriented assessment, without claiming causal inference.

The function SurvRegCens() of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) <doi:10.48550/arXiv.1402.0432>.

This package provides functions for self-determination motivation theory (SDT) to compute measures of motivation internalization, motivation simplex structure, and of the original and adjusted self-determination or relative autonomy index. SDT was introduced by Deci and Ryan (1985) <doi:10.1007/978-1-4899-2271-7>. See package?SDT for an overview.

Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.

Facilitates extraction of geospatial data from the Office for National Statistics Open Geography and nomis Application Programming Interfaces (APIs). Simplifies process of querying nomis datasets <https://www.nomisweb.co.uk/> and extracting desired datasets in dataframe format. Extracts area shapefiles at chosen resolution from Office for National Statistics Open Geography <https://geoportal.statistics.gov.uk/>.

Constructs cell-typeâ specific gene regulatory networks from single-cell RNA-sequencing data. The method implements the SCORPION algorithm, which first aggregates individual cells into super-cells and then applies PANDA (Passing Attributes between Networks for Data Assimilation) to infer transcription factorâ target regulatory relationships. It also provides statistical methods for differential edge analysis.

Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at <https://sampling.warwick.ac.uk>.

Interface to Sudachi <https://github.com/WorksApplications/Sudachi>, a Japanese morphological analyzer. This is a port of what is available in Python.

This package provides a suite of functions that allow a full, fast, and efficient Bayesian treatment of the Bradley--Terry model. Prior assumptions about the model parameters can be encoded through a multivariate normal prior distribution. Inference is performed using a latent variable representation of the model.

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.

Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.

This package implements a method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details. For this purpose B-splines are used, and the density is obtained by penalized least squares based on a Brier entropy penalty. The package provides methods for fitting the distribution as well as methods for evaluating the underlying density and cdf. In addition methods for plotting the distribution, drawing random numbers and calculating quantiles of the obtained distribution are provided.

This package provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM.

This package provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.

Imbibition causes seeds to expand, which results in the seed coat or testa being broken. Seed germination begins with imbibition. Imbibition aids in the transport of water into the developing ovules. Imbibition is required during the first stages of root water absorption.

This package implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the vars package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).

Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project PvSTATEM', which is an international project aiming for malaria elimination.

It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high resolution. 2. It is efficient for time series data to reveal periodic patterns.

This package provides three types of datetime pickers for usage in a Shiny UI. A datetime picker is an input field for selecting both a date and a time.

This package provides a collection of classes and methods for working with times and dates. The code was originally available in S-PLUS'.

This package implements the Shimazaki-Shinomoto method for optimizing the bin width of a histogram. This method minimizes the mean integrated squared error (MISE) and features a C++ backend for high performance and shift-averaging to remove edge-position bias. Ideally suits for time-dependent rate estimation and identifying intrinsic data structures. Supports both 1D and 2D data distributions. For more details see Shimazaki and Shinomoto (2007) "A Method for Selecting the Bin Size of a Time Histogram" <doi:10.1162/neco.2007.19.6.1503>.

Implementations of classical and machine learning models for survival analysis, including deep neural networks via keras and tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from Python via reticulate <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using Rcpp <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the Python package pycox <https://github.com/havakv/pycox>.

This package provides functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See <http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more information.

This package provides a comprehensive toolkit for extracting latent signals from panel data through multivariate time series analysis. Implements spectral decomposition methods including wavelet multiresolution analysis via maximal overlap discrete wavelet transform, Percival and Walden (2000) <doi:10.1017/CBO9780511841040>, empirical mode decomposition for non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features Bayesian variable selection through regularized Horseshoe priors, Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, for identifying structurally relevant predictors from high-dimensional candidate sets. Includes dynamic factor model estimation, principal component analysis with bootstrap significance testing, and automated technical interpretation of signal morphology and variance topology.