Package: likelihood.contr 0.2.0

Alexander Towell

likelihood.contr: Likelihood Contribution Models for Heterogeneous Observation Types

Constructs likelihood models from heterogeneous observation types by composing named contributions. Each observation type (exact, left-censored, right-censored, interval-censored, or custom) contributes independently to the total log-likelihood, which is summed under an i.i.d. assumption. Provides contr_name() for standard R distributions and contr_fn() for user-defined contributions, composed via likelihood_contr() into objects compatible with the likelihood.model inference framework.

Authors:Alexander Towell [aut, cre]

likelihood.contr_0.2.0.tar.gz
likelihood.contr_0.2.0.zip(r-4.7-any)likelihood.contr_0.2.0.zip(r-4.6-any)
likelihood.contr_0.2.0.tar.gz(r-4.7-any)likelihood.contr_0.2.0.tar.gz(r-4.6-any)
likelihood.contr_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
likelihood.contr/json (API)

# Install 'likelihood.contr' in R:
install.packages('likelihood.contr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/queelius/likelihood.contr/issues

On CRAN:

Conda:

3.75 score 7 scripts 274 downloads 7 mentions 9 exports 9 dependencies

Last updated from:68602f09c8. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK111
source / vignettesOK275
linux-releaseOK111
windows-develOK79
windows-releaseOK86
wasm-releaseOK95

Exports:assumptionscontr_fncontr_freqcontr_namehess_logliklikelihood_contrloglikrdatascore

Dependencies:algebraic.distalgebraic.mlebootgenericslikelihood.modelMASSmvtnormnumDerivR6

Timeout-aware service and AI latency benchmarks
The decision: does this backend meet a deadline? | A reproducible demonstration | When only histogram bins are retained | Where this fits

Last update: 2026-10-28
Started: 2026-10-28

Custom Contributions and Model Comparison
When contr_name() Is Not Enough | Analytical Derivatives | Model Comparison with LRT | Fisher Information via Monte Carlo

Last update: 2026-04-01
Started: 2026-04-01

Introduction to likelihood.contr
The Problem | Declaring Contributions | Composing a Model | Simulating Censored Data | Fitting the Model | Evaluating the Log-Likelihood Directly | Function-Based Dispatch | Next Steps

Last update: 2026-04-01
Started: 2026-04-01

Masked Series Systems
Competing Risks with Masked Failure Causes | Building the Model | Simulating Masked Data | Fitting | Why This Works

Last update: 2026-04-01
Started: 2026-04-01