Perform statistical writership analysis of scanned handwritten documents with a shiny app for handwriter'.

Fitting hidden Markov models of learning under the cognitive diagnosis framework. The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparameterized unified learning model, and the joint learning model for responses and response times.

We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). <doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008) <doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008) <doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X. (2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.

Simulate and analyze hierarchical composite endpoints with univariate distributions by Gasparyan, Koch, Brunner in (2025) in â The Univariate Distribution of Hierarchical Composite Endpoints and the Condorcet Non-transitivity Paradox.â (Biometrical Journal 68 (3), <doi:10.1002/bimj.70140>). Includes implementation for the kidney hierarchical composite endpoint as defined in Heerspink HL et al (2023) â Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progressionâ (Journal of the American Society of Nephrology 34 (2): 2025â 2038, <doi:10.1681/ASN.0000000000000243>). Win odds, also called Wilcoxon-Mann-Whitney or success odds, is the main analysis method, but other win statistics (win probability, win ratio, net benefit) are also implemented in the univariate case. The win probability analysis is based on the Brunner-Munzel test and uses the DeLong-DeLong-Clarke-Pearson variance estimator, as described by Brunner and Konietschke (2025) in â An unbiased rank-based estimator of the Mannâ Whitney variance including the case of tiesâ (Statistical Papers 66 (1): 20, <doi:10.1007/s00362-024-01635-0>). Includes implementation of a new Wilson-type, compatible confidence interval for the win odds, as proposed by Schüürhuis, Konietschke, Brunner (2025) in â A new approach to the nonparametric Behrensâ Fisher problem with compatible confidence intervals.â (Biometrical Journal 67 (6), <doi:10.1002/bimj.70096>). Stratification and covariate adjustment are performed based on the methodology presented by Koch GG et al. in â Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing themâ (Statistics in Medicine 17 (15-16): 1863â 92). For a review, see Gasparyan SB et al (2021) â Adjusted win ratio with stratification: Calculation methods and interpretationâ (Statistical Methods in Medical Research 30 (2): 580â 611, <doi:10.1177/0962280220942558>).

Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets).

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

Computes specialist biomarker indices and risk scores for metabolic, cardiovascular, renal, hepatic, inflammatory, frailty, and psychiatric health assessment. Includes fasting and OGTT insulin sensitivity/resistance indices, ASCVD/QRISK3/KFRE risk equations, liver and kidney markers, frailty and comorbidity indices, biofluid marker panels, and utilities for column mapping, normalization, imputation, and combined marker dispatch.

Wrapper for Stan that offers a number of in-built models to implement a hierarchical Bayesian longitudinal model for repeat observation data. Model choice selects the differential equation that is fit to the observations. Single and multi-individual models are available. O'Brien et al. (2024) <doi:10.1111/2041-210X.14463>.

This package implements a structured, reproducible framework for budget impact modelling (BIM) in health technology assessment (HTA), following the ISPOR Task Force guidelines (Sullivan et al. (2014) <doi:10.1016/j.jval.2013.08.2291> and Mauskopf et al. (2007) <doi:10.1111/j.1524-4733.2007.00187.x>). Provides functions for epidemiology-driven population estimation, market share modelling with flexible uptake dynamics, per-patient cost calculation across multiple cost categories, multi-year budget projections, payer perspective analysis, deterministic sensitivity analysis (DSA), and probabilistic sensitivity analysis (PSA) via Monte Carlo simulation. Produces submission-quality outputs including ISPOR-aligned summary tables, scenario comparison tables, per-patient cost breakdowns, tornado diagrams, PSA histograms, and text and HTML reports compatible with NICE, CADTH, and EU-HTA dossier formats. Ships with an interactive shiny dashboard built on bslib for point-and-click model building and exploration.

Using hybrid data, this package created a vividly colored hybrid heat map. The input is two files which are auto-selected. The first file has three columns, the first two for pairs of species, with the third column for the hybrid experiment code (an integer). The second file is a list of code and their descriptions in two columns. The output is a figure showing the hybrid heat map with a color legend.

R interface for H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).

Input multiple versions of a source document, and receive HTML code for a highlighted version of the source document indicating the frequency of occurrence of phrases in the different versions. This method is described in Chapter 3 of Rogers (2024) <https://digitalcommons.unl.edu/dissertations/AAI31240449/>.

In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.

This package provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel-coding method (only simple indel-coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapsing identical DNA sequences into haplotypes or inferring haplotypes using user-provided absolute pairwise character difference matrix. This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks.

This package contains ten datasets used in the chapters and exercises of Paul, Alice (2023) "Health Data Science in R" <https://alicepaul.github.io/health-data-science-using-r/>.

Converts among many citation formats, including BibTeX', Citeproc', Codemeta', RDF XML', RIS', Schema.org', and Citation File Format'. A low level R6 class is provided, as well as stand-alone functions for each citation format for both read and write.

Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) <doi:10.1093/jxb/50.334.655> and Bradford (2002) <https://www.jstor.org/stable/4046371>.

We provide the monthly number of HIV and antiretroviral therapy (ART) cases of male, female, children and transgender as well as for the whole of Pakistan reported at various treatment centers in Pakistan from January 2016 to December 2021. Related works include: a) Imran, M., Nasir, J. A., & Riaz, S. (2018). Regional pattern of HIV cases in Pakistan. Journal of Postgraduate Medical Institute, 32(1), 9-13. <https://jpmi.org.pk/index.php/jpmi/article/view/2108>.

Most common exact, asymptotic and resample based tests are provided for testing the homogeneity of variances of k normal distributions under normality. These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters. Bartlett, M. S. (1937) <doi:10.1098/rspa.1937.0109> Bhandary, M., & Dai, H. (2008) <doi:10.1080/03610910802431011> Brown, M. B., & Forsythe, A. B. (1974).<doi:10.1080/01621459.1974.10482955> Chang, C. H., Pal, N., & Lin, J. J. (2017) <doi:10.1080/03610918.2016.1202277> Gokpinar E. & Gokpinar F. (2017) <doi:10.1080/03610918.2014.955110> Liu, X., & Xu, X. (2010) <doi:10.1016/j.spl.2010.05.017> Levene, H. (1960) <https://cir.nii.ac.jp/crid/1573950400526848896> Gökpınar, E. (2020) <doi:10.1080/03610918.2020.1800037>.

This package performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.

Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) <doi:10.32614/RJ-2022-055>.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as GoMiner (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I now present an enhancement of the existing two-dimensional version of High-Throughput GoMiner ('HTGM2D'), which is called HTGM4D'. The original HTGM2D is augmented by adding two instances of the original GoMiner genes versus categories heatmaps, aligned with the categories axes of the HTGM2D heatmap.

This package provides utilities for runtime hotpatching of locked R package namespaces. The package enables dynamic injection of function patches into sealed package environments without rebuilding or redeploying the package. This is particularly useful for legacy containerized workflows where package versions are frozen in place. The core functionality includes inject_patch() to inject patches into package namespaces, undo_patch() to restore original functions, apply_hotfix_file() to apply patches from external R scripts, and test_patched_dir() to run test suites against patched packages. The package implements namespace surgery techniques that allow internal callers to automatically see patched functions.

The half-weight index gregariousness (HWIG) is an association index used in social network analyses. It extends the half-weight association index (HWI), correcting for level of gregariousness in individuals. It is calculated using group by individual data according to methods described in Godde et al. (2013) <doi:10.1016/j.anbehav.2012.12.010>.