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Explainable Outlier Detection Through Decision Tree Conditioning
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)
Distance Sampling Detection Function and Abundance Estimation
A simple way of fitting detection functions to distance sampling
data for both line and point transects. Adjustment term selection, left and
right truncation as well as monotonicity constraints and binning are
supported. Abundance and density estimates can also be calculated (via a
Horvitz-Thompson-like estimator) if survey area information is provided. See
Miller et al. (2019)
Detect and Check for Separation and Infinite Maximum Likelihood Estimates
Provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses. The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007, < https://ora.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a>) using 'ROI' < https://cran.r-project.org/package=ROI> or 'lpSolveAPI' < https://cran.r-project.org/package=lpSolveAPI>. The post-fit methods apply to models with categorical responses, including binomial-response generalized linear models and multinomial-response models, such as baseline category logits and adjacent category logits models; for example, the models implemented in the 'brglm2' < https://cran.r-project.org/package=brglm2> package. The post-fit methods successively refit the model with increasing number of iteratively reweighted least squares iterations, and monitor the ratio of the estimated standard error for each parameter to what it has been in the first iteration. According to the results in Lesaffre & Albert (1989, < https://www.jstor.org/stable/2345845>), divergence of those ratios indicates data separation.
Vector Generalized Linear and Additive Models
An implementation of about 6 major classes of
statistical regression models. The central algorithm is
Fisher scoring and iterative reweighted least squares.
At the heart of this package are the vector generalized linear
and additive model (VGLM/VGAM) classes. VGLMs can be loosely
thought of as multivariate GLMs. VGAMs are data-driven
VGLMs that use smoothing. The book "Vector Generalized
Linear and Additive Models: With an Implementation in R"
(Yee, 2015)
Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.
Agent-Based Bird Point Count Simulator
A highly scientific and utterly addictive
bird point count simulator
to test statistical assumptions, aid survey design,
and have fun while doing it (Solymos 2024
Signal Detection Analysis
Exploring time series for signal detection. It is specifically designed
to detect possible outbreaks using infectious disease surveillance data
at the European Union / European Economic Area or country level.
Automatic detection tools used are presented in the paper
"Monitoring count time series in R: aberration detection in public health surveillance",
by Salmon (2016)
Spatiotemporal Boundary Detection Model for Areal Unit Data
Implements a spatiotemporal boundary detection model with a dissimilarity
metric for areal data with inference in a Bayesian setting using Markov chain
Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget),
probit or Tobit link and spatial correlation is introduced at each time point
through a conditional autoregressive (CAR) prior. Temporal correlation is introduced
through a hierarchical structure and can be specified as exponential or first-order
autoregressive. Full details of the package can be found in the accompanying vignette.
Furthermore, the details of the package can be found in "Diagnosing Glaucoma
Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method",
by Berchuck et al (2018),
Event Detection Framework
Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.
Dimension Reduction for Outlier Detection
A dimension reduction technique for outlier detection. DOBIN: a Distance
based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier
detection. This is not an outlier detection method; rather it is a pre-processing
method for outlier detection. It brings outliers to the fore-front using fewer basis
vectors (Kandanaarachchi, Hyndman 2020)