Available methods

mbconcat Concatenation of multi-block X-data
mbpca Multiblock PCA (MBPCA), a.k.a Consensus principal component analysis (CPCA)
comdim Common components and specific weights analysis (ComDim), a.k.a CCSWA or HPCA

Utilities

mblock Make blocks from a matrix
rd Redundancy coefficients between two matrices
rv RV correlation coefficient

REGRESSION

Ordinary least squares (OLS)

Multiple linear regression (MLR)

mlr QR algorithm
mlrchol Normal equations and Choleski factorization
mlrpinv Pseudo-inverse
mlrpinvn Normal equations and pseudo-inverse
mlrvec Simple (Univariate x) linear regression

Anova

aov1 One-factor ANOVA

Partial least squares (PLSR)

Usual (asymetric regression mode)

plskern Fast "improved kernel #1" algorithm of Dayal & McGregor 1997
plsnipals Nipals
plswold Nipals Wold 1984
plsrosa ROSA Liland et al. 2016
plssimp SIMPLS de Jong 1993

Variants of regularization using latent variables

cglsr Conjugate gradient for the least squares normal equations (CGLS)
pcr Principal components regression (SVD factorization)
rrr Reduced rank regression (RRR), a.k.a Redundancy analysis regression

Robust

plsrout Outlierness

Sparse

splsr
- sPLSR Lê Cao et al. 2008
- Covsel regression Roger et al. 2011
spcr
- sPCR Shen & Huang 2008

Averaging PLSR models of different dimensionalities

plsravg PLSR-AVG

Non linear

kplsr Non linear kernel (KPLSR) Rosipal & Trejo 2001
dkplsr Direct non linear kernel (DKPLSR) Bennett & Embrechts 2003

Multiblock

mbplsr Multiblock PLSR (MBPLSR) - Fast version (PLSR on concatenated blocks)
mbplswest MBPLSR - Nipals algorithm Westerhuis et al. 1998
rosaplsr ROSA Liland et al. 2016
soplsr Sequentially orthogonalized (SO-PLSR)

Ridge (RR, KRR)

rr SVD factorization
rrchol Choleski factorization

Non linear

krr Non linear kernel (KRR), a.k.a Least squares SVM (LS-SVMR)

Local models

loessr LOESS regression model – With package Loess.jl

kNN

knnr kNN weighted regression (kNNR)
lwmlr kNN locally weighted MLR (kNN-LWMLR)
lwplsr kNN locally weighted PLSR (kNN-LWPLSR)

Averaging

lwplsravg kNN-LWPLSR-AVG

Support vector machines

Wrapper to LIBSVM.jl

svmr Epsilon-SVR (SVM-R)

Trees

Wrapper to DecisionTree.jl

treer Single tree
rfr Random forest

DISCRIMINATION ANALYSIS (DA)

Based on the prediction of the Y-dummy table

Linear

mlrda MLR-DA
plsrda PLSR-DA, a.k.a usual PLSDA
rrda RR-DA

Sparse

splsrda Sparse PLSR-DA

Non linear

kplsrda KPLSR-DA
dkplsrda DKPLSR-DA
krrda KRR-DA

Multiblock

mbplsrda MBPLSR-DA

Probabilistic DA

Parametric

lda Linear discriminant analysis (LDA)
qda Quadratic discriminant analysis (QDA, with continuum towards LDA)
rda Regularized discriminant analysis (RDA)

Non parametric

kdeda DA by kernel Gaussian density estimation (KDE-DA)

On PLS latent variables

PLSDA
- plslda PLS-LDA
- plsqda PLS-QDA (with continuum)
- plskdeda PLS-KDEDA
Sparse
- splslda: Sparse PLS-LDA
- splsqda: Sparse PLS-QDA
- splskdeda: Sparse PLS-KDEDA
Non linear
- kplslda KPLS-LDA
- kplsqda KPLS-QDA
- kplskdeda KPLS-KDEDA
- dkplslda Direct KPLS-LDA
- dkplsqda Direct KPLS-QDA
- dkplskdeda Direct KPLS-KDEDA
Multiblock
- mbplslda MBPLS-LDA
- mbplsqda MBPLS-QDA
- mbplskdeda MBPLS-KDEDA

Local models

knnda kNN-DA (Vote within neighbors)
lwmlrda kNN locally weighted MLR-DA (kNN-LWMLR-DA)
lwplsrda kNN Locally weighted PLSR-DA (kNN-LWPLSR-DA)
lwplslda kNN Locally weighted PLS-LDA (kNN-LWPLS-LDA)
lwplsqda kNN Locally weighted PLS-QDA (kNN-LWPLS-QDA, with continuum)

Support vector machines

Wrapper to LIBSVM.jl

svmda C-SVC (SVM-DA)

Trees

Wrapper to DecisionTree.jl

treeda Single tree
rfda Random forest

ONE-CLASS CLASSIFICATION (OCC)

From a PCA or PLS score space

occsd Score distance (SD)
occod Orthogonal distance (OD)
occsdod Compromise between SD and OD (a.k.a Simca approach)

Other methods

occstah Stahel-Donoho outlierness

Utilities

outstah Stahel-Donoho outlierness
outeucl: Outlierness from Euclidean distances to center

DISTRIBUTIONS

dmnorm Normal probability density estimation
dmnormlog Logarithm of the normal probability density estimation
dmkern Gaussian kernel density estimation (KDE)
pval Compute p-value(s) for a distribution, a vector or an ECDF
out Return if elements of a vector are strictly outside of a given range

VARIABLE IMPORTANCE

isel! Interval variable selection (e.g. Interval PLSR).
vip Variable importance on projections (VIP)
viperm! Variable importance by direct permutations

TUNING MODELS

Test-set validation

gridscore Compute an error rate over a grid of parameters

Cross-validation (CV)

gridcv Compute an error rate over a grid of parameters

Utilities

mpar Expand a grid of parameter values
segmkf Build segments for K-fold CV
segmts Build segments for test-set validation

Performance scores

Regression

ssr SSR
msep MSEP
rmsep, rmsepstand RMSEP
rrmsep Relative RMSEP
sep SEP
bias Bias
cor2 Squared correlation coefficient
r2 R2
rpd, rpdr Ratio of performance to deviation
mse Summary for regression
conf Confusion matrix

Discrimination

errp Classification error rate
merrp Mean intra-class classification error rate

Model dimensionality

aicplsr AIC and Cp for PLSR
selwold Wold's criterion to select dimensionality in LV models (e.g. PLSR)

DATA PROCESSING

Checking

finduniq Find the indexes making unique the IDs in a ID vector
dupl Find replicated rows in a dataset
tabdupl Tabulate duplicated values in a vector
findmiss Find rows with missing data in a dataset

Pre-processing

De-trend transformation (baseline correction)
- detrend_pol Polynomial linear regression
- detrend_lo LOESS
- detrend_asls Asymmetric least squares (ASLS)
- detrend_airpls Adaptive iteratively reweighted penalized least squares (AIRPLS)
- detrend_arpls Asymmetrically reweighted penalized least squares smoothing (ARPLS)
snv Standard-normal-deviation transformation
fdif Finite differences
mavg Smoothing by moving average
savgk, savgol Savitsky-Golay filtering
rmgap Remove vertical gaps in spectra, e.g. for ASD NIR data

Scaling

center Column centering
scale Column scaling
cscale Column centering and scaling
blockscal Scaling of multiblock data

Interpolation

interpl Sampling spectra by interpolation – From DataInterpolations.jl

Calibration transfer

difmean Compute a detrimental matrix (for calibration transfer) by difference of two matrix-column means.
eposvd Compute an orthogonalization matrix for calibration transfer
calds Direct standardization (DS)
calpds Piecewise direct standardization (PDS)

Build training vs. test sets by sampling

samprand Random (without replacement)
sampsys Systematic over a quantitative variable
sampcla Stratified by class
sampdf From each column of a dataframe (where missing values are allowed)
sampks Kennard-Stone
sampdp Duplex
sampwsp WSP

PLOTTING

plotsp Plot spectra
plotxy x-y scatter plot
plotgrid Plot error/performance rates of a model
plotconf Plot confusion matrix

MODELS AND PIPELINES

model Build a model
pip Build a pipeline of models

UTILITIES

Macros

@head Display the first rows of a dataset
@mod Shortcut for function parentmodule
@names Return the names of the sub-objects contained in a object
@pars Display the keyword arguments (with their default values) of a function
@plist Display each element of a list
@type Display the type and size of a dataset

Others

aggmean Compute column-wise means by class in a dataset
aggstat Compute column-wise statistics by class in a dataset
aggsumv Compute sub-total sums by class of a categorical variable
sumv, meanv, stdv, varv, madv, iqrv, normv Vector operations
covv, covm, corv, corm Weighted covariances and correlations
cosv, cosm Cosinus
colmad, colmean, colmed, colnorm, colstd, colsum, colvar Column-wise operations
colmeanskip, colstdskip, colsumskip, colvarskip Column-wise operations allowing missing data
convertdf Convert the columns of a dataframe to given types
dummy Build dummy table
euclsq, mahsq, mahsqchol Distances (Euclidean, Mahalanobis) between rows of matrices
fblockscal_col, _frob, _mfa, _sd Scale blocks
fcenter, fscale, fcscale Column-wise centering and scaling of a matrix
fconcat Concatenate multiblock data
findmax_cla Find the most occurent level in a categorical variable
frob, frob2 Frobenius norm of a matrix
fweight Weight each row of a matrix
getknn Find nearest neighbours between rows of matrices
iqrv Interval inter-quartiles
krbf, kpol Build kernel Gram matrices
locw Working function for local (kNN) models
mad Median absolute deviation (not exported)
matB, matW Between- and within-class covariance matrices
mlev Return the sorted levels of a vecor or a dataset
mweight Normalize a vector to sum to 1
mweightcla Compute observation weights for a categorical variable, given specified sub-total weights for the classes
nco, nro, Nb. rows and columns of an object
normv Norm of a vector
parsemiss Parsing a string vector allowing missing data
pval Compute p-value(s) for a distribution, an ECDF or vector
recod_catbydict Recode a categorical variable to dictionnary levels
recod_catbyind Recode a categorical variable to indexes of levels
recod_catbyint Recode a categorical variable to integers
recod_catbylev Recode a categorical variable to levels
recod_indbylev Recode an index variable to levels
recod_numbyint Recode a continuous variable to integers
recod_miss Declare data as missing in a dataset
rmcol Remove the columns of a matrix or the components of a vector having indexes s
rmrow Remove the rows of a matrix or the components of a vector having indexes s
rowmean, rownorm, rowstd, rowsum, rowvar: Row-wise operations
rowmeanskip, rowstdskip, rowsumskip, rowvarskip: Row-wise operations allowing missing data
thresh_soft, thresh_hard Thresholding functions
softmax Softmax function
sourcedir Include all the files contained in a directory
summ Summarize the columns of a dataset
tab, tabdupl Tabulations for categorical variables
vcatdf Vertical concatenation of a list of dataframes
wdis Different functions to compute weights from distances
wtal Compute weights from distances using the 'talworth' distribution
winvs Compute weights from distances using an inverse scaled exponential function
Other utility functions in files _util.jl