Package index • specleanr

All functions

abdata: Alburnoides bipunctatus species data from GBIF and iNaturalist

adjustboxplots(): Adjust the boxplots bounding fences using medcouple to flag suspicious outliers.

bestmethod(): Identifies the best method for outlier detection for a single species.

boots(): To implement bootstrapping procedures. Sampling with replacement.

broad_classify(): Outlier detection method broad classification.

check.exclude(): indicate excluded columns.

check_names(): Check species names for inconsistencies

check_packages(): Check for packages to install and respond to use

classify_data(): Extract final clean data using either absolute or best method generated outliers.

cosine(): Cosine similarity index based on (Gautam & Kulkarni 2014; Joy & Renumol 2020)

datacleaner-class: Outlier detection class for multiple methods

distboxplot(): Distribution boxplot

ecological_ranges(): Check for environmental outliers using species optimal ranges.

efidata: EFIPLUS data used to develop ecological sensitivity parameters for riverine species in European streams and rivers.

eif(): Computes the empirical influence function for each values in the dataset

extentvalues(): To check for a bounding box

extractMethods(): List of outlier detection methods implemented in this package.

extract_clean_data(): Extract final clean data using either absolute or best method generated outliers.

extractoutliers(): Extract outliers for a one species

geo_ranges(): Checks for geographic ranges from FishBase

getdata(): Download species records from online database.

getdiff(): get dataframe from the large dataframe.

ggenvironmentalspace(): Title Plotting to show the quality controlled data in environmental space.

ggoutlieraccum(): Identify if enough methods are selected for the outlier detection.

ggoutliers(): Visualize the outliers identified by each method

hamming(): Identify best outlier detection method using Hamming distance.

hampel(): Flag suspicious outliers based on the Hampel filter method..

handle_true_errors(): Catch errors during methods implementation.

interquartile(): Computes interquartile range to flag environmental outliers

isoforest(): Identify outliers using isolation forest model.

jaccard(): Identifies the best outlier detection method using Jaccard coefficient.

jdsdata: Joint Danube Survey Data

jknife(): Identifies outliers using Reverse Jackknifing method based on Chapman et al., (2005).

kdat: Sequential fences constants

logboxplot(): Log boxplot based for outlier detection.

mahal(): Flags outliers based on Mahalanobis distance matrix for all records.

match.argc(): Customized match function

match_datasets(): Data harmonizing for offline data based on Darwin Core terms .

medianrule(): Median rule method

mixediqr(): Mixed Interquartile range and semiInterquartile range Walker et al., 2018

mth: mth datasets with constant at each confidence interval levels.

multiabsolute(): Identifies absolute outliers for multiple species.

multibestmethod(): Identify best method for outlier removal for multiple species using majority votes.

multidetect(): Ensemble multiple outlier detection methods.

ocindex(): Identifies absolute outliers and their proportions for a single species.

onesvm(): Identify outliers using One Class Support Vector Machines

optimal_threshold(): Optimize threshold for clean data extraction.

overlap(): Identifies best outlier detection method using Overlap coefficient.

pca(): Implement principal component analysis for dimension reduction

pcboot(): To package both principal component analysis and bootstrapping.

pred_extract(): Preliminary data cleaning including removing duplicates, records outside a particular basin, and NAs.

search_threshold(): Determine the threshold using Locally estimated or weighted Scatterplot Smoothing.

semiIQR(): Computes semi-interquantile range to flag suspicious outliers

seqfences(): Sequential fences method

show(<datacleaner>): set method for displaying output details after outlier detection.

smc(): Identify best outlier detection method using simple matching coefficient.

sorensen(): Identifies best outlier detection method suing Sorensen Similarity Index.

thermal_ranges(): Collates minimum, maximum, and preferable temperatures from FishBase.

xglosh(): Global-Local Outlier Score from Hierarchies

xkmeans(): Flags outliers using kmeans clustering method

xknn(): k-nearest neighbors for outlier detection

xlof(): Flags suspicious using the local outlier factor or Density-Based Spatial Clustering of Applications with Noise.

zscore(): Computes z-scores to flag environmental outliers.