This package provides a utility to quickly obtain clean and tidy sports odds from The Odds API <https://the-odds-api.com>.

Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor. If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions). Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change. This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.

This package provides goodness-of-fit tests for ordinal regression models, including the Fagerland-Hosmer ordinal test, reproducing same output as Stata'. Supports polr(), vglm(), and binary glm() models. See Fagerland and Hosmer (2013) <doi:10.1002/sim.5645> and Fagerland and Hosmer (2017) <doi:10.1177/1536867X1701700308> for details.

Supplemental functions and data for OpenIntro resources, which includes open-source textbooks and resources for introductory statistics (<https://www.openintro.org/>). The package contains datasets used in our open-source textbooks along with custom plotting functions for reproducing book figures. Note that many functions and examples include color transparency; some plotting elements may not show up properly (or at all) when run in some versions of Windows operating system.

Allows performing forwards prediction for the General Unified Threshold model of Survival using compiled ode code. This package was created to avoid dependency with the morse package that requires the installation of JAGS'. This package is based on functions from the morse package v3.3.1: Virgile Baudrot, Sandrine Charles, Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.

This package provides a simple wrapper for the Octopus Energy API <https://developer.octopus.energy/docs/api/>. It handles authentication, by storing a provided API key and meter details. Implemented endpoints include products for viewing tariff details and consumption for viewing meter consumption data.

Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the gurobi software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.

Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).

Detection of overdispersion in count data for multiple regression analysis. Log-linear count data regression is one of the most popular techniques for predictive modeling where there is a non-negative discrete quantitative dependent variable. In order to ensure the inferences from the use of count data models are appropriate, researchers may choose between the estimation of a Poisson model and a negative binomial model, and the correct decision for prediction from a count data estimation is directly linked to the existence of overdispersion of the dependent variable, conditional to the explanatory variables. Based on the studies of Cameron and Trivedi (1990) <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), the overdisp() command is a contribution to researchers, providing a fast and secure solution for the detection of overdispersion in count data. Another advantage is that the installation of other packages is unnecessary, since the command runs in the basic R language.

This package provides a system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power.

Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.

Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. omicsTools is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality controlâ based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) <doi:10.1038/nprot.2011.335>.

This package implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.

Various tools developed as part of the Open-CESP (Centre de recherche en Epidémiologie et Santé des Populations) initiative to generate and evaluate synthetic datasets for statistical disclosure control. This includes tools to investigate the risk-utility tradeoff achievable with given synthesis methods, as well as statistical tools to estimate (conditional) probability distributions. The main eventual aim is to help researchers and statisticians disseminate open research data.

Identify the optimal timing for new treatment initiation during multiple state disease transition, including multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) <doi:10.18637/jss.v038.i07>.

Solves linear systems of form Ax=b via Gauss elimination, LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods.

Microarray probe ID is not convenient for further enrichment analysis and target gene selection. The package is created for the rice microarray probe ID conversion. This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research. For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes. Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.

Provide methods for estimating optimal treatment regimes in survival contexts with Kaplan-Meier-like estimators when no unmeasured confounding assumption is satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) <doi:10.1111/rssb.12201>) and when no unmeasured confounding assumption fails to hold and a binary instrument is available (Xia, J., Zhan, Z., Zhang, J. (2022) <arXiv:2210.05538>).

Automates and standardizes the import of raw data from Oregon RFID (radio-frequency identification) ORMR (Oregon RFID Multi-Reader) and ORSR (Oregon RFID Single Reader) antenna readers. Compiled data can then be combined within multi-reader arrays for further analysis, including summarizing tag and reader detections, determining tag direction, and calculating antenna efficiency.

Turn tidymodels workflows into objects containing the sufficient sequential equations to perform predictions. These smaller objects allow for low dependency prediction locally or directly in databases.

Data integration Web application for biobanks by OBiBa'. Opal is the core database application for biobanks. Participant data, once collected from any data source, must be integrated and stored in a central data repository under a uniform model. Opal is such a central repository. It can import, process, validate, query, analyze, report, and export data. Opal is typically used in a research center to analyze the data acquired at assessment centres. Its ultimate purpose is to achieve seamless data-sharing among biobanks. This Opal client allows to interact with Opal web services and to perform operations on the R server side. DataSHIELD administration tools are also provided.

The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importance in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions. Moreover, the variable importance values can also be based on the class probability predictions. Preliminary results indicate that this might lead to a better discrimination between influential and non-influential covariates. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4â 17. <doi:10.1007/s00357-018-9302-x>.

The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001) <DOI:10.1023/A:1010933404324> respectively.

Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series. Method is based on KubÃ¡Ä ek (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G. and Witkovský, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.