@@ -41,7 +41,7 @@

 #'## The following call produces plots and genome browser files for all BAM files in "my-data-folder"

 #'Aneufinder(inputfolder="my-data-folder", outputfolder="my-output-folder")}

 #'

-Aneufinder <- function(inputfolder, outputfolder, configfile=NULL, numCPU=1, reuse.existing.files=TRUE, binsizes=1e6, variable.width.reference=NULL, reads.per.bin=NULL, pairedEndReads=FALSE, assembly=NULL, chromosomes=NULL, remove.duplicate.reads=TRUE, min.mapq=10, blacklist=NULL, use.bamsignals=FALSE, reads.store=FALSE, correction.method=NULL, GC.BSgenome=NULL, method=c('dnacopy','HMM'), strandseq=FALSE, eps=0.1, max.time=60, max.iter=5000, num.trials=15, states=c('zero-inflation',paste0(0:10,'-somy')), most.frequent.state='2-somy', most.frequent.state.strandseq='1-somy', resolution=c(3,6), min.segwidth=2, bw=4*binsizes[1], pval=1e-8, cluster.plots=TRUE) {

+Aneufinder <- function(inputfolder, outputfolder, configfile=NULL, numCPU=1, reuse.existing.files=TRUE, binsizes=1e6, stepsizes=NULL, variable.width.reference=NULL, reads.per.bin=NULL, pairedEndReads=FALSE, assembly=NULL, chromosomes=NULL, remove.duplicate.reads=TRUE, min.mapq=10, blacklist=NULL, use.bamsignals=FALSE, reads.store=FALSE, correction.method=NULL, GC.BSgenome=NULL, method=c('dnacopy','HMM'), strandseq=FALSE, eps=0.1, max.time=60, max.iter=5000, num.trials=15, states=c('zero-inflation',paste0(0:10,'-somy')), most.frequent.state='2-somy', most.frequent.state.strandseq='1-somy', resolution=c(3,6), min.segwidth=2, bw=4*binsizes[1], pval=1e-8, cluster.plots=TRUE) {

 #=======================

 ### Helper functions ###

@@ -87,7 +87,7 @@ if (class(GC.BSgenome)=='BSgenome') {

 numCPU <- as.numeric(numCPU)

 ## Put options into list and merge with conf

-params <- list(numCPU=numCPU, reuse.existing.files=reuse.existing.files, binsizes=binsizes, variable.width.reference=variable.width.reference, reads.per.bin=reads.per.bin, pairedEndReads=pairedEndReads, assembly=assembly, chromosomes=chromosomes, remove.duplicate.reads=remove.duplicate.reads, min.mapq=min.mapq, blacklist=blacklist, reads.store=reads.store, use.bamsignals=use.bamsignals, correction.method=correction.method, GC.BSgenome=GC.BSgenome, method=method, strandseq=strandseq, eps=eps, max.time=max.time, max.iter=max.iter, num.trials=num.trials, states=states, most.frequent.state=most.frequent.state, most.frequent.state.strandseq=most.frequent.state.strandseq, min.segwidth=min.segwidth, resolution=resolution, bw=bw, pval=pval, cluster.plots=cluster.plots)

+params <- list(numCPU=numCPU, reuse.existing.files=reuse.existing.files, binsizes=binsizes, stepsizes=stepsizes, variable.width.reference=variable.width.reference, reads.per.bin=reads.per.bin, pairedEndReads=pairedEndReads, assembly=assembly, chromosomes=chromosomes, remove.duplicate.reads=remove.duplicate.reads, min.mapq=min.mapq, blacklist=blacklist, reads.store=reads.store, use.bamsignals=use.bamsignals, correction.method=correction.method, GC.BSgenome=GC.BSgenome, method=method, strandseq=strandseq, eps=eps, max.time=max.time, max.iter=max.iter, num.trials=num.trials, states=states, most.frequent.state=most.frequent.state, most.frequent.state.strandseq=most.frequent.state.strandseq, min.segwidth=min.segwidth, resolution=resolution, bw=bw, pval=pval, cluster.plots=cluster.plots)

 conf <- c(conf, params[setdiff(names(params),names(conf))])

 ## Check user input

@@ -108,6 +108,7 @@ if (any(formats == 'bed') & is.null(conf[['assembly']])) {

 ## Helpers

 binsizes <- conf[['binsizes']]

+stepsizes <- conf[['stepsizes']]

 reads.per.bins <- conf[['reads.per.bin']]

 patterns <- c(paste0('reads.per.bin_',reads.per.bins,'_'), paste0('binsize_',format(binsizes, scientific=TRUE, trim=TRUE),'_'))

 patterns <- setdiff(patterns, c('reads.per.bin__','binsize__'))

@@ -206,9 +207,9 @@ if (!is.null(conf[['variable.width.reference']])) {

 	} else if (format == 'bed') {

 		reads <- bed2GRanges(conf[['variable.width.reference']], assembly=chrom.lengths.df, chromosomes=conf[['chromosomes']], remove.duplicate.reads=conf[['remove.duplicate.reads']], min.mapq=conf[['min.mapq']], blacklist=conf[['blacklist']])

 	}

-	bins <- variableWidthBins(reads, binsizes=conf[['binsizes']], chromosomes=conf[['chromosomes']])

+	bins <- variableWidthBins(reads, binsizes=conf[['binsizes']], stepsizes=conf[['stepsizes']], chromosomes=conf[['chromosomes']])

 } else {

-  bins <- fixedWidthBins(chrom.lengths=chrom.lengths, chromosomes=conf[['chromosomes']], binsizes=conf[['binsizes']])

+  bins <- fixedWidthBins(chrom.lengths=chrom.lengths, chromosomes=conf[['chromosomes']], binsizes=conf[['binsizes']], stepsizes=conf[['stepsizes']])

 }

 message("==| Finished making bins.")

@@ -315,6 +315,7 @@ binReads <- function(file, assembly, ID=basename(file), bamindex=file, chromosom

 			### ID ###

 			attr(bins, 'ID') <- ID

+			stopTimedMessage(ptm)

 			### Save or return the bins ###

 			if (save.as.RData==TRUE) {

@@ -326,7 +327,6 @@ binReads <- function(file, assembly, ID=basename(file), bamindex=file, chromosom

 			} else {

 				bins.list[[ibinsize]] <- bins

 			}

-			stopTimedMessage(ptm)

 	} ### end loop binsizes ###

@@ -23,7 +23,7 @@

 #'## Check the fit

 #'plot(model, type='histogram')

 #'

-findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1, max.iter=1000, num.trials=15, eps.try=10*eps, num.threads=1, count.cutoff.quantile=0.999, strand='*', states=c("zero-inflation",paste0(0:10,"-somy")), most.frequent.state="2-somy", method="HMM", algorithm="EM", initial.params=NULL) {

+findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1, max.iter=1000, num.trials=15, eps.try=10*eps, num.threads=1, count.cutoff.quantile=0.999, strand='*', states=c("zero-inflation",paste0(0:10,"-somy")), most.frequent.state="2-somy", method="HMM", algorithm="EM", initial.params=NULL, verbosity=1) {

 	## Intercept user input

   binned.data <- loadFromFiles(binned.data, check.class=c('GRanges', 'GRangesList'))[[1]]

@@ -46,7 +46,7 @@ findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1

 	message("Method = ", method)

 	if (method == 'HMM') {

-		model <- univariate.findCNVs(binned.data, ID, eps=eps, init=init, max.time=max.time, max.iter=max.iter, num.trials=num.trials, eps.try=eps.try, num.threads=num.threads, count.cutoff.quantile=count.cutoff.quantile, strand=strand, states=states, most.frequent.state=most.frequent.state, algorithm=algorithm, initial.params=initial.params)

+		model <- univariate.findCNVs(binned.data, ID, eps=eps, init=init, max.time=max.time, max.iter=max.iter, num.trials=num.trials, eps.try=eps.try, num.threads=num.threads, count.cutoff.quantile=count.cutoff.quantile, strand=strand, states=states, most.frequent.state=most.frequent.state, algorithm=algorithm, initial.params=initial.params, verbosity=verbosity)

 	} else if (method == 'dnacopy') {

 	  model <- DNAcopy.findCNVs(binned.data, ID, CNgrid.start=1.5, count.cutoff.quantile=count.cutoff.quantile, strand=strand)

 	}

@@ -82,9 +82,10 @@ findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1

 #' @param most.frequent.state One of the states that were given in \code{states}. The specified state is assumed to be the most frequent one. This can help the fitting procedure to converge into the correct fit.

 #' @param algorithm One of \code{c('baumWelch','EM')}. The expectation maximization (\code{'EM'}) will find the most likely states and fit the best parameters to the data, the \code{'baumWelch'} will find the most likely states using the initial parameters.

 #' @param initial.params A \code{\link{aneuHMM}} object or file containing such an object from which initial starting parameters will be extracted.

+#' @param verbosity Integer specifying the verbosity of printed messages.

 #' @return An \code{\link{aneuHMM}} object.

 #' @importFrom stats runif

-univariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1, max.iter=-1, num.trials=1, eps.try=NULL, num.threads=1, count.cutoff.quantile=0.999, strand='*', states=c("zero-inflation",paste0(0:10,"-somy")), most.frequent.state="2-somy", algorithm="EM", initial.params=NULL) {

+univariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1, max.iter=-1, num.trials=1, eps.try=NULL, num.threads=1, count.cutoff.quantile=0.999, strand='*', states=c("zero-inflation",paste0(0:10,"-somy")), most.frequent.state="2-somy", algorithm="EM", initial.params=NULL, verbosity=1) {

 	### Define cleanup behaviour ###

 	on.exit(.C("C_univariate_cleanup", PACKAGE = 'AneuFinder'))

@@ -183,6 +184,7 @@ univariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard",

       init <- 'initial.params'

     	algorithm <- factor('baumWelch', levels=c('baumWelch','viterbi','EM'))

       num.trials <- 1

+      verbosity <- 0

     }

   	# Check if there are counts in the data, otherwise HMM will blow up

@@ -213,7 +215,7 @@ univariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard",

   	## Call univariate in a for loop to enable multiple trials

   	modellist <- list()

   	for (i_try in 1:num.trials) {

-  		message(paste0("Trial ",i_try," / ",num.trials))

+  		if (verbosity >= 1) { message(paste0("Trial ",i_try," / ",num.trials)) }

   		## Initial parameters

   		if (init == 'initial.params') {

@@ -305,6 +307,7 @@ univariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard",

   			error = as.integer(0), # int* error (error handling)

   			count.cutoff = as.integer(count.cutoff), # int* count.cutoff

   			algorithm = as.integer(algorithm), # int* algorithm

+  			verbosity = as.integer(verbosity), # int* verbosity

   			PACKAGE = 'AneuFinder'

   		)

@@ -412,6 +415,7 @@ univariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard",

   				error = as.integer(0), # int* error (error handling)

   				count.cutoff = as.integer(count.cutoff), # int* count.cutoff

   				algorithm = as.integer(algorithm), # int* algorithm

+    			verbosity = as.integer(verbosity), # int* verbosity

   				PACKAGE = 'AneuFinder'

   			)

   		}

@@ -558,7 +562,7 @@ univariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard",

 #' @inheritParams findCNVs

 #' @return An \code{\link{aneuBiHMM}} object.

 #' @importFrom stats pgeom pnbinom qnorm

-bivariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1, max.iter=-1, num.trials=1, eps.try=NULL, num.threads=1, count.cutoff.quantile=0.999, states=c("zero-inflation",paste0(0:10,"-somy")), most.frequent.state="1-somy", algorithm='EM', initial.params=NULL) {

+bivariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", max.time=-1, max.iter=-1, num.trials=1, eps.try=NULL, num.threads=1, count.cutoff.quantile=0.999, states=c("zero-inflation",paste0(0:10,"-somy")), most.frequent.state="1-somy", algorithm='EM', initial.params=NULL, verbosity=1) {

 	## Intercept user input

   binned.data <- loadFromFiles(binned.data, check.class='GRanges')[[1]]

@@ -864,6 +868,7 @@ bivariate.findCNVs <- function(binned.data, ID=NULL, eps=0.1, init="standard", m

 		num.threads = as.integer(num.threads), # int* num_threads

 		error = as.integer(0), # error handling

 		algorithm = as.integer(algorithm), # int* algorithm

+		verbosity = as.integer(verbosity), # int* verbosity

 		PACKAGE = 'AneuFinder'

 		)

@@ -72,7 +72,7 @@ writeConfig <- function(conf, configfile) {

 		cat(i1," = ",formatstring(conf[[i1]]),"\n", file=f)

 	}

 	cat("\n[Binning]\n", file=f)

-	for (i1 in c('binsizes', 'variable.width.reference', 'reads.per.bin', 'pairedEndReads', 'assembly', 'chromosomes', 'remove.duplicate.reads', 'min.mapq', 'blacklist', 'reads.store', 'use.bamsignals')) {

+	for (i1 in c('binsizes', 'stepsizes', 'variable.width.reference', 'reads.per.bin', 'pairedEndReads', 'assembly', 'chromosomes', 'remove.duplicate.reads', 'min.mapq', 'blacklist', 'reads.store', 'use.bamsignals')) {

 		cat(i1," = ",formatstring(conf[[i1]]),"\n", file=f)

 	}

 	cat("\n[Correction]\n", file=f)

@@ -5,7 +5,7 @@

 \title{Wrapper function for the \code{\link{AneuFinder}} package}

 \usage{

 Aneufinder(inputfolder, outputfolder, configfile = NULL, numCPU = 1,

-  reuse.existing.files = TRUE, binsizes = 1e+06,

+  reuse.existing.files = TRUE, binsizes = 1e+06, stepsizes = NULL,

   variable.width.reference = NULL, reads.per.bin = NULL,

   pairedEndReads = FALSE, assembly = NULL, chromosomes = NULL,

   remove.duplicate.reads = TRUE, min.mapq = 10, blacklist = NULL,

@@ -30,6 +30,8 @@ Aneufinder(inputfolder, outputfolder, configfile = NULL, numCPU = 1,

 \item{binsizes}{An integer vector with bin sizes. If more than one value is given, output files will be produced for each bin size.}

+\item{stepsizes}{A vector of step sizes the same length as \code{binsizes}.}

+

 \item{variable.width.reference}{A BAM file that is used as reference to produce variable width bins. See \code{\link{variableWidthBins}} for details.}

 \item{reads.per.bin}{Approximate number of desired reads per bin. The bin size will be selected accordingly. Output files are produced for each value.}

@@ -9,7 +9,7 @@ static double** multiD;

 // ===================================================================================================================================================

 // This function takes parameters from R, creates a univariate HMM object, creates the distributions, runs the EM and returns the result to R.

 // ===================================================================================================================================================

-void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, double* prob, int* maxiter, int* maxtime, double* eps, double* maxPosterior, int* states, double* A, double* proba, double* loglik, double* weights, int* distr_type, double* initial_size, double* initial_prob, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* read_cutoff, int* algorithm)

+void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, double* prob, int* maxiter, int* maxtime, double* eps, double* maxPosterior, int* states, double* A, double* proba, double* loglik, double* weights, int* distr_type, double* initial_size, double* initial_prob, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* read_cutoff, int* algorithm, int* verbosity)

 {

 	// Define logging level

@@ -23,34 +23,34 @@ void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, dou

 	// Print some information

 	//FILE_LOG(logINFO) << "number of states = " << *N;

-	Rprintf("number of states = %d\n", *N);

+	if (*verbosity>=1) Rprintf("number of states = %d\n", *N);

 	//FILE_LOG(logINFO) << "number of bins = " << *T;

-	Rprintf("number of bins = %d\n", *T);

+	if (*verbosity>=1) Rprintf("number of bins = %d\n", *T);

 	if (*maxiter < 0)

 	{

 		//FILE_LOG(logINFO) << "maximum number of iterations = none";

-		Rprintf("maximum number of iterations = none\n");

+		if (*verbosity>=1) Rprintf("maximum number of iterations = none\n");

 	} else {

 		//FILE_LOG(logINFO) << "maximum number of iterations = " << *maxiter;

-		Rprintf("maximum number of iterations = %d\n", *maxiter);

+		if (*verbosity>=1) Rprintf("maximum number of iterations = %d\n", *maxiter);

 	}

 	if (*maxtime < 0)

 	{

 		//FILE_LOG(logINFO) << "maximum running time = none";

-		Rprintf("maximum running time = none\n");

+		if (*verbosity>=1) Rprintf("maximum running time = none\n");

 	} else {

 		//FILE_LOG(logINFO) << "maximum running time = " << *maxtime << " sec";

-		Rprintf("maximum running time = %d sec\n", *maxtime);

+		if (*verbosity>=1) Rprintf("maximum running time = %d sec\n", *maxtime);

 	}

 	//FILE_LOG(logINFO) << "epsilon = " << *eps;

-	Rprintf("epsilon = %g\n", *eps);

+	if (*verbosity>=1) Rprintf("epsilon = %g\n", *eps);

 	//FILE_LOG(logDEBUG3) << "observation vector";

 	for (int t=0; t<50; t++) {

 		//FILE_LOG(logDEBUG3) << "O["<<t<<"] = " << O[t];

 	}

-	// Flush Rprintf statements to console

+	// Flush if (*verbosity>=1) Rprintf statements to console

 	R_FlushConsole();

 	// Create the HMM

@@ -75,7 +75,7 @@ void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, dou

 	}

 	variance = variance / *T;

 	//FILE_LOG(logINFO) << "data mean = " << mean << ", data variance = " << variance;		

-	Rprintf("data mean = %g, data variance = %g\n", mean, variance);		

+	if (*verbosity>=1) Rprintf("data mean = %g, data variance = %g\n", mean, variance);		

 	// Create the emission densities and initialize

 	for (int i_state=0; i_state<*N; i_state++)

@@ -112,7 +112,7 @@ void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, dou

 		}

 	}

-	// Flush Rprintf statements to console

+	// Flush if (*verbosity>=1) Rprintf statements to console

 	R_FlushConsole();

 	// Do the EM to estimate the parameters

@@ -132,7 +132,7 @@ void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, dou

 	catch (std::exception& e)

 	{

 		//FILE_LOG(logERROR) << "Error in EM/baumWelch: " << e.what();

-		Rprintf("Error in EM/baumWelch: %s\n", e.what());

+		if (*verbosity>=1) Rprintf("Error in EM/baumWelch: %s\n", e.what());

 		if (strcmp(e.what(),"nan detected")==0) { *error = 1; }

 		else { *error = 2; }

 	}

@@ -214,7 +214,7 @@ void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, dou

 // =====================================================================================================================================================

 // This function takes parameters from R, creates a multivariate HMM object, runs the EM and returns the result to R.

 // =====================================================================================================================================================

-void multivariate_hmm(double* D, int* T, int* N, int *Nmod, int* comb_states, int* maxiter, int* maxtime, double* eps, int* states, double* A, double* proba, double* loglik, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* algorithm)

+void multivariate_hmm(double* D, int* T, int* N, int *Nmod, int* comb_states, int* maxiter, int* maxtime, double* eps, int* states, double* A, double* proba, double* loglik, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* algorithm, int* verbosity)

 {

 	// Define logging level {"ERROR", "WARNING", "INFO", "ITERATION", "DEBUG", "DEBUG1", "DEBUG2", "DEBUG3", "DEBUG4"}

@@ -229,31 +229,31 @@ void multivariate_hmm(double* D, int* T, int* N, int *Nmod, int* comb_states, in

 	// Print some information

 	//FILE_LOG(logINFO) << "number of states = " << *N;

-	Rprintf("number of states = %d\n", *N);

+	if (*verbosity>=1) Rprintf("number of states = %d\n", *N);

 	//FILE_LOG(logINFO) << "number of bins = " << *T;

-	Rprintf("number of bins = %d\n", *T);

+	if (*verbosity>=1) Rprintf("number of bins = %d\n", *T);

 	if (*maxiter < 0)

 	{

 		//FILE_LOG(logINFO) << "maximum number of iterations = none";

-		Rprintf("maximum number of iterations = none\n");

+		if (*verbosity>=1) Rprintf("maximum number of iterations = none\n");

 	} else {

 		//FILE_LOG(logINFO) << "maximum number of iterations = " << *maxiter;

-		Rprintf("maximum number of iterations = %d\n", *maxiter);

+		if (*verbosity>=1) Rprintf("maximum number of iterations = %d\n", *maxiter);

 	}

 	if (*maxtime < 0)

 	{

 		//FILE_LOG(logINFO) << "maximum running time = none";

-		Rprintf("maximum running time = none\n");

+		if (*verbosity>=1) Rprintf("maximum running time = none\n");

 	} else {

 		//FILE_LOG(logINFO) << "maximum running time = " << *maxtime << " sec";

-		Rprintf("maximum running time = %d sec\n", *maxtime);

+		if (*verbosity>=1) Rprintf("maximum running time = %d sec\n", *maxtime);

 	}

 	//FILE_LOG(logINFO) << "epsilon = " << *eps;

-	Rprintf("epsilon = %g\n", *eps);

+	if (*verbosity>=1) Rprintf("epsilon = %g\n", *eps);

 	//FILE_LOG(logINFO) << "number of modifications = " << *Nmod;

-	Rprintf("number of modifications = %d\n", *Nmod);

+	if (*verbosity>=1) Rprintf("number of modifications = %d\n", *Nmod);

-	// Flush Rprintf statements to console

+	// Flush if (*verbosity>=1) Rprintf statements to console

 	R_FlushConsole();

 	// Recode the densities vector to matrix representation

@@ -303,7 +303,7 @@ void multivariate_hmm(double* D, int* T, int* N, int *Nmod, int* comb_states, in

 	catch (std::exception& e)

 	{

 		//FILE_LOG(logERROR) << "Error in EM/baumWelch: " << e.what();

-		Rprintf("Error in EM/baumWelch: %s\n", e.what());

+		if (*verbosity>=1) Rprintf("Error in EM/baumWelch: %s\n", e.what());

 		if (strcmp(e.what(),"nan detected")==0) { *error = 1; }

 		else { *error = 2; }

 	}

@@ -379,10 +379,10 @@ void array2D_which_max(double* array2D, int* dim, int* ind_max, double* value_ma

     for (int i1=0; i1<dim[1]; i1++)

     {

 			value_per_i0[i1] = array2D[i1 * dim[0] + i0];

-      // Rprintf("i0=%d, i1=%d, value_per_i0[%d] = %g\n", i0, i1, i1, value_per_i0[i1]);

+      // if (*verbosity>=1) Rprintf("i0=%d, i1=%d, value_per_i0[%d] = %g\n", i0, i1, i1, value_per_i0[i1]);

     }

 		ind_max[i0] = 1 + std::distance(value_per_i0.begin(), std::max_element(value_per_i0.begin(), value_per_i0.end()));

     value_max[i0] = *std::max_element(value_per_i0.begin(), value_per_i0.end());

   }

-}

\ No newline at end of file

+}

@@ -13,10 +13,10 @@

 // #endif

 extern "C"

-void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, double* prob, int* maxiter, int* maxtime, double* eps, double* maxPosterior, int* states, double* A, double* proba, double* loglik, double* weights, int* distr_type, double* initial_size, double* initial_prob, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* read_cutoff, int* algorithm);

+void univariate_hmm(int* O, int* T, int* N, int* state_labels, double* size, double* prob, int* maxiter, int* maxtime, double* eps, double* maxPosterior, int* states, double* A, double* proba, double* loglik, double* weights, int* distr_type, double* initial_size, double* initial_prob, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* read_cutoff, int* algorithm, int* verbosity);

 extern "C"

-void multivariate_hmm(double* D, int* T, int* N, int *Nmod, int* comb_states, int* maxiter, int* maxtime, double* eps, int* states, double* A, double* proba, double* loglik, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* algorithm);

+void multivariate_hmm(double* D, int* T, int* N, int *Nmod, int* comb_states, int* maxiter, int* maxtime, double* eps, int* states, double* A, double* proba, double* loglik, double* initial_A, double* initial_proba, bool* use_initial_params, int* num_threads, int* error, int* algorithm, int* verbosity);

 extern "C"

 void univariate_cleanup();

@@ -3,14 +3,14 @@

 #include "R_interface.h"

-R_NativePrimitiveArgType arg1[] = {INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, LGLSXP, INTSXP, INTSXP, INTSXP, INTSXP};

-R_NativePrimitiveArgType arg2[] = {REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, REALSXP, LGLSXP, INTSXP, INTSXP, INTSXP};

+R_NativePrimitiveArgType arg1[] = {INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, LGLSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP};

+R_NativePrimitiveArgType arg2[] = {REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, REALSXP, LGLSXP, INTSXP, INTSXP, INTSXP, INTSXP};

 R_NativePrimitiveArgType arg4[] = {INTSXP};

 R_NativePrimitiveArgType arg5[] = {REALSXP, INTSXP, INTSXP, REALSXP};

 static const R_CMethodDef CEntries[]  = {

-    {"C_univariate_hmm", (DL_FUNC) &univariate_hmm, 25, arg1},

-    {"C_multivariate_hmm", (DL_FUNC) &multivariate_hmm, 18, arg2},

+    {"C_univariate_hmm", (DL_FUNC) &univariate_hmm, 26, arg1},

+    {"C_multivariate_hmm", (DL_FUNC) &multivariate_hmm, 19, arg2},

     {"C_univariate_cleanup", (DL_FUNC) &univariate_cleanup, 0, NULL},

     {"C_multivariate_cleanup", (DL_FUNC) &multivariate_cleanup, 1, arg4},

     {"C_array2D_which_max", (DL_FUNC) &array2D_which_max, 4, arg5},