Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.

Variable selection for generalized linear models and the Cox proportional-hazards model in ultrahigh dimensions via the iterated block Gibbs sampler (IBGS). The sampler is implemented in C with parallel block screening through OpenMP', and supports the gaussian, binomial and poisson families (fitted by least squares or iteratively reweighted least squares) as well as the Cox model for survival analysis (fitted by its Efron partial likelihood), together with the AIC, BIC, AICc and extended BIC model selection criteria.

The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.

This package provides functions to download, clean, and structure open data from the Brazilian Federal Revenue (Receita Federal do Brasil - RFB) regarding Personal Income Tax (IRPF) statements. Includes a data dictionary and categorized metadata for several sections such as assets, rights, debts, and income brackets. More information about the data source can be found at <https://dados.gov.br/dados/conjuntos-dados/grandes-nmeros-do-imposto-de-renda-da-pessoa-fsica>.

Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.

This package provides functions to compute a continuum of information-based measures for quantifying the temporal stability of populations, communities, and ecosystems, as well as their associated synchrony, based on species (or species assemblage) biomass, or other key variables. When biodiversity data are available, the package also enables the assessment of the corresponding diversityâ stability and diversityâ synchrony relationships. All measures are applicable in both temporal and spatial contexts. The theoretical and methodological background is detailed in Chao et al. (2025) <doi:10.1101/2025.08.20.671203>.

Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics â A novel and practical method for estimating the â intrinsicâ value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>.

The marginal treatment effect was introduced by Heckman and Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) <doi:10.2307/2951620>. This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs Rcplex and cplexAPI are available from CRAN. MOSEK and the MOSEK R API can be obtained from <https://www.mosek.com/>. The lp_solve library is freely available from <http://lpsolve.sourceforge.net/5.5/>, and is included when installing its API lpSolveAPI', which is available from CRAN.

This package provides bibliographic information and term-frequency text analysis tools for publications of the U.S. Geological Survey (USGS) Idaho National Laboratory (INL) Project Office. Includes datasets of publications, authors, and term frequencies, along with functions to search terms, build word clouds, and extract text and cover images from publication documents.

This package provides a set of tools to i) identify geographic areas with significant change over time in drug utilization, and ii) characterize common change over time patterns among the time series for multiple geographic areas. For reference, see below: 1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) <doi:10.1504/IJCBDD.2018.10011910> 2. Wu, S., Wu, H. (2013) <doi:10.1186/1471-2105-14-6> 3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) <doi:10.1016/j.idm.2016.07.001>.

Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package IDF provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].

Identity by Descent (IBD) distributions in pedigrees. A Hidden Markov Model is used to compute identity coefficients, simulate IBD segments and to derive the distribution of total IBD sharing and segment count across chromosomes. The methods are applied in Kruijver (2025) <doi:10.3390/genes16050492>. The probability that the total IBD sharing is zero can be computed using the method of Donnelly (1983) <doi:10.1016/0040-5809(83)90004-7>.

Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms.

Run quality checks on data sets using the same checks that are conducted on the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk>.

This package provides a systematic framework for integrating multiple modalities of assays profiled on the same set of samples. The goal is to identify genes that are altered in cancer either marginally or consistently across different assays. The heterogeneity among different platforms and different samples are automatically adjusted so that the overall alteration magnitude can be accurately inferred. See Tong and Coombes (2012) <doi:10.1093/bioinformatics/bts561>.

Improved methods based on inverse probability weighting and outcome regression for causal inference and missing data problems.

Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

Distributional regression under stochastic order restrictions for numeric and binary response variables and partially ordered covariates, including right-censored responses via Survival-IDR. See Henzi, Ziegel, Gneiting (2021) <doi:10.1111/rssb.12450> and Bladt, Henzi, van den Heuvel, Ziegel (2026) <doi:10.48550/arXiv.2608.02914>.

This package provides a Bayesian framework that integrates several regression models to identify a parsimonious set of biomarkers shared across disparate data platforms (for example genomic, transcriptomic and proteomic assays). Subjects are partitioned into subgroups defined by their pattern of platform availability, so that no subject with partially missing platform data is excluded, and information is borrowed across subgroups through a Markov random field prior on the variable-selection indicators together with non-local (product moment) priors on the regression effects. The methodology was introduced for time-to-event outcomes by Chekouo, Stingo, Doecke and Do (2017) <doi:10.1111/biom.12587>; this package additionally supports continuous (Gaussian) and binary (probit) outcomes. Posterior inference is carried out by a Markov chain Monte Carlo sampler implemented in C for computational efficiency.

Helps with the thoughtful saving, reading, and management of result files (using rds files). The core functions take a list of parameters that are used to generate a unique hash to save results under. Then, the same parameter list can be used to read those results back in. This is helpful to avoid clunky file naming when running a large number of simulations. Additionally, helper functions are available for compiling a flat file of parameters of saved results, monitoring result usage, and cleaning up unwanted or unused results. For more information, visit the indexr homepage <https://lharris421.github.io/indexr/>.

Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.

You can access to open data published in Instituto Canario De Estadistica (ISTAC) APIs at <https://datos.canarias.es/api/estadisticas/>.

This package provides functions for the identification, classification, and ranking of influential nodes and candidate features from network and omics data. The package implements the Integrated Value of Influence (IVI) for integrative network centrality analysis, the SIR-based Influence Ranking (SIRIR) model for unsupervised influence ranking, and the Experimental data-based Integrative Ranking (ExIR) model for prioritizing candidate driver, biomarker, and mediator features from experimental omics data. Functions are provided for network reconstruction from adjacency matrices and data frames, topological analysis, centrality calculation, assessment of associations between centrality measures, and conditional probability analysis. ExIR supports bulk and single-cell omics data, including matrices, sparse matrices, data frames, tibbles, and Seurat objects.

Automatically detects Copy Number Variations (CNV) from Next Generation Sequencing data using a machine learning algorithm, Isolation forest. More details about the method can be found in the paper by Cabello-Aguilar (2022) <doi:10.1101/2022.01.03.474771>.