Estimates optimal number of biomarkers for two-group classification based on microarray data.

The OLStrajr package provides comprehensive functions for ordinary least squares (OLS) trajectory analysis and case-by-case OLS regression as outlined in Carrig, Wirth, and Curran (2004) <doi:10.1207/S15328007SEM1101_9> and Rogosa and Saner (1995) <doi:10.3102/10769986020002149>. It encompasses two primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies the estimation of individual growth curves over time via OLS regression, with options for visualizing both group-level and individual-level growth trajectories and support for linear and quadratic models. The cbc_lm() function facilitates case-by-case OLS estimates and provides unbiased mean population intercept and slope estimators by averaging OLS intercepts and slopes across cases. It further offers standard error calculations across bootstrap replicates and computation of 95% confidence intervals based on empirical distributions from the resampling processes.

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.

This package provides functions for detecting outliers in datasets using statistical methods. The package supports identification of anomalous observations in numerical data and is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows.

This package provides details such as Morphine Equivalent Dose (MED), brand name and opioid content which are calculated of all oral opioids authorized for sale by Health Canada and the FDA based on their Drug Identification Number (DIN) or National Drug Code (NDC). MEDs are calculated based on recommendations by Canadian Institute for Health Information (CIHI) and Von Korff et al (2008) and information obtained from Health Canada's Drug Product Database's monthly data dump or FDA Daily database for Canadian and US databases respectively. Please note in no way should output from this package be a substitute for medical advise. All medications should only be consumed on prescription from a licensed healthcare provider.

Three-dimensional rendering for grid and ggplot2 graphics using cubes and cuboids drawn with an oblique projection. As a special case also supports primary view orthographic projections. Can be viewed as an extension to the isocubes package <https://github.com/coolbutuseless/isocubes>.

It implements the online Bayesian methods for change point analysis. It can also perform missing data imputation with methods from VIM'. The reference is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>. The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.

This package provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.

Inspired by S-PLUS function objects.summary(), provides a function with the same name that returns data class, storage mode, mode, type, dimension, and size information for R objects in the specified environment. Various filtering and sorting options are also proposed.

Generalise the starting point of the array index.

Evaluates the Owen Q-function for an integer value of the degrees of freedom, by applying Owen's algorithm (1965) <doi:10.1093/biomet/52.3-4.437>. It is useful for the calculation of the power of equivalence tests.

This package provides tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function. This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed. The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines.

An R wrapper for the OneMap.Sg API <https://www.onemap.gov.sg/docs/>. Functions help users query data from the API and return raw JSON data in "tidy" formats. Support is also available for users to retrieve data from multiple API calls and integrate results into single dataframes, without needing to clean and merge the data themselves. This package is best suited for users who would like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.

The restricted optimal design method is implemented to optimally allocate a set of items that require calibration to a group of examinees. The optimization process is based on the method described in detail by Ul Hassan and Miller in their works published in (2019) <doi:10.1177/0146621618824854> and (2021) <doi:10.1016/j.csda.2021.107177>. To use the method, preliminary item characteristics must be provided as input. These characteristics can either be expert guesses or based on previous calibration with a small number of examinees. The item characteristics should be described in the form of parameters for an Item Response Theory (IRT) model. These models can include the Rasch model, the 2-parameter logistic model, the 3-parameter logistic model, or a mixture of these models. The output consists of a set of rules for each item that determine which examinees should be assigned to each item. The efficiency or gain achieved through the optimal design is quantified by comparing it to a random allocation. This comparison allows for an assessment of how much improvement or advantage is gained by using the optimal design approach. This work was supported by the Swedish Research Council (Vetenskapsrådet) Grant 2019-02706.

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.

Calculate the ratio of iron oxides, hematite and goethite, in soil using the diffuse reflectance technique. The Kubelka-Munk theory, second derivative analysis, and spectral region amplitudes related to hematite and goethite content are used for quantification (Torrent, J., & Barron, V. (2008) <doi:10.2136/sssabookser5.5.c13>). Additionally, the package calculates soil color in the visible spectrum using Munsell and RGB color spaces, based on color theory (Viscarra et al. (2006) <doi:10.1016/j.geoderma.2005.07.017>).

This package provides a wrapper for the OpenTripPlanner <http://www.opentripplanner.org/> REST API. Queries are submitted to the relevant OpenTripPlanner API resource, the response is parsed and useful R objects are returned.

This package provides functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. Specifically, OPL allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about OPL is provided by several international articles via Athey et al (2021, <doi:10.3982/ECTA15732>), Kitagawa et al (2018, <doi:10.3982/ECTA13288>), Cerulli (2022, <doi:10.1080/13504851.2022.2032577>), the paper by Cerulli (2021, <doi:10.1080/13504851.2020.1820939>) and the book by Gareth et al (2013, <doi:10.1007/978-1-4614-7138-7>).

Calculates D-, Ds-, A-, I- and L-optimal designs for non-linear models, via an implementation of the cocktail algorithm (Yu, 2011, <doi:10.1007/s11222-010-9183-2>). Compares designs via their efficiency, and augments any design with a controlled efficiency. An efficient rounding function has been provided to transform approximate designs to exact designs.

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>.

This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where â 0â may stand for negative and â 1â for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).

Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.

Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.

This package provides a novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC.