Compare dissolution profiles with confidence interval of similarity factor f2 using bootstrap methodology as described in the literature, such as Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997, ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>. The package can also be used to simulate dissolution profiles based on mathematical modelling and multivariate normal distribution.

Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.

This is a sub national population projection model for calculating population development. The model uses a cohort component method. Further reading: Stanley K. Smith: A Practitioner's Guide to State and Local Population Projections. 2013. <doi:10.1007/978-94-007-7551-0>.

This package provides a framework for data manipulation and visualization using a web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks.

This package provides functions to get and download city bike data from the website and API service of each city bike service in Norway. The package aims to reduce time spent on getting Norwegian city bike data, and lower barriers to start analyzing it. The data is retrieved from Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.

This package provides functions to specify, fit and visualize nested partially-latent class models ( Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>; Wu and Chen (2021) <doi:10.1002/sim.8804>) for inference of population disease etiology and individual diagnosis. In the motivating Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities of interest sum to one hundred percent, the PERCH scientists frequently refer to them as population etiology pie and individual etiology pie, hence the name of the package.

This package provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The models include country-specific VARs that share a global prior distribution that extend the model by JarociÅ ski (2010) <doi:10.1002/jae.1082>. Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. An extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in C++'. The bpvars package is aligned regarding objects, workflows, and code structure with the R packages bsvars by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars> and bsvarSIGNs by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset. Copyright: 2025 International Labour Organization.

Stock, Options and Futures Trading Strategies for Traders and Investors with Bearish Outlook. The indicators, strategies, calculations, functions and all other discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (â The Bible of Options Strategies (2nd ed.)â , 2015, ISBN: 9780133964028). Juan A. Serur, Juan A. Serur (â 151 Trading Strategiesâ , 2018, ISBN: 9783030027919). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 385-453)", 2019, ISBN: 9781119593577). John C. Hull (â Options, Futures, and Other Derivatives (11th ed.)â , 2022, ISBN: 9780136939979).

Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed.

Computation of key characteristics and plots for blinded sample size recalculation. Continuous as well as binary endpoints are supported in superiority and non-inferiority trials. See Baumann, Pilz, Kieser (2022) <doi:10.32614/RJ-2022-001> for a detailed description. The implemented methods include the approaches by Lu, K. (2019) <doi:10.1002/pst.1737>, Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>, Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.

Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see Vock and Simon (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. bakR includes a maximally efficient implementation of this model for conservative initial investigations of datasets. bakR also provides more highly powered implementations using the probabilistic programming language Stan to sample from the full posterior distribution. bakR performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.

Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.

Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial.

Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.

Collect your data on digital marketing campaigns from bing Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

This package implements a backward procedure for single and multiple change point detection proposed by Shin et al. <arXiv:1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.

As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in C++ and R.

Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.

Twelve confidence intervals for one binomial proportion or a vector of binomial proportions are computed. The confidence intervals are: Jeffreys, Wald, Wald corrected, Wald, Blyth and Still, Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. <doi:10.1002/sim.4780120902>.

Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential (Hursh & Silberberg, 2008, <doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data.

This package provides tools for fitting bivariate hurdle negative binomial models with horseshoe priors, Bayesian Model Averaging (BMA) via stacking, and comprehensive causal inference methods including G-computation, transfer entropy, Threshold Vector Autoregressive (TVAR) and Smooth Transition Autoregressive (STAR) models, Dynamic Bayesian Networks (DBN), Hidden Markov Models (HMM), and sensitivity analysis.

Search and access more than ten thousand datasets included in BCRPDATA (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).

This package provides a GUI with which users can construct and interact with biplots.