Found 954 packages in 0.05 seconds
Probability of Detection for Grab Sample Selection
Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988)
Probability of Detection for Qualitative PCR Methods
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified.
This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' < http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015)
Sleep Cycle Detection
Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979)
Detection of Outliers in Circular-Circular Regression
Detection of outliers in circular-circular regression models, modifying its and estimating of models parameters.
A Tidy Framework for Changepoint Detection Analysis
Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.
Network-Adjusted Covariates for Community Detection
Incorporating node-level covariates for community detection has gained increasing attention these years. This package provides the function for implementing the novel community detection algorithm known as Network-Adjusted Covariates for Community Detection (NAC), which is designed to detect latent community structure in graphs with node-level information, i.e., covariates. This algorithm can handle models such as the degree-corrected stochastic block model (DCSBM) with covariates. NAC specifically addresses the discrepancy between the community structure inferred from the adjacency information and the community structure inferred from the covariates information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2023)
Automatic Groove Identification via Bayesian Changepoint Detection
Provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step
of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994)
Eye Movement Analysis Package for Fixation and Saccade Detection
Fixation and saccade detection in eye movement recordings. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Detecting Structural Change with Heteroskedasticity
Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (
Detect Subgroup with an Enhanced Treatment Effect
A test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.