Package index • ramp.falciparum

Superinfection Dynamics

Queuing models for malaria

MMinfinity: MMinfinity
SquIP: SquIP
SquIPz: SquIPz

Solve a queuing model

Solve the equations

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

Enhanced Compartmental Models

Queuing models for malaria

SIPm: SIPm
solve_SIPm(): Solve the queuing model \(M/M/\infty\)

Infection Dynamics

Dynamics of MoI, AoI, and the AoY

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

Youngest of N Infections (YoN)

Methods to compute the YoN density and distribution functions & random numbers

dYoN(): The youngest of N infections, density function
pYoN(): The youngest of N infections, distribution function
rYoN(): 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)

A function to compute 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

Omega defined

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

Omega.beta

A modified beta distribution for log10 parasite distributions

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

Compute Omega(Fmu(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

Clone Density

Parasite density distributions by host cohort age

d_clone_density(): Compute \(P_\bday(a |h)\)
moments_clone_density(): Compute the moments of P_density

Parasite Density

Compute parasite density distributions as a function of the AoI

parasite_density(): Compute \(B_\bday(a | h)\)
d_parasite_density(): Compute the distribution function for \(B_\bday(a)\)
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

Basic sampling functions

Parasite positivity and counts given log10 parasites

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(): Detection of infection given parasitemia
d_clone_detect(): Detection of infection given parasitemia
d_parasite_detect_moi(): Detection of infection given parasitemia
d_parasite_detect(): Detection of infection given parasitemia
FQ(): The proportion of zero counts in

Observed MoI

Number of Clones Counted

d_moi_count(): Detection of infection given parasitemia

Parasite 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

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

MLE

Methods for maximum likelihood estimation

llik_count(): Compute the likelihood for a single parasite count datum
llik_counts(): Compute the log likelihood for a set of parasites counts data
counts_MLE(): Compute the MLE for parasite counts data
llik_counts_sz(): Compute the log likelihood for a set of parasites counts data
counts_MLE_sz(): Compute the MLE for parasite counts data