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.

Aids in the analysis of genes influencing cancer survival by including a principal function, calculator(), which calculates the P-value for each provided gene under the optimal cutoff in cancer survival studies. Grounded in methodologies from significant works, this package references Therneau's survival package (Therneau, 2024; <https://CRAN.R-project.org/package=survival>) and the survival analysis extensions by Therneau and Grambsch (2000, ISBN 0-387-98784-3). It also integrates the survminer package by Kassambara et al. (2021; <https://CRAN.R-project.org/package=survminer>), enhancing survival curve visualizations with ggplot2'.

It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator Omega-generic was proposed by Mai, Srivastava, and Krull (2021) <https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package OmegaG was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of OmegaG do not necessarily represent the policy of the ALSAC.

This package provides functionality to construct standardised tables from health care data formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. The package includes tools to build key tables such as observation period and drug era, among others.

Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug.

This package provides a suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011)<doi:10.18637/jss.v043.i11>.

Allows access to a proof-of-concept database containing Open Access species range models and relevant metadata. Access to the database is via both PostgreSQL connection and API <https://github.com/EnquistLab/Biendata-Frontend>, allowing diverse use-cases.

Generate and analyze Optimal Channel Networks (OCNs): oriented spanning trees reproducing all scaling features characteristic of real, natural river networks. As such, they can be used in a variety of numerical experiments in the fields of hydrology, ecology and epidemiology. See Carraro et al. (2020) <doi:10.1002/ece3.6479> for a presentation of the package; Rinaldo et al. (2014) <doi:10.1073/pnas.1322700111> for a theoretical overview on the OCN concept; Furrer and Sain (2010) <doi:10.18637/jss.v036.i10> for the construct used.

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.

An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the orf package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the ranger package (Wright & Ziegler, 2017) <arXiv:1508.04409>.

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Designed for performing impact analysis of opinions in a digital text document (DTD). The package allows a user to assess the extent to which a theme or subject within a document impacts the overall opinion expressed in the document. The package can be applied to a wide range of opinion-based DTD, including commentaries on social media platforms (such as Facebook', Twitter and Youtube'), online products reviews, and so on. The utility of opitools was originally demonstrated in Adepeju and Jimoh (2021) <doi:10.31235/osf.io/c32qh> in the assessment of COVID-19 impacts on neighbourhood policing using Twitter data. Further examples can be found in the vignette of the package.

Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data.

Obtain optimum block from Non-overlapping Block Bootstrap method.

After develop a ODK <https://opendatakit.org/> frame, we can link the frame to Google Sheets <https://www.google.com/sheets/about/> and collect data through Android <https://www.android.com/>. This data uploaded to a Google sheets'. odk2spss() function help to convert the odk frame into SPSS <https://www.ibm.com/analytics/us/en/technology/spss/> frame. Also able to add downloaded Google sheets data or read data from Google sheets by using ODK frame submission_url'.

Inference using a class of Hidden Markov models (HMMs) called oHMMed'(ordered HMM with emission densities <doi:10.1186/s12859-024-05751-4>): The oHMMed algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. These are modelled as discrete hidden states; the observed data points are then realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are then inferred. Relevant for application to genomic sequences, time series, or any other sequence data with serial autocorrelation.

Utilizes the Black-Scholes-Merton option pricing model to calculate key option analytics and perform graphical analysis of various option strategies. Provides functions to calculate the option premium and option greeks of European-style options.

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.

Ordination comprises several multivariate exploratory and explanatory techniques with theoretical foundations in geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. Greenacre (2010, ISBN:978-84-923846) shows how the most established of these, including principal components analysis, correspondence analysis, multidimensional scaling, factor analysis, and discriminant analysis, rely on eigen-decompositions or singular value decompositions of pre-processed numeric matrix data. These decompositions give rise to a set of shared coordinates along which the row and column elements can be measured. The overlay of their scatterplots on these axes, introduced by Gabriel (1971) <doi:10.1093/biomet/58.3.453>, is called a biplot. ordr provides inspection, extraction, manipulation, and visualization tools for several popular ordination classes supported by a set of recovery methods. It is inspired by and designed to integrate into Tidyverse workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.

Anomaly detection in dynamic, temporal networks. The package oddnet uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.

Implementation of a likelihood ratio test of differential onset of senescence between two groups. Given two groups with measures of age and of an individual trait likely to be subjected to senescence (e.g. body mass), OnAge provides an asymptotic p-value for the null hypothesis that senescence starts at the same age in both groups. The package implements the procedure used in Douhard et al. (2017) <doi:10.1111/oik.04421>.

Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio.

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.

This package implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) <doi:10.1007/s10742-022-00284-w> and Griffin et al. (2022) <doi:10.1186/s12874-021-01471-y> for a description of our methods.