Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units. Maximum, minimum, or an allowable range of differences between units on one variable can be set. Randomly assign units to treatment conditions. Diagnose potential interference between units assigned to different treatment conditions. Write outputs to .tex and .csv files. For more information on the methods implemented, see Moore (2012) <doi:10.1093/pan/mps025>.

The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.

Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity. Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.

This package provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) <doi:10.1007/s11336-012-9301-5>. Zhang (2014) <doi:10.3758/s13428-013-0424-0>.

Download and read US Census Bureau data relationship files. Provides support for cleaning and using block assignment files since 2010, as described in <https://www.census.gov/geographies/reference-files/time-series/geo/block-assignment-files.html>. Also includes support for working with block equivalency files, used for years outside of decennial census years.

Single linkage clustering and connected component analyses are often performed on biological images. Bioi provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions.

This package provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006) <doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al. (2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso (Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others. A function generating simulated survival data set is also provided.

Calculation of physical (e.g. aerodynamic conductance, surface temperature), and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. Calculations assume the land surface to behave like a big-leaf and return bulk ecosystem/canopy variables.

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return. CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577). Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132). Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143). Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211).

Compute bounds for the treatment effect after adjusting for the presence of omitted variables in linear econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. You supply the data, identify the outcome and treatment variables and additional regressors. The main functions will compute bounds for the bias-adjusted treatment effect. Many plot functions allow easy visualization of results.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event. A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) <doi:10.1038/clpt.2010.233>). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages. This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.

This package provides methods for estimation and hypothesis testing of proportions in group testing designs: methods for estimating a proportion in a single population (assuming sensitivity and specificity equal to 1 in designs with equal group sizes), as well as hypothesis tests and functions for experimental design for this situation. For estimating one proportion or the difference of proportions, a number of confidence interval methods are included, which can deal with various different pool sizes. Further, regression methods are implemented for simple pooling and matrix pooling designs. Methods for identification of positive items in group testing designs: Optimal testing configurations can be found for hierarchical and array-based algorithms. Operating characteristics can be calculated for testing configurations across a wide variety of situations.

Usually, it is difficult to plot choropleth maps for Bangladesh in R'. The bangladesh package provides ready-to-use shapefiles for different administrative regions of Bangladesh (e.g., Division, District, Upazila, and Union). This package helps users to draw thematic maps of administrative regions of Bangladesh easily as it comes with the sf objects for the boundaries. It also provides functions allowing users to efficiently get specific area maps and center coordinates for regions. Users can also search for a specific area and calculate the centroids of those areas.

Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported for semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Iachine (1995) with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach.

This package provides a Bayesian smoothing method for post-processing of remote sensing image classification which refines the labelling in a classified image in order to enhance its classification accuracy. Combines pixel-based classification methods with a spatial post-processing method to remove outliers and misclassified pixels.

Analyse single case analyses against a control group. Its purpose is to provide a flexible, with good power and low first type error approach that can manage at the same time controls and patient's data. The use of Bayesian statistics allows to test both the alternative and null hypothesis. Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq> Scandola, M., & Romano, D. (2021). <doi:10.1016/j.neuropsychologia.2021.107834>.

Simultaneously clusters the Periodontal diseases (PD) patients and their tooth sites after taking the patient- and site-level covariates into consideration. BAREB uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Essentially, BAREB is a cluster-wise linear model based on Yuliang (2020) <doi:10.1002/sim.8536>.

Maximum likelihood estimation, random values generation, density computation and other functions for the bivariate Poisson distribution. References include: Kawamura K. (1984). "Direct calculation of maximum likelihood estimator for the bivariate Poisson distribution". Kodai Mathematical Journal, 7(2): 211--221. <doi:10.2996/kmj/1138036908>. Kocherlakota S. and Kocherlakota K. (1992). "Bivariate discrete distributions". CRC Press. <doi:10.1201/9781315138480>. Karlis D. and Ntzoufras I. (2003). "Analysis of sports data by using bivariate Poisson models". Journal of the Royal Statistical Society: Series D (The Statistician), 52(3): 381--393. <doi:10.1111/1467-9884.00366>.

This package provides a comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.

Calculate robust measures of effect sizes using the bootstrap.

This package implements functions for multi-class discriminant analysis using binary predictors, for corresponding variable selection, and for dichotomizing continuous data.