fix installation bug and pandas based future warning issues.

NEWS.md

@@ -1,4 +1,5 @@
-# causact (development version)
+# causact 0.5.4
+* patch release to fix installation issues. The default installation workflow now uses `numpyro==0.13.2` to avoid `ModuleNotFoundError: No module named 'jax.linear_util'`.
 
 # causact 0.5.3
 * Fixed bug where beta distribution was being treated as Laplace distribution.

R/dag_numpyro.R

@@ -224,9 +224,9 @@ from jax.numpy import (exp, log, log1p, expm1, abs, mean,
       plateDataStatements = paste(paste0(
         abbrevLabelPad(paste0(plateDimDF$indexLabel)),# four spaces to have invis _dim
                               " = ",
-                              "pd.factorize(r.",
+                              "pd.factorize(np.array(r.",
         gsub("\\$", ".", plateDimDF$dataPy),
-                              ",use_na_sentinel=True)[0]   #DIM"),
+                              "),use_na_sentinel=True)[0]   #DIM"),
                               sep = "\n")
     ###make labels for dim variables = to label_dim
       dimStatements = paste(
@@ -240,9 +240,9 @@ from jax.numpy import (exp, log, log1p, expm1, abs, mean,
       coordLabelsStatements = paste(paste0(
       abbrevLabelPad(paste0(plateDimDF$indexLabel,"_crd")),# four spaces to have invis _dim
       " = ",
-      "pd.factorize(r.",
+      "pd.factorize(np.array(r.",
       gsub("\\$", ".", plateDimDF$dataPy),
-      ",use_na_sentinel=True)[1]   #DIM"),
+      "),use_na_sentinel=True)[1]   #DIM"),
 sep = "\n")
       functionArguments = paste(c(functionArguments,
                                 plateDimDF$indexLabel),

R/data.R

@@ -79,7 +79,7 @@
 #'   \item{CPI2017}{The Corruption Perceptions Index score for 2017: A country/territory’s score indicates the perceived level of public sector corruption on a scale of 0-100, where 0 means that a country is perceived as highly corrupt and a 100 means that a country is perceived as very clean. }
 #'   \item{HDI2017}{The human development index score for 2017: the Human Development Index (HDI) is a measure of achievement in the basic dimensions of human development across countries.  It is an index made from a simple unweighted average of a nation’s longevity, education and income and is widely accepted in development discourse.}
 #' }
-#' @source <https://www.transparency.org/cpi> CPI data available from www.transparency.org/cpi.  Accessed Oct 1, 2018. Consumer Perception Index 2017 by Transparency International is licensed under CC-BY- ND 4.0.
+#' @source <https://www.transparency.org/en/cpi/2017> CPI data available from https://www.transparency.org/en/cpi/2017.  Accessed Feb 24, 2024. Consumer Perception Index 2017 by Transparency International is licensed under CC-BY- ND 4.0.
 #' @source <https://hdr.undp.org/data-center/human-development-index#/indicies/HDI> HDA data accessed on Oct 1, 2018.
 #' @source <https://data.worldbank.org/> Population data accessed on Oct 1, 2018.
 "corruptDF"

README.md

@@ -182,16 +182,16 @@ drawsDF  ### see top of data frame
 #> # A tibble: 4,000 × 4
 #>    theta_Toyota.Corolla theta_Subaru.Outback theta_Kia.Forte theta_Jeep.Wrangler
 #>                   <dbl>                <dbl>           <dbl>               <dbl>
-#>  1                0.192                0.633           0.210               0.794
-#>  2                0.215                0.602           0.282               0.892
-#>  3                0.200                0.621           0.299               0.890
-#>  4                0.223                0.621           0.213               0.778
-#>  5                0.220                0.611           0.261               0.788
-#>  6                0.228                0.619           0.247               0.811
-#>  7                0.228                0.619           0.247               0.811
-#>  8                0.227                0.635           0.231               0.810
-#>  9                0.222                0.600           0.291               0.888
-#> 10                0.205                0.605           0.236               0.841
+#>  1                0.214                0.609           0.199               0.844
+#>  2                0.228                0.563           0.232               0.818
+#>  3                0.217                0.611           0.255               0.848
+#>  4                0.206                0.648           0.261               0.876
+#>  5                0.187                0.624           0.313               0.819
+#>  6                0.166                0.606           0.229               0.848
+#>  7                0.213                0.622           0.234               0.839
+#>  8                0.206                0.588           0.253               0.855
+#>  9                0.211                0.646           0.219               0.855
+#> 10                0.196                0.590           0.271               0.840
 #> # ℹ 3,990 more rows
 ```
 
@@ -237,9 +237,9 @@ numpyroCode = graph %>% dag_numpyro(mcmc = FALSE)
 #> ## note that above is from JAX numpy package, not numpy.
 #> 
 #> y = np.array(r.carModelDF.getCard)   #DATA
-#> x      = pd.factorize(r.carModelDF.carModel,use_na_sentinel=True)[0]   #DIM
+#> x      = pd.factorize(np.array(r.carModelDF.carModel),use_na_sentinel=True)[0]   #DIM
 #> x_dim  = len(np.unique(x))   #DIM
-#> x_crd  = pd.factorize(r.carModelDF.carModel,use_na_sentinel=True)[1]   #DIM
+#> x_crd  = pd.factorize(np.array(r.carModelDF.carModel),use_na_sentinel=True)[1]   #DIM
 #> def graph_model(y,x):
 #>  ## Define random variables and their relationships
 #>  with npo.plate('x_dim',x_dim):
@@ -250,7 +250,7 @@ numpyroCode = graph %>% dag_numpyro(mcmc = FALSE)
 #> 
 #> # computationally get posterior
 #> mcmc = MCMC(NUTS(graph_model), num_warmup = 1000, num_samples = 4000)
-#> rng_key = random.PRNGKey(seed = 111)
+#> rng_key = random.PRNGKey(seed = 1234567)
 #> mcmc.run(rng_key,y,x)
 #> drawsDS = az.from_numpyro(mcmc,
 #>  coords = {'x_dim': x_crd},

cran-comments.md

@@ -1,7 +1,7 @@
-This is a patch release to fix two small bugs.
+This is a patch release to fix a bug with installing python dependencies.
 
 ## Test environments
-* local R installation, R 4.3.1
+* local R installation, R 4.3.2
 * ubuntu 16.04 (on travis-ci), R 4.3.1
 * win-builder (devel)