Merge pull request #504 from spsanderson/development

fixes pre cran submission

Author: spsanderson

R/est-param-gen-pareto.R

@@ -74,7 +74,7 @@ util_generalized_pareto_param_estimate <- function(.x, .auto_gen_empirical = TRU
   initial_params <- c(shape1 = 1, shape2 = 1, scale = 1)
 
   # Optimize to minimize the negative log-likelihood
-  opt_result <- optim(
+  opt_result <- stats::optim(
     par = initial_params,
     fn = genpareto_lik,
     data = x_term,

R/est-param-inv-pareto.R

@@ -74,7 +74,7 @@ util_inverse_pareto_param_estimate <- function(.x, .auto_gen_empirical = TRUE) {
   initial_params <- c(shape = 1, scale = min(x_term))
 
   # Optimize to minimize the negative log-likelihood
-  opt_result <- optim(
+  opt_result <- stats::optim(
     par = initial_params,
     fn = neg_log_lik_invpareto,
     data = x_term,

R/est-param-inv-weibull.R

@@ -68,7 +68,7 @@ util_inverse_weibull_param_estimate <- function(.x, .auto_gen_empirical = TRUE)
   initial_params <- c(shape = 1, scale = 1)
 
   # Optimize to minimize the negative log-likelihood
-  opt_result <- optim(
+  opt_result <- stats::optim(
     par = initial_params,
     fn = neg_log_lik,
     data = x_term,

R/est-param-negative-binomial.R

@@ -112,14 +112,14 @@ util_negative_binomial_param_estimate <- function(.x, .size = 1,
   nll_func <- function(params) {
     size <- params[1]
     mu <- params[2]
-    -sum(dnbinom(x_term, size = size, mu = mu, log = TRUE))
+    -sum(stats::dnbinom(x_term, size = size, mu = mu, log = TRUE))
   }
 
   # Initial parameter guesses (you might need to adjust these based on your data)
   initial_params <- c(size = 1, mu = mean(x_term))
 
   # Optimize using optim()
-  optim_result <- optim(initial_params, nll_func)
+  optim_result <- stats::optim(initial_params, nll_func)
 
   # Extract estimated parameters
   mle_size <- optim_result$par[1]

R/est-param-zt-binom.R

@@ -77,7 +77,7 @@ util_zero_truncated_binomial_param_estimate <- function(.x, .auto_gen_empirical
   initial_params <- c(size = max(x_term), prob = 0.5)  # Adjust based on your data
 
   # Optimization using optim()
-  optim_result <- optim(
+  optim_result <- stats::optim(
     par = initial_params,
     fn = nll_func
   ) |>

R/est-param-ztn-binmoial.R

@@ -76,7 +76,7 @@ util_zero_truncated_negative_binomial_param_estimate <- function(.x, .auto_gen_e
   initial_params <- c(size = 1, prob = 0.5)  # Adjust based on your data
 
   # Optimization using optim()
-  optim_result <- optim(initial_params, nll_func) |>
+  optim_result <- stats::optim(initial_params, nll_func) |>
     suppressWarnings()
 
   # Extract estimated parameters

R/stats-inv-weibull-tbl.R

@@ -64,7 +64,7 @@ util_inverse_weibull_stats_tbl <- function(.data) {
   initial_params <- c(shape = t, scale = q)
 
   # Optimize to minimize the negative log-likelihood
-  opt_result <- optim(
+  opt_result <- stats::optim(
     par = initial_params,
     fn = neg_log_lik,
     data = data_tbl$y,
@@ -78,7 +78,7 @@ util_inverse_weibull_stats_tbl <- function(.data) {
 
   # Compute statistics
   stat_mean <- mean(actuar::rinvweibull(1e5, shape = iw_shape, scale = iw_scale))
-  stat_median <- quantile(data_tbl$y, 0.5)
+  stat_median <- stats::quantile(data_tbl$y, 0.5)
   stat_mode <- iw_scale * (1 - 1 / iw_shape)^(1 / iw_shape)
   stat_sd <- sqrt(var(actuar::rinvweibull(1e5, shape = iw_shape, scale = iw_scale)))
   stat_coef_var <- stat_sd / stat_mean

R/utils-aic-inv-weibull.R

@@ -61,7 +61,7 @@ util_inverse_weibull_aic <- function(.x) {
   initial_params <- c(shape = 1, scale = 1)
 
   # Fit inverse Weibull distribution using optim
-  fit_invweibull <- optim(
+  fit_invweibull <- stats::optim(
     par = initial_params,
     fn = neg_log_lik_invweibull,
     data = x,

R/utils-aic-negative-binomial.R

@@ -58,7 +58,7 @@ util_negative_binomial_aic <- function(.x) {
 
   # Calculate AIC
   k_negative_binomial <- 2 # Number of parameters for negative binomial distribution (r and prob)
-  logLik_negative_binomial <- sum(dnbinom(x, size = r, prob = prob, log = TRUE))
+  logLik_negative_binomial <- sum(stats::dnbinom(x, size = r, prob = prob, log = TRUE))
   AIC_negative_binomial <- 2 * k_negative_binomial - 2 * logLik_negative_binomial
 
   # Return AIC

R/utils-aic-zt-binom.R

@@ -77,7 +77,7 @@ util_zero_truncated_binomial_aic <- function(.x) {
   pe <- util_zero_truncated_binomial_param_estimate(x)$parameter_tbl
 
   # Fit zero-truncated binomial distribution to data
-  fit_rztbinom <- optim(
+  fit_rztbinom <- stats::optim(
     par = c(size = round(pe$size, 3), prob = round(pe$prob, 3)),
     fn = neg_log_lik_rztbinom,
     data = x

R/utils-aic-ztn-binomial.R

@@ -74,7 +74,7 @@ util_zero_truncated_negative_binomial_aic <- function(.x) {
   }
 
   # Fit zero-truncated negative binomial distribution to data
-  fit_rztnbinom <- optim(
+  fit_rztnbinom <- stats::optim(
     par = c(size = round(pe$size, 3), prob = round(pe$prob, 3)),
     fn = neg_log_lik_rztnbinom,
     data = x

README.md

@@ -50,18 +50,18 @@ library(ggplot2)
 
 tidy_normal()
 #> # A tibble: 50 × 7
-#>    sim_number     x      y    dx       dy     p      q
-#>    <fct>      <int>  <dbl> <dbl>    <dbl> <dbl>  <dbl>
-#>  1 1              1  0.227 -2.97 0.000238 0.590  0.227
-#>  2 1              2  1.12  -2.84 0.000640 0.869  1.12 
-#>  3 1              3  1.26  -2.71 0.00153  0.897  1.26 
-#>  4 1              4  0.204 -2.58 0.00326  0.581  0.204
-#>  5 1              5  1.04  -2.44 0.00620  0.852  1.04 
-#>  6 1              6 -0.180 -2.31 0.0106   0.429 -0.180
-#>  7 1              7  0.299 -2.18 0.0167   0.618  0.299
-#>  8 1              8  1.73  -2.04 0.0243   0.959  1.73 
-#>  9 1              9 -0.770 -1.91 0.0338   0.221 -0.770
-#> 10 1             10  0.385 -1.78 0.0463   0.650  0.385
+#>    sim_number     x       y    dx       dy     p       q
+#>    <fct>      <int>   <dbl> <dbl>    <dbl> <dbl>   <dbl>
+#>  1 1              1  0.991  -3.18 0.000487 0.839  0.991 
+#>  2 1              2 -0.163  -3.05 0.00163  0.435 -0.163 
+#>  3 1              3  2.19   -2.92 0.00454  0.986  2.19  
+#>  4 1              4 -0.226  -2.78 0.0106   0.411 -0.226 
+#>  5 1              5 -1.07   -2.65 0.0208   0.141 -1.07  
+#>  6 1              6 -0.708  -2.52 0.0345   0.239 -0.708 
+#>  7 1              7  0.343  -2.39 0.0488   0.634  0.343 
+#>  8 1              8  0.264  -2.26 0.0600   0.604  0.264 
+#>  9 1              9 -0.0531 -2.13 0.0667   0.479 -0.0531
+#> 10 1             10  0.444  -2.00 0.0705   0.671  0.444 
 #> # ℹ 40 more rows
 ```