Users can build a single shiny app for exploring population characterization, population-level causal effect estimation, and patient-level prediction results generated via the R analyses packages in HADES (see <https://ohdsi.github.io/Hades/>). Learn more about OhdsiShinyAppBuilder at <https://ohdsi.github.io/OhdsiShinyAppBuilder/>.

All the methods in this package generate a vector of uniform order statistics using a beta distribution and use an inverse cumulative distribution function for some distribution to give a vector of random order statistic variables for some distribution. This is much more efficient than using a loop since it is directly sampling from the order statistic distribution.

Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.

This package implements the Bayesian online changepoint detection method by Adams and MacKay (2007) <arXiv:0710.3742> for univariate or multivariate data. Gaussian and Poisson probability models are implemented. Provides post-processing functions with alternative ways to extract changepoints.

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 implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models. They are Crossover model with subject dropout, crossover model with proportional first order residual effects and interference model. You can use it to find either A-opt or D-opt approximate designs. Exact designs can be automatically rounded from approximate designs and relative efficiency is provided as well.

In the context of data fusion, the package provides a set of functions dedicated to the solving of recoding problems using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.

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.

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.

This package provides tools to analyze and infer orthology and paralogy relationships between glutamine synthetase proteins in seed plants.

Growing collection of helper functions for point pattern analysis. Most functions are designed to work with the spatstat (<http://spatstat.org>) package. The focus of most functions are either null models or summary functions for spatial point patterns. For a detailed description of all null models and summary functions, see Wiegand and Moloney (2014, ISBN:9781420082548).

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.

O-statistics, or overlap statistics, measure the degree of community-level trait overlap. They are estimated by fitting nonparametric kernel density functions to each speciesâ trait distribution and calculating their areas of overlap. For instance, the median pairwise overlap for a community is calculated by first determining the overlap of each species pair in trait space, and then taking the median overlap of each species pair in a community. This median overlap value is called the O-statistic (O for overlap). The Ostats() function calculates separate univariate overlap statistics for each trait, while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. O-statistics can be evaluated against null models to obtain standardized effect sizes. Ostats is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale drivers of biodiversity across the National Ecological Observatory Network (NEON)." For more information on this project, see the Macrosystems Biodiversity Website (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the underlying biological concepts at an undergraduate level is described in Grady et al. (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.

This package provides functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software <doi:10.18637/jss.v114.i10>.

Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of RcppArmadillo to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the findHOGFeatures function of the SimpleCV computer vision platform, the average_hash(), dhash() and phash() functions are based on the ImageHash python library. The Gabor Feature Extraction functions are based on Matlab code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The SLIC and SLICO superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.

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.

Geocode with the OpenCage API, either from place name to longitude and latitude (forward geocoding) or from longitude and latitude to the name and address of a location (reverse geocoding), see <https://opencagedata.com/>.

The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see <https://opendataformat.github.io/>.

Accesses OpenWeatherMap's (owm) <https://openweathermap.org/> API. owm itself is a service providing weather data in the past, in the future and now. Furthermore, owm serves weather map layers usable in frameworks like leaflet'. In order to access the API, you need to sign up for an API key. There are free and paid plans. Beside functions for fetching weather data from owm', owmr supplies tools to tidy up fetched data (for fast and simple access) and to show it on leaflet maps.

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 a DBI-compatible interface to ODBC databases.

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the GritBot <https://www.rulequest.com/gritbot-info.html> software.

Simplified odds ratio calculation of GAM(M)s & GLM(M)s. Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using exp(coef(model)) (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple predict() calls of the chosen predictor while holding other predictors constant. Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. In both cases, confident intervals are returned additionally. Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot.

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.