Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm the required action. It also provides functions that will install Github starred R packages whether available on CRAN or not.

This package provides a suite of open-source R functions designed to produce standard metrics for forest management and ecology from forest inventory data. The overarching goal is to minimize potential inconsistencies introduced by the algorithms used to compute and summarize core forest metrics. Learn more about the purpose of the package and the specific algorithms used in the package at <https://github.com/kearutherford/BerkeleyForestsAnalytics>.

Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.

Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>. It is particularly useful when the sensitivity or specificity in the sample is 100%. Note that this does not perform the test on nested models--for that, see epicalc::lrtest'.

Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.

Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

We perform linear, logistic, and cox regression using the base functions lm(), glm(), and coxph() in the R software and the survival package. Likewise, we can use ols(), lrm() and cph() from the rms package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. base.rms package can help you to switch between the two sets of commands easily.

Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis.

This package provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

Estimates cumulative history for time-series for continuously viewed bistable perceptual rivalry displays. Computes cumulative history via a homogeneous first order differential process. I.e., it assumes exponential growth/decay of the history as a function time and perceptually dominant state, Pastukhov & Braun (2011) <doi:10.1167/11.10.12>. Supports Gamma, log normal, and normal distribution families. Provides a method to compute history directly and example of using the computation on a custom Stan code.

Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.

Simplify bivariate and regression analyses by automating result generation, including summary tables, statistical tests, and customizable graphs. It supports tests for continuous and dichotomous data, as well as stepwise regression for linear, logistic, and Firth penalized logistic models. While not a substitute for tailored analysis, BiVariAn accelerates workflows and is expanding features like multilingual interpretations of results.The methods for selecting significant statistical tests, as well as the predictor selection in prediction functions, can be referenced in the works of Marc Kery (2003) <doi:10.1890/0012-9623(2003)84[92:NORDIG]2.0.CO;2> and Rainer Puhr (2017) <doi:10.1002/sim.7273>.

Interact with the Brandwatch API <https://developers.brandwatch.com/docs>. Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories. Also allows you to directly obtain mentions and aggregate data for a specified query or query group.

An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.

This package provides a GUI to correct measurement bias in DNA methylation analyses. The BiasCorrector package just wraps the functions implemented in the R package rBiasCorrection into a shiny web application in order to make them more easily accessible. Publication: Kapsner et al. (2021) <doi:10.1002/ijc.33681>.

Generates confidence intervals for standardized regression coefficients using delta method standard errors for models fitted by lm() as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6> and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>. The package can also be used to generate confidence intervals for differences of standardized regression coefficients and as a general approach to performing the delta method. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

This package provides a minimalist web framework for developing application programming interfaces in R that provides a flexible framework for handling common HTTP-requests, errors, logging, and an ability to integrate any R code as server middle-ware.

Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.

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

Easily talk to Google's BigQuery Storage API from R (<https://cloud.google.com/bigquery/docs/reference/storage/rpc>).

Transforms focal observations data, where different types of social interactions can be recorded by multiple observers, into asymmetric data matrices. Each cell in these matrices provides counts on the number of times a specific type of social interaction was initiated by the row subject and directed to the column subject.

Write blog posts and web pages in R Markdown. This package supports the static site generator Hugo (<https://gohugo.io>) best, and it also supports Jekyll (<https://jekyllrb.com>) and Hexo (<https://hexo.io>).

Bayesian Nonparametric sensitivity analysis of multiple testing procedures for p values with arbitrary dependencies, based on the Dirichlet process prior distribution.