interpretations.tsv

name	interpretation
\cbl	left curly brace (open only)
\cb	auto-sized curly braces around argument
\cbmat	matrix wrapped in auto-sized curly braces
\paren	auto-sized parentheses around argument
\pmat	matrix formatted with parentheses (pmatrix)
\sparen	fixed-size parentheses around argument
\sb	auto-sized square brackets around argument
\distop	operator-name distribution/function dispatcher
\sbmat	matrix formatted with square brackets (bmatrix)
\Prf	Pr(·) probability expression
\am	arg max operator
\argmax	arg max operator (alias for \am)
\amin	arg min operator
\argmin	arg min operator (alias for \amin)
\p	probability density/mass function symbol p
\f	probability density function symbol f
\F	cumulative distribution function symbol F
\h	hazard function symbol h
\H	cumulative hazard function symbol H
\pr	probability function symbol (alias for \p)
\dens	probability density symbol (alias for \p)
\pdens	probability density symbol (alias for \p)
\fdens	f-style density symbol (alias for \f)
\mass	probability mass symbol (alias for \P)
\pmass	probability mass symbol (alias for \P)
\fmass	F-style mass symbol (alias for \F)
\S	survival function symbol S
\Sf	survival function S(·) applied to argument
\surv	survival function symbol (alias for \S)
\survf	survival function applied to argument (alias for \Sf)
\survratio	survival ratio ϙ_S (ratio symbol with survival subscript)
\rsurv	survival ratio (alias for \survratio)
\survr	survival ratio (alias for \survratio)
\survratiof	survival ratio function ϙ_S(·)
\rsurvf	survival ratio function (alias for \survratiof)
\survrf	survival ratio function (alias for \survratiof)
\riskset	risk set symbol ℛ (survival analysis)
\risksetf	risk set at time t: ℛ(t)
\Q	quantile function symbol Q
\quantile	quantile function symbol (alias for \Q)
\Qf	quantile function Q(·) applied to argument
\quantilef	quantile function applied to argument (alias for \Qf)
\hQ	estimated quantile function Q̂; deprecated, use \eQ
\hquantile	estimated quantile function (alias for \hQ); deprecated, use \equantile
\hQf	estimated quantile function Q̂(·) applied to argument; deprecated, use \eQf
\hquantilef	estimated quantile function applied to argument (alias for \hQf); deprecated, use \equantilef
\ph	estimated probability p-hat; deprecated, use \ep
\P	probability operator ℙ (blackboard bold)
\hp	estimated probability (alias for \ph); deprecated, use \ep
\ga	Greek letter alpha (α)
\a	Greek letter alpha (alias for \ga)
\b	Greek letter beta (β)
\g	Greek letter gamma (γ)
\floor	floor function ⌊·⌋
\ceiling	ceiling function ⌈·⌉
\ceil	ceiling function (alias for \ceiling)
\Ber	Bernoulli distribution label
\Bernoulli	Bernoulli distribution label (long form)
\Pois	Poisson distribution label
\Poisson	Poisson distribution label (long form)
\Gaus	Gaussian distribution label
\Normal	Normal distribution label N
\ndist	Normal distribution expression N(·)
\NB	Negative Binomial distribution label
\NegBin	Negative Binomial label (long form)
\vbeta	beta coefficient vector
\vcoef	coefficient vector (alias for \vbeta)
\regcoef	regression coefficient symbol β
\Regcoef	uppercase regression coefficient symbol B (italic, because a coefficient is a variable)
\coef	regression coefficient for covariate (β_x)
\Coef	uppercase regression coefficient for covariate (B_x)
\intcoef	regression intercept (β_0)
\Intcoef	uppercase regression intercept (B_0)
\vb	beta vector (alias for \vbeta)
\vzero	zero vector
\vone	vector of ones
\gb	Greek letter beta (alias for \b)
\gg	Greek letter gamma (alias for \g)
\gd	Greek letter delta (δ)
\eps	varepsilon error term (ε)
\cdev	deviation from conditional mean (alias for \popdev, symbol δ; the noise is \Noise, which prints ε)
\veps	error vector (ε-vector)
\epsp	ε(·) error function
\epsf	ε(·) error function (alias for \epsp)
\om	Greek letter omega (ω)
\m	Greek letter mu / population mean (μ)
\mean	population mean symbol μ
\pmean	population mean symbol (alias for \mean)
\popmean	population mean symbol (alias for \mean)
\vm	mean vector (μ-vector)
\s	Greek letter sigma (σ)
\l	Greek letter lambda (λ)
\gl	Greek letter lambda (alias for \l)
\lam	Greek letter lambda (alias for \l)
\k	Greek letter kappa (κ)
\cs	conditional survival probability Pr(T>t|T≥t)
\condsurv	conditional survival probability (alias for \cs)
\gs	Greek letter sigma (alias for \s)
\gm	Greek letter mu (alias for \m)
\M	uppercase M symbol (italic variable, alias for mean/moment)
\gM	uppercase M symbol (alias for \M)
\Mu	uppercase M symbol (alias for \M)
\cd	centered dot multiplication symbol (·)
\cds	centered ellipsis (⋯)
\lds	lower ellipsis (…)
\eqdef	equals-by-definition symbol (≝)
\defeq	equals-by-definition (alias for \eqdef)
\eqby	equals sign with the reason written over it (prints \eqby{\text{Thm 2}})
\hb	estimated beta coefficient β̂; deprecated, use \eb
\hcoef	estimated regression coefficient for covariate (β̂_x); deprecated, use \ecoef
\hintcoef	estimated regression intercept (β̂_0); deprecated, use \eintcoef
\hvb	estimated beta vector (β̂-vector); deprecated, use \evb
\hvcoef	estimated coefficient vector (alias for \hvb); deprecated, use \evcoef
\vhcoef	estimated coefficient vector (transposed-letter alias for \hvb)
\hR	Gelman-Rubin convergence diagnostic (potential scale reduction factor, R-hat); deprecated, use \eR
\hk	estimated kappa κ̂; deprecated, use \ek
\hkappa	estimated kappa (alias for \hk); deprecated, use \ekappa
\hl	estimated lambda λ̂; deprecated, use \el
\hy	fitted / predicted value ŷ; deprecated, use \ey
\yh	fitted value ŷ (alias for \hy); deprecated, use \ey
\fitted	fitted or predicted value (alias for \hy)
\V	variance operator symbol Var
\hs	estimated sigma σ̂; deprecated, use \es
\hsig	estimated sigma (alias for \hs); deprecated, use \esig
\hS	estimated covariance matrix Σ̂; deprecated, use \eS
\hSig	estimated Sigma (alias for \hS); deprecated, use \eSig
\hSigma	estimated Sigma (alias for \hS); deprecated, use \eSigma
\hsurv	estimated survival function; deprecated, use \esurv
\hSurv	estimated survival function (alias for \hsurv); deprecated, use \eSurv
\hSurvf	estimated survival function Ŝ(·) applied to argument; deprecated, use \eSurvf
\dist	distributes as relation (∼)
\ddist	approximately distributes as (∿)
\dsim	approximately distributes as (alias for \ddist)
\za	normal critical value z_{1−α/2}
\ciradsym	confidence interval radius symbol (ξ)
\cirad	confidence interval radius expression
\ciradf	confidence interval radius with argument
\ci	full confidence interval expression
\th	Greek letter theta (θ)
\vth	theta parameter vector
\Th	uppercase Theta (Θ)
\smean	sample mean of the quantity in the argument, a bar over it (prints a bar over its argument)
\xbar	sample mean x̄
\samplemean	sample mean of n random variables, X̄_n (arguments: the variable and the sample size)
\hth	estimated theta θ̂; deprecated, use \eth
\hthml	maximum likelihood estimate of theta; deprecated, use \ethml
\ba	begin aligned math environment
\ea	end aligned math environment
\ind	statistical independence symbol (⊥⊥)
\indpt	independence symbol (alias for \ind)
\indold	independence symbol (legacy Unicode)
\all	universal quantifier (∀)
\iid	i.i.d. label
\ciid	conditionally i.i.d. label
\simind	independently distributed relation
\siid	i.i.d. distributed relation
\sciid	c.i.i.d. distributed relation
\simiid	i.i.d. distributed (alias for \siid)
\distiid	i.i.d. distributed (alias for \siid)
\tf	therefore symbol (∴)
\Lik	likelihood script symbol (ℒ)
\llik	log-likelihood symbol (ℓ)
\loss	loss function symbol (L)
\lossf	loss function applied to its arguments, L(·)
\obsloss	loss on one observation, ℓ_i (argument: the observation index)
\poprisk	population (true) risk symbol R: the expected loss of a model
\popriskf	population risk of a model, R(·)
\lpnorm	name of an ℓ_p norm or penalty, such as ℓ_1 or ℓ_2 (argument: p)
\lbound	a lower bound of a set (ℓ)
\ubound	an upper bound of a set (u)
\hf	fitted prediction function (f̂); deprecated, use \epredfn
\hfx	fitted prediction function applied to its argument, f̂(·); deprecated, use \epredfnf
\loglik	log-likelihood symbol (alias for \llik)
\logl	log-likelihood symbol (alias for \llik)
\llikf	log-likelihood function ℓ(·)
\Likf	likelihood function ℒ(·)
\loglikf	log-likelihood function ℓ(·) (alias for \llikf)
\loglf	log-likelihood function ℓ(·) (alias for \llikf)
\likf	log-likelihood function ℓ(·) (alias for \llikf)
\deviance	deviance of a fitted model: twice the gap between the saturated model's log-likelihood and its own (prints the upright name dev, because an abbreviation is a name)
\nulldeviance	null deviance: the deviance of the intercept-only model
\satllik	maximized log-likelihood of the saturated model
\satdeviance	deviance relative to the saturated model: twice the difference between the saturated model's and the fitted model's maximized log-likelihoods
\dicdev	Bayesian deviance used by the deviance information criterion: minus twice the log-likelihood, with no saturated term (prints \deviance_{\mathrm{DIC}})
\dicdevf	Bayesian deviance at a parameter value, dev_DIC(·) (prints \dicdev followed by its argument in parentheses)
\deviation	deviation symbol δ
\devn	deviation symbol (alias for \deviation)
\deviationv	deviation vector (the vec accent applied to δ)
\devnv	deviation vector (alias for \deviationv)
\deviationf	deviation of the first argument from the reference value in the second, δ_r(z)
\smeandev	deviation of the sample mean, the sample mean of the deviations (prints \smean{\deviation}, δ with a bar)
\popdev	deviation from a population mean, symbol δ (alias for \deviation; the noise is \Noise, which prints ε)
\popdevf	deviation of the argument from its population mean, δ_z
\Deviation	uppercase deviation symbol Δ
\Devn	uppercase deviation (alias for \Deviation)
\residual	residual symbol ε̂: the estimate of the noise \Noise (via \est)
\resid	residual, observed minus predicted outcome (alias for \residual)
\residv	residual vector ε̰̂
\Residual	residual symbol ε̂ (alias for \residual; a residual has no separate capital)
\Resid	residual (alias for \Residual)
\stdresidual	standardized residual ε̂′
\stdresid	standardized residual (alias for \stdresidual)
\StdResidual	standardized residual ε̂′ (alias for \stdresidual)
\StdResid	standardized residual (alias)
\modresidual	modified residual ε̂*
\modresid	modified residual (alias for \modresidual)
\ModResidual	modified residual ε̂* (alias for \modresidual)
\ModResid	modified residual (alias)
\devof	deviation of the first argument from the reference value in the second (prints the first minus the second inside parentheses; see notation-choices for compound references)
\esterr	estimation error: the error ε of the estimate in the first argument for the estimand in the second, ε_estimand(estimate); the estimate minus the estimand, written explicitly with \devof{estimate}{estimand}
\prederr	prediction error, predicted minus observed outcome, symbol ε with the upright label pred; same meaning as \esterr{\predval}{\Outvar}
\prederrf	prediction error of the predictor in the argument, ε_pred(·) (prints \prederr followed by its argument in parentheses); the estimation error of that predictor for the new outcome, \esterr{predictor}{\Outvar}
\score	score function ℓ′
\scorev	score vector
\scorei	per-observation score vector ℓ′_i
\scorevi	per-observation score vector (alias for \scorei)
\scoref	score function ℓ′(·)
\hess	Hessian of log-likelihood ℓ″
\hessm	Hessian matrix (alias for \hess)
\hessian	Hessian (alias for \hess)
\hessij	(i,j) element of the Hessian
\hessijalt	(i,j) Hessian in bracket notation
\hessf	Hessian function ℓ″(·)
\hessianf	Hessian function (alias for \hessf)
\starf	starred / optimal version of argument
\lik	log-likelihood (alias for \llik)
\est	estimated / hat version of argument
\Est	estimated / wide-hat version of argument (for multi-character targets)
\esttmp	alternative estimate notation (·̂*)
\esttmpl	alternative estimate of lambda
\cR	range / codomain symbol ℛ
\range	range / codomain (alias for \cR)
\Range	range / codomain (alias for \cR)
\rangef	range of a function ℛ(·)
\seq	sequence (colon range) from first to second argument, e.g. 1:n
\~	approximately equal (≈)
\dapp	approximately equal dot notation (≐)
\deriv	partial derivative operator ∂/∂(·)
\dderiv	second partial derivative ∂²/∂(·)²
\derivf	partial derivative ∂f/∂x with numerator
\derivff	partial derivative ∂f/∂x (reversed argument order)
\derivfnd	alias for \derivf (numerator first, denominator second)
\derivfdn	alias for \derivff (denominator first, numerator second)
\dderivf	second partial derivative ∂²f/∂x²
\xderiv	cross partial derivative ∂²/∂x∂y
\xderivf	cross partial ∂²f/∂x∂y with numerator
\xderivff	cross partial (reversed argument order)
\grad	gradient operator ∇
\gradf	gradient of a function, ∇f
\gradwrt	gradient with respect to a variable, ∇_θ f
\dif	total derivative fraction dF/dt (numerator first, denominator second)
\red	typeset argument in red
\green	typeset argument in green
\blue	typeset argument in blue
\cyan	typeset argument in cyan
\magenta	typeset argument in magenta
\yellow	typeset argument in yellow
\black	typeset argument in black
\gray	typeset argument in gray
\white	typeset argument in white
\darkgray	typeset argument in dark gray
\lightgray	typeset argument in light gray
\brown	typeset argument in brown
\lime	typeset argument in lime green
\olive	typeset argument in olive
\orange	typeset argument in orange
\pink	typeset argument in pink
\purple	typeset argument in purple
\teal	typeset argument in teal
\violet	typeset argument in violet
\hE	estimated expected value (prints the hat of \Expp, with the argument in square brackets); deprecated, use \eE
\hExp	estimated expected value (alias for \hE); deprecated, use \eExp
\hExpf	estimated expected value (alias for \hE); deprecated, use \eExpf
\hmu	estimated mean μ̂; deprecated, use \emu
\hmuf	estimated mean function μ̂(·); deprecated, use \emuf
\Expp	expectation operator 𝔼 (no argument; blackboard bold)
\Ep	expectation operator (alias for \Expp)
\expit	expit / logistic function
\expitf	expit applied to argument
\dexpitf	derivative of expit function
\logit	logit function
\sigmoid	logistic (sigmoid) function symbol (σ)
\sigmoidf	logistic (sigmoid) function applied to its argument, σ(·)
\logitf	logit applied to argument
\E	expected value (prints \Expp, with the argument in square brackets)
\Ef	expected value (alias for \E)
\Exp	expected value (alias for \E)
\Expf	expected value (alias for \E)
\Expfc	conditional expected value (prints \Expp with the conditioning subscript c and the argument in square brackets)
\ATT	average treatment effect on the treated label ATT
\ATTf	average treatment effect on the treated function ATT(·)
\evalbar	expression followed by an evaluation bar ·|
\evalAt	evaluate expression at a given point
\Varr	variance operator symbol Var
\Vart	variance operator symbol (alias for \Varr)
\hvar	estimated variance operator (hat); deprecated, use \evar
\hvart	estimated variance operator (alias for \hvar); deprecated, use \evart
\Covt	covariance operator symbol Cov
\hcovt	estimated covariance operator; deprecated, use \ecovt
\hCovt	estimated covariance operator (alias); deprecated, use \eCovt
\var	variance function Var(·)
\varf	variance function (alias for \var)
\Var	variance function (alias for \var)
\hVar	estimated variance function V̂ar(·); deprecated, use \eVar
\Varf	variance function (alias for \var)
\hvarf	estimated variance function (alias for \hVar); deprecated, use \evarf
\Cov	covariance function Cov(·)
\Covs	bivariate covariance Cov(·,·)
\Covf	covariance function (alias for \Cov)
\covh	estimated covariance function Ĉov(·)
\covhf	estimated covariance function (alias)
\hcovf	estimated covariance function (alias); deprecated, use \ecovf
\aVar	asymptotic variance aVar(·)
\aCov	asymptotic covariance aCov(·)
\cum	cumulative-exposure operator cum(·)
\Cort	correlation operator symbol Cor
\hcort	estimated correlation operator; deprecated, use \ecort
\Cor	correlation function Cor(·)
\Corf	correlation function (alias for \Cor)
\Cors	bivariate correlation Cor(·,·)
\hCor	estimated correlation function Ĉor(·); deprecated, use \eCor
\hCorf	estimated correlation function (alias); deprecated, use \eCorf
\hCors	estimated bivariate correlation; deprecated, use \eCors
\med	median operator symbol
\medf	median function med(·)
\IQR	interquartile range operator symbol
\IQRf	interquartile range function IQR(·)
\varht	estimated variance operator (hat, alias); deprecated, use \evar
\varh	estimated variance function (alias)
\varhf	estimated variance function (alias)
\vc	bold vector notation for argument
\sd	standard deviation function sd(·)
\SD	standard deviation function SD(·)
\hSD	estimated standard deviation SD̂(·); deprecated, use \eSD
\hSDf	estimated standard deviation (alias); deprecated, use \eSDf
\se	standard error function se(·)
\hset	estimated standard error operator ŝe; deprecated, use \eset
\hSE	estimated standard error operator ŜE; deprecated, use \eSE
\hSEt	estimated SE operator (alias for \hSE); deprecated, use \eSEt
\sef	standard error function (alias for \se)
\hse	estimated standard error ŝe(·); deprecated, use \ese
\hsef	estimated standard error (alias for \hse); deprecated, use \esef
\SE	standard error function SE(·)
\SEf	standard error function (alias for \SE)
\SDf	standard deviation function (alias for \SD)
\HSE	estimated standard error function (alias); deprecated, use \eSEf
\hSEf	estimated standard error function (alias); deprecated, use \eSEf
\logt	log operator symbol
\logf	log{·} function applied to argument
\lnf	natural log function applied to its argument, ln(·)
\maxf	maximum of a comma-separated list, max(·)
\minf	minimum of a comma-separated list, min(·)
\sinf	sine function applied to its argument, sin(·)
\cosf	cosine function applied to its argument, cos(·)
\tanhf	hyperbolic tangent function applied to its argument, tanh(·)
\detf	determinant of its argument, det(·)
\dlog	derivative of log (log′)
\dlogf	derivative of log applied to argument
\exp	exponential function exp{·}; takes one argument, so never write it bare: use \expf{·} or \expt with your own delimiters
\expt	exp operator symbol
\expf	exponential function (alias for \exp)
\expfn	exponential function (alias for \exp)
\dexp	derivative of exponential (exp′)
\dexpf	derivative of exp applied to argument
\euler	Euler's number e (roman)
\e	e^(·) exponential notation
\ef	e^(·) exponential notation (alias for \e)
\inv	multiplicative inverse (·)^{−1} with parentheses
\invf	multiplicative inverse (alias for \inv)
\invp	multiplicative inverse (alias for \inv)
\invcb	inverse with curly braces {·}^{−1}
\invsb	inverse with square brackets [·]^{−1}
\oinf	observed information symbol I
\oinfm	observed information matrix symbol
\Nat	natural numbers set ℕ (blackboard-bold)
\oinff	observed information function I(·)
\einf	expected (Fisher) information symbol ℐ
\EInf	expected information (alias for \einf)
\FInf	Fisher information (alias for \einf)
\einfm	expected information matrix symbol
\einff	expected information function ℐ(·)
\heinf	estimated Fisher information ℐ̂; deprecated, use \eeinf
\heinff	estimated Fisher information function ℐ̂(·); deprecated, use \eeinff
\1	indicator function 1_{·} (upright subscript style)
\indic	indicator function 𝟙_{·} (blackboard bold)
\indicp	indicator function 𝟙(·) with parentheses
\indiccb	indicator function 𝟙{·} with curly braces
\indicf	indicator function (alias for \indic)
\set	set notation {·}
\pf	probability density/mass p(·)
\Pf	probability (prints \P with the argument in parentheses)
\Bias	bias function Bias(·)
\bias	bias function (alias for \Bias)
\ss	variance σ²
\ssqf	σ²(·) variance as a function
\mselr	mean squared error function MSE(·)
\maelr	mean absolute error function MAE(·)
\abs	absolute value |·|
\norm	Euclidean norm ‖·‖
\normsq	squared norm ‖·‖²
\card	cardinality (number of elements) of a finite set |·|
\factorial	factorial of a non-negative integer n! (prints its argument followed by an exclamation mark)
\binomial	binomial coefficient, "n choose k" (prints \binom{n}{k})
\powf	first argument (·) raised to the power of the second argument, with parentheses around the base
\sqf	square of argument (·)²
\devsq	squared deviation of the first argument from the reference value in the second (δ_r(z))²
\ssdev	sum over i = 1, ..., n of the squared deviation of the observation from the argument (·), via \devsq
\sq	square superscript ² (postfix)
\err	error term ε (alias for \eps); the same symbol as the noise \Noise
\erf	error function ε(·); for an estimate, the estimation error (estimate minus truth), written \esterr{estimate}{truth}
\vec	vector notation (undertilde style)
\vecf	vector notation (alias for \vec)
\v	vector shorthand (alias for \vecf)
\matr	matrix notation (bold style)
\mX	predictor matrix X (bold): the matrix of covariate values; the design matrix built from a feature map is \design
\mx	design matrix x lowercase (bold)
\va	vector a (a̰)
\vx	vector x (x̰)
\vxs	starred/reference vector x* (x̰*)
\diff	difference operator Δ
\diffvx	difference in x-vector Δx̰
\vxi	i-th observation vector x̰_i
\vX	random vector X (X̰)
\vy	outcome vector y (y̰)
\vY	uppercase outcome vector Y (Y̰)
\outvar	outcome variable, observed value (prints y)
\Outvar	outcome variable, random variable (prints Y)
\voutvar	vector of observed outcomes (prints y̰)
\vOutvar	random vector of outcomes (prints Y̰)
\eoutvar	predicted outcome, through \est (prints ŷ)
\veoutvar	vector of predicted outcomes (prints a tilde under ŷ)
\predvar	predictor variable, observed value (prints x)
\Predvar	predictor variable, random variable (prints X)
\vpredvar	vector of one observation's predictor values (prints x̰)
\mpredvar	input matrix: the predictor vectors of all n observations, one per row (prints bold X, like \mX, the usual textbook form)
\vPredvar	random vector of predictors (prints X̰)
\vresid	vector of residuals, observed minus predicted outcomes (the negative of the prediction errors \prederr) (prints ε̰̂)
\vprederr	vector of prediction errors, predicted minus observed outcomes (the vec accent applied to \prederr)
\vz	vector z (z̰)
\vZ	random vector Z (Z̰)
\vD	exposure/treatment vector D (D̰)
\vd	exposure vector d lowercase (d̰)
\vT	time vector T (T̰)
\vt	time vector t lowercase (t̰)
\vpi	vector of probabilities π (π̰)
\vw	weight vector w (w̰)
\vv	vector v (v̰)
\vh	vector h, such as hidden-unit values (h̰)
\ve	residual vector e (ḛ)
\vu	vector u (ṵ)
\vg	vector g, such as a gradient (g̰)
\vp	vector p, such as a probability distribution (p̰)
\vq	vector q, such as a second probability distribution (q̰)
\vr	vector r (r̰)
\vconst	constant vector c (c̰)
\vdelta	vector δ, such as backpropagated errors (δ̰)
\vepsilon	vector ϵ (ϵ̰); see also \veps for an error vector
\vphi	vector φ, such as features (φ̰)
\mat	matrix notation (alias for \matr)
\dsn	sequence x₁, …, xₙ
\Xin	sequence of random variables X₁, …, Xₙ
\xin	sequence of observations x₁, …, xₙ
\yin	sequence of outcomes y₁, …, yₙ
\'	transpose superscript ⊤
\pt	parenthesized transpose (x)′
\tp	transpose of argument x′
\dpr	dot product symbol (·)
\Xxin	paired sequence X₁=x₁, …, Xₙ=xₙ
\dprod	dot product of two arguments a·b
\dprodf	dot product a·b (alias for \dprod)
\dpf	dot product a·b (alias for \dprodf)
\tprod	transpose-based product x′y
\tprodf	transpose-based product (alias for \tprod)
\angb	angle brackets ⟨·⟩ sized to their contents
\iprod	inner product ⟨x, y⟩
\iprodf	inner product (alias for \iprod)
\oprod	outer product x y′
\stprod	self transpose-based product x′x
\qform	quadratic form of a matrix M in a vector x: x′Mx, written \qform{M}{x}
\siprod	self inner product ⟨x, x⟩
\soprod	self outer product x x′
\sdprod	self dot product x·x
\sdprodf	self dot product (alias for \sdprod)
\cossim	cosine similarity: dot product divided by the product of the lengths
\cossimf	cosine similarity applied to two arguments, cossim(x, y)
\dsvn	paired sequence X₁=x₁, …, Xₙ=xₙ (alias)
\sumn	sum over index from 1 to n
\sumnf	sum over the first argument from 1 to n of the second argument (·)
\sumto	sum over the first argument from 1 to the second
\sumtof	sum over the first argument from 1 to the second of the third argument (·)
\sumin	sum over i from 1 to n
\suminf	sum over i from 1 to n of the argument (·)
\sumkK	sum over the K distinct covariate patterns, k from 1 to K
\sumkKf	sum over k from 1 to K of the argument (·)
\sumiN	sum over i from 1 to N
\sumiNf	sum over i from 1 to N of the argument (·)
\avgin	average over i from 1 to n: one over n times the sum
\avginf	average over i from 1 to n of the argument (·)
\avgiN	average over i from 1 to N: one over N times the sum
\avgiNf	average over i from 1 to N of the argument (·)
\avgto	average over the first argument from 1 to the second: one over the second argument times the sum
\avgtof	average over the first argument from 1 to the second of the third argument (·)
\avgon	average over the observations in an index set: one over the set's size times the sum over its members
\avgonf	average over the observations in an index set of the second argument (·)
\sumip	sum over i from 1 to p
\sumipf	sum over i from 1 to p of the argument (·)
\sumjn	sum over j from 1 to n
\sumjnf	sum over j from 1 to n of the argument (·)
\sumjp	sum over j from 1 to p
\sumjpf	sum over j from 1 to p of the argument (·)
\prodin	product over i from 1 to n
\prodinf	product over i from 1 to n of the argument (·)
\lp	linear predictor x′β
\lpdp	linear predictor at x (dot product x⃗·β⃗)
\lincomp	linear combination symbol η
\odds	odds symbol ω
\OR	odds ratio label OR
\RD	risk difference label RD
\RR	risk ratio label RR
\ratio	generic ratio symbol ϙ (base for \ror, \hr, and similar ratio macros; subscript denotes the ratio type)
\ratiof	generic ratio function ϙ(·)
\ror	odds ratio ϙ_ω (ratio symbol with odds subscript)
\orf	odds ratio function ϙ_ω(·)
\logodds	log-odds symbol η
\logoddsf	log-odds function η(·)
\difflogodds	difference in log-odds Δη
\hdifflogodds	estimated difference in log-odds ΔΗ̂; deprecated, use \edifflogodds
\diffeta	difference in eta Δη (alias for \difflogodds)
\oddst	odds operator label
\probst	probs operator label
\probt	prob operator label
\riskt	risk operator label
\logoddst	log-odds text label
\prob	probability symbol π
\probs	probability symbol (alias for \prob)
\risk	risk symbol π (used as the subscript for \riskratio)
\prev	prevalence symbol π (used as the subscript for \prevratio)
\potout	potential outcome Y_a (outcome under intervened exposure a)
\potprob	potential outcome probability π_a (e.g. Pr(Y_a=1) for binary Y)
\potmean	potential outcome mean μ_a (e.g. E[Y_a])
\obsprob	observed outcome probability π(a) (given observed exposure a)
\obsmean	observed outcome mean μ(a) (given observed exposure a)
\py	potential outcome Y_a (shorthand for \potout)
\ppi	potential outcome probability π_a (shorthand for \potprob)
\pmu	potential outcome mean μ_a (shorthand for \potmean)
\opi	observed outcome probability π(a) (shorthand for \obsprob)
\omu	observed outcome mean μ(a) (shorthand for \obsmean)
\probit	probit link function
\oddsf	odds function odds{·}
\doddsf	derivative of odds function odds′{·}
\oddsinv	inverse odds operator
\invodds	inverse odds (alternative notation)
\oddsinvf	inverse odds function
\invoddsf	inverse odds function (alias)
\doddsinvf	derivative of inverse odds function
\dinvoddsf	derivative of inverse odds (alias)
\rate	rate / hazard symbol λ
\lograte	log-rate symbol η
\haz	hazard function symbol λ (alias for \rate)
\hazratio	hazard ratio ϙ_λ (ratio symbol with hazard subscript)
\hr	hazard ratio (alias for \hazratio)
\rhaz	hazard ratio (alias for \hazratio)
\hazr	hazard ratio (alias for \hazratio)
\hrf	hazard ratio function ϙ_λ(·)
\rhazf	hazard ratio function (alias for \hrf)
\hazrf	hazard ratio function (alias for \hrf)
\rateratio	rate ratio ϙ_λ (ratio symbol with rate subscript)
\rater	rate ratio (alias for \rateratio)
\rrate	rate ratio (alias for \rateratio)
\rateratiof	rate ratio function ϙ_λ(·)
\raterf	rate ratio function (alias for \rateratiof)
\rratef	rate ratio function (alias for \rateratiof)
\riskratio	risk ratio ϙ_π (ratio symbol with risk subscript)
\rrisk	risk ratio (alias for \riskratio)
\riskr	risk ratio (alias for \riskratio)
\riskratiof	risk ratio function ϙ_π(·)
\rriskf	risk ratio function (alias for \riskratiof)
\riskrf	risk ratio function (alias for \riskratiof)
\prevratio	prevalence ratio ϙ_π (ratio symbol with prevalence subscript)
\prr	prevalence ratio (alias for \prevratio)
\prevratiof	prevalence ratio function ϙ_π(·)
\prrf	prevalence ratio function (alias for \prevratiof)
\loghaz	log-hazard symbol η
\diffloghaz	difference in log-hazard Δη
\cuhaz	cumulative hazard symbol Λ
\cumhaz	cumulative hazard (alias for \cuhaz)
\cuhazratio	cumulative hazard ratio ϙ_Λ (ratio symbol with cumulative-hazard subscript)
\rcuhaz	cumulative hazard ratio (alias for \cuhazratio)
\cuhazr	cumulative hazard ratio (alias for \cuhazratio)
\cuhazratiof	cumulative hazard ratio function ϙ_Λ(·)
\rcuhazf	cumulative hazard ratio function (alias for \cuhazratiof)
\cuhazrf	cumulative hazard ratio function (alias for \cuhazratiof)
\Surv	survival function (alias for \surv)
\incidence	incidence rate (average hazard)
\phaz	expected hazard E[λ] (Poisson hazard)
\idmat	identity matrix 𝕀
\and	set intersection (∩)
\reglincomb	regression linear combination x̰·β̰
\regbetasum	expanded regression sum β₁x₁+⋯+βₚxₚ
\pdf	probability density function symbol f
\pmf	probability mass function ℙ(·)
\cdf	cumulative distribution function symbol F
\cdff	cumulative distribution function F(·) applied to argument
\hcdf	estimated cumulative distribution function F̂; deprecated, use \ecdf
\hcdff	estimated cumulative distribution function F̂(·) applied to argument; deprecated, use \ecdff
\defLik	definitional expression for likelihood ℒ(θ)
\defLogLik	definitional expression for log-likelihood ℓ
\defScore	definitional expression for score function ℓ′
\defHess	definitional expression for Hessian matrix ℓ″
\thmHessij	formula for (i,j) element of the Hessian
\defOInf	definitional expression for observed information I
\defEInf	definitional expression for expected information ℐ
\thmADistMLE	asymptotic normal distribution of the MLE
\eqGLMRatio	GLM hazard or odds ratio expression
\defHR	definitional expression for hazard ratio
\kerngaus	Gaussian distribution kernel exponent
\likgaus	Gaussian likelihood expression
\llgaus	Gaussian log-likelihood expression
\scoreexpf	score function for exponential family
\llexpmean	log-likelihood expression for exponential mean model
\signt	sign operator symbol
\signf	sign function sign{·}
\reals	real numbers ℝ
\NA	not-available / missing-value placeholder (text "NA")
\ub	underbrace shorthand (alias for \underbrace)
\ubf	underbrace with subscript label \underbrace{·}_{label}
\dims	underbrace annotation labeling an expression with its dimensions, e.g. \dims{X}{n \times p}
\factor	generic factor symbol θ (base for \hazfactor, \riskfactor, and similar factor macros; compares one covariate pattern to the baseline; subscript denotes the factor type)
\factorf	generic factor function θ(·)
\oddsfactor	odds factor θ_ω (factor symbol with odds subscript; odds for a covariate pattern vs. baseline)
\oddsfactorf	odds factor function θ_ω(·)
\fodds	odds factor (alias for \oddsfactor)
\foddsf	odds factor function (alias for \oddsfactorf)
\hazfactor	hazard factor θ_λ (factor symbol with hazard subscript; hazard multiplier for a covariate pattern vs. baseline)
\hazfactorf	hazard factor function θ_λ(·)
\fhaz	hazard factor (alias for \hazfactor)
\fhazf	hazard factor function (alias for \hazfactorf)
\ratefactor	rate factor θ_λ (factor symbol with rate subscript; rate for a covariate pattern vs. baseline)
\ratefactorf	rate factor function θ_λ(·)
\frate	rate factor (alias for \ratefactor)
\fratef	rate factor function (alias for \ratefactorf)
\riskfactor	risk factor θ_π (factor symbol with risk subscript; risk for a covariate pattern vs. baseline)
\riskfactorf	risk factor function θ_π(·)
\frisk	risk factor (alias for \riskfactor)
\friskf	risk factor function (alias for \riskfactorf)
\prevfactor	prevalence factor θ_π (factor symbol with prevalence subscript; prevalence for a covariate pattern vs. baseline)
\prevfactorf	prevalence factor function θ_π(·)
\fprev	prevalence factor (alias for \prevfactor)
\fprevf	prevalence factor function (alias for \prevfactorf)
\cuhazfactor	cumulative hazard factor θ_Λ (factor symbol with cumulative-hazard subscript; cumulative hazard for a covariate pattern vs. baseline)
\cuhazfactorf	cumulative hazard factor function θ_Λ(·)
\fcuhaz	cumulative hazard factor (alias for \cuhazfactor)
\fcuhazf	cumulative hazard factor function (alias for \cuhazfactorf)
\survfactor	survival factor θ_S (factor symbol with survival subscript; survival for a covariate pattern vs. baseline)
\survfactorf	survival factor function θ_S(·)
\fsurv	survival factor (alias for \survfactor)
\fsurvf	survival factor function (alias for \survfactorf)
\ff	generic factor function (shorthand for \factorf)
\oddsff	odds factor function (shorthand for \oddsfactorf)
\hazff	hazard factor function (shorthand for \hazfactorf)
\rateff	rate factor function (shorthand for \ratefactorf)
\riskff	risk factor function (shorthand for \riskfactorf)
\prevff	prevalence factor function (shorthand for \prevfactorf)
\cuhazff	cumulative hazard factor function (shorthand for \cuhazfactorf)
\survff	survival factor function (shorthand for \survfactorf)
\hprob	estimated probability π̂; deprecated, use \eprob
\hprobs	estimated probability symbol π̂ (alias for \hprob); deprecated, use \eprobs
\hPr	estimated probability with brackets Pr̂[·]; deprecated, use \ePr
\hrisk	estimated risk π̂ (alias for \hprob); deprecated, use \erisk
\hprev	estimated prevalence π̂ (alias for \hprob); deprecated, use \eprev
\hpoprisk	estimated population risk R̂ (hat-wrapped \poprisk); the empirical risk; deprecated, use \epoprisk
\hpopriskf	estimated population risk function R̂(·) (hat-wrapped \popriskf); deprecated, use \epopriskf
\hodds	estimated odds ω̂; deprecated, use \eodds
\hATT	estimated average treatment effect on the treated ATT̂; deprecated, use \eATT
\hATTf	estimated average treatment effect on the treated function ATT̂(·); deprecated, use \eATTf
\hrate	estimated rate λ̂; deprecated, use \erate
\hhaz	estimated hazard λ̂ (alias for \hrate); deprecated, use \ehaz
\hcuhaz	estimated cumulative hazard Λ̂; deprecated, use \ecuhaz
\hcumhaz	estimated cumulative hazard (alias for \hcuhaz); deprecated, use \ecumhaz
\hratio	generic estimated ratio ϙ̂ (hat-wrapped \ratio); deprecated, use \eratio
\hratiof	generic estimated ratio function ϙ̂(·); deprecated, use \eratiof
\hror	estimated odds ratio ϙ̂_ω (\hratio subscripted by \odds, so the hat covers only the main symbol); deprecated, use \eror
\horf	estimated odds ratio function ϙ̂_ω(·); deprecated, use \eorf
\hhazratio	estimated hazard ratio ϙ̂_λ (\hratio subscripted by \haz, so the hat covers only the main symbol); deprecated, use \ehazratio
\hhr	estimated hazard ratio (alias for \hhazratio); deprecated, use \ehr
\hrhaz	estimated hazard ratio (alias for \hhazratio); deprecated, use \erhaz
\hhazr	estimated hazard ratio (alias for \hhazratio); deprecated, use \ehazr
\hhrf	estimated hazard ratio function ϙ̂_λ(·); deprecated, use \ehrf
\hrhazf	estimated hazard ratio function (alias for \hhrf); deprecated, use \erhazf
\hhazrf	estimated hazard ratio function (alias for \hhrf); deprecated, use \ehazrf
\hrateratio	estimated rate ratio ϙ̂_λ (\hratio subscripted by \rate, so the hat covers only the main symbol); deprecated, use \erateratio
\hrater	estimated rate ratio (alias for \hrateratio); deprecated, use \erater
\hrrate	estimated rate ratio (alias for \hrateratio); deprecated, use \errate
\hrateratiof	estimated rate ratio function ϙ̂_λ(·); deprecated, use \erateratiof
\hraterf	estimated rate ratio function (alias for \hrateratiof); deprecated, use \eraterf
\hrratef	estimated rate ratio function (alias for \hrateratiof); deprecated, use \erratef
\hriskratio	estimated risk ratio ϙ̂_π (\hratio subscripted by \risk, so the hat covers only the main symbol); deprecated, use \eriskratio
\hrrisk	estimated risk ratio (alias for \hriskratio); deprecated, use \errisk
\hriskr	estimated risk ratio (alias for \hriskratio); deprecated, use \eriskr
\hriskratiof	estimated risk ratio function ϙ̂_π(·); deprecated, use \eriskratiof
\hrriskf	estimated risk ratio function (alias for \hriskratiof); deprecated, use \erriskf
\hriskrf	estimated risk ratio function (alias for \hriskratiof); deprecated, use \eriskrf
\hRD	estimated risk difference RD̂; deprecated, use \eRD
\hRR	estimated risk ratio RR̂; deprecated, use \eRR
\hprevratio	estimated prevalence ratio ϙ̂_π (\hratio subscripted by \prev, so the hat covers only the main symbol); deprecated, use \eprevratio
\hprr	estimated prevalence ratio (alias for \hprevratio); deprecated, use \eprr
\hprevratiof	estimated prevalence ratio function ϙ̂_π(·); deprecated, use \eprevratiof
\hprrf	estimated prevalence ratio function (alias for \hprevratiof); deprecated, use \eprrf
\hsurvratio	estimated survival ratio ϙ̂_S (\hratio subscripted by \surv, so the hat covers only the main symbol); deprecated, use \esurvratio
\hrsurv	estimated survival ratio (alias for \hsurvratio); deprecated, use \ersurv
\hsurvr	estimated survival ratio (alias for \hsurvratio); deprecated, use \esurvr
\hsurvratiof	estimated survival ratio function ϙ̂_S(·); deprecated, use \esurvratiof
\hrsurvf	estimated survival ratio function (alias for \hsurvratiof); deprecated, use \ersurvf
\hsurvrf	estimated survival ratio function (alias for \hsurvratiof); deprecated, use \esurvrf
\hcuhazratio	estimated cumulative hazard ratio ϙ̂_Λ (\hratio subscripted by \cuhaz, so the hat covers only the main symbol); deprecated, use \ecuhazratio
\hrcuhaz	estimated cumulative hazard ratio (alias for \hcuhazratio); deprecated, use \ercuhaz
\hcuhazr	estimated cumulative hazard ratio (alias for \hcuhazratio); deprecated, use \ecuhazr
\hcuhazratiof	estimated cumulative hazard ratio function ϙ̂_Λ(·); deprecated, use \ecuhazratiof
\hrcuhazf	estimated cumulative hazard ratio function (alias for \hcuhazratiof); deprecated, use \ercuhazf
\hcuhazrf	estimated cumulative hazard ratio function (alias for \hcuhazratiof); deprecated, use \ecuhazrf
\hfactor	generic estimated factor θ̂ (hat-wrapped \factor); deprecated, use \efactor
\hfactorf	generic estimated factor function θ̂(·); deprecated, use \efactorf
\hff	generic estimated factor function (shorthand for \hfactorf); deprecated, use \eff
\hoddsfactor	estimated odds factor θ̂_ω (\hfactor subscripted by \odds, so the hat covers only the main symbol); deprecated, use \eoddsfactor
\hoddsfactorf	estimated odds factor function θ̂_ω(·); deprecated, use \eoddsfactorf
\hfodds	estimated odds factor (alias for \hoddsfactor); deprecated, use \efodds
\hfoddsf	estimated odds factor function (alias for \hoddsfactorf); deprecated, use \efoddsf
\hoddsff	estimated odds factor function (shorthand for \hoddsfactorf); deprecated, use \eoddsff
\hhazfactor	estimated hazard factor θ̂_λ (\hfactor subscripted by \haz, so the hat covers only the main symbol); deprecated, use \ehazfactor
\hhazfactorf	estimated hazard factor function θ̂_λ(·); deprecated, use \ehazfactorf
\hfhaz	estimated hazard factor (alias for \hhazfactor); deprecated, use \efhaz
\hfhazf	estimated hazard factor function (alias for \hhazfactorf); deprecated, use \efhazf
\hhazff	estimated hazard factor function (shorthand for \hhazfactorf); deprecated, use \ehazff
\hratefactor	estimated rate factor θ̂_λ (\hfactor subscripted by \rate, so the hat covers only the main symbol); deprecated, use \eratefactor
\hratefactorf	estimated rate factor function θ̂_λ(·); deprecated, use \eratefactorf
\hfrate	estimated rate factor (alias for \hratefactor); deprecated, use \efrate
\hfratef	estimated rate factor function (alias for \hratefactorf); deprecated, use \efratef
\hrateff	estimated rate factor function (shorthand for \hratefactorf); deprecated, use \erateff
\hriskfactor	estimated risk factor θ̂_π (\hfactor subscripted by \risk, so the hat covers only the main symbol); deprecated, use \eriskfactor
\hriskfactorf	estimated risk factor function θ̂_π(·); deprecated, use \eriskfactorf
\hfrisk	estimated risk factor (alias for \hriskfactor); deprecated, use \efrisk
\hfriskf	estimated risk factor function (alias for \hriskfactorf); deprecated, use \efriskf
\hriskff	estimated risk factor function (shorthand for \hriskfactorf); deprecated, use \eriskff
\hprevfactor	estimated prevalence factor θ̂_π (\hfactor subscripted by \prev, so the hat covers only the main symbol); deprecated, use \eprevfactor
\hprevfactorf	estimated prevalence factor function θ̂_π(·); deprecated, use \eprevfactorf
\hfprev	estimated prevalence factor (alias for \hprevfactor); deprecated, use \efprev
\hfprevf	estimated prevalence factor function (alias for \hprevfactorf); deprecated, use \efprevf
\hprevff	estimated prevalence factor function (shorthand for \hprevfactorf); deprecated, use \eprevff
\hcuhazfactor	estimated cumulative hazard factor θ̂_Λ (\hfactor subscripted by \cuhaz, so the hat covers only the main symbol); deprecated, use \ecuhazfactor
\hcuhazfactorf	estimated cumulative hazard factor function θ̂_Λ(·); deprecated, use \ecuhazfactorf
\hfcuhaz	estimated cumulative hazard factor (alias for \hcuhazfactor); deprecated, use \efcuhaz
\hfcuhazf	estimated cumulative hazard factor function (alias for \hcuhazfactorf); deprecated, use \efcuhazf
\hcuhazff	estimated cumulative hazard factor function (shorthand for \hcuhazfactorf); deprecated, use \ecuhazff
\hsurvfactor	estimated survival factor θ̂_S (\hfactor subscripted by \surv, so the hat covers only the main symbol); deprecated, use \esurvfactor
\hsurvfactorf	estimated survival factor function θ̂_S(·); deprecated, use \esurvfactorf
\hfsurv	estimated survival factor (alias for \hsurvfactor); deprecated, use \efsurv
\hfsurvf	estimated survival factor function (alias for \hsurvfactorf); deprecated, use \efsurvf
\hsurvff	estimated survival factor function (shorthand for \hsurvfactorf); deprecated, use \esurvff
\emp	empirical estimator label ("emp" subscript)
\km	Kaplan-Meier estimator label ("KM" subscript)
\naa	Nelson-Aalen estimator label ("NA" subscript)
\hsemp	empirical survival function estimator Ŝ_emp; deprecated, use \esemp
\hskm	Kaplan-Meier survival function estimator Ŝ_KM; deprecated, use \eskm
\hsnaa	Nelson-Aalen survival function estimator Ŝ_NA; deprecated, use \esnaa
\hsempf	empirical survival function estimator Ŝ_emp(·) applied to argument; deprecated, use \esempf
\hskmf	Kaplan-Meier survival function estimator Ŝ_KM(·) applied to argument; deprecated, use \eskmf
\hsnaaf	Nelson-Aalen survival function estimator Ŝ_NA(·) applied to argument; deprecated, use \esnaaf
\eQ	estimated quantile function Q̂
\equantile	estimated quantile function (alias for \eQ)
\eQf	estimated quantile function Q̂(·) applied to argument
\equantilef	estimated quantile function applied to argument (alias for \eQf)
\ep	estimated probability density (hat p)
\eb	estimated beta coefficient β̂
\ecoef	estimated regression coefficient for covariate (β̂_x)
\eintcoef	estimated regression intercept (β̂_0)
\evb	estimated beta vector (β̂-vector)
\evcoef	estimated coefficient vector (alias for \evb)
\evhcoef	estimated coefficient vector (transposed-letter alias for \evb)
\eR	Gelman-Rubin convergence diagnostic (potential scale reduction factor, R-hat)
\ek	estimated kappa κ̂
\ekappa	estimated kappa (alias for \ek)
\el	estimated lambda λ̂
\ey	estimated value of y
\efitted	fitted or predicted value (alias for \ey)
\es	estimated sigma σ̂
\esig	estimated sigma (alias for \es)
\eS	estimated covariance matrix Σ̂
\eSig	estimated Sigma (alias for \eS)
\eSigma	estimated Sigma (alias for \eS)
\esurv	estimated survival function
\eSurv	estimated survival function (alias for \esurv)
\eSurvf	estimated survival function Ŝ(·) applied to argument
\esemp	empirical survival function estimator Ŝ_emp
\eskm	Kaplan-Meier survival function estimator Ŝ_KM
\esnaa	Nelson-Aalen survival function estimator Ŝ_NA
\esempf	empirical survival function estimator Ŝ_emp(·) applied to argument
\eskmf	Kaplan-Meier survival function estimator Ŝ_KM(·) applied to argument
\esnaaf	Nelson-Aalen survival function estimator Ŝ_NA(·) applied to argument
\eth	estimated theta θ̂
\ethml	maximum likelihood estimate of theta
\eE	estimated expected value (prints \est of \Expp, with the argument in square brackets)
\eExp	estimated expected value (alias for \eE)
\eExpf	estimated expected value (alias for \eE)
\emu	estimated mean μ̂
\emuf	estimated mean function μ̂(·)
\evar	estimated variance operator (hat)
\evart	estimated variance operator (alias for \evar)
\ecovt	estimated covariance operator
\eCovt	estimated covariance operator (alias)
\eVar	estimated variance function V̂ar(·)
\evarf	estimated variance function (alias for \eVar)
\ecovf	estimated covariance function (alias)
\ecort	estimated correlation operator
\eCor	estimated correlation function Ĉor(·)
\eCorf	estimated correlation function (alias)
\eCors	estimated bivariate correlation
\eSD	estimated standard deviation SD̂(·)
\eSDf	estimated standard deviation (alias)
\eset	estimated standard error operator ŝe
\eSE	estimated standard error operator ŜE
\eSEt	estimated SE operator (alias for \eSE)
\ese	estimated standard error ŝe(·)
\esef	estimated standard error (alias for \ese)
\eSEf	estimated standard error function (alias)
\eeinf	estimated Fisher information ℐ̂
\eeinff	estimated Fisher information function ℐ̂(·)
\edifflogodds	estimated difference in log-odds ΔΗ̂
\ecdf	estimated cumulative distribution function F̂
\ecdff	estimated cumulative distribution function F̂(·) applied to argument
\eprob	estimated probability π̂
\eprobs	estimated probability symbol π̂ (alias for \eprob)
\ePr	estimated probability with brackets Pr̂[·] (via \Est)
\erisk	estimated risk π̂ (alias for \eprob)
\eprev	estimated prevalence π̂ (alias for \eprob)
\epoprisk	estimated population risk R̂ (via \est); the empirical risk
\epopriskf	estimated population risk function R̂(·) (via \est)
\emprisk	empirical risk R̂ (alias for \epoprisk)
\empriskf	empirical risk function R̂(·) (alias for \epopriskf)
\eodds	estimated odds ω̂
\eATT	estimated average treatment effect on the treated ATT̂ (via \Est)
\eATTf	estimated average treatment effect on the treated function ATT̂(·) (via \Est)
\erate	estimated rate λ̂
\ehaz	estimated hazard λ̂ (alias for \erate)
\ecuhaz	estimated cumulative hazard Λ̂
\ecumhaz	estimated cumulative hazard (alias for \ecuhaz)
\eratio	generic estimated ratio ϙ̂ (hat-wrapped \ratio)
\eratiof	generic estimated ratio function ϙ̂(·)
\eror	estimated odds ratio ϙ̂_ω (\eratio subscripted by \odds, so the hat covers only the main symbol)
\eorf	estimated odds ratio function ϙ̂_ω(·)
\ehazratio	estimated hazard ratio ϙ̂_λ (\eratio subscripted by \haz, so the hat covers only the main symbol)
\ehr	estimated hazard ratio (alias for \ehazratio)
\erhaz	estimated hazard ratio (alias for \ehazratio)
\ehazr	estimated hazard ratio (alias for \ehazratio)
\ehrf	estimated hazard ratio function ϙ̂_λ(·)
\erhazf	estimated hazard ratio function (alias for \ehrf)
\ehazrf	estimated hazard ratio function (alias for \ehrf)
\erateratio	estimated rate ratio ϙ̂_λ (\eratio subscripted by \rate, so the hat covers only the main symbol)
\erater	estimated rate ratio (alias for \erateratio)
\errate	estimated rate ratio (alias for \erateratio)
\erateratiof	estimated rate ratio function ϙ̂_λ(·)
\eraterf	estimated rate ratio function (alias for \erateratiof)
\erratef	estimated rate ratio function (alias for \erateratiof)
\eriskratio	estimated risk ratio ϙ̂_π (\eratio subscripted by \risk, so the hat covers only the main symbol)
\errisk	estimated risk ratio (alias for \eriskratio)
\eriskr	estimated risk ratio (alias for \eriskratio)
\eriskratiof	estimated risk ratio function ϙ̂_π(·)
\erriskf	estimated risk ratio function (alias for \eriskratiof)
\eriskrf	estimated risk ratio function (alias for \eriskratiof)
\eRD	estimated risk difference RD̂ (via \Est)
\eRR	estimated risk ratio RR̂ (via \Est)
\eprevratio	estimated prevalence ratio ϙ̂_π (\eratio subscripted by \prev, so the hat covers only the main symbol)
\eprr	estimated prevalence ratio (alias for \eprevratio)
\eprevratiof	estimated prevalence ratio function ϙ̂_π(·)
\eprrf	estimated prevalence ratio function (alias for \eprevratiof)
\esurvratio	estimated survival ratio ϙ̂_S (\eratio subscripted by \surv, so the hat covers only the main symbol)
\ersurv	estimated survival ratio (alias for \esurvratio)
\esurvr	estimated survival ratio (alias for \esurvratio)
\esurvratiof	estimated survival ratio function ϙ̂_S(·)
\ersurvf	estimated survival ratio function (alias for \esurvratiof)
\esurvrf	estimated survival ratio function (alias for \esurvratiof)
\ecuhazratio	estimated cumulative hazard ratio ϙ̂_Λ (\eratio subscripted by \cuhaz, so the hat covers only the main symbol)
\ercuhaz	estimated cumulative hazard ratio (alias for \ecuhazratio)
\ecuhazr	estimated cumulative hazard ratio (alias for \ecuhazratio)
\ecuhazratiof	estimated cumulative hazard ratio function ϙ̂_Λ(·)
\ercuhazf	estimated cumulative hazard ratio function (alias for \ecuhazratiof)
\ecuhazrf	estimated cumulative hazard ratio function (alias for \ecuhazratiof)
\efactor	generic estimated factor θ̂ (hat-wrapped \factor)
\efactorf	generic estimated factor function θ̂(·)
\eff	generic estimated factor function (shorthand for \efactorf)
\eoddsfactor	estimated odds factor θ̂_ω (\efactor subscripted by \odds, so the hat covers only the main symbol)
\eoddsfactorf	estimated odds factor function θ̂_ω(·)
\efodds	estimated odds factor (alias for \eoddsfactor)
\efoddsf	estimated odds factor function (alias for \eoddsfactorf)
\eoddsff	estimated odds factor function (shorthand for \eoddsfactorf)
\ehazfactor	estimated hazard factor θ̂_λ (\efactor subscripted by \haz, so the hat covers only the main symbol)
\ehazfactorf	estimated hazard factor function θ̂_λ(·)
\efhaz	estimated hazard factor (alias for \ehazfactor)
\efhazf	estimated hazard factor function (alias for \ehazfactorf)
\ehazff	estimated hazard factor function (shorthand for \ehazfactorf)
\eratefactor	estimated rate factor θ̂_λ (\efactor subscripted by \rate, so the hat covers only the main symbol)
\eratefactorf	estimated rate factor function θ̂_λ(·)
\efrate	estimated rate factor (alias for \eratefactor)
\efratef	estimated rate factor function (alias for \eratefactorf)
\erateff	estimated rate factor function (shorthand for \eratefactorf)
\eriskfactor	estimated risk factor θ̂_π (\efactor subscripted by \risk, so the hat covers only the main symbol)
\eriskfactorf	estimated risk factor function θ̂_π(·)
\efrisk	estimated risk factor (alias for \eriskfactor)
\efriskf	estimated risk factor function (alias for \eriskfactorf)
\eriskff	estimated risk factor function (shorthand for \eriskfactorf)
\eprevfactor	estimated prevalence factor θ̂_π (\efactor subscripted by \prev, so the hat covers only the main symbol)
\eprevfactorf	estimated prevalence factor function θ̂_π(·)
\efprev	estimated prevalence factor (alias for \eprevfactor)
\efprevf	estimated prevalence factor function (alias for \eprevfactorf)
\eprevff	estimated prevalence factor function (shorthand for \eprevfactorf)
\ecuhazfactor	estimated cumulative hazard factor θ̂_Λ (\efactor subscripted by \cuhaz, so the hat covers only the main symbol)
\ecuhazfactorf	estimated cumulative hazard factor function θ̂_Λ(·)
\efcuhaz	estimated cumulative hazard factor (alias for \ecuhazfactor)
\efcuhazf	estimated cumulative hazard factor function (alias for \ecuhazfactorf)
\ecuhazff	estimated cumulative hazard factor function (shorthand for \ecuhazfactorf)
\esurvfactor	estimated survival factor θ̂_S (\efactor subscripted by \surv, so the hat covers only the main symbol)
\esurvfactorf	estimated survival factor function θ̂_S(·)
\efsurv	estimated survival factor (alias for \esurvfactor)
\efsurvf	estimated survival factor function (alias for \esurvfactorf)
\esurvff	estimated survival factor function (shorthand for \esurvfactorf)
\bbN	blackboard bold N (natural numbers)
\bbZ	blackboard bold Z (integers)
\bbQ	blackboard bold Q (rational numbers)
\bbR	blackboard bold R (real numbers)
\bbC	blackboard bold C (complex numbers)
\mathscr	script font operator (fallback to \mathcal)
\scrA	script A symbol 𝒜
\scrB	script B symbol ℬ
\scrC	script C symbol 𝒞
\scrF	script F symbol ℱ
\scrS	script S symbol 𝒮
\scrT	script T symbol 𝒯
\salg	sigma-algebra on S symbol 𝒮
\salgS	sigma-algebra on S symbol 𝒮 (alias for \salg)
\salgT	sigma-algebra on T symbol 𝒯
\sigmaalg	sigma-algebra symbol (alias for \salg)
\sigmaalgebra	sigma-algebra symbol (alias for \salg)
\borel	Borel sigma-algebra symbol ℬ
\Borel	Borel sigma-algebra symbol (alias for \borel)
\borelalg	Borel sigma-algebra symbol (alias for \borel)
\events	event space / events sigma-algebra symbol ℱ
\eventspace	event space symbol (alias for \events)
\coll	collection of sets symbol 𝒞
\collection	collection of sets symbol (alias for \coll)
\gensalg	sigma-algebra generated by argument σ(·)
\gensigmaalg	generated sigma-algebra (alias for \gensalg)
\inas	convergence almost surely arrow
\indist	convergence in distribution arrow
\inprob	convergence in probability arrow
\plim	probability limit operator (plim)
\plimf	probability limit applied to argument
\tr	matrix trace operator (tr)
\trf	matrix trace applied to argument
\diag	diagonal matrix operator (diag)
\diagf	diagonal of matrix applied to argument
\rank	matrix rank operator (rank)
\rankf	matrix rank applied to argument
\vct	matrix vectorization operator (vec)
\vctf	matrix vectorization applied to argument
\vcs	matrix strict half-vectorization operator (vecs)
\vcsf	matrix strict half-vectorization applied to argument
\vch	matrix half-vectorization operator (vech)
\vchf	matrix half-vectorization applied to argument
\matvec	matrix vectorization operator (alias for \vct)
\matvecf	matrix vectorization applied to argument (alias for \vctf)
\dBeta	Beta distribution operator
\dBetaf	Beta distribution applied to parameters
\dBern	Bernoulli distribution operator (statmath prefix)
\dBernf	Bernoulli distribution applied to parameters
\dBin	Binomial distribution operator
\dBinf	Binomial distribution applied to parameters
\dF	F-distribution operator
\dFf	F-distribution applied to parameters
\dGam	Gamma distribution operator
\dGamf	Gamma distribution applied to parameters
\dInvGam	Inverse-Gamma distribution operator
\dInvGamf	Inverse-Gamma distribution applied to parameters
\dInvW	Inverse-Wishart distribution operator
\dInvWf	Inverse-Wishart distribution applied to parameters
\dLaplace	Laplace distribution operator
\dLaplacef	Laplace distribution applied to parameters
\dMN	Multinomial/Matrix-Normal distribution operator
\dMNf	Multinomial/Matrix-Normal distribution applied to parameters
\dN	Normal distribution operator (statmath prefix)
\dNf	Normal distribution applied to parameters
\dPo	Poisson distribution operator (statmath prefix)
\dPof	Poisson distribution applied to parameters
\dt	Student-t distribution operator
\dtf	Student-t distribution applied to parameters
\dW	Wishart distribution operator
\dWf	Wishart distribution applied to parameters
\dWeib	Weibull distribution operator
\dWeibf	Weibull distribution applied to parameters
\Ebar	expectation operator with bar
\Ebarf	expectation with bar applied to subscript
\Ehat	expectation operator with hat
\Ehatf	expectation with hat applied to subscript
\Etilde	expectation operator with tilde
\Etildef	expectation with tilde applied to subscript
\Corrt	correlation operator Corr
\Corr	correlation Corr(·) applied to argument
\Corrf	correlation Corr(·) applied to argument (alias for \Corr)
\Corrs	correlation Corr(·, ·) applied to two arguments
\MSEt	mean squared error operator MSE
\MSE	mean squared error MSE(·) applied to argument
\MSEf	mean squared error MSE(·) applied to argument (alias for \MSE)
\RMSEt	root mean squared error operator RMSE
\RMSE	root mean squared error RMSE(·) applied to argument
\RMSEf	root mean squared error RMSE(·) applied to argument (alias for \RMSE)
\pMSEt	population mean squared error operator pMSE
\pMSE	population mean squared error pMSE(·) applied to argument
\pMSEf	population mean squared error pMSE(·) applied to argument (alias for \pMSE)
\ESEt	expected squared error operator ESE, of a predicted value or a parameter estimate
\ESE	expected squared error ESE(·) of a predicted value or a parameter estimate, applied to argument
\ESEf	expected squared error ESE(·) applied to argument (alias for \ESE)
\ESEEt	expected squared estimation error operator ESEE, of a parameter estimate
\ESEE	expected squared estimation error ESEE(·) of a parameter estimate, applied to argument
\ESEEf	expected squared estimation error ESEE(·) applied to argument (alias for \ESEE)
\ESPEt	expected squared prediction error operator ESPE, of a predicted value
\ESPE	expected squared prediction error ESPE(·) of a predicted value, applied to argument
\ESPEf	expected squared prediction error ESPE(·) applied to argument (alias for \ESPE)
\pRMSEt	population root mean squared error operator pRMSE
\pRMSE	population root mean squared error pRMSE(·) applied to argument
\pRMSEf	population root mean squared error pRMSE(·) applied to argument (alias for \pRMSE)
\pMAEt	population mean absolute error operator pMAE
\pMAE	population mean absolute error pMAE(·) applied to argument
\pMAEf	population mean absolute error pMAE(·) applied to argument (alias for \pMAE)
\sMSEt	sample mean squared error operator sMSE, the estimator of pMSE (no hat form is needed)
\sMSE	sample mean squared error sMSE(·) applied to argument
\sMSEf	sample mean squared error sMSE(·) applied to argument (alias for \sMSE)
\sRMSEt	sample root mean squared error operator sRMSE, the estimator of pRMSE (no hat form is needed)
\sRMSE	sample root mean squared error sRMSE(·) applied to argument
\sRMSEf	sample root mean squared error sRMSE(·) applied to argument (alias for \sRMSE)
\sMAEt	sample mean absolute error operator sMAE, the estimator of pMAE (no hat form is needed)
\sMAE	sample mean absolute error sMAE(·) applied to argument
\sMAEf	sample mean absolute error sMAE(·) applied to argument (alias for \sMAE)
\SEtildet	standard error operator with tilde
\SEtilde	standard error with tilde applied to argument
\SEtildef	standard error with tilde applied to argument (alias for \SEtilde)
\Biast	bias operator Bias
\biast	bias operator Bias (lowercase alias)
\sign	sign operator (alias for \signt)
\bfA	bold Roman capital letter A
\bfB	bold Roman capital letter B
\bfC	bold Roman capital letter C
\bfD	bold Roman capital letter D
\bfE	bold Roman capital letter E
\bfF	bold Roman capital letter F
\bfG	bold Roman capital letter G
\bfH	bold Roman capital letter H
\bfI	bold Roman capital letter I
\bfJ	bold Roman capital letter J
\bfK	bold Roman capital letter K
\bfL	bold Roman capital letter L
\bfM	bold Roman capital letter M
\bfN	bold Roman capital letter N
\bfO	bold Roman capital letter O
\bfP	bold Roman capital letter P
\bfQ	bold Roman capital letter Q
\bfR	bold Roman capital letter R
\bfS	bold Roman capital letter S
\bfT	bold Roman capital letter T
\bfU	bold Roman capital letter U
\bfV	bold Roman capital letter V
\bfW	bold Roman capital letter W
\bfX	bold Roman capital letter X
\bfY	bold Roman capital letter Y
\bfZ	bold Roman capital letter Z
\bfa	bold Roman lowercase letter a
\bfb	bold Roman lowercase letter b
\bfc	bold Roman lowercase letter c
\bfd	bold Roman lowercase letter d
\bfe	bold Roman lowercase letter e
\bff	bold Roman lowercase letter f
\bfg	bold Roman lowercase letter g
\bfh	bold Roman lowercase letter h
\bfi	bold Roman lowercase letter i
\bfj	bold Roman lowercase letter j
\bfk	bold Roman lowercase letter k
\bfl	bold Roman lowercase letter l
\bfm	bold Roman lowercase letter m
\bfn	bold Roman lowercase letter n
\bfo	bold Roman lowercase letter o
\bfp	bold Roman lowercase letter p
\bfq	bold Roman lowercase letter q
\bfr	bold Roman lowercase letter r
\bfs	bold Roman lowercase letter s
\bft	bold Roman lowercase letter t
\bfu	bold Roman lowercase letter u
\bfv	bold Roman lowercase letter v
\bfw	bold Roman lowercase letter w
\bfx	bold Roman lowercase letter x
\bfy	bold Roman lowercase letter y
\bfz	bold Roman lowercase letter z
\bfalpha	bold Greek lowercase letter alpha
\bfbeta	bold Greek lowercase letter beta
\bfdelta	bold Greek lowercase letter delta
\bfepsilon	bold Greek lowercase letter epsilon
\bfvarepsilon	bold Greek lowercase letter varepsilon
\bfzeta	bold Greek lowercase letter zeta
\bfeta	bold Greek lowercase letter eta
\bftheta	bold Greek lowercase letter theta
\bfvartheta	bold Greek lowercase letter vartheta
\bfgamma	bold Greek lowercase letter gamma
\bfkappa	bold Greek lowercase letter kappa
\bflambda	bold Greek lowercase letter lambda
\bfmu	bold Greek lowercase letter mu
\bfnu	bold Greek lowercase letter nu
\bfxi	bold Greek lowercase letter xi
\bfpi	bold Greek lowercase letter pi
\bfvarpi	bold Greek lowercase letter varpi
\bfrho	bold Greek lowercase letter rho
\bfvarrho	bold Greek lowercase letter varrho
\bfsigma	bold Greek lowercase letter sigma
\bfvarsigma	bold Greek lowercase letter varsigma
\bftau	bold Greek lowercase letter tau
\bfupsilon	bold Greek lowercase letter upsilon
\bfphi	bold Greek lowercase letter phi
\bfvarphi	bold Greek lowercase letter varphi
\bfchi	bold Greek lowercase letter chi
\bfpsi	bold Greek lowercase letter psi
\bfomega	bold Greek lowercase letter omega
\bfiota	bold Greek lowercase letter iota
\bfGamma	bold Greek capital letter Gamma
\bfDelta	bold Greek capital letter Delta
\bfTheta	bold Greek capital letter Theta
\bfLambda	bold Greek capital letter Lambda
\bfXi	bold Greek capital letter Xi
\bfPi	bold Greek capital letter Pi
\bfSigma	bold Greek capital letter Sigma
\bfUpsilon	bold Greek capital letter Upsilon
\bfPhi	bold Greek capital letter Phi
\bfPsi	bold Greek capital letter Psi
\bfOmega	bold Greek capital letter Omega
\bfzero	bold zero vector/number
\hCorrt	estimated correlation operator Corr̂ (legacy \hat form); deprecated, use \eCorrt
\hCorr	estimated correlation Corr̂(·) applied to argument (legacy \hat form); deprecated, use \eCorr
\hCorrf	estimated correlation Corr̂(·) applied to argument (alias for \hCorr); deprecated, use \eCorrf
\hCorrs	estimated correlation Corr̂(·, ·) applied to two arguments (legacy \hat form); deprecated, use \eCorrs
\eCorrt	estimated correlation operator (composable \Est form)
\eCorr	estimated correlation applied to argument (composable \Est form)
\eCorrf	estimated correlation applied to argument (alias for \eCorr)
\eCorrs	estimated correlation applied to two arguments (composable \Est form)
\hMSEt	estimated mean squared error operator MSÊ (legacy \hat form); deprecated, use \eMSEt
\hMSE	estimated mean squared error applied to argument (legacy \hat form); deprecated, use \eMSE
\hMSEf	estimated mean squared error applied to argument (alias for \hMSE); deprecated, use \eMSEf
\hRMSEt	estimated root mean squared error operator (legacy \hat form); deprecated, use \eRMSEt
\hRMSE	estimated root mean squared error applied to argument (legacy \hat form); deprecated, use \eRMSE
\hRMSEf	estimated root mean squared error applied to argument (alias for \hRMSE); deprecated, use \eRMSEf
\eMSEt	estimated mean squared error operator (composable \Est form)
\eMSE	estimated mean squared error applied to argument (composable \Est form)
\eMSEf	estimated mean squared error applied to argument (alias for \eMSE)
\eRMSEt	estimated root mean squared error operator (composable \Est form)
\eRMSE	estimated root mean squared error applied to argument (composable \Est form)
\eRMSEf	estimated root mean squared error applied to argument (alias for \eRMSE)
\hBiast	estimated bias operator Biaŝ (legacy \hat form); deprecated, use \eBiast
\hBias	estimated bias applied to argument (legacy \hat form); deprecated, use \eBias
\hbias	estimated bias applied to argument (lowercase alias); deprecated, use \ebias
\eBiast	estimated bias operator (composable \Est form)
\eBias	estimated bias applied to argument (composable \Est form)
\ebias	estimated bias applied to argument (lowercase alias)
\koppa	archaic Greek letter koppa ϙ (lowercase; base symbol for quotient and ratios)
\Koppa	archaic Greek letter Koppa Ϙ (uppercase; capital quotient/ratio symbol)
\qoppa	archaic Greek letter qoppa (alias for \koppa)
\Qoppa	archaic Greek letter Qoppa (alias for \Koppa)
\Ratio	capital ratio symbol Ϙ (\Koppa)
\Ratiof	capital ratio function Ϙ(·)
\hkoppa	estimated koppa ϙ̂ (hat-wrapped \koppa); deprecated, use \ekoppa
\hKoppa	estimated Koppa Ϙ̂ (hat-wrapped \Koppa); deprecated, use \eKoppa
\hqoppa	estimated qoppa (alias for \hkoppa); deprecated, use \eqoppa
\hQoppa	estimated Qoppa (alias for \hKoppa); deprecated, use \eQoppa
\hRatio	estimated capital ratio Ϙ̂ (hat-wrapped \Ratio); deprecated, use \eRatio
\hRatiof	estimated capital ratio function Ϙ̂(·); deprecated, use \eRatiof
\ekoppa	estimated koppa ϙ̂ (estimator-wrapped \koppa)
\eKoppa	estimated Koppa Ϙ̂ (estimator-wrapped \Koppa)
\eqoppa	estimated qoppa (alias for \ekoppa)
\eQoppa	estimated Qoppa (alias for \eKoppa)
\eRatio	estimated capital ratio Ϙ̂ (estimator-wrapped \Ratio)
\eRatiof	estimated capital ratio function Ϙ̂(·)
\sdpar	a standard-deviation parameter (prints \sigma)
\varpar	a variance parameter (prints \sigma^2)
\hsdpar	an estimated standard deviation (prints \hat{\sigma}); deprecated, use \esdpar
\esdpar	an estimated standard deviation (forward-looking form) (prints \est{\sdpar})
\hvarpar	an estimated variance (prints \hat{\sigma}^2); deprecated, use \evarpar
\evarpar	an estimated variance (forward-looking form) (prints \est{\sdpar}^2)
\covmat	a covariance matrix (prints \Sigma)
\hcovmat	an estimated covariance matrix (prints \hat{\Sigma}); deprecated, use \ecovmat
\ecovmat	an estimated covariance matrix (forward-looking form) (prints \est{\covmat})
\corpar	a correlation parameter (prints \rho)
\noise	a realized value of the noise (prints ε, the same symbol as \Noise, following ISL and ESL)
\Noise	the noise: a latent random variable, the deviation \devof{\Outvar}{\truemeanf{\predvar}} of an outcome from its mean given the input (prints ε, following ISL and ESL; \noise prints the same symbol for a realized value)
\vnoise	a vector of realized noise values (prints \vecf{\noise})
\vNoise	a vector of noise random variables (prints \vecf{\Noise})
\biassym	short bias operator 𝔹 (no argument, blackboard bold like \Expp)
\biassymf	short bias operator applied to an estimator, like \E (prints \biassym with its argument in square brackets)
\varsym	short variance operator 𝕍 for any random variable (no argument, blackboard bold like \Expp)
\varsymf	short variance operator applied to a random variable, like \E (prints \varsym with its argument in square brackets)
\irrerr	short symbol for the irreducible error, the variance of the noise \Noise (alias for \varpar)
\rvar	a generic value of a random variable in probability expressions (prints x)
\Rvar	a generic random variable in probability expressions (prints X)
\learnrate	a learning rate (gradient-descent step size) (prints \alpha)
\regpar	a regularization penalty (tuning) parameter (prints \lambda)
\eigval	an eigenvalue (prints \lambda)
\Eigvals	a diagonal matrix of eigenvalues (prints \Lambda)
\featmap	a feature map (basis expansion) (prints \design)
\vfeatmap	a vector of feature-map outputs (hidden-unit features) (alias for \vdesign)
\design	a design matrix built from a feature map (prints D)
\vdesign	the design vector for one input: the feature-map outputs (prints \vecf{\design})
\normpdf	the standard normal density (prints \phi)
\normcdf	the standard normal CDF (prints \Phi)
\backerr	a backpropagation error signal (prints \delta)
\vbackerr	a vector of backpropagation error signals (prints \vecf{\delta})
\mbackerr	a batch matrix of backpropagation error signals (prints \Delta)
\vhidgrad	the gradient of the loss with respect to a hidden layer's output (prints \vecf{\epsilon})
\stepvec	an optimization step (vector) (prints \vecf{\delta})
\step	an optimization step (scalar) (prints \delta)
\tolout	an output tolerance (the epsilon of an epsilon-delta statement) (prints \varepsilon)
\tolin	an input radius (the delta of an epsilon-delta statement), or a small allowed failure probability (prints \delta)
\smallconst	a small positive constant (a small step, cutoff or half-width) (prints \varepsilon)
\machineps	machine epsilon (prints \varepsilon)
\kron	Kronecker delta; write \kron_{ij} (prints \delta)
\slack	an SVM slack variable (prints \xi)
\dualvar	an SVM dual variable (Lagrange multiplier of a margin constraint) (prints \alpha)
\lagmult	a Lagrange multiplier (prints \lambda)
\vlagmult	a vector of Lagrange multipliers (prints \vecf{\lambda})
\siglevel	the significance level of a test (prints \alpha)
\thresh	a classification threshold (prints \theta)
\param	a generic parameter (prints \theta)
\vparam	a generic parameter vector (prints \vec{\theta})
\hparam	an estimated parameter (prints \hat{\theta}); deprecated, use \eparam
\eparam	an estimated parameter (forward-looking form) (prints \est{\param})
\hvparam	an estimated parameter vector (prints \hat{\vec{\theta}}); deprecated, use \evparam
\evparam	an estimated parameter vector (forward-looking form) (prints \est{\vparam})
\Param	a parameter as a random variable (prints \Theta)
\Paramspace	a parameter space (prints \Theta)
\angpar	an angle (prints \theta)
\shape	a shape parameter (Weibull, gamma, first Beta shape) (prints \alpha)
\shapeb	the second shape parameter of a Beta distribution (prints \beta)
\df	degrees of freedom (prints the upright name df, because an abbreviation is a name)
\hdf	estimated degrees of freedom (prints the hat of the upright name df); deprecated, use \edf
\edf	estimated degrees of freedom (forward-looking form) (prints \est{\df})
\gammafn	the gamma function (prints \Gamma)
\samplespace	a sample space (prints \Omega)
\outcome	an outcome (sample point) (prints \omega)
\measure	a measure (prints \mu)
\measureb	a second measure (prints \nu)
\lebesgue	Lebesgue measure (prints \lambda)
\condnum	a condition number (prints \kappa)
\change	a change or difference (prints \Delta)
\vprob	a vector of probabilities (prints \vecf{\pi})
\hmean	an estimated mean (prints \hat{\mu}); deprecated, use \emean
\emean	an estimated mean (forward-looking form) (prints \est{\mean})
\hregcoef	an estimated unsubscripted regression coefficient (prints \hat{\beta}); deprecated, use \eregcoef
\eregcoef	an estimated unsubscripted regression coefficient (forward-looking form) (prints \est{\regcoef})
\armijo	the Armijo sufficient-decrease constant of a backtracking line search (prints c_1)
\schedfrac	the fraction of a learning-rate schedule that has elapsed (prints \zeta)
\schedlen	the number of updates a learning-rate schedule runs over (prints \tau)
\decay	a multiplicative learning-rate decay factor (prints \gamma)
\clipthresh	a gradient-clipping threshold (prints \tau)
\convrate	the per-step contraction factor of gradient descent (prints \rho)
\coordfactor	the per-coordinate contraction factor of gradient descent (prints \mu)
\leak	the negative-side slope of a leaky ReLU (prints \alpha)
\linefn	the one-variable function a line search minimizes (prints \phi)
\bandwidth	a kernel bandwidth (prints \sigma)
\attnweight	an attention weight (prints \alpha)
\degmat	the degree matrix of a similarity graph (prints \Delta)
\penaltygrid	a grid of candidate penalty values (prints \Lambda)
\prevalence	the prevalence of the positive class (prints \pi)
\recallweight	the F-beta weight on recall (prints \beta)
\resp	a mixture component's responsibility for a point (prints \gamma)
\shrinkage	the gradient-boosting shrinkage factor (prints \nu)
\singval	a singular value (prints \sigma)
\svfrac	the nu-SVM bound on the fraction of margin errors (prints \nu)
\truthind	a 0/1 truth indicator written delta(condition) (prints \delta)
\truthindf	prints \truthind\paren{...}, the truth indicator of its condition
\typetwoerr	the probability of a type II error (prints \beta)
\unitthresh	a neuron's firing threshold (prints \Theta)
\votewt	an AdaBoost vote weight (prints \alpha)
\wterr	an AdaBoost weighted training error (prints \varepsilon)
\strongconv	a strong-convexity constant (prints \mu)
\combwt	a scalar weight in a linear combination (prints \alpha)
\combwtb	a second scalar weight in a linear combination (prints \beta)
\errcov	the covariance matrix of observation errors (prints \Omega)
\midpt	an intermediate point in a mean-value or Taylor remainder (prints \tau)
\nullity	the nullity of a matrix (prints \nu)
\lsresid	a residual written as epsilon (prints \varepsilon)
\vlsresid	a vector of residuals written as epsilon (prints \vec{\varepsilon})
\subdiv	a subdivision of a region into rectangles (prints \Delta)
\universe	a universal set (prints \Omega)
\lowend	the lower endpoint of an interval (prints \alpha)
\highend	the upper endpoint of an interval (prints \beta)
\precision	a precision (reciprocal variance) (prints \tau)
\hprecision	an estimated precision (prints \hat{\tau}); deprecated, use \eprecision
\eprecision	an estimated precision (forward-looking form) (prints \est{\precision})
\mahal	the Mahalanobis distance function (prints \Delta)
\mahaldist	a Mahalanobis distance value (prints \delta)
\rvrate	a random event rate (prints \Lambda)
\overdisp	the negative binomial overdispersion parameter (prints \rho)
\adjlevel	a BCa-adjusted percentile level (prints \alpha)
\foldassign	the cross-validation fold-assignment function (prints \kappa)
\accept	a Metropolis-Hastings acceptance probability (prints \alpha)
\estimand	a quantity of interest in Bayesian model averaging (prints \Delta)
\stationary	the stationary distribution of a Markov chain (prints \pi)
\sdbetween	a between-group standard deviation (prints \tau)
\event	an event indicator (1 = event, 0 = censored) (prints \delta)
\propstep	a random-walk proposal increment (prints \varepsilon)
\sdprior	the standard deviation of a Gaussian prior (prints \tau)
\poismean	a Poisson mean in the zeros trick (prints \phi)
\sdest	the standard deviation of an estimator (prints \tau)
\lrstat	a likelihood-ratio test statistic (prints \Lambda)
\stat	a statistic, a function of the sample (prints T)
\statf	a statistic evaluated at a sample, \statf{\vx} (prints T(x))
\sfac	the factor of a density that depends on the data only through a statistic, in the factorization theorem for sufficiency (prints g)
\sfacf	the statistic factor evaluated at a statistic value and a parameter, \sfacf{t}{\param} (prints g(t ; \theta))
\rfac	the factor of a density that does not depend on the parameter, in the factorization theorem for sufficiency (prints h)
\rfacf	the parameter-free factor evaluated at a sample, \rfacf{\vx} (prints h(x))
\nuisance	a nuisance parameter (prints \lambda)
\hnuisance	an estimated nuisance parameter (prints \hat{\lambda}); deprecated, use \enuisance
\enuisance	an estimated nuisance parameter (forward-looking form) (prints \est{\nuisance})
\interest	a parameter of interest (prints \psi)
\quantlevel	a quantile level (prints \beta)
\tuning	a generic tuning parameter (prints \lambda)
\hist	history of a variable through a time point, as in \hist{X}{k} (prints \bar{X}_{k})
\trthist	treatment history through time k (prints \bar{A}_k)
\trtvalhist	a treatment-history value through time k (prints \bar{a}_k)
\covhist	covariate history through time k (prints \bar{L}_k)
\covvalhist	a covariate-history value through time k (prints \bar{l}_k)
\censhist	censoring history through time k (prints \bar{C}_k)
\censvalhist	a censoring-history value through time k (prints \bar{c}_k)
\eventnocens	event indicator at time m under the intervention that eliminates censoring (prints D^{\bar{c}=0}_m)
\modelfam	mean-function model family (hypothesis class) H: the set of mean-function models that training chooses from (mean-function level; the distribution level is \distfam)
\growthfn	growth function m_Y (prints m_{\predfam}): the most labelings of n points that the prediction function family can produce
\growthfnf	growth function at n points, m_Y(n) (prints \growthfn\paren{n})
\vcdim	Vapnik-Chervonenkis dimension d_VC: the largest number of points the model family shatters
\vcdimf	VC dimension of a model family, d_VC(·)
\failprob	failure probability delta of a probabilistic bound (one minus the confidence level)
\bestmod	best-in-family model h*: the member of the mean-function model family closest to the target function
\bestmodf	best-in-family model applied to its argument, h*(·)
\avgfit	average fit: the population mean over training sets of the fitted model, written as the population mean with the model as subscript (mu_h; no bar or hat)
\avgfitf	average fit at input x: the population expectation over training sets of the fitted model's value at x, mu_h(x)
\hidval	value of a hidden unit in a neural network, h
\hidvalof	value of the hidden unit given as the argument, h with the unit as subscript
\vhidval	vector of hidden-unit values (or a recurrent network's hidden state), h̰
\imgheight	height of an image or feature map, in pixels
\imgwidth	width of an image or feature map, in pixels
\poolsize	side length q of a square pooling block, in pixels
\hatmat	hat matrix H, which maps observed responses to fitted responses
\leverage	leverage of the observation given as the argument, h with the observation as subscript
\avgleverage	mean leverage, h-bar
\cutheight	height h at which a dendrogram is cut
\clusterassign	cluster-assignment function (the encoder) of a clustering method such as k-means, C
\clusterassignf	cluster assigned to the argument, C applied to it
\modmem	generic member h of a mean-function model family
\modmemf	generic member of a mean-function model family applied to its argument, h(·)
\modmemof	member (or fit) h of the mean-function model family given as the argument, h with the family as subscript
\nobs	sample size n: the number of observations or training examples
\nmodels	number M of models (classifiers) in a model family
\ninputvars	number p of input variables (predictors): the columns of the data matrix, not the number of inputs
\inputdim	alias for \ninputvars, kept for existing uses
\nparams	number p* of parameters: the columns of the design matrix after any feature map
\nnetparams	number P of parameters (weights and biases) in a neural network
\iter	index t of the step of an iterative algorithm, such as gradient descent
\layeridx	index ℓ of a layer of a neural network
\RSS	residual sum of squares, RSS: the sum over the observations of the squared residuals of a fitted model
\TSS	total sum of squares, TSS: the sum over the observations of the squared differences of the outcome from its sample mean
\npatterns	number K of distinct covariate patterns in a dataset
\nreps	number m of replicates (observations) that share one covariate pattern; written with a pattern index, as \nreps_k
\nevents	number s of events among the replicates of one covariate pattern; written with a pattern index, as \nevents_k
\popdist	population distribution P that the data are drawn from
\targetfn	target function \mu: the true relationship between inputs and outcomes
\truemeanfn	true mean function \mu: the population mean of the outcome given the inputs; deprecated, use \truemean
\truemean	true mean function \mu: the population mean of the outcome given the inputs; replaces the deprecated \truemeanfn
\dataset	data set: the whole collection of labelled observations that the training, validation and test sets are subsets of (prints a calligraphic D)
\trainset	training set: the labelled data a model is fitted to (prints a calligraphic D with a train subscript)
\valset	validation set: the held-out observations used to choose among fitted models (prints a calligraphic D with a val subscript)
\testset	test set: the held-out observations used once, to estimate the risk of the chosen model (prints a calligraphic D with a test subscript)
\trainidx	index set of the training set (prints I with a train subscript)
\validx	index set of the validation set (prints I with a val subscript)
\testidx	index set of the test set (prints I with a test subscript)
\targetfnf	target function applied to its argument, \mu(·)
\truemeanfnf	true mean function applied to its argument, \mu(·); deprecated, use \truemeanf
\truemeanf	true mean function applied to its argument, \mu(·); replaces the deprecated \truemeanfnf
\truemeansubf	true mean function with the subscript given as the first argument, applied to the second argument; with the subscript \Outvar \given \Predvar it is the conditional population mean \condE{\Outvar}{\Predvar = x}
\cmean	conditional population mean function of the outcome given the predictor, the subscripted \truemean with the subscript \Outvar \given \Predvar and no argument
\cmeanf	conditional population mean of the outcome given the predictor at x, \cmeanf{x}; the same as \truemeansubf{\Outvar \given \Predvar}{x} and \condE{\Outvar}{\Predvar = x}
\hmodmem	fitted model (hat-wrapped \modmem), legacy form; deprecated, use \emodmem
\hmodmemf	fitted model applied to its argument (hat-wrapped), h-hat(·); deprecated, use \emodmemf
\hmodmemof	fitted model with its mean-function model family as subscript (hat-wrapped); deprecated, use \emodmemof
\htargetfn	estimated target function: fitted counterpart of the target function (hat-wrapped), legacy form; deprecated, use \etargetfn
\htargetfnf	estimated target function applied to its argument (hat-wrapped), \mu-hat(·); deprecated, use \etargetfnf
\emodmem	fitted model: estimate of a member of a mean-function model family (via \est)
\emodmemf	fitted model applied to its argument (via \est), h-hat(·)
\emodmemof	fitted model with its mean-function model family as subscript (via \est)
\emodmemat	fitted model indexed by the input value, which is the subscript (stochastic-process style), h-hat_x (via \est)
\emodmematf	fitted model indexed by the input value in the subscript, applied to the data set in parentheses, h-hat_x(D) (via \est)
\etargetfn	estimated target function: fitted counterpart of the target function (via \est), \mu-hat
\etargetfnf	estimated target function applied to its argument (via \est), \mu-hat(·)
\targeterr	error of the fitted target function: target function minus its fitted counterpart (the deviation symbol subscripted by the target function)
\targeterrf	error of the fitted target function at a point (via \targeterr)
\codealph	alphabet of a block code (the set of symbols codewords use), Sigma
\srcalph	alphabet of a source: the finite set of values a random variable takes, calligraphic X
\ffield	finite field with the given number of elements, F_q
\hamdist	Hamming distance: number of positions where two strings differ, Delta(x, y)
\errfrac	fraction of a received word's positions that are corrupted, delta
\codeparams	parameters of a linear code: block length, message length, minimum distance, alphabet size, [n, k, d]_q
\asymub	big-O asymptotic upper bound applied to its argument, O(g)
\entropy	Shannon entropy symbol H
\entropyf	Shannon entropy of a random variable, H(X)
\kolm	Kolmogorov complexity symbol K
\kolmf	Kolmogorov complexity of a string, K(x)
\ckolmf	conditional Kolmogorov complexity of a string given an input, K(x | n)
\strlen	length of a string (number of symbols), |p|
\fitproc	fitting procedure: a rule that turns data and a covariate value into a prediction, g
\fitprocf	fitting procedure applied to its argument, g(·); write \fitprocf{D} for the model g(D) fitted to the data set D, and \fitprocf{D}\paren{x} for its prediction at x
\fitmodf	fitted model obtained from the training set given as the argument, the prediction function ŷ with the training set as subscript (prints \fitted_{D}, where the subscript is a data set, not an observation index); \fitmodf{D}\paren{x} is its prediction at x, and \fitprocf{D} is the same model written as the fitting procedure g applied to D
\predvalf	prediction at the covariate value given as the argument, from the model that a fitting procedure fits to the training set (prints \fitted\paren{x}, the fitted prediction function applied to x; unlike \predval, which takes no argument and names no covariate value, this applies the fitted function to the covariate value given, and unlike \epredfnf, which applies any estimated prediction function, it is fixed to the training-set fit)
\estmeanf	estimate of the mean function value at the covariate value given as the argument (prints \emuf{x}, the estimated mean function applied to x; the value that the fitted model's prediction \predvalf{x} estimates, here named by its role as an estimator of \truemeanf{x} rather than as a prediction)
\candproc	candidate fitting procedure with the index given as the argument, g with that subscript
\candfitted	fitted model of the candidate procedure with the index given as the argument, the prediction function that the candidate fits to the training set (prints \fitted_{l}, where the subscript is the candidate index, not an observation index; the candidate counterpart of \fitted, which names the fitted model of the single fitting procedure; unlike \candproc, which is the procedure and takes a data set, this is the model that results and takes a covariate value)
\candpredvalf	prediction at the covariate value given as the second argument, from the model that the candidate procedure with the index given as the first argument fits to the training set (prints \fitted_{l}\paren{x}, where the subscript is the candidate index, not an observation index; the candidate counterpart of \predvalf; unlike \predval and unlike \epredfnf, which applies any estimated prediction function, it is fixed to the candidate's training-set fit)
\candproctunedf	fitted model that the tuned procedure with the label given as the first argument fits to the data set given as the second argument (prints \candproctuned{\text{CV}}\paren{D}; the f form of \candproctuned, which names the procedure and leaves the data set implicit)
\fitmodvalf	prediction at the covariate value given as the second argument, from the model that the fitting procedure fits to the data set given as the first argument (prints \fitmodf{D}\paren{x}, that is the fitted model ŷ_D applied to x; the counterpart of \predvalf for a data set other than the full training set, such as the observations outside a fold; unlike \candpredvalf, which indexes a candidate procedure rather than a data set; unlike \predval and unlike \epredfnf, which applies any estimated prediction function, it is fixed to the fit to that data set)
\candproctuned	fitting procedure with the label given as the argument as a superscript, such as CV for the cross-validation choice procedure
\candfnset	set of candidate prediction functions of a fitting procedure, with the index of the procedure as the subscript
\ncand	number of candidate fitting procedures, L
\icand	index of a candidate fitting procedure, l
\nfolds	number of cross-validation folds, k
\ifold	index of a cross-validation fold, j
\iobs	index of an observation, i
\obsidx	index set of the observations of a data set, I
\nheld	number of observations in a held-out set, m
\termfn	term of a linear-in-terms model, a function of the covariate value, t with the index given as the argument as a subscript
\polydeg	degree of a polynomial model, d
\objfn	objective function that a fitting procedure minimizes over a model family (prints \mathcal{J})
\objfnf	value of the objective function at a candidate model, the argument (prints \objfn applied to the argument)
\optalg	optimization algorithm that searches a model family for a minimizer of the objective function (prints \mathcal{A})
\seed	random seed that starts a pseudorandom number generator (prints \mathfrak{s})
\sscp	sum-of-squares-and-cross-products matrix of the term vectors, A
\newpredvar	covariate of a new observation (random), X with subscript star
\newoutvar	outcome of a new observation (random), Y with subscript star
\newpredval	covariate value of a new observation (fixed), x with subscript star
\newoutval	outcome value of a new observation (fixed), y with subscript star
\heldpredvar	covariate of a held-out observation (random), X with subscript star and the index of the observation
\heldoutvar	outcome of a held-out observation (random), Y with subscript star and the index of the observation
\heldpredval	covariate value of a held-out observation (fixed), x with subscript star and the index of the observation
\heldoutval	outcome value of a held-out observation (fixed), y with subscript star and the index of the observation
\posclass	positive class or positive test result in binary classification (prints a plus sign)
\negclass	negative class or negative test result in binary classification (prints a minus sign)
\ntp	number of true positives in a confusion matrix (prints n_{T\posclass})
\nfp	number of false positives in a confusion matrix (prints n_{F\posclass})
\nfn	number of false negatives in a confusion matrix (prints n_{F\negclass})
\ntn	number of true negatives in a confusion matrix (prints n_{T\negclass})
\given	the conditioning bar "given" (prints |), for conditional expressions such as E[Y | X]
\condE	conditional expected value E[A | B], \condE{A}{B}
\condVar	conditional variance Var(A | B), \condVar{A}{B}
\condCov	conditional covariance Cov(A, B | C), \condCov{A}{B}{C}
\condPr	conditional probability Pr(A | B), \condPr{A}{B}
\condpmf	conditional probability mass function (prints \pmf of A given B), \condpmf{A}{B}
\condpdf	conditional probability density function f(A | B), \condpdf{A}{B}
\condcdf	conditional cumulative distribution function F(A | B), \condcdf{A}{B}
\generr	generalization error operator Err (expected loss of a prediction rule on new data)
\expgenerr	expected generalization error of fitting procedure g trained on n observations, averaged over training sets, Err^g(n); \expgenerr{g}{n}
\cvest	cross-validation estimate of generalization error, Êrr_CV(k)
\cvestof	k-fold cross-validation estimate of the generalization error of fitting procedure g, Êrr^g_CV(k); \cvestof{k}{g}
\cvestoff	k-fold cross-validation estimate of the generalization error of the fitting procedure given as the second argument, computed on the data set given as the third argument (prints \cvestof{k}{g}\paren{D}; the f form of \cvestof, which leaves the data set implicit)
\fold	cross-validation fold F_j (set of held-out observation indices)
\foldsize	number of observations n_j in fold j
\foldof	fold κ(i) containing observation i
\fittedwo	fitted value at input x from a fit leaving out fold j, ŷ^(-j)(x); \fittedwo{j}{x}
\lev	leverage h_i of observation i (diagonal of the hat matrix)
\foldmse	mean squared error MSE_j on fold j
\foldmseof	mean squared error of fitting procedure g on fold j, MSE^g_j; \foldmseof{j}{g}
\foldmseoff	mean squared error on the fold given as the first argument, of the fitting procedure given as the second argument, computed on the data set given as the third argument (prints \foldmseof{j}{g}\paren{D}; the f form of \foldmseof, which leaves the data set implicit)
\cvse	standard error SE_CV(k) of the cross-validation estimate
\cvseof	standard error of the k-fold cross-validation estimate for fitting procedure g, SE^g_CV(k); \cvseof{k}{g}
\cvseoff	standard error of the k-fold cross-validation estimate for the fitting procedure given as the second argument, computed on the data set given as the third argument (prints \cvseof{k}{g}\paren{D}; the f form of \cvseof, which leaves the data set implicit)
\rmselr	root mean squared error function RMSE(·), counterpart of \mselr
\epe	expected prediction error at input x of fitting procedure g trained on n observations, EPE^g_n(x); \epe{g}{n}{x}
\epl	expected prediction loss at input x of fitting procedure g trained on n observations, for any loss, EPL^g_n(x); \epl{g}{n}{x}
\termvec	vector t_k of a model term evaluated at every observation
\trainerr	training error (average loss on the training set), over-barred err
\valerr	validation error (average loss on the validation set), over-barred err with subscript val
\testerr	test error (average loss on the test set), over-barred err with subscript test
\arcoef	autoregressive coefficient ρ
\truenulls	index set I_0 of the true null hypotheses
\levelset	symbol for a level set of a function
\levelsetof	level set of the function given as the second argument at the value given as the first, written with the value as a subscript
\predfn	symbol for a prediction function, which maps predictor values to a predicted outcome
\epredfn	estimated prediction function, such as one fitted from training data; replaces the deprecated \hf
\predfnf	prediction function applied to the argument
\epredfnf	estimated prediction function applied to the argument; replaces the deprecated \hfx
\cent	mark for the centered version of the argument (the data with its mean subtracted)
\aug	mark for the augmented version of the argument (extended with additional entries)
\bestpredfn	best prediction function f*: the conditional mean of the outcome given the inputs, which minimizes the squared-error risk
\bestpredfnf	best prediction function applied to its argument, f*(·)
\predfam	prediction function family Y-script: the set of prediction functions that training chooses from (prediction level; a member is \predfn, a fitted member is \epredfn)
\predfamof	prediction function family with its label as the subscript, Y-script with the label below
\distfam	probability distribution model family P-script: a set of probability distribution models, each a candidate for the distribution of the outcome given the covariate (distribution level)
\distfamof	distribution model family with its label as the subscript, P-script with the label below
\distmem	generic member P of a distribution model family: a probability distribution model for the outcome given the covariate; the mean-function model it contains is \meanfnof{\distmem}
\distmemof	member of a distribution model family, with its label as the subscript, P with the label below
\edistmem	fitted distribution model: estimate of a member of a distribution model family (via \est)
\edistmemof	fitted distribution model with its label as the subscript (via \est)
\meanfnof	mean function of the distribution model given as the argument, \mean with the model below: the mean-function component of a distribution model (for the true distribution \popdist it is the same function as \targetfn, though it prints mu with the distribution as the subscript)
\meanfnoff	mean function of the distribution model in the first argument, applied to the covariate value in the second
\predval	predicted value of the outcome (alias of \eoutvar)
\vpredval	vector of predicted values (alias of \veoutvar)
\predbar	sample mean of the predictor values (prints \bar{\predvar})
\outbar	sample mean of the outcome values (prints \bar{\outvar})
\rvarbar	sample mean of a generic variable (prints \bar{\rvar})
\sspred	centered sum of squares of the predictor values (prints S_{\predvar\predvar})
\ssout	centered sum of squares of the outcome values (prints S_{\outvar\outvar})
\sspredout	centered sum of cross-products of the predictor and outcome values (prints S_{\predvar\outvar})
\Epredvalf	expected predicted value at the covariate value given as the argument: the population expectation over training sets of the fitted model's prediction at that value (prints the expectation over the training set of \fitted\paren{x}; the same quantity \avgfitf prints as mu_h(x), written as an expectation of a prediction rather than as a function in a model family; unlike \Epredval, it names the covariate value)
\Epredval	expected predicted value when no covariate value is salient: the population expectation over training sets of \predval
\sumsym	symbol for the sum of the elements of a vector of values (prints s)
\Sumsym	symbol for the sum of the elements of a vector of random variables (prints S)
\sumof	sum of the elements of a vector of values (prints s_{#1})
\Sumof	sum of the elements of a vector of random variables (prints S_{#1})