Package index • L0glm

Fit, tune and compare sparse models

L0glm.fit(): Fit generalized linear model with \(L_0\) or grouped row-\(L_0\) regularization subject to optional nonnegativity constraints
L0glm.tune(): Tunes the regularisation parameter lambda of an L0glm fit
L0nnpois(): Fast nonnegative identity-link Poisson L0 or grouped L0 penalized fit
sparseglm() coef(<sparseglm>) plot(<sparseglm>): Fit, tune and compare sparse regression methods
L0glm.metrics(): Essential fit and recovery metrics for an L0glm model
preclustering: Preclustering response channels before sparse Poisson fitting
compulsory_terms: Nonnegative compulsory terms and smooth backgrounds

predict(<L0glm_fit>) predict(<L0glm_extended_fit>) predict(<sparseglm>) fitted(<L0glm_fit>) fitted(<sparseglm>): Predictions and fitted values from sparse regression models
print(<L0glm_fit>) summary(<L0glm_fit>) print(<summary.L0glm_fit>) print(<sparseglm>) summary(<sparseglm>) print(<summary.sparseglm>): Print and summarize sparse regression fits
logLik(<L0glm_fit>) nobs(<L0glm_fit>) coef(<L0glm_fit>) GIC() EBIC() MBIC(): Likelihood, information criteria and coefficients of an L0glm fit
information_criteria: Information-criterion presets for sparse GLMs
preset.lambda(): Calculate an information-criterion penalty preset

extended_families: Negative Binomial, Cox and Multinomial Fits
L0glm_negative_binomial: L0-Penalized Negative Binomial Regression
L0glm_multinomial: Multinomial Regression with Grouped or CLR Entrywise L0 Penalties
L0glm_cox: L0-Penalized Cox Models with Cached Matrix-Free IRLS

sis_glm(): Efficient-score screening for GLMs and special families
simul(): Simulate sparse GLMs and deconvolution problems
simulate_spike_train(): Simulate a blurred single or multichannel spike train
simulate_GCMS(): Simulate synthetic GC-MS chromatograms with ground-truth signal + background
lmridge(): Fits a ridge regularised weighted least squares regression using a chosen solver subject to optional nonnegativity or box constraints on the coefficients.
control.solver.gen(): Set Options of Constrained Ridge Regularised Weighted Least Squares Regression Solvers

residuals(<GCMSdeco>) GCMSdeco() coef(<GCMSdeco>) fitted(<GCMSdeco>) nobs(<GCMSdeco>) logLik(<GCMSdeco>) AIC(<GCMSdeco>) BIC(<GCMSdeco>) GIC(<GCMSdeco>) EBIC(<GCMSdeco>) MBIC(<GCMSdeco>) print(<GCMSdeco>): Deconvolve a GC/MS chromatogram and export its compound spectra
plot(<GCMSdeco>): Plot GC/MS signals, compound profiles and recovered spectra
L0glm_sparse_deco(): Sparse deconvolution with a fixed PSF via L0-penalised GLMs
L0glm_blind_deco(): Alternating sparse L0 glm powered blind deconvolution
L0glm_deco_estkernel(): Estimate the PSF given a spike train estimate via L0-penalised GLMs
splogconNMF() fitted(<splogconNMF>) nobs(<splogconNMF>) coef(<splogconNMF>) logLik(<splogconNMF>) AIC(<splogconNMF>) BIC(<splogconNMF>) GIC(<splogconNMF>) EBIC(<splogconNMF>) MBIC(<splogconNMF>) print(<splogconNMF>): Sparse log-concave nonnegative matrix factorization
convmatrix(): Make banded or rectangular convolution design matrices
convkernel() shift_kernel(): Generate normalised 1-D convolution kernels (point-spread functions)
estimate_psf_from_std(): Estimate a time-varying PSF matrix from an alkane ladder TIC
log_conc_smooth(): Log-concave spline smoothing of a discrete convolution kernel estimate
nb1() Gamma_scale() Gamma_censored(): Scalar noise models for nonnegative signals
temporal_dispersion(): Estimate noise dispersion from temporal differences

plotmat(): Plot a (Possibly Sparse) Matrix as a False-Colour Image
plot_L0glm_benchmark(): Plot benchmark examples
glm_cluster() glm_aggregate(): Likelihood-based clustering and aggregation of GLM response channels
glm_dist(): Dissimilarities between scalar GLM response channels
L0BestRankGNMF(): L0-Penalised Generalised Best-Rank NMF

GCMS_data1: GC/MS Chromatogram Section - Cuticular Hydrocarbon Profile of a Cataglyphis iberica Ant Queen
GCMS_data2: GC/MS Chromatogram Section - Beer Headspace Volatiles
simulated_spectra: Simulated Electron Impact Mass Spectra Dataset