| Type: | Package |
| Title: | Fit Two-Component Normal and Lognormal Mixture Models |
| Version: | 0.1.1 |
| Description: | Fits, bootstraps, and evaluates two-component normal and lognormal mixture models. Includes diagnostic plots and statistical evaluation of mixture model fits using differential evolution optimization. |
| Imports: | DEoptim (≥ 2.0.0), pbapply (≥ 1.0.0), parallelly (≥ 1.0.0) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-09-21 18:17:46 UTC; mac |
| Author: | Farrokh Habibzadeh [aut, cre] |
| Maintainer: | Farrokh Habibzadeh <farrokh.habibzadeh@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-27 08:40:08 UTC |
Internal function for multiple DEoptim runs
Description
Internal function for multiple DEoptim runs
Usage
.fit_mix2_core(
x,
family = c("lognormal", "normal"),
lower = NULL,
upper = NULL,
NP = 100,
itermax = 10000,
reltol = 5e-06,
steptol = 50,
F = 0.8,
CR = 0.9,
strategy = 2,
parallelType = 0,
packages = c("stats"),
parVar = NULL,
n_runs = 20,
quiet = 2,
par_init = NULL,
pgtol = 1e-08
)
Internal function for multiple DEoptim runs
Description
Internal function for multiple DEoptim runs
Usage
.run_demulti(
loglik_fn,
x,
lower,
upper,
NP = 100,
itermax = 10000,
reltol = 5e-06,
steptol = 50,
F = 0.8,
CR = 0.9,
strategy = 2,
parallelType = 0,
packages = c("stats"),
parVar = NULL,
n_runs = 20,
quiet = 2
)
Bootstrap mixture parameters
Description
Bootstrap mixture parameters
Usage
bootstrap_mix2(
fit = NULL,
x = NULL,
par = NULL,
family = NULL,
B = 1000,
parametric = TRUE,
boot_size = NULL,
parallelType = 0,
quiet = 2,
ci_level = 0.95
)
Arguments
fit |
fitted object from fit_lognorm2 or fit_norm2 |
x |
numeric vector (if fit not provided) |
par |
numeric vector of parameters (if fit not provided) |
family |
"lognormal" or "normal" (if fit not provided) |
B |
number of bootstrap replicates |
parametric |
logical, parametric bootstrap if TRUE |
boot_size |
size or fraction (if between 0 and 1) of bootstrap sample |
parallelType |
integer for DEoptim/pbapply parallelism |
quiet |
0/1/2 for verbosity |
ci_level |
confidence level |
Value
list with cleaned bootstrap estimates, central tendency, and CI
Default bounds for lognormal mixture
Description
Default bounds for lognormal mixture
Usage
default_bounds_lognorm2(x)
Default bounds for normal mixture
Description
Default bounds for normal mixture
Usage
default_bounds_norm2(x)
Evaluate initial parameter values for mixture fitting
Description
Evaluate initial parameter values for mixture fitting
Usage
evaluate_init(
par_init,
x,
family = c("lognormal", "normal"),
lower = NULL,
upper = NULL,
pgtol = 1e-08
)
Arguments
par_init |
numeric vector of initial parameters |
x |
numeric vector of data |
family |
"lognormal" or "normal" |
lower |
numeric vector of lower bounds |
upper |
numeric vector of upper bounds |
pgtol |
numeric, gradient tolerance for optim |
Value
list with success flag, optimized parameters, log-likelihood, and convergence
Fit 2-component lognormal mixture
Description
Fit 2-component lognormal mixture
Usage
fit_lognorm2(x, ...)
Arguments
x |
numeric vector of data to fit |
... |
additional arguments passed to |
Value
list with fitted parameters and metrics
Fit 2-component normal mixture
Description
Fit 2-component normal mixture
Usage
fit_norm2(x, ...)
Arguments
x |
numeric vector of data to fit |
... |
additional arguments passed to |
Value
list with fitted parameters and metrics
Log-likelihood for 2-component lognormal mixture
Description
Log-likelihood for 2-component lognormal mixture
Usage
loglik_lognorm(params, x)
Log-likelihood for 2-component normal mixture
Description
Log-likelihood for 2-component normal mixture
Usage
loglik_norm(params, x)
Reorder mixture components to prevent label switching
Description
Reorder mixture components to prevent label switching
Usage
order_components(par)
Preliminary diagnostic plots
Description
Preliminary diagnostic plots
Usage
prelim_plots(
x,
which = c("hist"),
hist_bins = 60,
col_hist = "grey85",
col_density = "darkorange",
col_qq = "grey60",
col_line = "darkorange"
)
Arguments
x |
numeric vector |
which |
character vector: "hist", "qq", "pp", "logqq" |
hist_bins |
number of bins for histogram |
col_hist |
color for histogram |
col_density |
color for density line in histogram |
col_qq |
color for qq points |
col_line |
color for lines in "qq", "pp", "logqq" plots |
Value
no return value, called for side effects (generating plots)
Select best mixture model (lognormal or normal) based on BIC
Description
Select best mixture model (lognormal or normal) based on BIC
Usage
select_best_mixture(x, n_runs = 1, NP = 50, itermax = 10000, quiet = 2)
Arguments
x |
numeric vector |
n_runs |
number of DEoptim runs |
NP |
population size for DEoptim |
itermax |
maximum iterations |
quiet |
verbosity |
Value
list with best fit, all fits, and BICs