Function reference • maelstRom

All functions
`AdvancedFitter()`	Detects relationships between beta-binomial parameters (pi, theta) and independent variables
`AdvancedPvalcomb()`	Combines SNP-level dAD results to gene-level.
`AllelicMeta_est()`	Estimate sequencing error rate and inbreeding using an Expectation-Maximisation algorithm
`AllelicMeta_est_par()`	multi-locus wrapper function of AllelicMeta_est
`BetaBinomGenotyping()`	multi-locus wrapper function of EMfit_betabinom_robust for the purpose of genotyping
`BetaBinomMix_LLDeriv()`	Beta-binomial mixture model log-likelihood derivatives to its parameters involved in optimization
`BetaBinomMix_LLDeriv_transfo()`	Beta-binomial mixture model log-likelihood derivatives to its parameters involved in optimization
`BetaBinom_test_pvalTS()`	Performs an exact beta-binomial p-test
`BrolDB()`	UNUSED
`combine_p_gene()`	Combine p-values of SNPs per gene.
`CppCnT_Optim()`	Optimizer for pi, theta_control, and theta_case, for heterozygous samples in the differential control-case beta-binomial mixture fit using expectation-maximization
`CppHetH0_Optim()`	Optimizer for theta for heterozygous samples assuming a fixed pi parameter in the beta-binomial mixture fit using expectation-maximization
`CppHet_Optim()`	Optimizer for pi, theta for heterozygous samples in the beta-binomial mixture fit using expectation-maximization
`CppHom_Optim()`	Optimizer for theta for homozygous samples in the beta-binomial mixture fit using expectation-maximization
`dAD_analysis()`	Performs a differential Allelic Dispersion analysis on a control- and case-dataset
`dBetaBinom_cpp_old()`	Exact beta-binomial density using sums.
`dBetaBinom_MP()`	Exact beta-binomial density using a multiprecision library.
`dBetaBin_cppi()`	dBetaBinom implementation for internal use by C++
`dBetaBin_cppi_MP()`	dBetaBinom implementation for internal use by C++, accounting for numerical precision problems via increased memory usage (number of bits per number)
`dBetaBinom()` `pBetaBinom()` `qBetaBinom()` `rBetaBinom()`	The Beta-binomial distribution
`EMfit_betabinom()`	Models allelic RNAseq counts using an expectation-maximization fit of a beta-binomial mixture distribution
`EMfit_betabinom_popcomb()`	Models and estimates the allelic shift for cis-eQTL data, using beta-binomial models.
`EMfit_betabinom_robust()`	Models allelic RNAseq counts using a robust expectation-maximization fit of a beta-binomial mixture distribution
`EMfit_betabinom_SEslack()`	Models allelic RNAseq counts using an expectation-maximization fit of a beta-binomial mixture distribution, allowing slight deviations in the Sequencing Error (SE) metaparameter.
`EMfit_binom()`	Models allelic RNAseq counts using an expectation-maximization fit of a binomial mixture distribution
`final_filter()`	Final filtering of maelstRom analysis results and writing to output files
`GOF_uniform()`	Goodness-of-fit test comparing data following a uniform distribution to a Hardy-Weinberg distribution.
`GradPiPi_cppi()`	Beta-binomial second-order derivative with respect to its pi parameter (twice); for internal use by C++
`GradPiPi_cppi_MP()`	Beta-binomial second-order derivative with respect to its pi parameter (twice); for internal use by C++, accounting for numerical precision problems via increased memory usage (number of bits per number)
`GradPiTheta_cppi()`	Beta-binomial second-order derivative with respect to its pi parameter (once) and theta-parameter (once); for internal use by C++
`GradPiTheta_cppi_MP()`	Beta-binomial second-order derivative with respect to its pi parameter (once) and theta-parameter (once); for internal use by C++, accounting for numerical precision problems via increased memory usage (number of bits per number)
`GradPi_cppi()`	Beta-binomial gradient with respect to its pi parameter; for internal use by C++
`GradPi_cppi_MP()`	Beta-binomial gradient with respect to its pi parameter; for internal use by C++, accounting for numerical precision problems via increased memory usage (number of bits per number)
`GradThetaTheta_cppi()`	Beta-binomial second-order derivative with respect to its theta-parameter (twice); for internal use by C++
`GradThetaTheta_cppi_MP()`	Beta-binomial second-order derivative with respect to its theta-parameter (twice); for internal use by C++, accounting for numerical precision problems via increased memory usage (number of bits per number)
`GradTheta_cppi()`	Beta-binomial gradient with respect to its theta parameter; for internal use by C++
`GradTheta_cppi_MP()`	Beta-binomial gradient with respect to its theta parameter; for internal use by C++, accounting for numerical precision problems via increased memory usage (number of bits per number)
`grad_pi()`	Beta-binomial gradient with respect to its pi parameter
`grad_pi_MP()`	Exact gradient of the beta-binomial log-likelihood function for pi using a multiprecision library.
`grad_pi_old()`	Exact gradient of the beta-binomial log-likelihood function for pi using sums.
`grad_pi_pi()`	Beta-binomial second-order derivative with respect to its pi parameter (twice)
`grad_pi_theta()`	Beta-binomial second-order derivative with respect to its pi parameter (once) and theta-parameter (once)
`grad_theta()`	Beta-binomial gradient with respect to its theta parameter
`grad_theta_MP()`	Exact gradient of the beta-binomial log-likelihood function for theta using a multiprecision library.
`grad_theta_old()`	Exact gradient of the beta-binomial log-likelihood function for theta using sums.
`grad_theta_theta()`	Beta-binomial second-order derivative with respect to its theta-parameter (twice)
`HWE_chisquared()`	Chi-squared test assessing the Hardy-Weinberg assumption.
`imprinting_est()`	Estimate the degree of imprinting.
`impr_LOI_analysis()`	multi-locus wrapper function for imprinting detection and loss-of-imprinting analysis
`LikelyDistsHet()`	Returns parameter- and likelihood-distances based on leave-on-out (case-deletion) MLE's of `EMfit_betabinom_robust`'s fitted model
`LogGaussianQuad_CPP()`	Internal helper function for TumPur_LogLik_CPP when NumIntMethod == "GaussianQuad"
`LogNewtonCotes_CPP()`	Internal helper function for TumPur_LogLik_CPP when NumIntMethod == "NewtonCotes"
`logSums_MaxMethod()`	Returns an (approximate) log of a sum based on individual logs of the terms being summed
`logSums_MaxMethodSigned_CPP()`	Returns an (approximate) log of a sum based on individual logs of the terms being summed, but can include negative terms (see further)
`logSums_MaxMethod_CPP()`	Returns an (approximate) log of a sum based on individual logs of the terms being summed
`logSums_MaxMethod_CPP_R()`	Returns an (approximate) log of a sum based on individual logs of the terms being summed
`LogTanhSinhQuad_CPP()`	Internal helper function for TumPur_LogLik_CPP when NumIntMethod == "TanhSinhQuad"
`LogTrapezoidalInt_CPP()`	Internal helper function for TumPur_LogLik_CPP when NumIntMethod == "Gregory"
`LOItest_logreg()`	Logistic regression on the degree of heterozygosity between cases and controls
`LOI_calling()`	Call LOI samples over al imprinted loci.
`maelstRom-package` `maelstRom`	maelstRom: A package for RNAseq-based allelic analyses
`maelstRom_ADChromplot()`	Make a plot summarizing allelic divergence (-related phenomena) across a chromosome
`maelstRom_EMfitplot()`	Plots the result of maelstRom's beta-binomial mixture EM-fit.
`maelstRom_imprintplot()`	Plot maelstRom's imprinting detection results.
`median_AB()`	Calculates the median allelic bias
`median_imprinting()`	Calculate median imprinting.
`MomentEst_MixedBetaBinom()`	Calculates moment estimates for the beta-binomial parameters given observed data
`MomentEst_MixedBetaBinom_SESlack()`	Similar to MomentEst_MixedBetaBinom, but allowing for slack on the SE parameter (see EMfit_betabinom_SEslack)
`MPhelper_GradThetaTheta()`	Helper function for GradThetaTheta_cppi
`multinomial_coeff()`	Calculate multinomial coefficient(s).
`MyOptTest()`	UNUSED test function for C++ driven numeric optimization
`pmf_betabinomMix()`	Probability Mass Function of the beta-binomial mixture distribution modeling population-level RNAseq data
`pmf_betabinomMix_SEslack()`	This function is a work in progress. As such, it is not exported yet; but it allows for some leniency on the sequencing error parameter, hence "SESlack"
`pmf_binomMix()`	Probability Mass Function of the binomial mixture distribution modeling population-level RNAseq data
`pmf_impr()`	Probability Mass Function for an imprinted SNP.
`prior_filter()`	Prior filtering of loci.
`qbeta_C()`	UNUSED alternative implementation of the qbeta funtion
`qbeta_C3()`	UNUSED alternative implementation of the qbeta funtion
`standard_alleles()`	Determine standard alleles of SNP positions.
`symmetry_gof()`	Goodness-of-fit test for symmetry.
`TumPurHelpFun_CPP()`	Internal helper function for TumPur_LogLik_CPP
`TumPurHelpFun_CPP_R()`	Internal helper function for TumPur_LogLik_CPP
`TumPur_LogLik_CPP()` `TumPur_LogLik_CPP2()` `TumPur_LogLik_CPP2X()` `TumPur_LogLik_CPP3()` `TumPur_LogLik_CPP3X()` `TumPur_LogLik_CPP4()` `TumPur_LogLik_CPP_DB1()` `TumPur_LogLik_CPP_DB2()` `TumPur_LogLik_CPP_DB3()` `TumPur_LogLik_CPP2_10()` `TumPur_LogLik_CPP2_100()` `TumPur_LogLik_CPP2_1000()` `TumPur_LogLik_CPP2Y()`	Various implementations of the tumor-purity-accounting (negative) log likelihood (sum of completely correlated beta-binomials)

Reference

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