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Keywords {🔍}

latent, likelihood, phenotype, epistasis, phenotypes, model, function, observed, global, parameters, base, fit, bottleneck, source, type, fitting, note, models, multiple, pre, class, abstractepistasis, number, calculation, wildtype, variants, nlatentphenotypes, float, variant, functional, bottle, gaussian, cauchy, set, effects, values, property, classes, bases, int, implements, site, preferences, monotonic, spline, monotonicsplineepistasis, data, modelonelesslatentnone, return, effect,

Topics {✒️}

𝑓 𝑣 post 𝑓 𝑣 pre optimize_method='l-bfgs-b' 𝑛 𝑣 post 𝑛 𝑣 bottle post-selection condition post- selection condition post-selection conditions package global epistasis function post-selection frequencies n_{\rm{bottle}] current log likelihood fitting fitting workflow evenly spaced points functional score measurement minimum functional score monotonic i-splines i-spline defining global epistasis functions multiple latent phenotypes measured functional scores global epistasis models global epistasis model 𝑐 𝛼 𝜕 𝐼 𝑚 𝐼 𝑚 pre-selection library {'l-bfgs-b' current predicted latent max functional score experimentally measured parameters f_post values calculated fitting epistasis model real theoretical basis models class dms_variants cauchy likelihood relative likelihood calculation method likelihood-calculation method likelihood calculation methods likelihood- calculation methods distinct latent phenotypes mutations contribute additively predicted latent phenotypes abstract base class post-selection = 𝑓 𝑣 𝑓 𝑣 𝑁 bottle cauchy distribution relative

Questions {❓}

• Binarymap) set the phenotypes to nan (not a number)?
• Clearcache (bool) – Clear the cache after model fitting?
• Phenotype ({'observed', 'latent'}) – Calculate the preferences from observed or latent phenotypes?

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