Stock, Options and Futures Trading Strategies for Traders and Investors with Bullish Outlook are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the 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). Zura Kakushadze, Juan A. Serur (â 151 Trading Strategiesâ , 2018, ISBN: 9783030027919). John C. Hull (â Options, Futures, and Other Derivatives (11th ed.)â , 2022, ISBN: 9780136939979).

These are miscellaneous functions for working with panel data, quantiles, and printing results. For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance. For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions. Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas. Additional utilities support staggered treatment adoption settings, including functions for identifying treatment groups, recovering pre-treatment outcomes and covariate averages, and computing lagged outcomes and first differences in panel data.

This package contains a split population survival estimator that models the misclassification probability of failure versus right-censored events. The split population survival estimator is described in Bagozzi et al. (2019) <doi:10.1017/pan.2019.6>.

This package provides functions to combine data on voting blocs size, turnout, and vote choice to estimate each bloc's vote contributions to the Democratic and Republican parties. The package also includes functions for uncertainty estimation and plotting. Users may define voting blocs along a discrete or continuous variable. The package implements methods described in Grimmer, Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.

The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed. The package is based on the work in Rebora P (2014) <doi:10.32614/RJ-2014-028>.

Barnard's unconditional test for 2x2 contingency tables.

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'.

Generate the James Blinding Index, as described in James et al (1996) <https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, as described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>. These are measures to assess whether or not satisfactory blinding has been maintained in a randomized, controlled, clinical trial. These can be generated for trial subjects, research coordinators and principal investigators, based upon standardized questionnaires that have been administered, to assess whether or not they can correctly guess to which treatment arm (e.g. placebo or treatment) subjects were assigned at randomization.

This package provides a computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.

This package provides tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.

This package implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian framework for estimating causal effects of clinical interventions on bounded continuous outcomes in longitudinal observational studies with irregular visits. The methodology is specifically designed for periodontal disease research, where discrete treatments and continuous disease states (e.g., proportion of periodontal pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. The package integrates a double-censored Tobit likelihood to handle boundary mass at zero and one, subject-specific random effects to capture within-subject correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) to model complex nonlinear interactions without parametric restrictions. Causal identification is established under the potential outcomes framework via the G-computation formula, with key estimands including the Mixed Average Potential Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The package provides functions for model fitting, posterior inference, and causal estimand estimation.

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'.

Propagate uncertainty from several estimates when combining these estimates via a function. This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter. Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage. Gaussian copulas are used for when parameters are assumed to be dependent / correlated. References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.

Finds the best block diagonal matrix approximation of a symmetric matrix. This can be exploited for divisive hierarchical clustering using singular vectors, named HC-SVD. The method is described in Bauer (202Xa) <doi:10.48550/arXiv.2308.06820>.

Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) <doi:10.1001/jama.1991.03470010097038>. This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>. The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses.

This package provides a client for retrieving data and metadata from central bank APIs including Banco de España (BdE), Banco de Portugal (BdP), Bank for International Settlements (BIS), Bank of Canada (BoC), Bank of England (BoE), Bank of Japan (BoJ), Banque de France (BdF), Czech National Bank (CNB), Deutsche Bundesbank (BBk), European Central Bank (ECB), National Bank of Poland (NBP), Norges Bank (NoB), Oesterreichische Nationalbank (OeNB), Sveriges Riksbank (SRb), and Swiss National Bank (SNB).

Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) <doi:10.1007/s11336-008-9089-5>.

Extend the bigmemory package with various analytics. Functions bigkmeans and binit may also be used with native R objects. For tapply'-like functions, the bigtabulate package may also be helpful. For linear algebra support, see bigalgebra'. For mutex (locking) support for advanced shared-memory usage, see synchronicity'.

This package provides a unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. BRCore implements the workflow proposed by Shade and Stopnisek (2019) and incorporates additional rarefaction steps. The proposed workflow aims to identify persistent microbiomes using abundance-occupancy distributions and neutral community model fitting. For more details on abundance-occupancy distributions see Shade A, Stopnisek N (2019) <doi:10.1016/j.mib.2019.09.008>, for neutral models, see Sloan et al. (2006) <doi:10.1111/j.1462-2920.2005.00956.x> and Burns et al. (2015) <doi:10.1038/ismej.2015.142>.

In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.

Generalization of the Bayesian classification and regression tree model that partitions subjects into terminal nodes and tailors predictive model to each terminal node.

Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <doi:10.48550/arXiv.1806.05738>.

The core algorithm is described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) <arXiv:1901.07410>. Please consult the following youtube video <https://www.youtube.com/watch?v=M9Dm1nl_zSQfor> the idea of functionality. Ball Mapper provide a topologically accurate summary of a data in a form of an abstract graph. To create it, please provide the coordinates of points (in the points array), values of a function of interest at those points (can be initialized randomly if you do not have it) and the value epsilon which is the radius of the ball in the Ball Mapper construction. It can be understood as the minimal resolution on which we use to create the model of the data.

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>.