Package index • DescToolsX

Package Overview & Interfaces

Package overview, shared interfaces, constants, options, and documentation tools.

DescToolsX DescToolsX-package: DescToolsX: Descriptive Statistics and Exploratory Data Analysis
attachAliases() detachAliases(): Attach and Remove Short Aliases for Selected DescToolsX Functions
Agreement: Agreement Measures - Common Interface
Association: Association Measures - Common Interface
ConfidenceIntervals: Confidence Interval Interface - Common Arguments
Formulas: Formula Interfaces - Common Arguments
getConcepts() conceptMap() conceptAudit(): Concept Utilities for Package Documentation
day.abb day.name: DescToolsX Constants
setDescToolsXOption(): Set DescToolsX Options

Describing Data & Tables

Compact inspection, descriptive summaries, and one- and two-dimensional tables.

abstract() print(<Abstract>): Display Compact Abstract of a Data Frame
desc() print(<Desc.list>) print(<Desc>) plot(<Desc>) plot(<Desc.factor>) print(<Desc.logical>) plot(<Desc.logical>) print(<Desc.numeric>) plot(<Desc.numeric>) print(<Desc.AllNA>) plot(<Desc.AllNA>): Describe Data
desc(<Date>): Descriptive statistics for calendar date variables
desc(<factor>) desc(<character>) print(<Desc.factor>): Describe a Factor
.descNN() print(<Desc.nn>) plot(<Desc.nn>): Describe a Numeric-Numeric Relationship
.descNQ() print(<Desc.nq>) plot(<Desc.nq>): Describe Relationship: Numeric x by Categorical g
desc(<numeric>): Describe a Numeric Variable
.descQN() print(<Desc.qn>): Describe Relationship: Categorical y vs Numeric x
.descQQ(): Describe Relationship: Categorical x by Categorical y
desc(<ts>) print(<Desc.ts>) plot(<Desc.ts>): Diagnostic Summary for Time Series Objects
print(<Desc.Date>): Print method for "Desc.Date" objects
desc(<table>) desc(<matrix>) desc(<array>) print(<Desc.table>) print(<Desc.qq>) plot(<Desc.qq>): Describe a Contingency Table
expFreq(): Expected Frequencies
freq() print(<Freq>): Frequency Table for a Single Variable
freq2D(): Bivariate (Two-Dimensional) Frequency Distribution
percTable() print(<PercTable>): Percentage Table
tOne() print(<tOne>) `[`(<tOne>): Create Table One Describing Baseline Characteristics

Location, Dispersion & Shape

Classical, weighted, robust, and nonlinear descriptive statistics.

gmean() gsd(): Geometric Mean and Standard Deviation
hmean(): Harmonic Mean and Its Confidence Interval
hodgesLehmann(): Hodges-Lehmann Estimator of Location
huberM(): Safe (Generalized) Huber M-Estimator of Location
meanX(): (Weighted) Arithmetic Mean
medianX(): (Weighted) Median Value
modeX(): Mode (most Frequent Value(s))
tukeyBiweight(): Tukey's Biweight Mean
coefVar() coefVarCI(): Coefficient of Variation
iqrX(): The (weighted) Interquartile Range
madX(): Median Absolute Deviation
meanAD(): Mean Absolute Deviation From a Center Point
meanSE(): Standard Error of Mean
rangeX(): (Robust) Range
sdX() varX(): (Weighted) Variance and Standard Deviation
large() small() highLow(): Kth Smallest/Largest Values
kurt(): Kurtosis
quantileX(): (Weighted) Sample Quantiles
skew(): Skewness

Association & Correlation

Nominal, ordinal, and continuous association measures and supporting tools.

contCoef(): Pearson's Contingency Coefficient
cramerV(): Cramer's V
gkTau(): Goodman Kruskal's Tau
lambda(): Goodman Kruskal Lambda
mutInf(): Mutual Information
phi(): Phi Coefficient
tschuprowT(): Tschuprow's T
uncertCoef(): Uncertainty Coefficient
yuleQ() yuleY(): Yule's Coefficients of Association (Q and Y)
conDisPairs(): Concordant and Discordant Pairs
kendallW(): Kendall's Coefficient of Concordance W
ordAssocs() gkGamma() kendallTauA() kendallTauB() stuartTauC() somersDelta(): Ordinal Association Measures
corPart(): Partial Correlation Matrix
corPolychor(): Polychoric Correlation
findCorrX(): Identify Highly Correlated Variables
hoeffdingD(): Hoeffding's D Statistic
keepSig(): Keep Only Significant Values in a Symmetric Matrix
pearsonCor(): Confidence Intervals for Pearson Correlation
spearmanCor(): Spearman Rank Correlation

Agreement & Reliability

Agreement measures, reliability coefficients, rater data, and supporting tools.

blandAltmanData(): Bland-Altman Agreement Data
ccc(): Lin's Concordance Correlation Coefficient
print(<BlandAltman>): Print a Bland-Altman Analysis
cohenKappa(): Cohen's Kappa and Weighted Kappa
kappaM(): Kappa for m Raters
krippAlpha(): Krippendorff's Alpha for Wide Data
pabak(): Prevalence-Adjusted and Bias-Adjusted Kappa (PABAK)
percAgreement(): Percent Agreement with Design-Based SE and CI
randolphKappa(): Randolph's Free-Marginal Multirater Kappa
cronbachAlpha(): Cronbach's Coefficient Alpha
icc(): Intraclass Correlation Coefficient (ICC)
isConfusionTable(): Detect Whether an Object Looks Like a Confusion/Coincidence Matrix
normalizeToConfusion(): Normalize Input to a Contingency or Agreement Table
raterFrame(): Create a Data.frame for Interrater Agreement

Effect Sizes & Binary Outcomes

Standardized effects, ANOVA effects, odds ratios, and relative risks.

cohenD(): Cohen's and Hedges' Effect Size
etaSq() aovlDetails() aovlErrorTerms(): Effect Size Calculations for ANOVAs
glassDelta(): Glass' Delta Effect Size
cohenH(): Cohen's h for a 2x2 Table
oddsRatio() print(<OddsRatio>): Compute Odds Ratios
relRisk(): Relative Risk

Inequality, Diversity & Concentration

Inequality curves and indices, diversity, entropy, and concentration measures.

atkinson(): Atkinson Index
gini(): Gini Coefficient
herfindahl(): Herfindahl Index
lc(<formula>) lc(<default>) predict(<Lc>): Lorenz Curve
rosenbluth(): Rosenbluth Index
theil(): Theil Index
divCoef(): Compute a diversity coefficient
entropy(): Shannon Entropy
simpson(): Simpson Diversity Indices

Classification & Model Evaluation

Classification metrics, calibration, discrimination, and prediction-error measures.

auc(): Compute Area Under the Curve
conf() print(<Conf>) plot(<Conf>) sensX() specX(): Confusion Matrix and Classification Metrics
cStat(): Concordance Statistic (C-Statistic / AUC)
averagePrecision(): Average Precision Score
logLoss(): Log Loss
brierScore(): Brier Score
mae(): Mean Absolute Error
mape(): Mean Absolute Percentage Error
mse(): Mean Squared Error
nmae(): Normalized Mean Absolute Error
nmse(): Normalized Mean Squared Error
rmse(): Root Mean Squared Error
smape(): Symmetric Mean Absolute Percentage Error

Transformations, Missing Data & Outliers

Binning, transformations, imputation, scaling, and outlier detection.

cut(<integer>): Cut an Integer Variable into Intervals
cutAge(): Create a Factor Variable by Cutting an Age Variable
cutQ(): Create a Factor Variable Using the Quantiles of a Continuous Variable
boxCox() boxCoxInv(): Box-Cox Transformation
boxCoxLambda(): Automatic Selection of Box-Cox Transformation Parameter
logSt() logStInv(): Started Logarithmic Transformation and Its Inverse
scaleX(): (Robust) Scaling and Centering
yeoJohnson() yeoJohnsonInv(): Yeo-Johnson Transformation
impute(): Impute Missing Values in a Vector
imputeKnn(): K-Nearest Neighbors Imputation
lof(): Local Outlier Factor
outlier(): Outlier

Dates & Time

Date classes, calendar arithmetic, extraction, conversion, and categorization.

as.ym() as.Date(<ym>) print(<ym>): A Class for Dealing with the Yearmonth Format
isDate() isTime() isDateTime() hasVaryingTime(): Date and Time Class Predicates
addMonths(): Add Months to a Date
countWorkDays(): Count Work Days Between Two Dates
year() month() week() day() `day<-`() weekday() quarter() today() now() hour() minute() second() timezone() yearMonth() yearWeek() yearDay() diffDays360() lastDayOfMonth() yearDays() monthDays() isWeekend() isLeapYear(): Basic Date Functions
hmsToMinute() hmsToSec() secToHms(): Convert h:m:s To/From seconds
generation(): Generation by Birth Year
zodiac(): Calculate the Zodiac of a Date