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CRAN Task View: Anomaly Detection with R 🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛍️🛒🛒
https://github.com/pridiltal/ctv-AnomalyDetection
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CRAN Task View: Anomaly Detection with R 🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛒🛍️🛒🛒
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URL: https://github.com/pridiltal/ctv-AnomalyDetection
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Created: 2019-10-07T00:22:28.000Z (about 5 years ago)
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README

        ## CRAN Task View: Anomaly Detection with R

                                                                     

--------------- --------------------------------------------------   

**Maintainer:** Priyanga Dilini Talagala, Rob J. Hyndman             

**Contact:**    pritalagala at gmail.com                             

**Version:**    2022-12-31                                           

**URL:**           



This CRAN task view contains a list of packages that can be used for

anomaly detection. Anomaly detection problems have many different facets

and the detection techniques can be highly influenced by the way we

define anomalies, the type of input data to the algorithm, the expected

output, etc. This leads to wide variations in problem formulations,

which need to be addressed through different analytical approaches.

Anomalies are often mentioned under several alternative names such as

outliers, novelty, odd values, extreme values, faults, aberration in

different application domains. These variants are also considered for

this task view.

**The development of this task view is fairly new and still in its early

stages and therefore subject to changes. Please send suggestions for

additions and extensions for this task view to the task view

maintainer.**

**Univariate Outlier Detection**

  - *Univariate outlier* detection methods focus on values in a single

    feature space. Package [univOutl](https://cran.r-project.org/package=univOutl)

    includes various methods for detecting univariate outliers, e.g. the

    Hidiroglou-Berthelot method. Methods to deal with skewed

    distribution are also included in this package.

  - The [dixonTest](https://cran.r-project.org/package=dixonTest) package provides

    Dixon's ratio test for outlier detection in small and normally

    distributed samples.

  - Univariate outliers detection is also supported by `outlier()`

    function in [GmAMisc](https://cran.r-project.org/package=GmAMisc) which

    implements three different methods (mean-base, median-based,

    boxplot-based).

  - The [hotspots](https://cran.r-project.org/package=hotspots) package supports

    univariate outlier detection by identifying values that are

    disproportionately high based on both the deviance of any given

    value from a statistical distribution and its similarity to other

    values.

  - The [outliers](https://cran.r-project.org/package=outliers) package provides a

    collection of tests commonly used for identifying *outliers* . For

    most functions the input is a numeric vector. If argument is a data

    frame, then outlier is calculated for each column by sapply. The

    same behavior is applied by apply when the matrix is given.

  - The [extremevalues](https://cran.r-project.org/package=extremevalues) package

    offers outlier detection and plot functions for univariate data. In

    this work a value in the data is an outlier when it is unlikely to

    be drawn from the estimated distribution.

  - The [funModeling](https://cran.r-project.org/package=funModeling) package

    provides tools for outlier detection using top/bottom X%, Tukey’s

    boxplot definition and Hampel’s method.

  - The [alphaOutlier](https://cran.r-project.org/package=alphaOutlier) package

    provides Alpha-Outlier regions (as proposed by Davies and Gather

    (1993)) for well-known probability distributions.

**Multivariate Outlier Detection**

  - Under *multivariate, high-dimensional or multidimensional scenario,*

    where the focus is on n (\>2) - dimensional space, all attributes

    might be of same type or might be a mixture of different types such

    as categorical or numerical, which has a direct impact on the

    implementation and scope of the algorithm. The problems of anomaly

    detection in high-dimensional data are threefold, involving

    detection of: (a) global anomalies, (b) local anomalies and (c)

    micro clusters or clusters of anomalies. Global anomalies are very

    different from the dense area with respect to their attributes. In

    contrast, a local anomaly is only an anomaly when it is distinct

    from, and compared with, its local neighbourhood. Micro clusters or

    clusters of anomalies may cause masking problems.

*Multivariate Outlier Detection: Density-based outlier detection*

  - The [DDoutlier](https://cran.r-project.org/package=DDoutlier) package provides a

    wide variety of distance- and density-based outlier detection

    functions mainly focusing local outliers in high-dimensional data.

  - *Local Outlier Factor (LOF)* is an algorithm for detecting anomalous

    data points by measuring the local deviation of a given data point

    with respect to its neighbours. This algorithm with some variations

    is supported by many packages. The

    [DescTools](https://cran.r-project.org/package=DescTools) package provides

    functions for outlier detection using LOF and Tukey’s boxplot

    definition. Functions `LOF()` and `GLOSH` in package

    [dbscan](https://cran.r-project.org/package=dbscan) provide density based

    anomaly detection methods using a kd-tree to speed up kNN search.

    Parallel implementation of LOF which uses multiple CPUs to

    significantly speed up the LOF computation for large datasets is

    available in [Rlof](https://cran.r-project.org/package=Rlof) package. Package

    [bigutilsr](https://cran.r-project.org/package=bigutilsr) provides utility

    functions for outlier detection in large-scale data. It includes LOF

    and outlier detection method based on departure from histogram.

  - The [SMLoutliers](https://cran.r-project.org/package=SMLoutliers) package

    provides an implementation of the Local Correlation Integral method

    (Lof: Identifying density-based local outliers) for outlier

    detection in multivariate data which consists of numeric values.

  - The [ldbod](https://cran.r-project.org/package=ldbod) package provides flexible

    functions for computing local density-based outlier scores. It

    allows for subsampling of input data or a user specified reference

    data set to compute outlier scores against, so both unsupervised and

    semi-supervised outlier detection can be done.

  - The [kernlab](https://cran.r-project.org/package=kernlab) package provides

    kernel-based machine learning methods including one-class Support

    Vector Machines for *novelty* detection.

  - The [amelie](https://cran.r-project.org/package=amelie) package implements

    anomaly detection as binary classification for multivariate

  - The estimated density ratio function in

    [densratio](https://cran.r-project.org/package=densratio) package can be used in

    many applications such as anomaly detection, change-point detection,

    covariate shift adaptation.

  - The [lookout](https://cran.r-project.org/package=lookout) package detects

    outliers using leave-one-out kernel density estimates and extreme

    value theory. The bandwidth for kernel density estimates is computed

    using persistent homology, a technique in topological data analysis.

    It also has the capability to explore the birth and the cessation of

    outliers with changing bandwidth and significance levels via

    `persisting_outliers().`

  - The Weighted BACON (blocked adaptive computationally-efficient

    outlier nominators) algorithms in

    [wbacon](https://cran.r-project.org/package=wbacon) implement a weighted variant

    of the BACON algorithms for multivariate outlier detection and

    robust linear regression. The methods assume that the typical data

    follows an elliptically contoured distribution.

*Multivariate Outlier Detection: Distance-based outlier detection*

  - The [HDoutliers](https://cran.r-project.org/package=HDoutliers) package provides

    an implementation of an algorithm for univariate and multivariate

    outlier detection that can handle data with a mixed categorical and

    continuous variables and outlier masking problem.

  - The [stray](https://cran.r-project.org/package=stray) package implements an

    algorithm for detecting anomalies in high-dimensional data that

    addresses the limitations of 'HDoutliers' algorithm. An approach

    based on extreme value theory is used for the anomalous threshold

    calculation.

  - The [Routliers](https://cran.r-project.org/package=Routliers) package provides

    robust methods to detect univariate (Median Absolute Deviation

    method) and multivariate outliers (Mahalanobis-Minimum Covariance

    Determinant method).

  - The [modi](https://cran.r-project.org/package=modi) package implements

    Mahalanobis distance or depth-based algorithms for multivariate

    outlier detection in the presence of missing values (incomplete

    survey data).

  - The

    [CerioliOutlierDetection](https://cran.r-project.org/package=CerioliOutlierDetection)

    package implements the iterated RMCD method of Cerioli (2010) for

    multivariate outlier detection via robust Mahalanobis distances.

  - The [rrcovHD](https://cran.r-project.org/package=rrcovHD) package performs

    outlier identification using robust multivariate methods based on

    robust mahalanobis distances and principal component analysis.

  - The [mvoutlier](https://cran.r-project.org/package=mvoutlier) package also

    provides various robust methods based multivariate outlier detection

    capabilities. This includes a Mahalanobis type method with an

    adaptive outlier cutoff value, a method incorporating local

    neighborhood and a method for compositional data.

  - Function `dm.mahalanobis` in [DJL](https://cran.r-project.org/package=DJL)

    package implements Mahalanobis distance measure for outlier

    detection. In addition to the basic distance measure, boxplots are

    provided with potential outlier(s) to give an insight into the early

    stage of data cleansing task.

*Multivariate Outlier Detection: Clustering-based outlier detection*

  - The [kmodR](https://cran.r-project.org/package=kmodR) package presents a unified

    approach for simultaneously clustering and discovering outliers in

    high dimensional data. Their approach is formalized as a

    generalization of the k-MEANS problem.

  - The [DMwR2](https://cran.r-project.org/package=DMwR2) package uses hierarchical

    clustering to obtain a ranking of outlierness for a set of cases.

    The ranking is obtained on the basis of the path each case follows

    within the merging steps of a agglomerative hierarchical clustering

    method.

*Multivariate Outlier Detection: Angle-based outlier detection*

  - The [abodOutlier](https://cran.r-project.org/package=abodOutlier) package

    performs angle-based outlier detection on high dimensional data. A

    complete, a randomized and a knn based methods are available.

*Multivariate Outlier Detection: Decision tree based approaches*

  - Explainable outlier detection method through decision tree

    conditioning is facilitated by

    [outliertree](https://cran.r-project.org/package=outliertree) package .

  - The

    [bagged.outliertrees](https://cran.r-project.org/package=bagged.outliertrees)

    package provides an explainable unsupervised outlier detection

    method based on an ensemble implementation of the existing

    OutlierTree procedure in

    [outliertree](https://cran.r-project.org/package=outliertree) package. The

    implementation takes advantage of bootstrap aggregating (bagging) to

    improve robustness by reducing the possible masking effect and

    subsequent high variance (similarly to Isolation Forest), hence the

    name "Bagged OutlierTrees".

  - The [isotree](https://cran.r-project.org/package=isotree) package provides fast

    and multi-threaded implementation of Extended Isolation Forest,

    Fair-Cut Forest, SCiForest (a.k.a. Split-Criterion iForest), and

    regular Isolation Forest, for isolation-based outlier detection,

    clustered outlier detection, distance or similarity approximation,

    and imputation of missing values based on random or guided decision

    tree splitting. It also supports categorical data.

  - The [outForest](https://cran.r-project.org/package=outForest) package provides a

    random forest based implementation for multivariate outlier

    detection. In this method each numeric variable is regressed onto

    all other variables by a random forest. If the scaled absolute

    difference between observed value and out-of-bag prediction of the

    corresponding random forest is suspiciously large, then a value is

    considered an outlier.

  - The [solitude](https://cran.r-project.org/package=solitude) package provides an

    implementation of Isolation forest which detects anomalies in

    cross-sectional tabular data purely based on the concept of

    isolation without employing any distance or density measures.

*Multivariate Outlier Detection: Other approaches*

  - The [abnormality](https://cran.r-project.org/package=abnormality) package

    measures a Subject's Abnormality with Respect to a Reference

    Population. A methodology is introduced to address this bias to

    accurately measure overall abnormality in high dimensional spaces.

    It can be applied to datasets in which the number of observations is

    less than the number of features/variables, and it can be abstracted

    to practically any number of domains or dimensions.

  - The [ICSOutlier](https://cran.r-project.org/package=ICSOutlier) package performs

    multivariate outlier detection using invariant coordinates and

    offers different methods to choose the appropriate components. The

    current implementation targets data sets with only a small

    percentage of outliers but future extensions are under preparation.

  - The [sGMRFmix](https://cran.r-project.org/package=sGMRFmix) package provides an

    anomaly detection method for multivariate noisy sensor data using

    sparse Gaussian Markov random field mixtures. It can compute

    variable-wise anomaly scores.

  - Artificial neural networks for anomaly detection is implemented in

    [ANN2](https://cran.r-project.org/package=ANN2) package.

  - The [probout](https://cran.r-project.org/package=probout) package estimates

    unsupervised outlier probabilities for multivariate numeric

  - The [mrfDepth](https://cran.r-project.org/package=mrfDepth) package provides

    tools to compute depth measures and implementations of related tasks

    such as outlier detection, data exploration and classification of

    multivariate, regression and functional data.

  - The [evtclass](https://cran.r-project.org/package=evtclass) package provides two

    classifiers for open set recognition and novelty detection based on

    extreme value theory.

  - The [FastHCS](https://cran.r-project.org/package=FastHCS) package implements

    robust algorithm for principal component analysis and thereby

    provide robust PCA modeling and associated outlier detection and

    diagnostic tools for high-dimensional data. PCA based outlier

    detection tools are also available via

    [FactoInvestigate](https://cran.r-project.org/package=FactoInvestigate) package.

  - *Cellwise outliers* are entries in the data matrix which are

    substantially higher or lower than what could be expected based on

    the other cells in its column as well as the other cells in its row,

    taking the relations between the columns into account. Package

    [cellWise](https://cran.r-project.org/package=cellWise) provides tools for

    detecting cellwise outliers and robust methods to analyze data which

    may contain them.

  - *The Projection Congruent Subset (PCS)* is a method for finding

    multivariate outliers by searching for a subset which minimizes a

    criterion. PCS is supported by

    [FastPCS](https://cran.r-project.org/package=FastPCS) package.

  - The [molic](https://cran.r-project.org/package=molic) package provides an

    outlier detection method for high‐dimensional contingency tables

    using decomposable graphical models

  - The [outlierensembles](https://cran.r-project.org/package=outlierensembles)

    package provides ensemble functions for outlier/anomaly detection.

    In addition to some exiting ensemble methods for outlier detcetion,

    it also provides an Item Response Theory based ensemble method.

**Temporal Data**

  - The problems of anomaly detection for temporal data are 3-fold: (a)

    the detection of contextual anomalies (point anomalies) within a

    given series; (b) the detection of anomalous subsequences within a

    given series; and (c) the detection of anomalous series within a

    collection of series

  - The [trendsegmentR](https://cran.r-project.org/package=trendsegmentR) package

    performs the detection of point anomalies and linear trend changes

    for univariate time series by implementing the bottom-up unbalanced

    wavelet transformation.

  - The [anomaly](https://cran.r-project.org/package=anomaly) package implements

    Collective And Point Anomaly (CAPA), Multi-Variate Collective And

    Point Anomaly (MVCAPA), Proportion Adaptive Segment Selection (PASS)

    and Bayesian Abnormal Region Detector (BARD) methods for the

    detection of *anomalies* in time series data.

  - The [anomalize](https://cran.r-project.org/package=anomalize) package enables a

    "tidy" workflow for detecting anomalies in data. The main functions

    are `time_decompose()`, `anomalize()`, and `time_recompose()`.

  - The [cbar](https://cran.r-project.org/package=cbar) package detect contextual

    anomalies in time-series data with Bayesian data analysis. It

    focuses on determining a normal range of target value, and provides

    simple-to-use functions to abstract the outcome.

  - The `detectAO` and `detectIO` functions in

    [TSA](https://cran.r-project.org/package=TSA) package support detecting additive

    outlier and innovative outlier in time series data.

  - The [washeR](https://cran.r-project.org/package=washeR) package performs time

    series outlier detection using non parametric test. An input can be

    a data frame (grouped time series: phenomenon+date+group+values) or

    a vector (single time series)

  - The [tsoutliers](https://cran.r-project.org/package=tsoutliers) package

    implements the Chen-Liu approach for detection of time series

    outliers such as innovational outliers, additive outliers, level

    shifts, temporary changes and seasonal level shifts.

  - The [seasonal](https://cran.r-project.org/package=seasonal) package provides

    easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment

    software by the US Census Bureau. It offers full access to almost

    all options and outputs of X-13, including outlier detection.

  - The [npphen](https://cran.r-project.org/package=npphen) package implements basic

    and high-level functions for detection of anomalies in vector data

    (numerical series/ time series) and raster data (satellite derived

    products). Processing of very large raster files is supported.

  - the [ACA](https://cran.r-project.org/package=ACA) package offers an interactive

    function for the detection of abrupt change-points or aberrations in

    point series.

  - A set of online fault (anomaly) detectors for time series using

    prediction-based and window-based techniques are available via

    [otsad](https://cran.r-project.org/package=otsad) package. It can handle both

    stationary and non-stationary environments.

  - The [oddstream](https://cran.r-project.org/package=oddstream) package implements

    an algorithm for early detection of anomalous series within a large

    collection of streaming time series data. The model uses time series

    features as inputs, and a density-based comparison to detect any

    significant changes in the distribution of the features.

  - The [composits](https://cran.r-project.org/package=composits) package provides

    an ensemble of time series outlier detection methods that can be

    used for compositional, multivariate and univariate data. It uses

    the four R packages [forecast](https://cran.r-project.org/package=forecast),

    [tsoutliers](https://cran.r-project.org/package=tsoutliers),

    [otsad](https://cran.r-project.org/package=otsad) and

    [anomalize](https://cran.r-project.org/package=anomalize) to detect time series

    outliers.

  - The [pasadr](https://cran.r-project.org/package=pasadr) package provides a novel

    stealthy-attack detection mechanism that monitors time series of

    sensor measurements in real time for structural changes in the

    process behavior. It has the capability of detecting both

    significant deviations in the process behavior and subtle

    attack-indicating changes, significantly raising the bar for

    strategic adversaries who may attempt to maintain their malicious

    manipulation within the noise level.

**Spatial Outliers**

  - Spatial objects whose non-spatial attribute values are markedly

    different from those of their spatial neighbors are known as Spatial

    outliers or abnormal spatial patterns.

  - The [RWBP](https://cran.r-project.org/package=RWBP) package detects spatial

    outliers using a Random Walk on Bipartite Graph.

  - Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies

    in an image. Package [EFDR](https://cran.r-project.org/package=EFDR) implements

    wavelet-based Enhanced FDR for detecting signals from complete or

    incomplete spatially aggregated data. The package also provides

    elementary tools to interpolate spatially irregular data onto a grid

    of the required size.

  - The function `spatial.outlier` in

    [depth.plot](https://cran.r-project.org/package=depth.plot) package helps to

    identify multivariate spatial outlier within a p-variate data cloud

    or if any p-variate observation is an outlier with respect to a

    p-variate data cloud.

**Spatio-Temporal Data**

  - Functions for error detection and correction in point data quality

    datasets that are used in species distribution modeling are

    available via [biogeo](https://cran.r-project.org/package=biogeo) package.

  - The [CoordinateCleaner](https://cran.r-project.org/package=CoordinateCleaner)

    package provides functions for flagging of common spatial and

    temporal outliers (errors) in biological and paleontological

    collection data, for the use in conservation, ecology and

    paleontology.

**Functional Data**

  - The `foutliers()` function from

    [rainbow](https://cran.r-project.org/package=rainbow) package provides

    functional outlier detection methods. Bagplots and boxplots for

    functional data can also be used to identify outliers, which have

    either the lowest depth (distance from the centre) or the lowest

    density, respectively.

  - The [adamethods](https://cran.r-project.org/package=adamethods) package provides

    a collection of several algorithms to obtain archetypoids with small

    and large databases and with both classical multivariate data and

    functional data (univariate and multivariate). Some of these

    algorithms also allow to detect anomalies.

  - The `shape.fd.outliers` function in

    [ddalpha](https://cran.r-project.org/package=ddalpha) package detects functional

    outliers of first three orders, based on the order extended

    integrated depth for functional data.

  - The [fda.usc](https://cran.r-project.org/package=fda.usc) package provides tools

    for outlier detection in functional data (atypical curves detection)

    using different approaches such as likelihood ratio test, depth

    measures, quantiles of the bootstrap samples.

  - The [fdasrvf](https://cran.r-project.org/package=fdasrvf) package supports

    outlier detection in functional data using the square-root velocity

    framework which allows for elastic analysis of functional data

    through phase and amplitude separation.

  - The [fdaoutlier](https://cran.r-project.org/package=fdaoutlier) package provides

    a collection of functions for outlier detection in functional data

    analysis. Methods implemented include directional outlyingness,

    MS-plot, total variation depth, and sequential transformations among

    others.

**Visualization of Anomalies**

  - The [OutliersO3](https://cran.r-project.org/package=OutliersO3) package provides

    tools to aid in the display and understanding of patterns of

    multivariate outliers. It uses the results of identifying outliers

    for every possible combination of dataset variables to provide

    insight into why particular cases are outliers.

  - The [Morpho](https://cran.r-project.org/package=Morpho) package provides a

    collection of tools for Geometric Morphometrics and mesh processing.

    Apart from the core functions it provides a graphical interface to

    find outliers and/or to switch mislabeled landmarks.

  - The [StatDA](https://cran.r-project.org/package=StatDA) package provides

    visualization tools to locate outliers in environmental data.

**Pre-processing Methods for Anomaly Detection**

  - The [dobin](https://cran.r-project.org/package=dobin) package provides dimension

    reduction technique for outlier detection using neighbours,

    constructs a set of basis vectors for outlier detection. It brings

    outliers to the fore-front using fewer basis vectors.

**Specific Application Fields**

*Epidemiology*

  - The [ABPS](https://cran.r-project.org/package=ABPS) package provides an

    implementation of the Abnormal Blood Profile Score (ABPS, part of

    the Athlete Biological Passport program of the World Anti-Doping

    Agency), which combines several blood parameters into a single score

    in order to detect blood doping. The package also contains functions

    to calculate other scores used in anti-doping programs, such as the

    OFF-score

  - The [surveillance](https://cran.r-project.org/package=surveillance) package

    implements statistical methods for aberration detection in time

    series of counts, proportions and categorical data, as well as for

    the modeling of continuous-time point processes of epidemic

    phenomena. The package also contains several real-world data sets,

    the ability to simulate outbreak data, and to visualize the results

    of the monitoring in a temporal, spatial or spatio-temporal fashion.

  - The [outbreaker2](https://cran.r-project.org/package=outbreaker2) package

    supports Bayesian reconstruction of disease outbreaks using

    epidemiological and genetic information. It is applicable to various

    densely sampled epidemics, and improves previous approaches by

    detecting unobserved and imported cases, as well as allowing

    multiple introductions of the pathogen.

  - The [outbreaks](https://cran.r-project.org/package=outbreaks) package provides

    empirical or simulated disease outbreak data, either as RData or as

    text files.

*Other*

  - The [precintcon](https://cran.r-project.org/package=precintcon) package contains

    functions to analyze the precipitation intensity, concentration and

    anomaly.

  - The [survBootOutliers](https://cran.r-project.org/package=survBootOutliers)

    package provides concordance based bootstrap methods for outlier

    detection in survival analysis.

  - The [pcadapt](https://cran.r-project.org/package=pcadapt) package provides

    methods to detect genetic markers involved in biological adaptation

    using statistical tools based on Principal Component Analysis.

  - The [rgr](https://cran.r-project.org/package=rgr) package supports exploratory

    data analysis with applied geochemical data, with special

    application to the estimation of background ranges and

    identification of anomalies to support mineral exploration and

    environmental studies.

  - The [NMAoutlier](https://cran.r-project.org/package=NMAoutlier) package

    implements the forward search algorithm for the detection of

    outlying studies (studies with extreme results) in network

    meta-analysis.

  - The [boutliers](https://cran.r-project.org/package=boutliers) package provides

    methods for outlier detection and influence diagnostics for

    meta-analysis based on Bootstrap distributions of the influence

    statistics.

  - The [dave](https://cran.r-project.org/package=dave) package provides a

    collection of functions for data analysis in vegetation ecology

    including outlier detection using nearest neighbour distances.

  - The [MALDIrppa](https://cran.r-project.org/package=MALDIrppa) package provides

    methods for quality control and robust pre-processing and analysis

    of MALDI mass spectrometry data.

  - The [MIPHENO](https://cran.r-project.org/package=MIPHENO) package contains

    functions to carry out processing of high throughput data analysis

    and detection of putative hits/mutants.

  - The [OutlierDM](https://cran.r-project.org/package=OutlierDM) package provides

    functions to detect outlying values such as genes, peptides or

    samples for multi-replicated high-throughput high-dimensional data.

  - The [qpcR](https://cran.r-project.org/package=qpcR) package implements methods

    for kinetic outlier detection (KOD) in real-time polymerase chain

    reaction (qPCR).

  - The [referenceIntervals](https://cran.r-project.org/package=referenceIntervals)

    package provides a collection of tools including outlier detcetion

    to allow the medical professional to calculate appropriate reference

    ranges (intervals) with confidence intervals around the limits for

    diagnostic purposes.

  - The Hampel filter is a robust outlier detector using Median Absolute

    Deviation (MAD). The

    [seismicRoll](https://cran.r-project.org/package=seismicRoll) package provides

    fast rolling functions for seismology including outlier detection

    with a rolling Hampel Filter.

  - The [spikes](https://cran.r-project.org/package=spikes) package provides tool to

    detect election fraud from irregularities in vote-share

    distributions using re-sampled kernel density method.

  - The [wql](https://cran.r-project.org/package=wql) package stands for \`water

    quality' provides functions including anomaly detection to assist in

    the processing and exploration of data from environmental monitoring

    programs.

  - The Grubbs‐Beck test is recommended by the federal guidelines for

    detection of low outliers in flood flow frequency computation in the

    United States. The [MGBT](https://cran.r-project.org/package=MGBT) computes the

    multiple Grubbs-Beck low-outlier test on positively distributed data

    and utilities for non-interpretive U.S. Geological Survey annual

    peak-stream flow data processing.

  - The [envoutliers](https://cran.r-project.org/package=envoutliers) package

    provides three semi-parametric methods for detection of outliers in

    environmental data based on kernel regression and subsequent

    analysis of smoothing residuals

  - The [rIP](https://cran.r-project.org/package=rIP) package supports detection of

    fraud in online surveys by tracing, scoring, and visualizing IP

    addresses

  - The [extremeIndex](https://cran.r-project.org/package=extremeIndex) computes an

    index measuring the amount of information brought by forecasts for

    extreme events, subject to calibration. This index is originally

    designed for weather or climate forecasts, but it may be used in

    other forecasting contexts.

  - The [clampSeg](https://cran.r-project.org/package=clampSeg) package provides

    tool to identify and idealize flickering events in filtered ion

    channel recordings.

**Data Sets**

  - The [anomaly](https://cran.r-project.org/package=anomaly) package contains

    lightcurve time series data from the Kepler telescope.

  - The [leri](https://cran.r-project.org/package=leri) package finds and downloads

    Landscape Evaporative Response Index (LERI) data, then reads the

    data into R. The LERI product measures anomalies in actual

    evapotranspiration, to support drought monitoring and early warning

    systems.

  - The [waterData](https://cran.r-project.org/package=waterData) package imports

    U.S. Geological Survey (USGS) daily hydrologic data from USGS web

    services and provides functions to calculate and plot anomalies.

**Miscellaneous**

  - The [CircOutlier](https://cran.r-project.org/package=CircOutlier) package

    enables detection of outliers in circular-circular regression

    models, modifying its and estimating of models parameters.

  - The Residual Congruent Subset (RCS) is a method for finding outliers

    in the regression setting. RCS is supported by

    [FastRCS](https://cran.r-project.org/package=FastRCS) package.

  - Package [quokar](https://cran.r-project.org/package=quokar) provides quantile

    regression outlier diagnostics with K Left Out Analysis.

  - The [oclust](https://cran.r-project.org/package=oclust) package provides a

    function to detect and trim outliers in Gaussian mixture model based

    clustering using methods described in Clark and McNicholas (2019).

  - The [semdiag](https://cran.r-project.org/package=semdiag) package implements

    outlier and leverage diagnostics for Structural equation modeling.

  - The [SeleMix](https://cran.r-project.org/package=SeleMix) package provides

    functions for detection of outliers and influential errors using a

    latent variable model. A mixture model (Gaussian contamination

    model) based on response(s) y and a depended set of covariates is

    fit to the data to quantify the impact of errors to the estimates.

  - The [compositions](https://cran.r-project.org/package=compositions) package

    provides functions to detect various types of outliers in

    compositional datasets.

  - The [kuiper.2samp](https://cran.r-project.org/package=kuiper.2samp) package

    performs the two-sample Kuiper test to assess the anomaly of

    continuous, one-dimensional probability distributions.

  - The `enpls.od()` function in [enpls](https://cran.r-project.org/package=enpls)

    package performs outlier detection with ensemble partial least

    squares.

  - The [surveyoutliers](https://cran.r-project.org/package=surveyoutliers) package

    helps manage outliers in sample surveys by calculating optimal

    one-sided winsorizing cutoffs.

  - The [faoutlier](https://cran.r-project.org/package=faoutlier) package provides

    tools for detecting and summarize influential cases that can affect

    exploratory and confirmatory factor analysis models and structural

    equation models.

  - The [crseEventStudy](https://cran.r-project.org/package=crseEventStudy) package

    provides a robust and powerful test of abnormal stock returns in

    long-horizon event

    studies



### CRAN packages:

  - [abnormality](https://cran.r-project.org/package=abnormality)

  - [abodOutlier](https://cran.r-project.org/package=abodOutlier)

  - [ABPS](https://cran.r-project.org/package=ABPS)

  - [ACA](https://cran.r-project.org/package=ACA)

  - [adamethods](https://cran.r-project.org/package=adamethods)

  - [alphaOutlier](https://cran.r-project.org/package=alphaOutlier)

  - [amelie](https://cran.r-project.org/package=amelie)

  - [ANN2](https://cran.r-project.org/package=ANN2)

  - [anomalize](https://cran.r-project.org/package=anomalize)

  - [anomaly](https://cran.r-project.org/package=anomaly)

  - [bagged.outliertrees](https://cran.r-project.org/package=bagged.outliertrees)

  - [bigutilsr](https://cran.r-project.org/package=bigutilsr)

  - [biogeo](https://cran.r-project.org/package=biogeo)

  - [boutliers](https://cran.r-project.org/package=boutliers)

  - [cbar](https://cran.r-project.org/package=cbar)

  - [cellWise](https://cran.r-project.org/package=cellWise)

  - [CerioliOutlierDetection](https://cran.r-project.org/package=CerioliOutlierDetection)

  - [CircOutlier](https://cran.r-project.org/package=CircOutlier)

  - [clampSeg](https://cran.r-project.org/package=clampSeg)

  - [compositions](https://cran.r-project.org/package=compositions)

  - [composits](https://cran.r-project.org/package=composits)

  - [CoordinateCleaner](https://cran.r-project.org/package=CoordinateCleaner)

  - [crseEventStudy](https://cran.r-project.org/package=crseEventStudy)

  - [dave](https://cran.r-project.org/package=dave)

  - [dbscan](https://cran.r-project.org/package=dbscan)

  - [ddalpha](https://cran.r-project.org/package=ddalpha)

  - [DDoutlier](https://cran.r-project.org/package=DDoutlier) (core)

  - [densratio](https://cran.r-project.org/package=densratio)

  - [depth.plot](https://cran.r-project.org/package=depth.plot)

  - [DescTools](https://cran.r-project.org/package=DescTools)

  - [dixonTest](https://cran.r-project.org/package=dixonTest)

  - [DJL](https://cran.r-project.org/package=DJL)

  - [DMwR2](https://cran.r-project.org/package=DMwR2)

  - [dobin](https://cran.r-project.org/package=dobin)

  - [EFDR](https://cran.r-project.org/package=EFDR)

  - [enpls](https://cran.r-project.org/package=enpls)

  - [envoutliers](https://cran.r-project.org/package=envoutliers)

  - [evtclass](https://cran.r-project.org/package=evtclass)

  - [extremeIndex](https://cran.r-project.org/package=extremeIndex)

  - [extremevalues](https://cran.r-project.org/package=extremevalues)

  - [FactoInvestigate](https://cran.r-project.org/package=FactoInvestigate)

  - [faoutlier](https://cran.r-project.org/package=faoutlier)

  - [FastHCS](https://cran.r-project.org/package=FastHCS)

  - [FastPCS](https://cran.r-project.org/package=FastPCS)

  - [FastRCS](https://cran.r-project.org/package=FastRCS)

  - [fda.usc](https://cran.r-project.org/package=fda.usc)

  - [fdaoutlier](https://cran.r-project.org/package=fdaoutlier)

  - [fdasrvf](https://cran.r-project.org/package=fdasrvf)

  - [forecast](https://cran.r-project.org/package=forecast)

  - [funModeling](https://cran.r-project.org/package=funModeling)

  - [GmAMisc](https://cran.r-project.org/package=GmAMisc)

  - [HDoutliers](https://cran.r-project.org/package=HDoutliers) (core)

  - [hotspots](https://cran.r-project.org/package=hotspots)

  - [ICSOutlier](https://cran.r-project.org/package=ICSOutlier)

  - [isotree](https://cran.r-project.org/package=isotree)

  - [kernlab](https://cran.r-project.org/package=kernlab)

  - [kmodR](https://cran.r-project.org/package=kmodR)

  - [kuiper.2samp](https://cran.r-project.org/package=kuiper.2samp)

  - [ldbod](https://cran.r-project.org/package=ldbod)

  - [leri](https://cran.r-project.org/package=leri)

  - [lookout](https://cran.r-project.org/package=lookout)

  - [MALDIrppa](https://cran.r-project.org/package=MALDIrppa)

  - [MGBT](https://cran.r-project.org/package=MGBT)

  - [MIPHENO](https://cran.r-project.org/package=MIPHENO)

  - [modi](https://cran.r-project.org/package=modi)

  - [molic](https://cran.r-project.org/package=molic)

  - [Morpho](https://cran.r-project.org/package=Morpho)

  - [mrfDepth](https://cran.r-project.org/package=mrfDepth)

  - [mvoutlier](https://cran.r-project.org/package=mvoutlier)

  - [NMAoutlier](https://cran.r-project.org/package=NMAoutlier)

  - [npphen](https://cran.r-project.org/package=npphen)

  - [oclust](https://cran.r-project.org/package=oclust)

  - [oddstream](https://cran.r-project.org/package=oddstream)

  - [otsad](https://cran.r-project.org/package=otsad)

  - [outbreaker2](https://cran.r-project.org/package=outbreaker2)

  - [outbreaks](https://cran.r-project.org/package=outbreaks)

  - [outForest](https://cran.r-project.org/package=outForest)

  - [OutlierDM](https://cran.r-project.org/package=OutlierDM)

  - [outlierensembles](https://cran.r-project.org/package=outlierensembles)

  - [outliers](https://cran.r-project.org/package=outliers)

  - [OutliersO3](https://cran.r-project.org/package=OutliersO3) (core)

  - [outliertree](https://cran.r-project.org/package=outliertree)

  - [pasadr](https://cran.r-project.org/package=pasadr)

  - [pcadapt](https://cran.r-project.org/package=pcadapt)

  - [precintcon](https://cran.r-project.org/package=precintcon)

  - [probout](https://cran.r-project.org/package=probout)

  - [qpcR](https://cran.r-project.org/package=qpcR)

  - [quokar](https://cran.r-project.org/package=quokar)

  - [rainbow](https://cran.r-project.org/package=rainbow)

  - [referenceIntervals](https://cran.r-project.org/package=referenceIntervals)

  - [rgr](https://cran.r-project.org/package=rgr)

  - [rIP](https://cran.r-project.org/package=rIP)

  - [Rlof](https://cran.r-project.org/package=Rlof)

  - [Routliers](https://cran.r-project.org/package=Routliers)

  - [rrcovHD](https://cran.r-project.org/package=rrcovHD)

  - [RWBP](https://cran.r-project.org/package=RWBP)

  - [seasonal](https://cran.r-project.org/package=seasonal)

  - [seismicRoll](https://cran.r-project.org/package=seismicRoll)

  - [SeleMix](https://cran.r-project.org/package=SeleMix)

  - [semdiag](https://cran.r-project.org/package=semdiag)

  - [sGMRFmix](https://cran.r-project.org/package=sGMRFmix)

  - [SMLoutliers](https://cran.r-project.org/package=SMLoutliers)

  - [solitude](https://cran.r-project.org/package=solitude)

  - [spikes](https://cran.r-project.org/package=spikes)

  - [StatDA](https://cran.r-project.org/package=StatDA)

  - [stray](https://cran.r-project.org/package=stray)

  - [survBootOutliers](https://cran.r-project.org/package=survBootOutliers)

  - [surveillance](https://cran.r-project.org/package=surveillance)

  - [surveyoutliers](https://cran.r-project.org/package=surveyoutliers)

  - [trendsegmentR](https://cran.r-project.org/package=trendsegmentR)

  - [TSA](https://cran.r-project.org/package=TSA)

  - [tsoutliers](https://cran.r-project.org/package=tsoutliers)

  - [univOutl](https://cran.r-project.org/package=univOutl)

  - [washeR](https://cran.r-project.org/package=washeR)

  - [waterData](https://cran.r-project.org/package=waterData)

  - [wbacon](https://cran.r-project.org/package=wbacon)

  - [wql](https://cran.r-project.org/package=wql)

### Related links:

  - CRAN Task View: [Cluster](Cluster.html)

  - CRAN Task View: [ExtremeValue](ExtremeValue.html)

  - [GitHub repository for this Task

    View](https://github.com/pridiltal/ctv-AnomalyDetection)