This package provides an efficient and robust implementation for estimating marginal Hazard Ratio (HR) and Restricted Mean Survival Time (RMST) with covariate adjustment using Daniel et al. (2021) <doi:10.1002/bimj.201900297> and Karrison et al. (2018) <doi:10.1177/1740774518759281>.

Species Distribution Modeling (SDM) is a practical methodology that aims to estimate the area of distribution of a species. However, most of the work has focused on estimating static expressions of the correlation between environmental variables. The outputs of correlative species distribution models can be interpreted as maps of the suitable environment for a species but not generally as maps of its actual distribution. Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4> presented the BAM scheme, a heuristic framework that states that the occupied area of a species occurs on sites that have been accessible through dispersal (M) and have both favorable biotic (B) and abiotic conditions (A). The bamm package implements classes and functions to operate on each element of the BAM and by using a cellular automata model where the occupied area of a species at time t is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B). The theoretical background of the package can be found in Soberón and Osorio-Olvera (2023) <doi:10.1111/jbi.14587>.

Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects. Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames.

Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".

Some very simple infrastructure for basis functions.

For studying recurrent disease and death with competing risks, comparisons based on the well-known cumulative incidence function can be confounded by different prevalence rates of the competing events. Alternatively, comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. This package implements a nonparametric estimator for the conditional cumulative incidence function and a nonparametric conditional bivariate cumulative incidence function for the bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.

Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.

Reads and plots phylogenetic placements.

Get a current financial year, start of current month, End of current month, start of financial year and end of it. Allow for offset from the date.

Generates nonparametric bootstrap confidence intervals (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>) for standardized regression coefficients (beta) and other effect sizes, including multiple correlation, semipartial correlations, improvement in R-squared, squared partial correlations, and differences in standardized regression coefficients, for models fitted by lm().

This package provides functions that allow users to quantify the relative contributions of geographic and ecological distances to empirical patterns of genetic differentiation on a landscape. Specifically, we use a custom Markov chain Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the inference model, as well as functions for performing MCMC diagnosis and assessing model adequacy.

This package provides functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) <arXiv:2205.15263> for technical details. Source code and more data sets are at <https://github.com/profyliu/bsnsing/>.

Developed for the following tasks. Simulating, computing maximum likelihood estimator, computing the Fisher information matrix, computing goodness-of-fit measures, and correcting bias of the ML estimator for a wide range of distributions fitted to units placed on progressive type-I interval censoring and progressive type-II censoring plans. The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and bootstrap method for computing the bias-corrected maximum likelihood estimator.

Defines operating characteristics of Bayesian Adaptive Trials considering a generalised linear model response via Monte Carlo simulations of Bayesian GLM fitted via integrated Laplace approximations (INLA).

Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, JAGS software and rjags package are required, and users need to pre-install them.

Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes: a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>. b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. <doi:10.3934/math.2023352>. c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. <doi:10.3390/axioms11090438>.

This package provides the functions for Brunner-Munzel test and permuted Brunner-Munzel test, which enable to use formula, matrix, and table as argument. These functions are based on Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U> and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>, and are written with FORTRAN.

The Bayesian optimal interval (BOIN) design is a novel phase I clinical trial design for finding the maximum tolerated dose (MTD). It can be used to design both single-agent and drug-combination trials. The BOIN design is motivated by the top priority and concern of clinicians when testing a new drug, which is to effectively treat patients and minimize the chance of exposing them to subtherapeutic or overly toxic doses. The prominent advantage of the BOIN design is that it achieves simplicity and superior performance at the same time. The BOIN design is algorithm-based and can be implemented in a simple way similar to the traditional 3+3 design. The BOIN design yields an average performance that is comparable to that of the continual reassessment method (CRM, one of the best model-based designs) in terms of selecting the MTD, but has a substantially lower risk of assigning patients to subtherapeutic or overly toxic doses. For tutorial, please check Yan et al. (2020) <doi:10.18637/jss.v094.i13>.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is a command-line tool. This package provides a way to call BEAST2 from an R function call.

From a given data frame, this package learns its Bayesian network structure based on a selected score.

This package provides a simple tool to quantify the amount of transmission of an infectious disease of interest occurring within and between population groups. bumblebee uses counts of observed directed transmission pairs, identified phylogenetically from deep-sequence data or from epidemiological contacts, to quantify transmission flows within and between population groups accounting for sampling heterogeneity. Population groups might include: geographical areas (e.g. communities, regions), demographic groups (e.g. age, gender) or arms of a randomized clinical trial. See the bumblebee website for statistical theory, documentation and examples <https://magosil86.github.io/bumblebee/>.

The function \codebarcode() produces a histogram-like plot of a distribution that shows granularity in the data.

This package provides tools and code snippets for summarizing nested data, adverse events and REDCap study information.

These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.