An implementation of several functions for feature extraction in ordinal time series datasets. Specifically, some of the features proposed by Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed. These features can be used to perform inferential tasks or to feed machine learning algorithms for ordinal time series, among others. The package also includes some interesting datasets containing financial time series. Practitioners from a broad variety of fields could benefit from the general framework provided by otsfeatures'.

This package provides a method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data.

Estimate the positron emission tomography (PET) neuroreceptor occupancies from the total volumes of distribution of a set of regions of interest. Fitting methods include the simple reference region', ordinary least squares (sometimes known as occupancy plot), and restricted maximum likelihood estimation'.

Non-spatial and spatial open-population capture-recapture analysis.

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.

Shiny Application to visualize Olympic Data. From 1896 to 2016. Even Winter Olympics events are included. Data is from Kaggle at <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.

Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported. Additionally raster maps may be constructed using custom tile servers. Maps can be plotted using either base graphics, or ggplot2. This package is not affiliated with the OpenStreetMap.org mapping project.

The Open Bodem Index (OBI) is a method to evaluate the quality of soils of agricultural fields in The Netherlands and the sustainability of the current agricultural practices. The OBI score is based on four main criteria: chemical, physical, biological and management, which consist of more than 21 indicators. By providing results of a soil analysis and management info the OBIC package can be use to calculate he scores, indicators and derivatives that are used by the OBI. More information about the Open Bodem Index can be found at <https://openbodemindex.nl/>.

Obtaining Bayes Expected A Posteriori (EAP) individual score estimates based on linear and non-linear extended Exploratoy Factor Analysis solutions that include a correlated-residual structure.

Provide functions for users or machines to quickly and easily retrieve datasets from the mindat.org API (<https://api.mindat.org/schema/redoc/>).

Quickly create numeric matrices for machine learning algorithms that require them. It converts factor columns into onehot vectors.

The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.

We proposes a framework that provides real time support for early detection of anomalous series within a large collection of streaming time series data. By definition, anomalies are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that is very unlikely given the forecast distribution. The algorithm first forecasts a boundary for the system's typical behaviour using a representative sample of the typical behaviour of the system. An approach based on extreme value theory is used for this boundary prediction process. Then a sliding window is used to test for anomalous series within the newly arrived collection of series. Feature based representation of time series is used as the input to the model. To cope with concept drift, the forecast boundary for the system's typical behaviour is updated periodically. More details regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) <doi:10.1080/10618600.2019.1617160>.

Analyze repertory grids, a qualitative-quantitative data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across various domains ranging from clinical psychology to marketing. The package contains functions to quantitatively analyze and visualize repertory grid data (e.g. Fransella', Bell', & Bannister', 2004, ISBN: 978-0-470-09080-0). The package is part of the The package is part of the <https://openrepgrid.org/> project.

In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.

Different measures which can be used to quantify similarities between regions. These measures are isonymy, isonymy between, Lasker distance, coefficients of Hedrick and Nei. In addition, it calculates biodiversity indices such as Margalef, Menhinick, Simpson, Shannon, Shannon-Wiener, Sheldon, Heip, Hill Numbers, Geometric Mean and Cressie and Read statistics.

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>).

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>.

Supports the modeling of ordinal random variables, like the outcomes of races, via Softmax regression, under the Harville <doi:10.1080/01621459.1973.10482425> and Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.

This package provides a comprehensive set of indexes and tests for social segregation analysis, as described in Tivadar (2019) - OasisR': An R Package to Bring Some Order to the World of Segregation Measurement <doi:10.18637/jss.v089.i07>. The package is the most complete existing tool and it clarifies many ambiguities and errors regarding the definition of segregation indices. Additionally, OasisR introduces several resampling methods that enable testing their statistical significance (randomization tests, bootstrapping, and jackknife methods).

This package provides functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.

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.

Intended to assist in the choice of the sampling strategy to implement in a survey.

An implementation of the Blinder-Oaxaca decomposition for linear regression models.