US baby names provided by the SSA. This package contains all names used for at least 5 children of either sex.

Reading and writing BibTeX files using data frames in R sessions.

Generate urls and hyperlinks to commonly used biological databases and resources based on standard identifiers. This is primarily useful when writing dynamic reports that reference things like gene symbols in text or tables, allowing you to, for example, convert gene identifiers to hyperlinks pointing to their entry in the NCBI Gene database. Currently supports NCBI Gene, PubMed', Gene Ontology, KEGG', CRAN and Bioconductor.

Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. BFAST can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. BFAST monitoring functionality is described in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST'- type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>. BFAST Lite approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks).

Enable users to evaluate long-term trends using a Generalized Additive Modeling (GAM) approach. The model development includes selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporation of hydrologic variability via either a river flow or salinity, the use of an intervention to deal with method or laboratory changes suspected to impact data values, and representation of left- and interval-censored data. The approach has been applied to water quality data in the Chesapeake Bay, a major estuary on the east coast of the United States to provide insights to a range of management- and research-focused questions. Methodology described in Murphy (2019) <doi:10.1016/j.envsoft.2019.03.027>.

Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) <doi:10.2307/3545584>.

This package provides methods for the binarization of one-dimensional data and some visualization functions.

Real-time quantitative polymerase chain reaction (qPCR) data sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package provides a dilution series for one PCR target: a random sequence that minimizes secondary structure and off-target primer binding. The data set is a six-point, ten-fold dilution series. For each concentration there are two replicates. Each amplification curve is 40 cycles long. Original raw data file: <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.

Carry out Bayesian estimation and forecasting for a variety of stochastic mortality models using vague prior distributions. Models supported include numerous well-established approaches introduced in the actuarial and demographic literature, such as the Lee-Carter (1992) <doi:10.1080/01621459.1992.10475265>, the Cairns-Blake-Dowd (2009) <doi:10.1080/10920277.2009.10597538>, the Li-Lee (2005) <doi:10.1353/dem.2005.0021>, and the Plat (2009) <doi:10.1016/j.insmatheco.2009.08.006> models. The package is designed to analyse stratified mortality data structured as a 3-dimensional array of dimensions p à A à T (strata à age à year). Stratification can represent factors such as cause of death, country, deprivation level, sex, geographic region, insurance product, marital status, socioeconomic group, or smoking behavior. While the primary focus is on analysing stratified data (p > 1), the package can also handle mortality data that are not stratified (p = 1). Model selection via the Deviance Information Criterion (DIC) is supported.

Bayesian adaptive trial algorithm implements multiple-stage interim analysis. Package includes data generating function, and Bayesian hypothesis testing function.

Analyze differences among time series curves with p-value adjustment for multiple comparisons introduced in Oleson et al (2015) <DOI:10.1177/0962280215607411>.

This package provides tools to fit Bayesian state-space models to animal tracking data. Models are provided for location filtering, location filtering and behavioural state estimation, and their hierarchical versions. The models are primarily intended for fitting to ARGOS satellite tracking data but options exist to fit to other tracking data types. For Global Positioning System data, consider the moveHMM package. Simplified Markov Chain Monte Carlo convergence diagnostic plotting is provided but users are encouraged to explore tools available in packages such as coda and boa'.

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>.

Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of Petri Net objects and visualization of process maps. See also packages edeaR','processmapR', eventdataR and processmonitR'.

This package provides a fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.

This package provides a family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.

This package provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation of confidence intervals around these indices. Furthermore, the package allows for the exploration of how variation in these indices changes with differing numbers of sites, making it a useful tool with which to begin an ecological analysis. Methods are based on the following references: Chao et al. (2014) <doi:10.1890/13-0133.1>, Chao and Colwell (2022) <doi:10.1002/9781119902911.ch2>, Hsieh, Ma,` and Chao (2016) <doi:10.1111/2041-210X.12613>.

Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially those of biomacromolecules such as proteins. Biological Magnetic Resonance Data Bank ('BMRB') is a repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules. Currently, BMRB offers an R package RBMRB to fetch data, however, it doesn't easily offer individual data file downloading and storing in a local directory. When using RBMRB', the data will stored as an R object, which fundamentally hinders the NMR researches to access the rich information from raw data, for example, the metadata. Here, BMRBr File Downloader ('BMRBr') offers a more fundamental, low level downloader, which will download original deposited .str format file. This type of file contains information such as entry title, authors, citation, protein sequences, and so on. Many factors affect NMR experiment outputs, such as temperature, resonance sensitivity and etc., approximately 40% of the entries in the BMRB have chemical shift accuracy problems [1,2] Unfortunately, current reference correction methods are heavily dependent on the availability of assigned protein chemical shifts or protein structure. This is my current research project is going to solve, which will be included in the future release of the package. The current version of the package is sufficient and robust enough for downloading individual BMRB data file from the BMRB database <http://www.bmrb.wisc.edu>. The functionalities of this package includes but not limited: * To simplifies NMR researches by combine data downloading and results analysis together. * To allows NMR data reaches a broader audience that could utilize more than just chemical shifts but also metadata. * To offer reference corrected data for entries without assignment or structure information (future release). Reference: [1] E.L. Ulrich, H. Akutsu, J.F. Doreleijers, Y. Harano, Y.E. Ioannidis, J. Lin, et al., BioMagResBank, Nucl. Acids Res. 36 (2008) D402â 8. <doi:10.1093/nar/gkm957>. [2] L. Wang, H.R. Eghbalnia, A. Bahrami, J.L. Markley, Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications, J. Biomol. NMR. 32 (2005) 13â 22. <doi:10.1007/s10858-005-1717-0>.

Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.

Dose-response modeling for negative-binomial distributed data with a variety of dose-response models. Covariate adjustment and Bayesian model averaging is supported. Functions are provided to easily obtain inference on the dose-response relationship and plot the dose-response curve.

This package provides a framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S=x where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.

This package provides functions to construct efficient block designs for 3-level factorial experiments in block size 3. The designs ensure the estimation of all main effects and two-factor interactions in minimum number of replications. For more details, see Dey and Mukerjee (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R. and Gupta, V.K. (2013) <doi:10.1007/s40003-013-0059-5>.

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.

This package provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.