Function reference
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zda()
- Compute infection density in a cohort of humans, \(z_\tau(\alpha, a |h)\)
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truePR()
- Compute the true PR in a cohort as a function of age and exposure
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meanMoI()
- The mean MoI in a host cohort of age \(a\)
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dMoIda()
- Derivatives for the queuing model \(M/M/\infty\)
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solveMMinfty()
- Solve the queuing model \(M/M/\infty\)
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MoIDistPlot()
- Plot the output of solveMMinfty
Age of Infection (AoI)
Methods to compute the AoI density and distribution functions, random numbers, and moments
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dAoI()
- Compute the density function for AoI
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pAoI()
- Compute the distribution function for AoI
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rAoI()
- Random numbers for the AoI
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momentAoI()
- Compute the moments for the AoI density function for a cohort of age a
Age of the Youngest Infection (AoY)
Methods to compute the AoY density and distribution functions, random numbers, and moments
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dAoY()
- The density function for the age of the youngest infection (AoY)
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pAoY()
- The distribution function for the age of the youngest infection (AoY)
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pAoY_long()
- Alternative method for computing the distribution function for the age of the youngest infection (AoY)
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rAoY()
- The random generation function for the age of the youngest infection (AoY)
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momentAoY()
- Compute the moments for the AoY density function for a cohort of age a
Age of the Youngest of N Infections (AoYN)
Methods to compute the AoYN density and distribution functions & random numbers
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dAoYN()
- The youngest of N infections, density function
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pAoYN()
- The youngest of N infections, distribution function
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rAoYN()
- The youngest of N infections, random numbers
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dmda()
- Compute the derivatives for MoI using a hybrid model
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solve_dm()
- Solve the hybrid model for the MoI
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dpda()
- Compute the derivatives for MoI and true PR
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solve_dpda()
- Solve a system of differential equations to compute the true PR and the MoI
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dAoIda()
- Compute the derivatives of the AoI moments dynamically
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solve_dAoI()
- Solve the system of differential equations to compute the moments of the AoI over time.
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dAoYda()
- Compute the derivatives of the approximate moments of the AoY dynamically
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solve_dAoYda()
- Solve the system of differential equations to compute the approximate moments of the AoY over time.
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Fmu()
- Compute mean, expected parasite densities
mu
as a function of the age of infectionalpha
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sFmu()
- Compute mean, expected parasite densities
mu
for multiple values of the age of infectionalpha
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Fmu(<W>)
- Compute expected log10 parasite densities,
mu
, as a function of the age of infectionalpha
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par_Fmu_W()
- Set up parameters for Fmu.W
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Fmu(<base>)
- Compute expected log10 parasite densities,
mu
, as a function of the age of infectionalpha
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par_Fmu_base()
- Set up parameters for Fmu.base
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Fmu(<chronic>)
- Compute expected log10 parasite densities,
mu
, as a function of the age of infectionalpha
-
par_Fmu_chronic()
- Set up parameters for Fmu.chronic
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d_Omega()
- The density function for parasite densities in a simple malaria infection
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p_Omega()
- The density function for parasite densities as a function of the mean
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q_Omega()
- The quantile function for parasite densities in a simple malaria infection
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r_Omega()
- Random generation of parasite densities from a simple malaria infection
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par_Omega_beta()
- The quantile function for parasite densities in a simple malaria infection
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d_Omega(<beta>)
- Modified beta distribution, density function
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dbeta1()
- Density function for the beta distribution, an alternative parameterization
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p_Omega(<beta>)
- Modified beta distribution, distribution function
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pbeta1()
- Disribution function for the beta distribution, an alternative parameterization
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q_Omega(<beta>)
- Modified beta distribution, distribution function
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qbeta1()
- The quantile function for the beta distribution, an alternative parameterization
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r_Omega(<beta>)
- Modified beta distribution, random numbers
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rbeta1()
- The random generation function for the beta distribution, an alternative parameterization Title
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sigma_mu()
- A function to compute the variance of the beta distrution as a function of the mean.
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sigma_mu(<abc>)
- A function that returns constrained values of the variance for the beta distrution as a function of the mean.
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par_sigma_abc()
- Parameters to configure sigma_mu.abc
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d_alpha2density()
- The density function for parasite densities in a simple malaria infection of age alpha
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p_alpha2density()
- The distribution function for parasite densities in a simple malaria infection of age alpha
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q_alpha2density()
- The quantile function for parasite densities in a simple malaria infection of age alpha
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r_alpha2density()
- The distribution function for parasite densities in a simple malaria infection of age alpha
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d_clone_density()
- Compute \(P_\tau(a |h)\)
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moments_clone_density()
- Compute the moments of P_density
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parasite_density()
- Compute \(B_\tau(a | h)\)
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d_parasite_density()
- Compute the distribution function for \(B_\tau(a)\)
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p_parasite_density()
- Call parasite_density and return the density vector
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r_parasite_density()
- Random generation for parasite densities in a host cohort
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moments_parasite_density()
- Compute the moments of P_density
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rRda()
- Random generation for M parasite densities in a host cohort with MoI
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dDensityPaConvolve2()
- The density function for the sum of two infections
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cdfConvolve2()
- The density function for the sum of two infections
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cdfConvolve2a()
- The density function for the sum of two infections, method b
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cdfConvolve2b()
- The density function for the sum of two infections, method a
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d_detect()
- The probability of detecting parasites
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d_counts()
- The PDF for counting \(x\) parasites
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p_counts()
- The CDF for counting \(x\) parasites
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d_nz_counts_log()
- The PDF for non-zero counts, \(\log_{10}\) transformed
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p_nz_counts_log()
- The CDF for non-zero counts, \(\log_{10}\) transformed
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d_nz_counts_log_binned()
- The binned PDF for non-zero counts, \(\log_{10}\) transformed
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p_nz_counts_log_binned()
- The binned PDF for non-zero counts, \(\log_{10}\) transformed
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par_pois()
- Parameters for Poisson sampling
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d_detect(<pois>)
- Detection of infection given parasitemia
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p_counts(<pois>)
- Detection of infection given parasitemia
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d_counts(<pois>)
- Detection of infection given parasitemia
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par_nb()
- Parameters for negative binomial sampling
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d_detect(<nb>)
- Detection of infection given parasitemia
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p_counts(<nb>)
- Detection of infection given parasitemia
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d_counts(<nb>)
- Negative binomial PDF for raw parasite counts
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d_detect_mesh()
d_detect_mesh()
- Detection of infection given parasitemia
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d_clone_detect()
- Detection of infection given parasitemia
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d_parasite_detect_moi()
d_parasite_detect_moi()
- Detection of infection given parasitemia
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d_parasite_detect()
d_parasite_detect()
- Detection of infection given parasitemia
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FQ()
- The proportion of zero counts in
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d_moi_count()
d_moi_count()
- Detection of infection given parasitemia
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d_clone_counts()
- Compute the density of parasite counts for simple infections
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p_clone_counts()
- Compute the distribution of parasite counts for simple infections
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mean_clone_counts()
- Compute the mean parasite counts in simple infections
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d_parasite_counts()
- Compute the density of parasite counts in complex infections
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p_parasite_counts()
- Compute the distribution of parasite counts in complex infections
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mean_parasite_counts()
- Compute the mean parasite counts in complex infections
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FoI()
- Force of Infection, \(h_\tau(a)\)
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ageFoI()
- Functions to modify the FoI by age
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ageFoI(<flat>)
- A function that does not modify the FoI by age
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par_flatAge()
- Make a parameter list to dispatch ageFoI.flat
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ageFoI(<type2>)
- A function that does not modify the FoI by age
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par_type2Age()
- Make a parameter list to dispatch ageFoI.type2
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seasonalFoI()
- Add a seasonal pattern to the FoI trace function
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seasonalFoI(<exp>)
- A generalized sinusoidal seasonal pattern, exponentiated
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par_expSeason()
- Return a list to dispatch seasonalFoI.exp
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seasonalFoI(<flat>)
- The "no seasonality" function
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par_flatSeason()
- Return a list that dispatches seasonalFoI.flat
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seasonalFoI(<sin>)
- A generalized sinusoidal seasonal pattern
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par_sinSeason()
- Return a list to configure and dispatch seasonalFoI.sin
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trendFoI()
- Add a trend to the FoI trace function
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trendFoI(<flat>)
- The "no trend" function
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par_flatTrend()
- Return a list that dispatches trendFoI.flat
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nzPois()
- Compute N non-zero values from a Poisson distribution with a given MoI
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Wda()
- Compute immune tracking variables as a function of host age and exposure
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Wda(<none>)
- Compute immune tracking variables as a function of host age and exposure
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par_Wda_none()
- Make a parameter set for Wda.none
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Wda(<delta>)
- Compute immune tracking variables as a function of host age and exposure
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par_Wda_delta()
- Make a parameter set for Wda.none
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log10RBC()
- Compute log10 of the red blood cell population
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log10RBC(<static>)
- Compute log10 of the red blood cell population
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par_lRBC_static()
- Set up parameters for log10RBC.static