Function reference • pf.memory

Infection Dynamics

Dynamics of MoI, AoI, and the AoY

zda(): Compute infection density in a cohort of humans, \(z_\tau(\alpha, a |h)\)

truePR(): Compute the true PR in a cohort as a function of age and exposure

Multiplicity of Infection (MoI)

Methods to compute the MoI

meanMoI(): The mean MoI in a host cohort of age \(a\)

dMoIda(): Derivatives for the queuing model \(M/M/\infty\)

solveMMinfty(): Solve the queuing model \(M/M/\infty\)

MoIDistPlot(): Plot the output of solveMMinfty

Age of Infection (AoI)

Methods to compute the AoI density and distribution functions, random numbers, and moments

dAoI(): Compute the density function for AoI

pAoI(): Compute the distribution function for AoI

rAoI(): Random numbers for the AoI

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

dAoY(): The density function for the age of the youngest infection (AoY)

pAoY(): The distribution function for the age of the youngest infection (AoY)

pAoY_long(): Alternative method for computing the distribution function for the age of the youngest infection (AoY)

rAoY(): The random generation function for the age of the youngest infection (AoY)

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

dAoYN(): The youngest of N infections, density function

pAoYN(): The youngest of N infections, distribution function

rAoYN(): The youngest of N infections, random numbers

Hybrid Models

Methods to solve hybrid models

dmda(): Compute the derivatives for MoI using a hybrid model

solve_dm(): Solve the hybrid model for the MoI

dpda(): Compute the derivatives for MoI and true PR

solve_dpda(): Solve a system of differential equations to compute the true PR and the MoI

dAoIda(): Compute the derivatives of the AoI moments dynamically

solve_dAoI(): Solve the system of differential equations to compute the moments of the AoI over time.

dAoYda(): Compute the derivatives of the approximate moments of the AoY dynamically

solve_dAoYda(): Solve the system of differential equations to compute the approximate moments of the AoY over time.

Parasite Densities

Expected densities by AoI

F_mu(alpha)

Expected log10 parasites (mu) from the AoI (alpha)

Fmu(): Compute mean, expected parasite densities mu as a function of the age of infection alpha

sFmu(): Compute mean, expected parasite densities mu for multiple values of the age of infection alpha

Fmu(<W>): Compute expected log10 parasite densities, mu, as a function of the age of infection alpha

par_Fmu_W(): Set up parameters for Fmu.W

Fmu(<base>): Compute expected log10 parasite densities, mu, as a function of the age of infection alpha

par_Fmu_base(): Set up parameters for Fmu.base

Fmu(<chronic>): Compute expected log10 parasite densities, mu, as a function of the age of infection alpha

par_Fmu_chronic(): Set up parameters for Fmu.chronic

P(mu)

log10 parasites distributions from mu

d_Omega(): The density function for parasite densities in a simple malaria infection

p_Omega(): The density function for parasite densities as a function of the mean

q_Omega(): The quantile function for parasite densities in a simple malaria infection

r_Omega(): Random generation of parasite densities from a simple malaria infection

par_Omega_beta(): The quantile function for parasite densities in a simple malaria infection

P(mu)

log10 parasites distributions from mu

d_Omega(<beta>): Modified beta distribution, density function

dbeta1(): Density function for the beta distribution, an alternative parameterization

p_Omega(<beta>): Modified beta distribution, distribution function

pbeta1(): Disribution function for the beta distribution, an alternative parameterization

q_Omega(<beta>): Modified beta distribution, distribution function

qbeta1(): The quantile function for the beta distribution, an alternative parameterization

r_Omega(<beta>): Modified beta distribution, random numbers

rbeta1(): The random generation function for the beta distribution, an alternative parameterization Title

sigma_mu(): A function to compute the variance of the beta distrution as a function of the mean.

sigma_mu(<abc>): A function that returns constrained values of the variance for the beta distrution as a function of the mean.

par_sigma_abc(): Parameters to configure sigma_mu.abc

P(alpha)

log10 parasites from AoI (alpha)

d_alpha2density(): The density function for parasite densities in a simple malaria infection of age alpha

p_alpha2density(): The distribution function for parasite densities in a simple malaria infection of age alpha

q_alpha2density(): The quantile function for parasite densities in a simple malaria infection of age alpha

r_alpha2density(): The distribution function for parasite densities in a simple malaria infection of age alpha

P(a)

Parasite density distributions by host cohort age

d_clone_density(): Compute \(P_\tau(a |h)\)

moments_clone_density(): Compute the moments of P_density

Complex Infections

Expected densities by AoI

parasite_density(): Compute \(B_\tau(a | h)\)

d_parasite_density(): Compute the distribution function for \(B_\tau(a)\)

p_parasite_density(): Call parasite_density and return the density vector

r_parasite_density(): Random generation for parasite densities in a host cohort

moments_parasite_density(): Compute the moments of P_density

rRda(): Random generation for M parasite densities in a host cohort with MoI

Convolutions

Other

dDensityPaConvolve2(): The density function for the sum of two infections

cdfConvolve2(): The density function for the sum of two infections

cdfConvolve2a(): The density function for the sum of two infections, method b

cdfConvolve2b(): The density function for the sum of two infections, method a

Sampling

Generic Functions

Parasite positivity

d_detect(): The probability of detecting parasites

d_counts(): The PDF for counting \(x\) parasites

p_counts(): The CDF for counting \(x\) parasites

d_nz_counts_log(): The PDF for non-zero counts, \(\log_{10}\) transformed

p_nz_counts_log(): The CDF for non-zero counts, \(\log_{10}\) transformed

d_nz_counts_log_binned(): The binned PDF for non-zero counts, \(\log_{10}\) transformed

p_nz_counts_log_binned(): The binned PDF for non-zero counts, \(\log_{10}\) transformed

Poisson Sampling

par_pois(): Parameters for Poisson sampling

d_detect(<pois>): Detection of infection given parasitemia

p_counts(<pois>): Detection of infection given parasitemia

d_counts(<pois>): Detection of infection given parasitemia

Negative Binomial Sampling

par_nb(): Parameters for negative binomial sampling

d_detect(<nb>): Detection of infection given parasitemia

p_counts(<nb>): Detection of infection given parasitemia

d_counts(<nb>): Negative binomial PDF for raw parasite counts

Parasite Detection and Counts

Observed Prevalence

Detection and observed prevalence

d_detect_mesh() d_detect_mesh(): Detection of infection given parasitemia

d_clone_detect(): Detection of infection given parasitemia

d_parasite_detect_moi() d_parasite_detect_moi(): Detection of infection given parasitemia

d_parasite_detect() d_parasite_detect(): Detection of infection given parasitemia

FQ(): The proportion of zero counts in

Observed MoI

Number of Clones Counted

d_moi_count() d_moi_count(): Detection of infection given parasitemia

Counts

Parasite counts

d_clone_counts(): Compute the density of parasite counts for simple infections

p_clone_counts(): Compute the distribution of parasite counts for simple infections

mean_clone_counts(): Compute the mean parasite counts in simple infections

d_parasite_counts(): Compute the density of parasite counts in complex infections

p_parasite_counts(): Compute the distribution of parasite counts in complex infections

mean_parasite_counts(): Compute the mean parasite counts in complex infections

Force of Infection

Methods to set up FoI trace functions

FoI(): Force of Infection, \(h_\tau(a)\)

FoI by Age

Specialized methods modify the FoI by Age

ageFoI(): Functions to modify the FoI by age

ageFoI(<flat>): A function that does not modify the FoI by age

par_flatAge(): Make a parameter list to dispatch ageFoI.flat

ageFoI(<type2>): A function that does not modify the FoI by age

par_type2Age(): Make a parameter list to dispatch ageFoI.type2

FoI by Season

Specialized methods set the seasonal FoI

seasonalFoI(): Add a seasonal pattern to the FoI trace function

seasonalFoI(<exp>): A generalized sinusoidal seasonal pattern, exponentiated

par_expSeason(): Return a list to dispatch seasonalFoI.exp

seasonalFoI(<flat>): The "no seasonality" function

par_flatSeason(): Return a list that dispatches seasonalFoI.flat

seasonalFoI(<sin>): A generalized sinusoidal seasonal pattern

par_sinSeason(): Return a list to configure and dispatch seasonalFoI.sin

FoI Trends

Specialized methods to set trends in the FoI

trendFoI(): Add a trend to the FoI trace function

trendFoI(<flat>): The "no trend" function

par_flatTrend(): Return a list that dispatches trendFoI.flat

Utilities

Other

nzPois(): Compute N non-zero values from a Poisson distribution with a given MoI

Immune Tracking Variables

Expected densities by AoI

Wda(): Compute immune tracking variables as a function of host age and exposure

Wda(<none>): Compute immune tracking variables as a function of host age and exposure

par_Wda_none(): Make a parameter set for Wda.none

Wda(<delta>): Compute immune tracking variables as a function of host age and exposure

par_Wda_delta(): Make a parameter set for Wda.none

Red Blood Cells

Other

log10RBC(): Compute log10 of the red blood cell population

log10RBC(<static>): Compute log10 of the red blood cell population

par_lRBC_static(): Set up parameters for log10RBC.static