@@ -24,9 +24,9 @@ export(featureModuleLookup)

 export(featureModuleTable)

 export(geneSetEnrich)

 export(logLikelihood)

-export(logLikelihoodCeldaC)

-export(logLikelihoodCeldaCG)

-export(logLikelihoodCeldaG)

+export(logLikelihood.celda_C)

+export(logLikelihood.celda_CG)

+export(logLikelihood.celda_G)

 export(logLikelihoodHistory)

 export(matrixNames)

 export(moduleHeatmap)

@@ -743,7 +743,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_C"),

 #' @return Numeric. The log likelihood for the given cluster assignments

 #' @seealso `celda_C()` for clustering cells

 #' @examples

-#' loglik <- logLikelihoodCeldaC(celdaCSim$counts,

+#' loglik <- logLikelihood.celda_C(celdaCSim$counts,

 #'     sampleLabel = celdaCSim$sampleLabel,

 #'     z = celdaCSim$z,

 #'     K = celdaCSim$K,

@@ -758,7 +758,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_C"),

 #'     alpha = celdaCSim$alpha,

 #'     beta = celdaCSim$beta)

 #' @export

-logLikelihoodCeldaC <- function(counts, sampleLabel, z, K, alpha, beta) {

+logLikelihood.celda_C <- function(counts, sampleLabel, z, K, alpha, beta) {

     if (sum(z > K) > 0) {

         stop("An entry in z contains a value greater than the provided K.")

@@ -933,7 +933,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_CG"),

 #' @return The log likelihood for the given cluster assignments

 #' @seealso `celda_CG()` for clustering features and cells

 #' @examples

-#' loglik <- logLikelihoodCeldaCG(celdaCGSim$counts,

+#' loglik <- logLikelihood.celda_CG(celdaCGSim$counts,

 #'     sampleLabel = celdaCGSim$sampleLabel,

 #'     z = celdaCGSim$z,

 #'     y = celdaCGSim$y,

@@ -956,7 +956,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_CG"),

 #'     gamma = celdaCGSim$gamma,

 #'     delta = celdaCGSim$delta)

 #' @export

-logLikelihoodCeldaCG <- function(counts,

+logLikelihood.celda_CG <- function(counts,

     sampleLabel,

     z,

     y,

@@ -713,7 +713,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_G"),

 #' @return The log-likelihood for the given cluster assignments.

 #' @seealso `celda_G()` for clustering features

 #' @examples

-#' loglik <- logLikelihoodCeldaG(celdaGSim$counts,

+#' loglik <- logLikelihood.celda_G(celdaGSim$counts,

 #'     y = celdaGSim$y,

 #'     L = celdaGSim$L,

 #'     beta = celdaGSim$beta,

@@ -728,7 +728,7 @@ setMethod("factorizeMatrix", signature(celdaMod = "celda_G"),

 #'     delta = celdaGSim$delta,

 #'     gamma = celdaGSim$gamma)

 #' @export

-logLikelihoodCeldaG <- function(counts, y, L, beta, delta, gamma) {

+logLikelihood.celda_G <- function(counts, y, L, beta, delta, gamma) {

     if (sum(y > L) > 0) {

         stop("An entry in y contains a value greater than the provided L.")

     }

similarity index 88%

rename from man/logLikelihoodCeldaC.Rd

rename to man/logLikelihood.celda_C.Rd

@@ -1,10 +1,10 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/celda_C.R

-\name{logLikelihoodCeldaC}

-\alias{logLikelihoodCeldaC}

+\name{logLikelihood.celda_C}

+\alias{logLikelihood.celda_C}

 \title{Calculate Celda_C log likelihood}

 \usage{

-logLikelihoodCeldaC(counts, sampleLabel, z, K, alpha, beta)

+logLikelihood.celda_C(counts, sampleLabel, z, K, alpha, beta)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent

@@ -33,7 +33,7 @@ Calculates the log likelihood for user-provided cell population

  clusters using the `celda_C()` model.

 }

 \examples{

-loglik <- logLikelihoodCeldaC(celdaCSim$counts,

+loglik <- logLikelihood.celda_C(celdaCSim$counts,

     sampleLabel = celdaCSim$sampleLabel,

     z = celdaCSim$z,

     K = celdaCSim$K,

similarity index 91%

rename from man/logLikelihoodCeldaCG.Rd

rename to man/logLikelihood.celda_CG.Rd

@@ -1,10 +1,10 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/celda_CG.R

-\name{logLikelihoodCeldaCG}

-\alias{logLikelihoodCeldaCG}

+\name{logLikelihood.celda_CG}

+\alias{logLikelihood.celda_CG}

 \title{Calculate Celda_CG log likelihood}

 \usage{

-logLikelihoodCeldaCG(counts, sampleLabel, z, y, K, L, alpha, beta, delta,

+logLikelihood.celda_CG(counts, sampleLabel, z, y, K, L, alpha, beta, delta,

   gamma)

 }

 \arguments{

@@ -44,7 +44,7 @@ Calculates the log likelihood for user-provided cell population

  and feature module clusters using the `celda_CG()` model.

 }

 \examples{

-loglik <- logLikelihoodCeldaCG(celdaCGSim$counts,

+loglik <- logLikelihood.celda_CG(celdaCGSim$counts,

     sampleLabel = celdaCGSim$sampleLabel,

     z = celdaCGSim$z,

     y = celdaCGSim$y,

similarity index 89%

rename from man/logLikelihoodCeldaG.Rd

rename to man/logLikelihood.celda_G.Rd

@@ -1,10 +1,10 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/celda_G.R

-\name{logLikelihoodCeldaG}

-\alias{logLikelihoodCeldaG}

+\name{logLikelihood.celda_G}

+\alias{logLikelihood.celda_G}

 \title{Calculate Celda_G log likelihood}

 \usage{

-logLikelihoodCeldaG(counts, y, L, beta, delta, gamma)

+logLikelihood.celda_G(counts, y, L, beta, delta, gamma)

 }

 \arguments{

 \item{counts}{Integer matrix. Rows represent features and columns represent

@@ -33,7 +33,7 @@ Calculates the log likelihood for user-provided feature module

  clusters using the `celda_G()` model.

 }

 \examples{

-loglik <- logLikelihoodCeldaG(celdaGSim$counts,

+loglik <- logLikelihood.celda_G(celdaGSim$counts,

     y = celdaGSim$y,

     L = celdaGSim$L,

     beta = celdaGSim$beta,